ggml : pass eps to ggml_norm
This commit is contained in:
Georgi Gerganov
parent
d561b7f724
commit
e3c52bd990
4 changed files with 49 additions and 40 deletions
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@ -938,7 +938,8 @@ void ggml_metal_graph_compute(
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} break;
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case GGML_OP_NORM:
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{
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const float eps = 1e-5f;
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float eps;
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memcpy(&eps, dst->op_params, sizeof(float));
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const int nth = 256;
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16
ggml.c
16
ggml.c
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@ -5789,6 +5789,7 @@ struct ggml_tensor * ggml_silu_back(
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static struct ggml_tensor * ggml_norm_impl(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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float eps,
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bool inplace) {
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bool is_node = false;
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@ -5799,7 +5800,7 @@ static struct ggml_tensor * ggml_norm_impl(
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struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
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// TODO: maybe store epsilon here?
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ggml_set_op_params(result, &eps, sizeof(eps));
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result->op = GGML_OP_NORM;
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result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
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@ -5810,14 +5811,16 @@ static struct ggml_tensor * ggml_norm_impl(
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struct ggml_tensor * ggml_norm(
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struct ggml_context * ctx,
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struct ggml_tensor * a) {
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return ggml_norm_impl(ctx, a, false);
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struct ggml_tensor * a,
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float eps) {
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return ggml_norm_impl(ctx, a, eps, false);
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}
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struct ggml_tensor * ggml_norm_inplace(
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struct ggml_context * ctx,
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struct ggml_tensor * a) {
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return ggml_norm_impl(ctx, a, true);
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struct ggml_tensor * a,
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float eps) {
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return ggml_norm_impl(ctx, a, eps, true);
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}
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// ggml_rms_norm
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@ -10619,7 +10622,8 @@ static void ggml_compute_forward_norm_f32(
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GGML_TENSOR_UNARY_OP_LOCALS;
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const float eps = 1e-5f; // TODO: make this a parameter
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float eps;
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memcpy(&eps, dst->op_params, sizeof(float));
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// TODO: optimize
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for (int64_t i03 = 0; i03 < ne03; i03++) {
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7
ggml.h
7
ggml.h
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@ -909,14 +909,15 @@ extern "C" {
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struct ggml_tensor * b);
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// normalize along rows
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// TODO: eps is hardcoded to 1e-5 for now
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GGML_API struct ggml_tensor * ggml_norm(
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struct ggml_context * ctx,
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struct ggml_tensor * a);
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struct ggml_tensor * a,
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float eps);
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GGML_API struct ggml_tensor * ggml_norm_inplace(
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struct ggml_context * ctx,
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struct ggml_tensor * a);
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struct ggml_tensor * a,
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float eps);
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GGML_API struct ggml_tensor * ggml_rms_norm(
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struct ggml_context * ctx,
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25
llama.cpp
25
llama.cpp
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@ -830,6 +830,7 @@ struct llama_hparams {
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uint32_t n_rot = 64;
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uint32_t n_ff = 11008;
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float f_norm_eps = 1e-5;
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float f_norm_rms_eps = 1e-5;
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float rope_freq_base = 10000.0f;
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@ -1557,6 +1558,7 @@ static void llm_load_hparams(
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} break;
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case LLM_ARCH_FALCON:
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{
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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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} break;
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default: (void)0;
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};
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@ -1684,7 +1686,8 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
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LLAMA_LOG_INFO("%s: n_layer = %u\n", __func__, hparams.n_layer);
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LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot); // a.k.a. n_embd_head, n_head_dim
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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_rms_eps);
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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: n_ff = %u\n", __func__, hparams.n_ff);
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LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, hparams.rope_freq_base);
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LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, hparams.rope_freq_scale);
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@ -1899,8 +1902,7 @@ static void llm_load_tensors(
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mmapped_size - vram_weights; // weights in VRAM not in memory
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// this is the memory required by one llama_state
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const size_t mem_required_state =
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scale*hparams.kv_size();
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const size_t mem_required_state = scale*hparams.kv_size();
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LLAMA_LOG_INFO("%s: mem required = %7.2f MB (+ %7.2f MB per state)\n", __func__,
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mem_required / 1024.0 / 1024.0, mem_required_state / 1024.0 / 1024.0);
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@ -2383,6 +2385,10 @@ static struct ggml_cgraph * llm_build_falcon(
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GGML_ASSERT(n_embd_head == hparams.n_rot);
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const float freq_base = hparams.rope_freq_base;
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const float freq_scale = hparams.rope_freq_scale;
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const float norm_eps = hparams.f_norm_eps;
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auto & buf_compute = lctx.buf_compute;
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struct ggml_init_params params = {
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@ -2436,7 +2442,7 @@ static struct ggml_cgraph * llm_build_falcon(
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// self-attention
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{
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attn_norm = ggml_norm(ctx0, inpL);
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attn_norm = ggml_norm(ctx0, inpL, norm_eps);
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attn_norm = ggml_add(ctx0,
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ggml_mul(ctx0,
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@ -2445,7 +2451,7 @@ static struct ggml_cgraph * llm_build_falcon(
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ggml_repeat(ctx0, model.layers[il].attn_norm_b, attn_norm));
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if (model.layers[il].attn_norm_2) { // Falcon-40B
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cur = ggml_norm(ctx0, inpL);
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cur = ggml_norm(ctx0, inpL, norm_eps);
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cur = ggml_add(ctx0,
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ggml_mul(ctx0,
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@ -2490,8 +2496,8 @@ static struct ggml_cgraph * llm_build_falcon(
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wsize * n_embd_head * (n_head + n_head_kv));
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// using mode = 2 for neox mode
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Qcur = ggml_rope_inplace(ctx0, Qcur, n_past, n_embd_head, 2, 0);
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Kcur = ggml_rope_inplace(ctx0, Kcur, n_past, n_embd_head, 2, 0);
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Qcur = ggml_rope_custom_inplace(ctx0, Qcur, n_past, n_embd_head, 2, 0, freq_base, freq_scale);
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Kcur = ggml_rope_custom_inplace(ctx0, Kcur, n_past, n_embd_head, 2, 0, freq_base, freq_scale);
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// store key and value to memory
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{
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@ -2522,8 +2528,6 @@ static struct ggml_cgraph * llm_build_falcon(
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// K * Q
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// K = ggml_cont(ctx0, ggml_repeat2(ctx0, K, repeat_dummy));
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struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
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struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
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@ -2549,7 +2553,6 @@ static struct ggml_cgraph * llm_build_falcon(
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n_embd_head, n_head_kv, n_past + N),
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0, 2, 1, 3);
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// V = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_repeat2(ctx0, V, repeat_dummy)));
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V = ggml_cont(ctx0, ggml_transpose(ctx0, V));
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// KQV = transpose(V) * KQ_soft_max
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@ -2589,7 +2592,7 @@ static struct ggml_cgraph * llm_build_falcon(
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// norm
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{
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cur = ggml_norm(ctx0, inpL);
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cur = ggml_norm(ctx0, inpL, norm_eps);
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cur = ggml_add(ctx0,
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ggml_mul(ctx0,
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