From 6e7cca404748dd4b1a3affd0d1296e37f4ac0a6f Mon Sep 17 00:00:00 2001 From: Xiao-Yong Jin Date: Sat, 15 Jul 2023 06:34:16 -0400 Subject: [PATCH] llama : add custom RoPE (#2054) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Implement customizable RoPE The original RoPE has pre-defined parameters theta_i = 10000^(āˆ’2(iāˆ’1)/d), for i in [1, 2, ..., d/2] Our customizable RoPE, ggml_rope_custom_inplace, uses theta_i = scale * base^(āˆ’2(iāˆ’1)/d), for i in [1, 2, ..., d/2] with the default matches the original scale = 1.0 base = 10000 The new command line arguments --rope-freq-base --rope-freq-scale set the two new RoPE parameter. Recent researches show changing these two parameters extends the context limit with minimal loss. 1. Extending Context to 8K kaiokendev https://kaiokendev.github.io/til#extending-context-to-8k 2. Extending Context Window of Large Language Models via Positional Interpolation Shouyuan Chen, Sherman Wong, Liangjian Chen, Yuandong Tian https://arxiv.org/abs/2306.15595 3. NTK-Aware Scaled RoPE allows LLaMA models to have extended (8k+) context size without any fine-tuning and minimal perplexity degradation. https://www.reddit.com/user/bloc97 https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ For the bold, try adding the following command line parameters to your favorite model: -c 16384 --rope-freq-base 80000 --rope-freq-scale 0.5 * ggml-metal: fix custom rope * common: fix argument names in help * llama: increase MEM_REQ_EVAL for MODEL_3B It avoids crashing for quantized weights on CPU. Better ways to calculate the required buffer size would be better. * llama: make MEM_REQ_EVAL depend on n_ctx * server: use proper Content-Type in curl examples Without the header Content-Type: application/json, curl will POST with Content-Type: application/x-www-form-urlencoded Though our simple server doesn't care, the httplib.h used has a limit with CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 8192 With Content-Type: application/json, we can send large json data. * style : minor fixes, mostly indentations * ggml : fix asserts --------- Co-authored-by: Georgi Gerganov --- examples/common.cpp | 16 ++++++++ examples/common.h | 2 + examples/main/main.cpp | 12 +++++- examples/server/README.md | 1 + examples/server/chat.sh | 2 + examples/server/server.cpp | 18 ++++++++ ggml-metal.m | 45 +++++++++++--------- ggml-metal.metal | 6 ++- ggml.c | 50 +++++++++++++++++------ ggml.h | 11 +++++ llama.cpp | 84 +++++++++++++++++++++++--------------- llama.h | 5 +++ 12 files changed, 185 insertions(+), 67 deletions(-) diff --git a/examples/common.cpp b/examples/common.cpp index 94875b054..8705127cb 100644 --- a/examples/common.cpp +++ b/examples/common.cpp @@ -168,6 +168,18 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.n_ctx = std::stoi(argv[i]); + } else if (arg == "--rope-freq-base") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.rope_freq_base = std::stof(argv[i]); + } else if (arg == "--rope-freq-scale") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.rope_freq_scale = std::stof(argv[i]); } else if (arg == "--memory-f32") { params.memory_f16 = false; } else if (arg == "--top-p") { @@ -493,6 +505,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { fprintf(stderr, " --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale); fprintf(stderr, " --cfg-smooth-factor N smooth factor between old and new logits (default: %f, 1.0 = no smoothing)\n", params.cfg_smooth_factor); fprintf(stderr, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); + fprintf(stderr, " --rope-freq-base N RoPE base frequency (default: %.1f)\n", params.rope_freq_base); + fprintf(stderr, " --rope-freq-scale N RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale); fprintf(stderr, " --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); fprintf(stderr, " --no-penalize-nl do not penalize newline token\n"); fprintf(stderr, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); @@ -573,6 +587,8 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param lparams.use_mlock = params.use_mlock; lparams.logits_all = params.perplexity; lparams.embedding = params.embedding; + lparams.rope_freq_base = params.rope_freq_base; + lparams.rope_freq_scale = params.rope_freq_scale; return lparams; } diff --git a/examples/common.h b/examples/common.h index 6315df961..f52fef629 100644 --- a/examples/common.h +++ b/examples/common.h @@ -32,6 +32,8 @@ struct gpt_params { int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. + float rope_freq_base = 10000.0f; // RoPE base frequency + float rope_freq_scale = 1.0f; // RoPE frequency scaling factor // sampling parameters std::unordered_map logit_bias; // logit bias for specific tokens diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 2248c2458..bcbcf12b0 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -84,9 +84,17 @@ int main(int argc, char ** argv) { return 0; } + if (params.rope_freq_base != 10000.0) { + fprintf(stderr, "%s: warning: changing RoPE frequency base to %g (default 10000.0)\n", __func__, params.rope_freq_base); + } + + if (params.rope_freq_scale != 1.0) { + fprintf(stderr, "%s: warning: scaling RoPE frequency by %g (default 1.0)\n", __func__, params.rope_freq_scale); + } + if (params.n_ctx > 2048) { - fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);" - "expect poor results\n", __func__, params.n_ctx); + fprintf(stderr, "%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified);" + " you are on your own\n", __func__, params.n_ctx); } else if (params.n_ctx < 8) { fprintf(stderr, "%s: warning: minimum context size is 8, using minimum size.\n", __func__); params.n_ctx = 8; diff --git a/examples/server/README.md b/examples/server/README.md index ad9b6bb08..e5ca8269b 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -66,6 +66,7 @@ Using [curl](https://curl.se/). On Windows `curl.exe` should be available in the ```sh curl --request POST \ --url http://localhost:8080/completion \ + --header "Content-Type: application/json" \ --data '{"prompt": "Building a website can be done in 10 simple steps:","n_predict": 128}' ``` diff --git a/examples/server/chat.sh b/examples/server/chat.sh index a89f8e908..014360121 100644 --- a/examples/server/chat.sh +++ b/examples/server/chat.sh @@ -32,6 +32,7 @@ tokenize() { --silent \ --request POST \ --url "${API_URL}/tokenize" \ + --header "Content-Type: application/json" \ --data-raw "$(jq -ns --arg content "$1" '{content:$content}')" \ | jq '.tokens[]' } @@ -64,6 +65,7 @@ chat_completion() { --no-buffer \ --request POST \ --url "${API_URL}/completion" \ + --header "Content-Type: application/json" \ --data-raw "${DATA}") printf "\n" diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 296c5d646..f442f2b56 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -608,6 +608,8 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, fprintf(stderr, " -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled"); fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); fprintf(stderr, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); + fprintf(stderr, " --rope-freq-base N RoPE base frequency (default: %.1f)\n", params.rope_freq_base); + fprintf(stderr, " --rope-freq-scale N RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale); fprintf(stderr, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); fprintf(stderr, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); fprintf(stderr, " not recommended: doubles context memory required and no measurable increase in quality\n"); @@ -722,6 +724,22 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.n_ctx = std::stoi(argv[i]); } + else if (arg == "--rope-freq-base") + { + if (++i >= argc) { + invalid_param = true; + break; + } + params.rope_freq_base = std::stof(argv[i]); + } + else if (arg == "--rope-freq-scale") + { + if (++i >= argc) { + invalid_param = true; + break; + } + params.rope_freq_scale = std::stof(argv[i]); + } else if (arg == "--memory-f32" || arg == "--memory_f32") { params.memory_f16 = false; diff --git a/ggml-metal.m b/ggml-metal.m index c795ee227..ee205bcdf 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -881,28 +881,35 @@ void ggml_metal_graph_compute( const int n_past = ((int32_t *)(src1->data))[0]; + float freq_base; + float freq_scale; + memcpy(&freq_base, (int32_t *) src1->data + 4, sizeof(float)); + memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float)); + [encoder setComputePipelineState:ctx->pipeline_rope]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&n_past length:sizeof( int) atIndex:18]; - [encoder setBytes:&n_dims length:sizeof( int) atIndex:19]; - [encoder setBytes:&mode length:sizeof( int) atIndex:20]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; + [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; + [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; + [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&n_past length:sizeof( int) atIndex:18]; + [encoder setBytes:&n_dims length:sizeof( int) atIndex:19]; + [encoder setBytes:&mode length:sizeof( int) atIndex:20]; + [encoder setBytes:&freq_base length:sizeof(float) atIndex:21]; + [encoder setBytes:&freq_scale length:sizeof(float) atIndex:22]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; diff --git a/ggml-metal.metal b/ggml-metal.metal index f094a1d40..9f9a4fbd7 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -656,17 +656,19 @@ kernel void kernel_rope( constant int & n_past, constant int & n_dims, constant int & mode, + constant float & freq_base, + constant float & freq_scale, uint3 tpig[[thread_position_in_grid]]) { const int64_t i3 = tpig[2]; const int64_t i2 = tpig[1]; const int64_t i1 = tpig[0]; const bool is_neox = mode & 2; - const float theta_scale = pow(10000.0, -2.0f/n_dims); + const float theta_scale = pow(freq_base, -2.0f/n_dims); const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2); - float theta = (float)p; + float theta = freq_scale * (float)p; if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { diff --git a/ggml.c b/ggml.c index 3ea8ba6ec..5ce1da0e9 100644 --- a/ggml.c +++ b/ggml.c @@ -6956,6 +6956,8 @@ struct ggml_tensor * ggml_rope_impl( int n_past, int n_dims, int mode, + float freq_base, + float freq_scale, int n_ctx, bool inplace) { GGML_ASSERT(n_past >= 0); @@ -6969,12 +6971,14 @@ struct ggml_tensor * ggml_rope_impl( ggml_scratch_save(ctx); - struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 4); + struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 6); ((int32_t *) b->data)[0] = n_past; ((int32_t *) b->data)[1] = n_dims; ((int32_t *) b->data)[2] = mode; ((int32_t *) b->data)[3] = n_ctx; + memcpy((int32_t *) b->data + 4, &freq_base, sizeof(float)); + memcpy((int32_t *) b->data + 5, &freq_scale, sizeof(float)); ggml_scratch_load(ctx); @@ -6993,7 +6997,7 @@ struct ggml_tensor * ggml_rope( int n_dims, int mode, int n_ctx) { - return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, false); + return ggml_rope_impl(ctx, a, n_past, n_dims, mode, 10000.0f, 1.0f, n_ctx, false); } struct ggml_tensor * ggml_rope_inplace( @@ -7003,7 +7007,19 @@ struct ggml_tensor * ggml_rope_inplace( int n_dims, int mode, int n_ctx) { - return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, true); + return ggml_rope_impl(ctx, a, n_past, n_dims, mode, 10000.0f, 1.0f, n_ctx, true); +} + +struct ggml_tensor * ggml_rope_custom_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_past, + int n_dims, + int mode, + float freq_base, + float freq_scale, + int n_ctx) { + return ggml_rope_impl(ctx, a, n_past, n_dims, mode, freq_base, freq_scale, n_ctx, true); } // ggml_rope_back @@ -12074,16 +12090,21 @@ static void ggml_compute_forward_rope_f32( const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(ggml_nelements(src1) == 4); + GGML_ASSERT(ggml_nelements(src1) == 6); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } + float freq_base; + float freq_scale; + const int n_past = ((int32_t *) src1->data)[0]; const int n_dims = ((int32_t *) src1->data)[1]; const int mode = ((int32_t *) src1->data)[2]; const int n_ctx = ((int32_t *) src1->data)[3]; + memcpy(&freq_base, (int32_t *) src1->data + 4, sizeof(float)); + memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float)); assert(n_past >= 0); @@ -12112,7 +12133,7 @@ static void ggml_compute_forward_rope_f32( // row index used to determine which thread to use int ir = 0; - const float theta_scale = powf(10000.0, -2.0f/n_dims); + const float theta_scale = powf(freq_base, -2.0f/n_dims); const bool is_neox = mode & 2; const bool is_glm = mode & 4; @@ -12124,7 +12145,7 @@ static void ggml_compute_forward_rope_f32( if (ir++ < ir0) continue; if (ir > ir1) break; - float theta = (float)p; + float theta = freq_scale * (float)p; if (is_glm) { theta = MIN(p, n_ctx - 2); @@ -12201,16 +12222,21 @@ static void ggml_compute_forward_rope_f16( const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(ggml_nelements(src1) == 4); + GGML_ASSERT(ggml_nelements(src1) == 6); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } + float freq_base; + float freq_scale; + const int n_past = ((int32_t *) src1->data)[0]; const int n_dims = ((int32_t *) src1->data)[1]; const int mode = ((int32_t *) src1->data)[2]; const int n_ctx = ((int32_t *) src1->data)[3]; + memcpy(&freq_base, (int32_t *) src1->data + 4, sizeof(float)); + memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float)); assert(n_past >= 0); @@ -12239,7 +12265,7 @@ static void ggml_compute_forward_rope_f16( // row index used to determine which thread to use int ir = 0; - const float theta_scale = powf(10000.0, -2.0f/n_dims); + const float theta_scale = powf(freq_base, -2.0f/n_dims); const bool is_neox = mode & 2; const bool is_glm = mode & 4; @@ -12251,7 +12277,7 @@ static void ggml_compute_forward_rope_f16( if (ir++ < ir0) continue; if (ir > ir1) break; - float theta = (float)p; + float theta = freq_scale * (float)p; if (is_glm) { theta = MIN(p, n_ctx - 2); @@ -12312,7 +12338,7 @@ static void ggml_compute_forward_rope_f16( const float x0 = GGML_FP16_TO_FP32(src[0]); const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); - dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); + dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); } } @@ -15710,7 +15736,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor // necessary for llama if (src0->grad) { assert(src1->type == GGML_TYPE_I32); - assert(ggml_nelements(src1) == 4); + assert(ggml_nelements(src1) == 6); const int n_past = ((int32_t *) src1->data)[0]; const int n_dims = ((int32_t *) src1->data)[1]; const int mode = ((int32_t *) src1->data)[2]; @@ -15731,7 +15757,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { if (src0->grad) { assert(src1->type == GGML_TYPE_I32); - assert(ggml_nelements(src1) == 4); + assert(ggml_nelements(src1) == 3); const int n_past = ((int32_t *) src1->data)[0]; const int n_dims = ((int32_t *) src1->data)[1]; const int mode = ((int32_t *) src1->data)[2]; diff --git a/ggml.h b/ggml.h index b88c35bae..24856a255 100644 --- a/ggml.h +++ b/ggml.h @@ -1121,6 +1121,17 @@ extern "C" { int mode, int n_ctx); + // custom RoPE, in-place, returns view(a) + GGML_API struct ggml_tensor * ggml_rope_custom_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_past, + int n_dims, + int mode, + float freq_base, + float freq_scale, + int n_ctx); + // rotary position embedding backward, i.e compute dx from dy // a - dy GGML_API struct ggml_tensor * ggml_rope_back( diff --git a/llama.cpp b/llama.cpp index b0cd9417c..27e1ee964 100644 --- a/llama.cpp +++ b/llama.cpp @@ -101,14 +101,15 @@ static void ggml_graph_compute_helper(std::vector & buf, ggml_cgraph * // memory sizes // -static const std::map & MEM_REQ_SCRATCH0() +static const std::map & MEM_REQ_SCRATCH0(int n_ctx) { static std::map k_sizes = { - { MODEL_3B, 256ull * MB }, - { MODEL_7B, 512ull * MB }, - { MODEL_13B, 512ull * MB }, - { MODEL_30B, 512ull * MB }, - { MODEL_65B, 1024ull * MB }, + /* empirical scaling, still a guess */ + { MODEL_3B, ((size_t) n_ctx / 16ull + 128ull) * MB }, + { MODEL_7B, ((size_t) n_ctx / 16ull + 256ull) * MB }, + { MODEL_13B, ((size_t) n_ctx / 12ull + 256ull) * MB }, + { MODEL_30B, ((size_t) n_ctx / 10ull + 256ull) * MB }, + { MODEL_65B, ((size_t) n_ctx / 8ull + 512ull) * MB }, }; return k_sizes; } @@ -140,14 +141,14 @@ static const std::map & MEM_REQ_KV_SELF() // this is mostly needed for temporary mul_mat buffers to dequantize the data // not actually needed if BLAS is disabled -static const std::map & MEM_REQ_EVAL() +static const std::map & MEM_REQ_EVAL(int n_ctx) { static std::map k_sizes = { - { MODEL_3B, 512ull * MB }, - { MODEL_7B, 768ull * MB }, - { MODEL_13B, 1024ull * MB }, - { MODEL_30B, 1280ull * MB }, - { MODEL_65B, 1536ull * MB }, + { MODEL_3B, ((size_t) n_ctx / 256ull + 512ull) * MB }, + { MODEL_7B, ((size_t) n_ctx / 256ull + 768ull) * MB }, + { MODEL_13B, ((size_t) n_ctx / 256ull + 1024ull) * MB }, + { MODEL_30B, ((size_t) n_ctx / 256ull + 1280ull) * MB }, + { MODEL_65B, ((size_t) n_ctx / 256ull + 1536ull) * MB }, }; return k_sizes; } @@ -189,6 +190,10 @@ struct llama_hparams { uint32_t n_head = 32; uint32_t n_layer = 32; uint32_t n_rot = 64; + + float rope_freq_base = 10000.0f; + float rope_freq_scale = 1.0f; + enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16; bool operator!=(const llama_hparams & other) const { @@ -647,7 +652,7 @@ struct llama_model_loader { *ctx_size_p = *mmapped_size_p = 0; for (const llama_load_tensor & lt : tensors_map.tensors) { *ctx_size_p += sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE; - *(use_mmap ? mmapped_size_p : ctx_size_p) += lt.size; + *(use_mmap ? mmapped_size_p : ctx_size_p) += lt.size + 16; } } @@ -843,6 +848,8 @@ struct llama_context_params llama_context_default_params() { /*.gpu_layers =*/ 0, /*.main_gpu =*/ 0, /*.tensor_split =*/ {0}, + /*.rope_freq_base =*/ 10000.0f, + /*.rope_freq_scale =*/ 1.0f, /*.progress_callback =*/ nullptr, /*.progress_callback_user_data =*/ nullptr, /*.low_vram =*/ false, @@ -966,6 +973,8 @@ static void llama_model_load_internal( int n_gpu_layers, int main_gpu, const float * tensor_split, + float rope_freq_base, + float rope_freq_scale, bool low_vram, ggml_type memory_type, bool use_mmap, @@ -1000,22 +1009,27 @@ static void llama_model_load_internal( } hparams.n_ctx = n_ctx; + + hparams.rope_freq_base = rope_freq_base; + hparams.rope_freq_scale = rope_freq_scale; } const uint32_t n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult; { - fprintf(stderr, "%s: format = %s\n", __func__, llama_file_version_name(file_version)); - fprintf(stderr, "%s: n_vocab = %u\n", __func__, hparams.n_vocab); - fprintf(stderr, "%s: n_ctx = %u\n", __func__, hparams.n_ctx); - fprintf(stderr, "%s: n_embd = %u\n", __func__, hparams.n_embd); - fprintf(stderr, "%s: n_mult = %u\n", __func__, hparams.n_mult); - fprintf(stderr, "%s: n_head = %u\n", __func__, hparams.n_head); - fprintf(stderr, "%s: n_layer = %u\n", __func__, hparams.n_layer); - fprintf(stderr, "%s: n_rot = %u\n", __func__, hparams.n_rot); + fprintf(stderr, "%s: format = %s\n", __func__, llama_file_version_name(file_version)); + fprintf(stderr, "%s: n_vocab = %u\n", __func__, hparams.n_vocab); + fprintf(stderr, "%s: n_ctx = %u\n", __func__, hparams.n_ctx); + fprintf(stderr, "%s: n_embd = %u\n", __func__, hparams.n_embd); + fprintf(stderr, "%s: n_mult = %u\n", __func__, hparams.n_mult); + fprintf(stderr, "%s: n_head = %u\n", __func__, hparams.n_head); + fprintf(stderr, "%s: n_layer = %u\n", __func__, hparams.n_layer); + fprintf(stderr, "%s: n_rot = %u\n", __func__, hparams.n_rot); + fprintf(stderr, "%s: freq_base = %.1f\n", __func__, hparams.rope_freq_base); + fprintf(stderr, "%s: freq_scale = %g\n", __func__, hparams.rope_freq_scale); fprintf(stderr, "%s: ftype = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype)); - fprintf(stderr, "%s: n_ff = %u\n", __func__, n_ff); - fprintf(stderr, "%s: model size = %s\n", __func__, llama_model_type_name(model.type)); + fprintf(stderr, "%s: n_ff = %u\n", __func__, n_ff); + fprintf(stderr, "%s: model size = %s\n", __func__, llama_model_type_name(model.type)); } if (file_version < LLAMA_FILE_VERSION_GGJT_V2) { @@ -1164,9 +1178,9 @@ static void llama_model_load_internal( const size_t mem_required = ctx_size + mmapped_size - vram_weights + // weights in VRAM not in memory - MEM_REQ_SCRATCH0().at(model.type) + + MEM_REQ_SCRATCH0(hparams.n_ctx).at(model.type) + MEM_REQ_SCRATCH1().at(model.type) + - MEM_REQ_EVAL().at (model.type); + MEM_REQ_EVAL(hparams.n_ctx).at(model.type); // this is the memory required by one llama_state const size_t mem_required_state = @@ -1270,6 +1284,8 @@ static bool llama_model_load( int n_gpu_layers, int main_gpu, float * tensor_split, + float rope_freq_base, + float rope_freq_scale, bool low_vram, ggml_type memory_type, bool use_mmap, @@ -1278,7 +1294,7 @@ static bool llama_model_load( llama_progress_callback progress_callback, void *progress_callback_user_data) { try { - llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gpu_layers, main_gpu, tensor_split, low_vram, memory_type, + llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gpu_layers, main_gpu, tensor_split, rope_freq_base, rope_freq_scale, low_vram, memory_type, use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data); return true; } catch (const std::exception & err) { @@ -1330,6 +1346,9 @@ static bool llama_eval_internal( const int n_rot = hparams.n_embd/hparams.n_head; const int n_gpu_layers = model.n_gpu_layers; + const float freq_base = hparams.rope_freq_base; + const float freq_scale = hparams.rope_freq_scale; + auto & mem_per_token = lctx.mem_per_token; auto & buf_compute = lctx.buf_compute; @@ -1427,11 +1446,11 @@ static bool llama_eval_internal( offload_func_kq(tmpq); ggml_set_name(tmpq, "tmpq"); - struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N), n_past, n_rot, 0, 0); + struct ggml_tensor * Kcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N), n_past, n_rot, 0, freq_base, freq_scale, 0); offload_func_kq(Kcur); ggml_set_name(Kcur, "Kcur"); - struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N), n_past, n_rot, 0, 0); + struct ggml_tensor * Qcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N), n_past, n_rot, 0, freq_base, freq_scale, 0); offload_func_kq(Qcur); ggml_set_name(Qcur, "Qcur"); @@ -2674,8 +2693,9 @@ struct llama_model * llama_load_model_from_file( ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32; if (!llama_model_load(path_model, *model, model->vocab, params.n_ctx, params.n_batch, params.n_gpu_layers, - params.main_gpu, params.tensor_split, params.low_vram, memory_type, params.use_mmap, params.use_mlock, - params.vocab_only, params.progress_callback, params.progress_callback_user_data)) { + params.main_gpu, params.tensor_split, params.rope_freq_base, params.rope_freq_scale,params.low_vram, + memory_type, params.use_mmap, params.use_mlock, params.vocab_only, params.progress_callback, + params.progress_callback_user_data)) { delete model; fprintf(stderr, "%s: failed to load model\n", __func__); return nullptr; @@ -2750,9 +2770,9 @@ struct llama_context * llama_new_context_with_model( ctx->embedding.resize(hparams.n_embd); } - ctx->buf_compute.resize(MEM_REQ_EVAL().at(ctx->model.type)); + ctx->buf_compute.resize(MEM_REQ_EVAL(hparams.n_ctx).at(ctx->model.type)); - ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0().at(ctx->model.type)); + ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type)); ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1().at(ctx->model.type)); } diff --git a/llama.h b/llama.h index e7c60f40c..e744584f2 100644 --- a/llama.h +++ b/llama.h @@ -89,6 +89,11 @@ extern "C" { int32_t n_gpu_layers; // number of layers to store in VRAM int32_t main_gpu; // the GPU that is used for scratch and small tensors float tensor_split[LLAMA_MAX_DEVICES]; // how to split layers across multiple GPUs + + // ref: https://github.com/ggerganov/llama.cpp/pull/2054 + float rope_freq_base; // RoPE base frequency + float rope_freq_scale; // RoPE frequency scaling factor + // called with a progress value between 0 and 1, pass NULL to disable llama_progress_callback progress_callback; // context pointer passed to the progress callback