llm : add MPT support (#3417)
* CUDA: added support for ggml_clamp (see also: https://github.com/ggerganov/ggml/issues/545) * mpt : added an implementation based (mostly) on falcon integration, modified with deltas from ggml/examples/mpt * mpt : protect against "clip_qkv": null in mpt-7b * mpt : quick fix to avoid "Strange model" warning when quantizing MPT models * mpt : addendum to changeset:84e30e8 - leave parameter clamp_kqv out from metadata rather than use 0.0 to indicate "no clamping" (more compliant with the current GGUF spec?) * mpt : standardized all tensor names to follow GGUF spec * mpt : addendum to changeset:1be89c40 - use "req" parameter of GGUF_GET_KEY macro instead of duplicate code * mpt : fixed comment s/gptneox/mpt/ * mpt : remove tabs, trailing whitespace * mpt : removed ne01 + n_past == ne00 assertion from alibi (cuda/f32) and rope_shift from build_mpt * mpt : updated convert-mpt-hf-to-gguf.py to reflect changes made to convert-gptneox-hf-to-gguf.py in pr:3252 * comment out n_past instead of marking it unused * mpt : removed hardcoded +178 from convert script in favor of utilizing hparams["vocab_size"] * mpt : remove unused tokenizer_json in convert script * ggml : remove obsolete n_past assert in ggml_alibi * llama : print clam_kqv and max_alibi_bias hparams --------- Co-authored-by: Cebtenzzre <cebtenzzre@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Jan Ploski
GitHub
parent
11ea5c7d96
commit
f5f9121de1
5 changed files with 685 additions and 9 deletions
425
llama.cpp
425
llama.cpp
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@ -424,6 +424,14 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
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LLM_ARCH_MPT,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_OUTPUT, "output" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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@ -1011,6 +1019,9 @@ struct llama_hparams {
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float rope_freq_base_train;
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float rope_freq_scale_train;
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float f_clamp_kqv;
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float f_max_alibi_bias;
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bool operator!=(const llama_hparams & other) const {
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if (this->vocab_only != other.vocab_only) return true;
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if (this->n_vocab != other.n_vocab) return true;
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@ -2060,6 +2071,20 @@ static void llm_load_hparams(
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_MPT:
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{
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hparams.f_clamp_kqv = 0.0f;
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GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_EPS));
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GGUF_GET_KEY(ctx, hparams.f_clamp_kqv, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ATTENTION_CLAMP_KQV));
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GGUF_GET_KEY(ctx, hparams.f_max_alibi_bias, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_MAX_ALIBI_BIAS));
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switch (hparams.n_layer) {
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case 32: model.type = e_model::MODEL_7B; break;
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case 48: model.type = e_model::MODEL_30B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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default: (void)0;
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}
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@ -2204,6 +2229,8 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
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LLAMA_LOG_INFO("%s: n_gqa = %u\n", __func__, hparams.n_gqa());
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LLAMA_LOG_INFO("%s: f_norm_eps = %.1e\n", __func__, hparams.f_norm_eps);
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LLAMA_LOG_INFO("%s: f_norm_rms_eps = %.1e\n", __func__, hparams.f_norm_rms_eps);
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LLAMA_LOG_INFO("%s: f_clamp_kqv = %.1e\n", __func__, hparams.f_clamp_kqv);
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LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n", __func__, hparams.f_max_alibi_bias);
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LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff);
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LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train);
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LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train);
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@ -2649,6 +2676,73 @@ static void llm_load_tensors(
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layer.attn_k_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {64}, backend);
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}
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} break;
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case LLM_ARCH_MPT:
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{
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model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
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// output
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{
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ggml_backend_type backend_norm;
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ggml_backend_type backend_output;
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if (n_gpu_layers > int(n_layer)) {
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// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
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// on Windows however this is detrimental unless everything is on the GPU
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#ifndef _WIN32
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backend_norm = LLAMA_BACKEND_OFFLOAD;
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#else
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backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
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#endif // _WIN32
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backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
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} else {
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backend_norm = GGML_BACKEND_CPU;
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backend_output = GGML_BACKEND_CPU;
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}
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model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
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model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
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if (backend_norm == GGML_BACKEND_GPU) {
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vram_weights += ggml_nbytes(model.output_norm);
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}
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if (backend_output == GGML_BACKEND_GPU_SPLIT) {
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vram_weights += ggml_nbytes(model.output);
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}
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}
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const uint32_t n_ff = hparams.n_ff;
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const int i_gpu_start = n_layer - n_gpu_layers;
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model.layers.resize(n_layer);
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for (uint32_t i = 0; i < n_layer; ++i) {
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const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
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const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
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auto & layer = model.layers[i];
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layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
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layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, 3*n_embd}, backend_split);
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layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
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layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
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layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
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layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
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if (backend == GGML_BACKEND_GPU) {
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vram_weights +=
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ggml_nbytes(layer.attn_norm) +
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ggml_nbytes(layer.wqkv) +
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ggml_nbytes(layer.wo) +
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ggml_nbytes(layer.ffn_norm) +
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ggml_nbytes(layer.w2) +
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ggml_nbytes(layer.w3);
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}
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}
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} break;
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default:
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throw std::runtime_error("unknown architecture");
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}
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@ -4505,7 +4599,6 @@ static struct ggml_cgraph * llm_build_starcoder(
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return gf;
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}
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static struct ggml_cgraph * llm_build_persimmon(
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llama_context & lctx,
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const llama_batch & batch) {
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@ -4903,6 +4996,326 @@ static struct ggml_cgraph * llm_build_persimmon(
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return gf;
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}
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static struct ggml_cgraph * llm_build_mpt(
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llama_context & lctx,
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const llama_batch & batch) {
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const auto & model = lctx.model;
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const auto & hparams = model.hparams;
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const auto & cparams = lctx.cparams;
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const auto & kv_self = lctx.kv_self;
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GGML_ASSERT(!!kv_self.ctx);
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const int64_t n_embd = hparams.n_embd;
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const int64_t n_layer = hparams.n_layer;
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const int64_t n_ctx = cparams.n_ctx;
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const int64_t n_head = hparams.n_head;
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const int64_t n_head_kv = hparams.n_head_kv; // == n_head for MPT, as there's no MQA/GQA
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const int64_t n_embd_head = hparams.n_embd_head();
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const int64_t n_embd_gqa = hparams.n_embd_gqa();
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const float norm_eps = hparams.f_norm_eps;
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const float clamp_kqv = hparams.f_clamp_kqv;
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const float max_alibi_bias = hparams.f_max_alibi_bias;
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const int n_gpu_layers = model.n_gpu_layers;
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const int32_t n_tokens = batch.n_tokens;
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const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n;
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const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
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//printf("kv_head = %d, n_kv = %d, n_tokens = %d, n_ctx = %d, is_measure = %d, has_shift = %d\n",
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// kv_head, n_kv, n_tokens, n_ctx, ggml_allocr_is_measure(lctx.alloc), kv_self.has_shift);
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auto & buf_compute = lctx.buf_compute;
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struct ggml_init_params params = {
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/*.mem_size =*/ buf_compute.size,
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/*.mem_buffer =*/ buf_compute.data,
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/*.no_alloc =*/ false,
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};
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params.no_alloc = true;
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struct ggml_context * ctx0 = ggml_init(params);
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ggml_cgraph * gf = ggml_new_graph(ctx0);
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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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//int warmup = 0;
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if (batch.token) {
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struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
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ggml_allocr_alloc(lctx.alloc, inp_tokens);
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if (!ggml_allocr_is_measure(lctx.alloc)) {
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memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
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//warmup = ((uint32_t*) inp_tokens->data)[0] == 0;
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}
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ggml_set_name(inp_tokens, "inp_tokens");
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inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
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} else {
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#ifdef GGML_USE_MPI
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GGML_ASSERT(false && "not implemented");
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#endif
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inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
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ggml_allocr_alloc(lctx.alloc, inpL);
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if (!ggml_allocr_is_measure(lctx.alloc)) {
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memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
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}
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}
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const int i_gpu_start = n_layer - n_gpu_layers;
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(void) i_gpu_start;
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// offload functions set the tensor output backend to GPU
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// tensors are GPU-accelerated if any input or the output has been offloaded
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offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
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offload_func_t offload_func_kq = llama_nop;
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offload_func_t offload_func_v = llama_nop;
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#ifdef GGML_USE_CUBLAS
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if (n_gpu_layers > n_layer) {
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offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
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}
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if (n_gpu_layers > n_layer + 1) {
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offload_func_v = ggml_cuda_assign_buffers_no_alloc;
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}
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if (n_gpu_layers > n_layer + 2) {
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offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
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}
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#endif // GGML_USE_CUBLAS
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// KQ_scale
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struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
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ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
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ggml_allocr_alloc(lctx.alloc, KQ_scale);
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if (!ggml_allocr_is_measure(lctx.alloc)) {
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ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
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}
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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
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offload_func_kq(KQ_mask);
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ggml_set_name(KQ_mask, "KQ_mask");
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ggml_allocr_alloc(lctx.alloc, KQ_mask);
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if (!ggml_allocr_is_measure(lctx.alloc)) {
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float * data = (float *) KQ_mask->data;
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memset(data, 0, ggml_nbytes(KQ_mask));
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for (int h = 0; h < 1; ++h) {
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for (int j = 0; j < n_tokens; ++j) {
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const llama_pos pos = batch.pos[j];
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const llama_seq_id seq_id = batch.seq_id[j];
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for (int i = 0; i < n_kv; ++i) {
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if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
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data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
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}
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}
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}
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}
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}
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * attn_norm;
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offload_func_t offload_func = llama_nop;
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#ifdef GGML_USE_CUBLAS
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if (il >= i_gpu_start) {
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offload_func = ggml_cuda_assign_buffers_no_alloc;
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}
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#endif // GGML_USE_CUBLAS
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// self-attention
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// TODO: refactor into common function (shared with LLaMA)
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{
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attn_norm = ggml_norm(ctx0, inpL, norm_eps);
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offload_func(attn_norm);
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attn_norm = ggml_mul(ctx0, attn_norm, model.layers[il].attn_norm);
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offload_func(attn_norm);
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if (1) {
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cur = attn_norm;
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}
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// compute QKV
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cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
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offload_func_kq(cur);
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if (clamp_kqv > 0.0f) {
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cur = ggml_clamp(ctx0, cur, -clamp_kqv, clamp_kqv);
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offload_func_kq(cur);
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}
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const size_t wsize = ggml_type_size(cur->type);
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struct ggml_tensor * Qcur = ggml_view_3d(
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ctx0, cur, n_embd_head, n_head, n_tokens,
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wsize * n_embd_head,
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wsize * n_embd_head * (n_head + 2 * n_head_kv),
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0);
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offload_func_kq(Qcur);
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struct ggml_tensor * Kcur = ggml_view_3d(
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ctx0, cur, n_embd_head, n_head_kv, n_tokens,
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wsize * n_embd_head,
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wsize * n_embd_head * (n_head + 2 * n_head_kv),
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wsize * n_embd_head * n_head);
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offload_func_kq(Kcur);
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struct ggml_tensor * tmpv = ggml_view_3d(
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ctx0, cur, n_embd_head, n_head_kv, n_tokens,
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wsize * n_embd_head,
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wsize * n_embd_head * (n_head + 2 * n_head_kv),
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wsize * n_embd_head * (n_head + n_head_kv));
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offload_func_kq(Kcur);
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ggml_set_name(Qcur, "Qcur");
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ggml_set_name(Kcur, "Kcur");
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{
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struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd_gqa, n_tokens));
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offload_func_v(Vcur);
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offload_func_v(Vcur->src[0]->src[0]);
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ggml_set_name(Vcur, "Vcur");
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struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
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offload_func_kq(k);
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ggml_set_name(k, "k");
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struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
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( n_ctx)*ggml_element_size(kv_self.v),
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(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
|
||||
offload_func_v(v);
|
||||
|
||||
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
|
||||
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
|
||||
}
|
||||
|
||||
struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
|
||||
offload_func_kq(Q);
|
||||
ggml_set_name(Q, "Q");
|
||||
|
||||
struct ggml_tensor * K =
|
||||
ggml_view_3d(ctx0, kv_self.k,
|
||||
n_embd_head, n_kv, n_head_kv,
|
||||
ggml_element_size(kv_self.k)*n_embd_gqa,
|
||||
ggml_element_size(kv_self.k)*n_embd_head,
|
||||
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
|
||||
offload_func_kq(K);
|
||||
ggml_set_name(K, "K");
|
||||
|
||||
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
||||
offload_func_kq(KQ);
|
||||
ggml_set_name(KQ, "KQ");
|
||||
|
||||
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
|
||||
offload_func_kq(KQ_scaled);
|
||||
ggml_set_name(KQ_scaled, "KQ_scaled");
|
||||
|
||||
// TODO: replace with ggml_add()
|
||||
struct ggml_tensor * KQ_scaled_alibi =
|
||||
ggml_alibi(ctx0, KQ_scaled, 0, n_head, max_alibi_bias);
|
||||
offload_func_kq(KQ_scaled_alibi);
|
||||
ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi");
|
||||
|
||||
struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask);
|
||||
offload_func_kq(KQ_masked);
|
||||
ggml_set_name(KQ_masked, "KQ_masked");
|
||||
|
||||
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
|
||||
offload_func_v(KQ_soft_max);
|
||||
ggml_set_name(KQ_soft_max, "KQ_soft_max");
|
||||
|
||||
struct ggml_tensor * V =
|
||||
ggml_view_3d(ctx0, kv_self.v,
|
||||
n_kv, n_embd_head, n_head_kv,
|
||||
ggml_element_size(kv_self.v)*n_ctx,
|
||||
ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
|
||||
ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
|
||||
offload_func_v(V);
|
||||
ggml_set_name(V, "V");
|
||||
|
||||
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
||||
offload_func_v(KQV);
|
||||
ggml_set_name(KQV, "KQV");
|
||||
|
||||
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
||||
offload_func_v(KQV_merged);
|
||||
ggml_set_name(KQV_merged, "KQV_merged");
|
||||
|
||||
cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
|
||||
offload_func_v(cur);
|
||||
ggml_set_name(cur, "KQV_merged_contiguous");
|
||||
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "result_wo");
|
||||
}
|
||||
|
||||
// Add the input
|
||||
cur = ggml_add(ctx0, cur, inpL);
|
||||
offload_func(cur);
|
||||
|
||||
struct ggml_tensor * attn_out = cur;
|
||||
|
||||
// feed forward
|
||||
{
|
||||
// Norm
|
||||
{
|
||||
cur = ggml_norm(ctx0, attn_out, norm_eps);
|
||||
offload_func(cur);
|
||||
|
||||
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
|
||||
offload_func(cur);
|
||||
}
|
||||
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].w3, cur);
|
||||
offload_func(cur);
|
||||
|
||||
cur = ggml_gelu(ctx0, cur);
|
||||
offload_func(cur);
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur);
|
||||
offload_func(cur);
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, attn_out);
|
||||
offload_func(cur);
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
// norm
|
||||
{
|
||||
cur = ggml_norm(ctx0, cur, norm_eps);
|
||||
offload_func_nr(cur);
|
||||
|
||||
cur = ggml_mul(ctx0, cur, model.output_norm);
|
||||
ggml_set_name(cur, "result_norm");
|
||||
}
|
||||
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
ggml_set_name(cur, "result_output");
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
ggml_free(ctx0);
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
||||
static struct ggml_cgraph * llama_build_graph(
|
||||
llama_context & lctx,
|
||||
const llama_batch & batch) {
|
||||
|
|
@ -4935,6 +5348,10 @@ static struct ggml_cgraph * llama_build_graph(
|
|||
{
|
||||
result = llm_build_refact(lctx, batch);
|
||||
} break;
|
||||
case LLM_ARCH_MPT:
|
||||
{
|
||||
result = llm_build_mpt(lctx, batch);
|
||||
} break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
|
|
@ -5065,7 +5482,8 @@ static int llama_decode_internal(
|
|||
const bool full_offload_supported = model.arch == LLM_ARCH_LLAMA ||
|
||||
model.arch == LLM_ARCH_BAICHUAN ||
|
||||
model.arch == LLM_ARCH_FALCON ||
|
||||
model.arch == LLM_ARCH_REFACT;
|
||||
model.arch == LLM_ARCH_REFACT ||
|
||||
model.arch == LLM_ARCH_MPT;
|
||||
const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3;
|
||||
if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) {
|
||||
n_threads = 1;
|
||||
|
|
@ -7161,7 +7579,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
const std::string name = ggml_get_name(meta);
|
||||
|
||||
// TODO: avoid hardcoded tensor names - use the TN_* constants
|
||||
if (name.find("attn_v.weight") != std::string::npos) {
|
||||
if (name.find("attn_v.weight") != std::string::npos ||
|
||||
name.find("attn_qkv.weight") != std::string::npos) {
|
||||
++n_attention_wv;
|
||||
}
|
||||
else if (name.find("ffn_down.weight") != std::string::npos) {
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue