# Token embeddings
MODEL_TENSOR.TOKEN_EMBD: (
"gpt_neox.embed_in", # gptneox
- "transformer.wte", # gpt2 gpt-j mpt refact qwen
+ "transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx
"transformer.word_embeddings", # falcon
"word_embeddings", # bloom
"model.embed_tokens", # llama-hf
# Output
MODEL_TENSOR.OUTPUT: (
"embed_out", # gptneox
- "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba
+ "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx
"output", # llama-pth bloom internlm2
"word_embeddings_for_head", # persimmon
"lm_head.linear", # phi2
"transformer.ln_f", # gpt2 gpt-j falcon
"model.norm", # llama-hf baichuan internlm2
"norm", # llama-pth
- "transformer.norm_f", # mpt
+ "transformer.norm_f", # mpt dbrx
"ln_f", # refact bloom qwen gpt2
"language_model.encoder.final_layernorm", # persimmon
"model.final_layernorm", # persimmon
"model.layers.{bid}.norm", # mamba-qbert
"backbone.layers.{bid}.norm", # mamba
"transformer.decoder_layer.{bid}.rms_norm", # Grok
+ "transformer.blocks.{bid}.norm_attn_norm.norm_1", # dbrx
),
# Attention norm 2
"gpt_neox.layers.{bid}.attention.query_key_value", # gptneox
"transformer.h.{bid}.attn.c_attn", # gpt2 qwen
"transformer.blocks.{bid}.attn.Wqkv", # mpt
+ "transformer.blocks.{bid}.norm_attn_norm.attn.Wqkv", # dbrx
"transformer.h.{bid}.self_attention.query_key_value", # falcon
"h.{bid}.self_attention.query_key_value", # bloom
"language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon
# Attention output
MODEL_TENSOR.ATTN_OUT: (
- "gpt_neox.layers.{bid}.attention.dense", # gptneox
- "transformer.h.{bid}.attn.c_proj", # gpt2 refact qwen
- "transformer.blocks.{bid}.attn.out_proj", # mpt
- "transformer.h.{bid}.self_attention.dense", # falcon
- "h.{bid}.self_attention.dense", # bloom
- "model.layers.{bid}.self_attn.o_proj", # llama-hf
- "layers.{bid}.attention.wo", # llama-pth
- "encoder.layer.{bid}.attention.output.dense", # bert
- "transformer.h.{bid}.attn.out_proj", # gpt-j
- "language_model.encoder.layers.{bid}.self_attention.dense", # persimmon
- "model.layers.{bid}.self_attn.dense", # persimmon
- "h.{bid}.attn.c_proj", # gpt2
- "transformer.h.{bid}.mixer.out_proj", # phi2
- "model.layers.layers.{bid}.self_attn.o_proj", # plamo
- "model.layers.{bid}.attention.wo", # internlm2
- "encoder.layers.{bid}.attn.out_proj", # nomic-bert
- "transformer.decoder_layer.{bid}.multi_head_attention.linear"# Grok
+ "gpt_neox.layers.{bid}.attention.dense", # gptneox
+ "transformer.h.{bid}.attn.c_proj", # gpt2 refact qwen
+ "transformer.blocks.{bid}.attn.out_proj", # mpt
+ "transformer.h.{bid}.self_attention.dense", # falcon
+ "h.{bid}.self_attention.dense", # bloom
+ "model.layers.{bid}.self_attn.o_proj", # llama-hf
+ "layers.{bid}.attention.wo", # llama-pth
+ "encoder.layer.{bid}.attention.output.dense", # bert
+ "transformer.h.{bid}.attn.out_proj", # gpt-j
+ "language_model.encoder.layers.{bid}.self_attention.dense", # persimmon
+ "model.layers.{bid}.self_attn.dense", # persimmon
+ "h.{bid}.attn.c_proj", # gpt2
+ "transformer.h.{bid}.mixer.out_proj", # phi2
+ "model.layers.layers.{bid}.self_attn.o_proj", # plamo
+ "model.layers.{bid}.attention.wo", # internlm2
+ "encoder.layers.{bid}.attn.out_proj", # nomic-bert
+ "transformer.decoder_layer.{bid}.multi_head_attention.linear", # Grok
+ "transformer.blocks.{bid}.norm_attn_norm.attn.out_proj", # dbrx
),
# Attention output norm
"encoder.layer.{bid}.attention.output.LayerNorm", # bert
"encoder.layers.{bid}.norm1", # nomic-bert
"transformer.decoder_layer.{bid}.rms_norm_1", # Grok
+ "transformer.blocks.{bid}.norm_attn_norm.norm_2", # dbrx
),
# Rotary embeddings
),
MODEL_TENSOR.FFN_GATE_INP: (
- "layers.{bid}.feed_forward.gate", # mixtral
- "model.layers.{bid}.block_sparse_moe.gate", # mixtral
- "transformer.decoder_layer.{bid}.router" # Grok
+ "layers.{bid}.feed_forward.gate", # mixtral
+ "model.layers.{bid}.block_sparse_moe.gate", # mixtral
+ "transformer.decoder_layer.{bid}.router", # Grok
+ "transformer.blocks.{bid}.ffn.router.layer", # dbrx
),
# Feed-forward up
MODEL_TENSOR.FFN_UP_EXP: (
"layers.{bid}.feed_forward.experts.w3", # mixtral (merged)
"transformer.decoder_layer.{bid}.moe.linear_v", # Grok (merged)
+ "transformer.blocks.{bid}.ffn.experts.mlp.v1", # dbrx
),
# AWQ-activation gate
),
MODEL_TENSOR.FFN_GATE_EXP: (
- "layers.{bid}.feed_forward.experts.w1", # mixtral (merged)
- "transformer.decoder_layer.{bid}.moe.linear" # Grok (merged)
+ "layers.{bid}.feed_forward.experts.w1", # mixtral (merged)
+ "transformer.decoder_layer.{bid}.moe.linear", # Grok (merged)
+ "transformer.blocks.{bid}.ffn.experts.mlp.w1", # dbrx
),
# Feed-forward down
MODEL_TENSOR.FFN_DOWN_EXP: (
"layers.{bid}.feed_forward.experts.w2", # mixtral (merged)
"transformer.decoder_layer.{bid}.moe.linear_1", # Grok (merged)
+ "transformer.blocks.{bid}.ffn.experts.mlp.w2", # dbrx
),
MODEL_TENSOR.ATTN_Q_NORM: (
#endif
#define LLAMA_MAX_NODES 8192
-#define LLAMA_MAX_EXPERTS 8
+#define LLAMA_MAX_EXPERTS 16
//
LLM_ARCH_MAMBA,
LLM_ARCH_XVERSE,
LLM_ARCH_COMMAND_R,
+ LLM_ARCH_DBRX,
LLM_ARCH_UNKNOWN,
};
{ LLM_ARCH_MAMBA, "mamba" },
{ LLM_ARCH_XVERSE, "xverse" },
{ LLM_ARCH_COMMAND_R, "command-r" },
+ { LLM_ARCH_DBRX, "dbrx" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
},
},
+ {
+ LLM_ARCH_DBRX,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
+ { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
+ { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
+ { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
+ { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
+ },
+ },
{
LLM_ARCH_UNKNOWN,
{
MODEL_XL,
MODEL_8x7B,
MODEL_8x22B,
+ MODEL_16x12B,
};
static const size_t kiB = 1024;
case MODEL_XL: return "1.5B";
case MODEL_8x7B: return "8x7B";
case MODEL_8x22B: return "8x22B";
+ case MODEL_16x12B: return "16x12B";
default: return "?B";
}
}
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
+ case LLM_ARCH_DBRX:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+ ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv);
+
+ switch (hparams.n_layer) {
+ case 40: model.type = e_model::MODEL_16x12B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
default: (void)0;
}
layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
}
} break;
+ case LLM_ARCH_DBRX:
+ {
+ if (n_expert == 0) {
+ throw std::runtime_error("DBRX model cannot have zero experts");
+ }
+
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
+ }
+
+ for (int i = 0; i < n_layer; ++i) {
+ ggml_context * ctx_layer = ctx_for_layer(i);
+ ggml_context * ctx_split = ctx_for_layer_split(i);
+
+ auto & layer = model.layers[i];
+
+ layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+
+ layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+
+ layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
+
+ layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
+ layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert});
+ layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert});
+ layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert});
+ }
+ } break;
case LLM_ARCH_BAICHUAN:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
- ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts]
- cb(logits, "ffn_moe_logits", il);
-
- ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts]
- cb(probs, "ffn_moe_probs", il);
-
- // select experts
- ggml_tensor * selected_experts = ggml_top_k(ctx0, probs, n_expert_used); // [n_tokens, num_experts_per_tok]
- cb(selected_experts->src[0], "ffn_moe_argsort", il);
-
- ggml_tensor * weights = ggml_get_rows(ctx0,
- ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts);
- cb(weights, "ffn_moe_weights", il);
-
- weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok]
-
- ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights);
- cb(weights_sum, "ffn_moe_weights_sum", il);
-
- weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok]
- cb(weights, "ffn_moe_weights_norm", il);
-
- // compute expert outputs
- ggml_tensor * moe_out = nullptr;
-
- for (int i = 0; i < n_expert_used; ++i) {
- ggml_tensor * cur_expert;
-
- ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exps, selected_experts, i, cur);
- cb(cur_up, "ffn_moe_up", il);
-
- ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur);
- cb(cur_gate, "ffn_moe_gate", il);
-
- cur_gate = ggml_silu(ctx0, cur_gate);
- cb(cur_gate, "ffn_moe_silu", il);
-
- cur_expert = ggml_mul(ctx0, cur_up, cur_gate);
- cb(cur_expert, "ffn_moe_gate_par", il);
-
- cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, selected_experts, i, cur_expert); // [n_tokens, n_embd]
- cb(cur_expert, "ffn_moe_down", il);
-
- cur_expert = ggml_mul(ctx0, cur_expert,
- ggml_view_2d(ctx0, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
- cb(cur_expert, "ffn_moe_weighted", il);
-
- if (i == 0) {
- moe_out = cur_expert;
- } else {
- moe_out = ggml_add(ctx0, moe_out, cur_expert);
- cb(moe_out, "ffn_moe_out", il);
- }
- }
-
- cur = moe_out;
+ cur = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
return gf;
}
+ // REVIEW: will be replaced by https://github.com/ggerganov/llama.cpp/pull/6505
+ ggml_tensor * build_moe_ffn(ggml_tensor * cur, int32_t n_tokens, llm_ffn_op_type type_op, int il) {
+ ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts]
+ cb(logits, "ffn_moe_logits", il);
+
+ ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts]
+ cb(probs, "ffn_moe_probs", il);
+
+ // select experts
+ ggml_tensor * selected_experts = ggml_top_k(ctx0, probs, n_expert_used); // [n_tokens, num_experts_per_tok]
+ cb(selected_experts->src[0], "ffn_moe_argsort", il);
+
+ ggml_tensor * weights = ggml_get_rows(ctx0,
+ ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts);
+ cb(weights, "ffn_moe_weights", il);
+
+ weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok]
+
+ ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights);
+ cb(weights_sum, "ffn_moe_weights_sum", il);
+
+ weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok]
+ cb(weights, "ffn_moe_weights_norm", il);
+
+ // compute expert outputs
+ ggml_tensor * moe_out = nullptr;
+
+ for (int i = 0; i < n_expert_used; ++i) {
+ ggml_tensor * cur_expert;
+
+ ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exps, selected_experts, i, cur);
+ cb(cur_up, "ffn_moe_up", il);
+
+ ggml_tensor * gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur);
+ cb(gate, "ffn_moe_gate", il);
+
+ switch (type_op) {
+ case LLM_FFN_SILU:
+ {
+ gate = ggml_silu(ctx0, gate);
+ cb(gate, "ffn_moe_silu", il);
+ } break;
+ case LLM_FFN_GELU:
+ {
+ gate = ggml_gelu(ctx0, gate);
+ cb(gate, "ffn_moe_gelu", il);
+ } break;
+ default:
+ GGML_ASSERT(false);
+ }
+
+ cur_expert = ggml_mul(ctx0, cur_up, gate);
+ cb(cur_expert, "ffn_moe_gate_par", il);
+
+ cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, selected_experts, i, cur_expert); // [n_tokens, n_embd]
+ cb(cur_expert, "ffn_moe_down", il);
+
+ cur_expert = ggml_mul(ctx0, cur_expert,
+ ggml_view_2d(ctx0, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
+ cb(cur_expert, "ffn_moe_weighted", il);
+
+ if (i == 0) {
+ moe_out = cur_expert;
+ } else {
+ moe_out = ggml_add(ctx0, moe_out, cur_expert);
+ cb(moe_out, "ffn_moe_out", il);
+ }
+ }
+
+ return moe_out;
+ }
+
struct ggml_cgraph * build_baichuan() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
- ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts]
- cb(logits, "ffn_moe_logits", il);
+ cur = build_moe_ffn(cur, n_tokens, LLM_FFN_GELU, il);
+
+ // Grok
+ // if layer_out_norm is present then apply it before adding the input
+ // Idea: maybe ffn_out_norm is a better name
+ if (model.layers[il].layer_out_norm) {
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.layers[il].layer_out_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "layer_out_norm", il);
+ }
+
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cb(cur, "ffn_out", il);
+
+ ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
+ if (layer_dir != nullptr) {
+ cur = ggml_add(ctx0, cur, layer_dir);
+ }
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
- ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts]
- cb(probs, "ffn_moe_probs", il);
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
- // select experts
- ggml_tensor * selected_experts = ggml_top_k(ctx0, probs, n_expert_used); // [n_tokens, num_experts_per_tok]
- cb(selected_experts->src[0], "ffn_moe_argsort", il);
+ // lm_head
+ cur = ggml_mul_mat(ctx0, model.output, cur);
- ggml_tensor * weights = ggml_get_rows(ctx0,
- ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts);
- cb(weights, "ffn_moe_weights", il);
+ // Grok
+ // multiply logits by output_multiplier_scale of 0.5773502691896257
- weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok]
+ cur = ggml_scale(ctx0, cur, 0.5773502691896257f);
- ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights);
- cb(weights_sum, "ffn_moe_weights_sum", il);
+ cb(cur, "result_output", -1);
- weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok]
- cb(weights, "ffn_moe_weights_norm", il);
+ ggml_build_forward_expand(gf, cur);
- // compute expert outputs
- ggml_tensor * moe_out = nullptr;
+ return gf;
+ }
- for (int i = 0; i < n_expert_used; ++i) {
- ggml_tensor * cur_expert;
+ struct ggml_cgraph * build_dbrx() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
- ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exps, selected_experts, i, cur);
- cb(cur_up, "ffn_moe_up", il);
+ // mutable variable, needed during the last layer of the computation to skip unused tokens
+ int32_t n_tokens = this->n_tokens;
- ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur);
- cb(cur_gate, "ffn_moe_gate", il);
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+ GGML_ASSERT(n_embd_head == hparams.n_rot);
- //GeLU
- cur_gate = ggml_gelu(ctx0, cur_gate);
- cb(cur_gate, "ffn_moe_gelu", il);
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
- cur_expert = ggml_mul(ctx0, cur_up, cur_gate);
- cb(cur_expert, "ffn_moe_gate_par", il);
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
- cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, selected_experts, i, cur_expert); // [n_tokens, n_embd]
- cb(cur_expert, "ffn_moe_down", il);
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
- cur_expert = ggml_mul(ctx0, cur_expert,
- ggml_view_2d(ctx0, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
- cb(cur_expert, "ffn_moe_weighted", il);
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
- if (i == 0) {
- moe_out = cur_expert;
- } else {
- moe_out = ggml_add(ctx0, moe_out, cur_expert);
- cb(moe_out, "ffn_moe_out", il);
- }
- }
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * inpSA = inpL;
- cur = moe_out;
+ // norm
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM, cb, il);
+ cb(cur, "attn_norm", il);
- // Grok
- // if layer_out_norm is present then apply it before adding the input
- // Idea: maybe ffn_out_norm is a better name
- if (model.layers[il].layer_out_norm) {
- cur = llm_build_norm(ctx0, cur, hparams,
- model.layers[il].layer_out_norm, NULL,
- LLM_NORM_RMS, cb, il);
- cb(cur, "layer_out_norm", il);
+ // self-attention
+ {
+ struct ggml_tensor * Qcur = nullptr;
+ struct ggml_tensor * Kcur = nullptr;
+ struct ggml_tensor * Vcur = nullptr;
+
+ cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+ cb(cur, "wqkv", il);
+
+ cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
+ cb(cur, "wqkv_clamped", il);
+
+ Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+ Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+ Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_rope_custom(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
+ n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_custom(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+ n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+ model.layers[il].wo, NULL,
+ Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ n_tokens = n_outputs;
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward network
+ // MoE branch
+ cur = llm_build_norm(ctx0, ffn_inp, hparams,
+ model.layers[il].attn_out_norm, NULL,
+ LLM_NORM, cb, il);
+ cb(cur, "attn_out_norm", il);
+
+ cur = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, il);
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "ffn_out", il);
cur = inpL;
cur = llm_build_norm(ctx0, cur, hparams,
- model.output_norm, NULL,
- LLM_NORM_RMS, cb, -1);
+ model.output_norm, NULL,
+ LLM_NORM, cb, -1);
cb(cur, "result_norm", -1);
// lm_head
cur = ggml_mul_mat(ctx0, model.output, cur);
- // Grok
- // multiply logits by output_multiplier_scale of 0.5773502691896257
-
- cur = ggml_scale(ctx0, cur, 0.5773502691896257f);
-
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
{
result = llm.build_command_r();
} break;
+ case LLM_ARCH_DBRX:
+ {
+ result = llm.build_dbrx();
+ } break;
default:
GGML_ASSERT(false);
}
// the pairs of head values are offset by n_rot/2
case LLM_ARCH_FALCON:
case LLM_ARCH_GROK:
+ case LLM_ARCH_DBRX:
case LLM_ARCH_PERSIMMON:
case LLM_ARCH_BERT:
case LLM_ARCH_NOMIC_BERT:
overview / pkg / ggml / sources / llama.cpp / commitdiff