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model : add support for Falcon-H1 family (#14534)
* v1 * push more fixes * another fix * fix * more fixes * minor fix * more cleaning on python code * python fixes * changed precision for multipliers float 32->64 * fixes * another fix * fix * pre-norm -> norm * fix * Revert "fix" This reverts commit 243e4d1a50bd73467d99f6b289b9a1826f83b94b. * fix * small fix ffn_norm * try * mix instead of max * fix vocab size * conflict solve * fixed multipliers * falcon-h1 specefic vocab resolved * read arch from gguf.MODEL_ARCH * mamba_d_ssm added to d_inner find_hparam * remove unused functions from gguf_writer.py * override modify_tensors instead of get_tensors * fix conversion and d_inner * added some cb functions for debugging puposes * inp_out_ids moved outside of layers loop * mup_vec create as float64 * fix rope_theta * injected mup * clean ups * rm extra space * rm unused MAMBA_CHUNK_SIZE * rm unused key * add bos False * changed ROPE_TYPE * cleaning debugging stuff * cleaning debug quant * fix comment * some cleanups * some cleanups * Update src/llama-model-loader.cpp * more cleanups * moe cleanuips * d_ssm -> d_inner; * cleaning unused hparams * cleanup * more cleanups * more cleanups on python conversion; * minor cleanups * Apply suggestions from code review Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * remove todo * added falcon-h1 * tensor not required * clean * remove unneeded attributes * more cleanups and fixed conversion * remove final_norm * flake8 fixes * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * flake8 fixes * Update src/llama-hparams.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update src/llama-arch.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * added hashes * Update src/llama-arch.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update src/llama-vocab.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update the update file * Revert "update the update file" This reverts commit 082ab4ad2a3927384d878666a5f8cae4eb15f577. * fix: address suggestions * fix: update convert_hf_to_gguf.py * Update gguf-py/gguf/constants.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update src/llama-model-loader.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * d_inner fixed * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * reshaping ssm_norm for 34B * removing generate_mup * remove duplicates metadata keys * rm comment * final comment * fix unused args * fix constants * fix bad merge * Update src/llama-model.cpp Co-authored-by: compilade <git@compilade.net> * falcon-h1: remove unused ssm_in_b and bad merge * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * falcon-h1: fix last comment * Update convert_hf_to_gguf.py Co-authored-by: compilade <git@compilade.net> * falcon-h1: revert add_add_bos(False) * falcon-h1: fix tied weights * falcon-h1: remove whitespace * falcon-h1: fix wrong size param * falcon-h1: fix whitespace issues --------- Co-authored-by: younesbelkada <younes.belkada@tii.ae> Co-authored-by: Younes B <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> Co-authored-by: compilade <git@compilade.net>
This commit is contained in:
ibrahim khadraoui
GitHub
parent
20b7bf8a32
commit
04655063c4
8 changed files with 585 additions and 9 deletions
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@ -1550,6 +1550,37 @@ void llama_model::load_hparams(llama_model_loader & ml) {
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default: type = LLM_TYPE_UNKNOWN;
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}
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} break;
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case LLM_ARCH_FALCON_H1:
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{
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// Common parameters
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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// SSM parameters
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ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
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ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
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ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
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ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
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ml.get_key(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group);
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std::fill(hparams.recurrent_layer_arr.begin(), hparams.recurrent_layer_arr.end(), true);
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switch (hparams.n_layer) {
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case 36:
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type = LLM_TYPE_0_5B; break;
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case 24:
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type = LLM_TYPE_1_5B; break;
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case 66:
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type = LLM_TYPE_1B; break;
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case 32:
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type = LLM_TYPE_3B; break;
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case 44:
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type = LLM_TYPE_7B; break;
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case 72:
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type = LLM_TYPE_34B; break;
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default:
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type = LLM_TYPE_UNKNOWN;
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}
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} break;
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case LLM_ARCH_HUNYUAN_MOE:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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@ -4497,6 +4528,83 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
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}
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} break;
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case LLM_ARCH_FALCON_H1:
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{
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// Common
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const int64_t hidden_size = hparams.n_embd; // hidden_size
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// mamba2 Mixer SSM params
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const int64_t ssm_conv_kernel_size = hparams.ssm_d_conv; // ssm_conv_kernel_size
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const int64_t ssm_n_groups = hparams.ssm_n_group; // ssm_n_groups
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const int64_t ssm_state_size = hparams.ssm_d_state; // ssm_state_size
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const int64_t ssm_intermediate_size = hparams.ssm_d_inner; // TODO expand
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const int64_t ssm_num_heads = hparams.ssm_dt_rank; // ssm_num_heads
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const int64_t ssm_conv_dim = ssm_intermediate_size + 2 * ssm_n_groups * ssm_state_size;
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const int64_t ssm_projection_size = ssm_intermediate_size + ssm_conv_dim + ssm_num_heads;
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// attn params
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const int64_t attn_num_attention_head = hparams.n_head(0); // rename to: attn_num_attention_head
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const int64_t attn_num_key_value_head = hparams.n_head_kv(0);
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// ffn params
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const int64_t ffn_intermediate_size = hparams.n_ff(0);
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// embeddings
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hidden_size, n_vocab}, 0);
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// output
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output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hidden_size, n_vocab}, TENSOR_NOT_REQUIRED);
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output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {hidden_size}, 0);
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// if output is NULL, init from the input tok embed
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if (output == NULL) {
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output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hidden_size, n_vocab}, TENSOR_DUPLICATED);
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}
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = layers[i];
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/*SSM LAYERS*/
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// ssm in
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layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {hidden_size, ssm_projection_size}, 0);
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// ssm 1d conv
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layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {ssm_conv_kernel_size, ssm_conv_dim}, 0);
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layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {ssm_conv_dim}, TENSOR_NOT_REQUIRED);
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// ssm_dt
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layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {ssm_num_heads}, 0);
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// no "weight" suffix for these
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layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, ssm_num_heads}, 0);
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layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, ssm_num_heads}, 0);
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// ssm_norm
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layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {ssm_intermediate_size / ssm_n_groups, ssm_n_groups}, TENSOR_NOT_REQUIRED);
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// out_proj
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layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {ssm_intermediate_size, hidden_size}, 0);
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/*ATTENTION LAYERS*/
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// attention layers (with optional bias)
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layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {hidden_size, n_embd_head_k * attn_num_attention_head}, 0);
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layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {hidden_size, attn_num_key_value_head * n_embd_head_k}, 0);
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layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {hidden_size, attn_num_key_value_head * n_embd_head_v}, 0);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * attn_num_attention_head, hidden_size}, 0);
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layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {hidden_size}, TENSOR_NOT_REQUIRED);
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layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {attn_num_key_value_head * n_embd_head_k}, TENSOR_NOT_REQUIRED);
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layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {attn_num_key_value_head * n_embd_head_v}, TENSOR_NOT_REQUIRED);
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layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {hidden_size}, TENSOR_NOT_REQUIRED);
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {hidden_size}, 0);
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// feed forward (w/ optional biases)
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layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, i), {hidden_size}, 0);
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layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
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layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {hidden_size, ffn_intermediate_size}, 0);
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { ffn_intermediate_size, hidden_size}, 0);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {hidden_size, ffn_intermediate_size}, 0);
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layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {ffn_intermediate_size}, TENSOR_NOT_REQUIRED);
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layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {hidden_size}, TENSOR_NOT_REQUIRED);
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layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {ffn_intermediate_size}, TENSOR_NOT_REQUIRED);
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}
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} break;
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case LLM_ARCH_HUNYUAN_MOE:
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{
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
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@ -10147,7 +10255,7 @@ struct llm_build_mamba : public llm_graph_context {
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// {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens}
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cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens * n_seqs);
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// cb(cur, "mamba_out", il);
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cb(cur, "mamba_out", il);
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return cur;
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}
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@ -14598,6 +14706,267 @@ struct llm_build_ernie4_5 : public llm_graph_context {
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}
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};
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struct llm_build_falcon_h1 : public llm_graph_context {
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const llama_model & model;
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llm_build_falcon_h1(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params), model(model) {
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const int64_t n_embd_head = hparams.n_embd_head_v;
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ggml_tensor * cur;
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ggml_tensor * inpL;
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inpL = build_inp_embd(model.tok_embd);
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// inp_pos - contains the positions
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ggml_tensor * inp_pos = build_inp_pos();
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// Build the inputs in the recurrent & kv cache
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auto * inp = build_inp_mem_hybrid();
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const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
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ggml_tensor * inp_out_ids = build_inp_out_ids();
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for (int il = 0; il < n_layer; ++il) {
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ggml_tensor * inpSA = inpL;
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cur = build_norm(inpL,
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model.layers[il].attn_norm, NULL,
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LLM_NORM_RMS, il);
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cb(cur, "attn_norm", il);
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// self-attention
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ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
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cb(Vcur, "Vcur", il);
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
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Qcur = ggml_rope_ext(
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ctx0, Qcur, inp_pos, nullptr,
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n_rot, hparams.rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow);
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Kcur = ggml_rope_ext(
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ctx0, Kcur, inp_pos, nullptr,
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n_rot, hparams.rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Qcur, "Qcur-post-rope", il);
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cb(Kcur, "Kcur-post-rope", il);
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cb(Vcur, "Vcur-post-rope", il);
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ggml_tensor * attn_out = build_attn(inp, gf,
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model.layers[il].wo, NULL,
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Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
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cb(attn_out, "attn_out", il);
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cur = build_norm(inpL,
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model.layers[il].attn_norm, NULL,
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LLM_NORM_RMS, il);
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// Mamba2 layer
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cb(cur, "ssm_in", il);
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ggml_tensor * ssm_out = build_mamba2_layer(inp, gf, cur, ubatch, il);
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cb(ssm_out, "ssm_out", il);
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// // Aggregation
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cur = ggml_add(ctx0, attn_out, ssm_out);
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inpSA = ggml_add(ctx0, cur, inpSA);
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cb(cur, "layer_out", il);
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if (il == n_layer - 1 && inp_out_ids) {
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cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
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}
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ggml_tensor * ffn_inp = inpSA;
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cb(ffn_inp, "ffn_inp", il);
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// feed-forward network
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cur = build_norm(ffn_inp,
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model.layers[il].ffn_norm, NULL,
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LLM_NORM_RMS, il);
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cb(cur, "ffn_norm", il);
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cur = build_ffn(cur,
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model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
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model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
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model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, il);
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cb(cur, "ffn_out", il);
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cur = ggml_add(ctx0, cur, inpSA);
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cur = build_cvec(cur, il);
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cb(cur, "l_out", il);
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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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cur = build_norm(cur,
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model.output_norm, NULL,
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LLM_NORM_RMS, -1);
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cb(cur, "result_norm", -1);
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res->t_embd = cur;
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// lm_head
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cur = build_lora_mm(model.output, cur);
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cb(cur, "result_output", -1);
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res->t_logits = cur;
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ggml_build_forward_expand(gf, cur);
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}
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ggml_tensor * build_mamba2_layer(
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llm_graph_input_mem_hybrid * inp,
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ggml_cgraph * gf,
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ggml_tensor * cur,
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const llama_ubatch & ubatch,
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int il) const {
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const auto * kv_state = static_cast<const llama_memory_hybrid_context *>(mctx)->get_recr();
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const auto kv_head = kv_state->get_head();
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const int64_t d_conv = hparams.ssm_d_conv;
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const int64_t d_inner = hparams.ssm_d_inner;
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const int64_t d_state = hparams.ssm_d_state;
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const int64_t n_head = hparams.ssm_dt_rank;
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const int64_t head_dim = d_inner / n_head;
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const int64_t n_group = hparams.ssm_n_group;
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const int64_t n_seqs = ubatch.n_seqs;
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const int64_t n_seq_tokens = ubatch.n_seq_tokens;
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GGML_ASSERT(n_seqs != 0);
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GGML_ASSERT(ubatch.equal_seqs);
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GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
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ggml_tensor * conv_states_all = kv_state->get_r_l(il);
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ggml_tensor * ssm_states_all = kv_state->get_s_l(il);
|
||||
|
||||
ggml_tensor * conv = build_rs(inp, gf, conv_states_all, hparams.n_embd_r(), n_seqs);
|
||||
conv = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_inner + 2*n_group*d_state, n_seqs);
|
||||
|
||||
// {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs}
|
||||
cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);
|
||||
|
||||
// d_in_proj = 2 * self.d_inner + 2 * self.ngroups * self.d_state + self.nheads
|
||||
|
||||
// {n_embd, d_in_proj} @ {n_embd, n_seq_tokens, n_seqs} => {d_in_proj, n_seq_tokens, n_seqs}
|
||||
ggml_tensor * zxBCdt = build_lora_mm(model.layers[il].ssm_in, cur);
|
||||
cb(zxBCdt, "zxBCdt", il);
|
||||
|
||||
// split the above in three
|
||||
ggml_tensor * z = ggml_view_4d(ctx0, zxBCdt, head_dim, n_head, n_seq_tokens, n_seqs, head_dim*zxBCdt->nb[0], zxBCdt->nb[1], zxBCdt->nb[2], 0);
|
||||
ggml_tensor * xBC = ggml_view_3d(ctx0, zxBCdt, d_inner + 2*n_group*d_state, n_seq_tokens, n_seqs, zxBCdt->nb[1], zxBCdt->nb[2], d_inner*ggml_element_size(zxBCdt));
|
||||
ggml_tensor * dt = ggml_view_3d(ctx0, zxBCdt, n_head, n_seq_tokens, n_seqs, zxBCdt->nb[1], zxBCdt->nb[2], (2*d_inner + 2*n_group*d_state)*ggml_element_size(zxBCdt));
|
||||
|
||||
// conv
|
||||
{
|
||||
// => {d_conv - 1 + n_seq_tokens, d_inner + 2*n_group*d_state, n_seqs}
|
||||
ggml_tensor * conv_x = ggml_concat(ctx0, conv, ggml_transpose(ctx0, xBC), 0);
|
||||
|
||||
// copy last (d_conv - 1) columns back into the state cache
|
||||
ggml_tensor * last_conv = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner + 2*n_group*d_state, n_seqs, conv_x->nb[1], conv_x->nb[2], n_seq_tokens*(conv_x->nb[0]));
|
||||
|
||||
ggml_build_forward_expand(gf,
|
||||
ggml_cpy(ctx0, last_conv,
|
||||
ggml_view_1d(ctx0, conv_states_all,
|
||||
(d_conv - 1)*(d_inner + 2*n_group*d_state)*(n_seqs),
|
||||
kv_head*(d_conv - 1)*(d_inner + 2*n_group*d_state)*ggml_element_size(conv_states_all))));
|
||||
|
||||
// 1D convolution
|
||||
// The equivalent is to make a self-overlapping view of conv_x
|
||||
// over d_conv columns at each stride in the 3rd dimension,
|
||||
// then element-wise multiply that with the conv1d weight,
|
||||
// then sum the elements of each row,
|
||||
// (the last two steps are a dot product over rows (also doable with mul_mat))
|
||||
// then permute away the ne[0] dimension,
|
||||
// and then you're left with the resulting x tensor.
|
||||
// For simultaneous sequences, all sequences need to have the same length.
|
||||
xBC = ggml_ssm_conv(ctx0, conv_x, model.layers[il].ssm_conv1d);
|
||||
|
||||
// bias
|
||||
xBC = ggml_add(ctx0, xBC, model.layers[il].ssm_conv1d_b);
|
||||
|
||||
xBC = ggml_silu(ctx0, xBC);
|
||||
}
|
||||
|
||||
// ssm
|
||||
{
|
||||
// These correspond to V K Q in SSM/attention duality
|
||||
ggml_tensor * x = ggml_view_4d(ctx0, xBC, head_dim, n_head, n_seq_tokens, n_seqs, head_dim*xBC->nb[0], xBC->nb[1], xBC->nb[2], 0);
|
||||
|
||||
ggml_tensor * B = ggml_view_4d(ctx0, xBC, d_state, n_group, n_seq_tokens, n_seqs, d_state*xBC->nb[0], xBC->nb[1], xBC->nb[2], d_inner*ggml_element_size(xBC));
|
||||
|
||||
ggml_tensor * C = ggml_view_4d(ctx0, xBC, d_state, n_group, n_seq_tokens, n_seqs, d_state*xBC->nb[0], xBC->nb[1], xBC->nb[2], (d_inner + n_group*d_state)*ggml_element_size(xBC));
|
||||
|
||||
// {n_head, n_seq_tokens, n_seqs}
|
||||
dt = ggml_add(ctx0, ggml_cont(ctx0, dt), model.layers[il].ssm_dt_b);
|
||||
|
||||
ggml_tensor * A = model.layers[il].ssm_a;
|
||||
|
||||
// use the states and the indices provided by build_rs
|
||||
// (this is necessary in order to properly use the states before they are overwritten,
|
||||
// while avoiding to make unnecessary copies of the states)
|
||||
auto get_ssm_rows = [&](ggml_context * ctx, ggml_tensor * states, ggml_tensor * ids) {
|
||||
ggml_tensor * ssm = ggml_reshape_4d(ctx, states, d_state, head_dim, n_head, kv_state->get_size());
|
||||
|
||||
// TODO: use semistructured matrices to implement state-space duality
|
||||
// => {d_inner, n_seq_tokens, n_seqs} and {d_state, d_inner, n_seqs}
|
||||
return ggml_ssm_scan(ctx, ssm, x, dt, A, B, C, ids);
|
||||
};
|
||||
|
||||
ggml_tensor * y_ssm = build_rs(inp, gf, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows);
|
||||
|
||||
// store last states
|
||||
ggml_build_forward_expand(gf,
|
||||
ggml_cpy(ctx0,
|
||||
ggml_view_1d(ctx0, y_ssm, d_state*d_inner*n_seqs, ggml_nelements(x)*x->nb[0]),
|
||||
ggml_view_1d(ctx0, ssm_states_all, d_state*d_inner*n_seqs, kv_head*d_state*d_inner*ggml_element_size(ssm_states_all))));
|
||||
|
||||
ggml_tensor * y = ggml_view_4d(ctx0, y_ssm, head_dim, n_head, n_seq_tokens, n_seqs, x->nb[1], n_head*x->nb[1], n_seq_tokens*n_head*x->nb[1], 0);
|
||||
|
||||
// TODO: skip computing output earlier for unused tokens
|
||||
|
||||
y = ggml_add(ctx0, y, ggml_mul(ctx0, x, model.layers[il].ssm_d));
|
||||
y = ggml_swiglu_split(ctx0, ggml_cont(ctx0, z), y);
|
||||
|
||||
// grouped RMS norm
|
||||
if (model.layers[il].ssm_norm) {
|
||||
y = ggml_reshape_4d(ctx0, y, d_inner / n_group, n_group, n_seq_tokens, n_seqs);
|
||||
y = build_norm(y, model.layers[il].ssm_norm, NULL, LLM_NORM_RMS, il);
|
||||
}
|
||||
|
||||
y = ggml_reshape_3d(ctx0, y, d_inner, n_seq_tokens, n_seqs);
|
||||
|
||||
// {d_inner, n_embd} @ {d_inner, n_seq_tokens, n_seqs} => {n_embd, n_seq_tokens, n_seqs}
|
||||
cur = build_lora_mm(model.layers[il].ssm_out, y);
|
||||
}
|
||||
|
||||
// {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens}
|
||||
cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens * n_seqs);
|
||||
cb(cur, "mamba_out", il);
|
||||
return cur;
|
||||
}
|
||||
};
|
||||
|
||||
struct llm_build_arcee : public llm_graph_context {
|
||||
llm_build_arcee(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
|
|
@ -15077,7 +15446,9 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
|
|||
/* recurrent_type_v */ GGML_TYPE_F32,
|
||||
/* recurrent_kv_size */ std::max((uint32_t) 1, cparams.n_seq_max),
|
||||
/* n_seq_max */ cparams.n_seq_max,
|
||||
/* offload */ cparams.offload_kqv);
|
||||
/* offload */ cparams.offload_kqv,
|
||||
/* filter_attn */ (arch == LLM_ARCH_FALCON_H1) ? [&](int32_t) { return true; } : (llama_memory_hybrid::layer_filter_cb)nullptr,
|
||||
/* filter_recr */ (arch == LLM_ARCH_FALCON_H1) ? [&](int32_t) { return true; } : (llama_memory_hybrid::layer_filter_cb)nullptr);
|
||||
} else {
|
||||
const auto padding = llama_kv_cache_unified::get_padding(cparams);
|
||||
|
||||
|
|
@ -15419,6 +15790,10 @@ llm_graph_result_ptr llama_model::build_graph(
|
|||
{
|
||||
llm = std::make_unique<llm_build_smollm3>(*this, params, gf);
|
||||
} break;
|
||||
case LLM_ARCH_FALCON_H1:
|
||||
{
|
||||
llm = std::make_unique<llm_build_falcon_h1>(*this, params, gf);
|
||||
} break;
|
||||
default:
|
||||
GGML_ABORT("fatal error");
|
||||
}
|
||||
|
|
@ -15577,6 +15952,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
|
|||
|
||||
// the pairs of head values are offset by n_rot/2
|
||||
case LLM_ARCH_FALCON:
|
||||
case LLM_ARCH_FALCON_H1:
|
||||
case LLM_ARCH_GROK:
|
||||
case LLM_ARCH_DBRX:
|
||||
case LLM_ARCH_BERT:
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue