mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2025-01-01 00:39:00 +01:00
add input embeddings handling
This commit is contained in:
Meng Zhang
parent
ab13d071e1
commit
8bc76a225d
329
llama.cpp
329
llama.cpp
@ -3424,6 +3424,331 @@ static struct ggml_cgraph * llm_build_falcon(
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return gf;
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}
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static struct ggml_cgraph * llm_build_starcoder(
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llama_context & lctx,
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const llama_token * tokens,
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const float * embd,
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int n_tokens,
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int n_past) {
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GGML_ASSERT((!tokens && embd) || (tokens && !embd)); // NOLINT
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const int N = n_tokens;
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const auto & model = lctx.model;
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const auto & hparams = model.hparams;
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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 = hparams.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;
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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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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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const int n_gpu_layers = model.n_gpu_layers;
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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 * token;
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struct ggml_tensor * position;
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struct ggml_tensor * inpL;
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if (tokens) {
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struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
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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, tokens, N*ggml_element_size(inp_tokens));
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}
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ggml_set_name(inp_tokens, "inp_tokens");
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token = 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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token = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N);
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ggml_allocr_alloc(lctx.alloc, token);
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if (!ggml_allocr_is_measure(lctx.alloc)) {
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memcpy(token->data, embd, N * n_embd * ggml_element_size(inpL));
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}
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}
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{
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// Compute position embeddings.
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struct ggml_tensor * inp_positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
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ggml_allocr_alloc(lctx.alloc, inp_positions);
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if (!ggml_allocr_is_measure(lctx.alloc)) {
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for (int i = 0; i < N; ++i) {
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((int32_t *) inp_positions->data)[i] = n_past + i;
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}
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}
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ggml_set_name(inp_positions, "inp_positions");
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position = ggml_get_rows(ctx0, model.pos_embeddings, inp_positions);
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}
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inpL = ggml_add(ctx0, token, position);
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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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//
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// with the low VRAM option VRAM scratch is disabled in llama_load_model_internal
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// in that case ggml_cuda_assign_buffers has no effect
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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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struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
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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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ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
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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_add(ctx0,
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ggml_mul(ctx0, attn_norm, model.layers[il].attn_norm),
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model.layers[il].attn_norm_b);
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offload_func(attn_norm->src[0]);
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offload_func(attn_norm);
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if (model.layers[il].attn_norm_2) { // Falcon-40B
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cur = ggml_norm(ctx0, inpL, norm_eps);
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offload_func(cur);
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cur = ggml_add(ctx0,
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ggml_mul(ctx0, cur, model.layers[il].attn_norm_2),
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model.layers[il].attn_norm_2_b);
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offload_func(cur->src[0]);
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offload_func(cur);
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} else { // Falcon 7B
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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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// Note that the strides for Kcur, Vcur are set up so that the
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// resulting views are misaligned with the tensor's storage
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// (by applying the K/V offset we shift the tensor's original
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// view to stick out behind the viewed QKV tensor's allocated
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// memory, so to say). This is ok because no actual accesses
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// happen to that out-of-range memory, but it can require some
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// trickery when trying to accurately dump these views for
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// debugging.
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const size_t wsize = ggml_type_size(cur->type);
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// TODO: these 2 ggml_conts are technically not needed, but we add them until CUDA support for
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// non-contiguous views is added for the rope operator
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struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_3d(
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ctx0, cur, n_embd_head, n_head, N,
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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(tmpq);
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struct ggml_tensor * tmpk = ggml_cont(ctx0, ggml_view_3d(
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ctx0, cur, n_embd_head, n_head_kv, N,
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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(tmpk);
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struct ggml_tensor * tmpv = ggml_view_3d(
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ctx0, cur, n_embd_head, n_head_kv, N,
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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_v(tmpv);
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// using mode = 2 for neox mode
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struct ggml_tensor * Qcur = ggml_rope_custom_inplace(ctx0, tmpq, n_past, n_embd_head, 2, 0, freq_base, freq_scale);
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offload_func_kq(Qcur);
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struct ggml_tensor * Kcur = ggml_rope_custom_inplace(ctx0, tmpk, n_past, n_embd_head, 2, 0, freq_base, freq_scale);
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offload_func_kq(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));
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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*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + n_past));
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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, 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 + n_past*ggml_element_size(kv_self.v));
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offload_func_v(v);
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ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
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ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
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}
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struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
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offload_func_kq(Q);
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ggml_set_name(Q, "Q");
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struct ggml_tensor * K =
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ggml_view_3d(ctx0, kv_self.k,
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n_embd_head, n_past + N, n_head_kv,
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ggml_element_size(kv_self.k)*n_embd_gqa,
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ggml_element_size(kv_self.k)*n_embd_head,
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ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
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offload_func_kq(K);
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ggml_set_name(K, "K");
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struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
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offload_func_kq(KQ);
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ggml_set_name(KQ, "KQ");
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struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
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offload_func_kq(KQ_scaled);
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ggml_set_name(KQ_scaled, "KQ_scaled");
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struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
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offload_func_kq(KQ_masked);
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ggml_set_name(KQ_masked, "KQ_masked");
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struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
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offload_func_v(KQ_soft_max);
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ggml_set_name(KQ_soft_max, "KQ_soft_max");
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struct ggml_tensor * V =
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ggml_view_3d(ctx0, kv_self.v,
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n_past + N, n_embd_head, n_head_kv,
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ggml_element_size(kv_self.v)*n_ctx,
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ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
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ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
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offload_func_v(V);
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ggml_set_name(V, "V");
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struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
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offload_func_v(KQV);
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ggml_set_name(KQV, "KQV");
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struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
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offload_func_v(KQV_merged);
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ggml_set_name(KQV_merged, "KQV_merged");
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cur = ggml_cpy(ctx0, KQV_merged, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
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offload_func_v(cur);
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ggml_set_name(cur, "KQV_merged_contiguous");
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cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
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offload_func(cur);
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ggml_set_name(cur, "result_wo");
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}
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struct ggml_tensor * attn_out = cur;
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// feed forward
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{
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struct ggml_tensor * inpFF = attn_norm;
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cur = ggml_mul_mat(ctx0, model.layers[il].w3, inpFF);
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offload_func(cur);
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cur = ggml_gelu(ctx0, cur);
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offload_func(cur);
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cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur);
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offload_func(cur);
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}
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cur = ggml_add(ctx0, cur, attn_out);
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offload_func(cur);
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cur = ggml_add(ctx0, cur, inpL);
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offload_func(cur);
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// input for next layer
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inpL = cur;
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}
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cur = inpL;
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// norm
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{
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cur = ggml_norm(ctx0, cur, norm_eps);
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offload_func_nr(cur);
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cur = ggml_add(ctx0,
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ggml_mul(ctx0, cur, model.output_norm),
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model.output_norm_b);
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ggml_set_name(cur, "result_norm");
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}
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cur = ggml_mul_mat(ctx0, model.output, cur);
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ggml_set_name(cur, "result_output");
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ggml_build_forward_expand(gf, cur);
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ggml_free(ctx0);
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return gf;
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}
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static struct ggml_cgraph * llama_build_graph(
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llama_context & lctx,
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const llama_token * tokens,
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@ -3447,6 +3772,10 @@ static struct ggml_cgraph * llama_build_graph(
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{
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result = llm_build_falcon(lctx, tokens, embd, n_tokens, n_past);
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} break;
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case LLM_ARCH_STARCODER:
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{
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result = llm_build_starcoder(lctx, tokens, embd, n_tokens, n_past);
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} break;
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default:
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GGML_ASSERT(false);
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};
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