sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf
+from convert import HfVocab
+
# check for any of the given keys in the dictionary and return the value of the first key found
def get_key_opts(d, keys):
return OrionModel
if model_architecture == "InternLM2ForCausalLM":
return InternLM2Model
+ if model_architecture == "MiniCPMForCausalLM":
+ return MiniCPMModel
return Model
def _is_model_safetensors(self) -> bool:
return gguf.MODEL_ARCH.ORION
if arch == "InternLM2ForCausalLM":
return gguf.MODEL_ARCH.INTERNLM2
+ if arch == "MiniCPMForCausalLM":
+ return gguf.MODEL_ARCH.MINICPM
raise NotImplementedError(f'Architecture "{arch}" not supported!')
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
+ def _set_vocab_hf(self):
+ path = self.dir_model
+ added_tokens_path = self.dir_model
+ vocab = HfVocab(
+ path, added_tokens_path if added_tokens_path.exists() else None
+ )
+ tokens = []
+ scores = []
+ toktypes = []
+
+ for text, score, toktype in vocab.all_tokens():
+ tokens.append(text)
+ scores.append(score)
+ toktypes.append(toktype)
+
+ assert len(tokens) == vocab.vocab_size
+
+ self.gguf_writer.add_tokenizer_model("llama")
+ self.gguf_writer.add_token_list(tokens)
+ self.gguf_writer.add_token_scores(scores)
+ self.gguf_writer.add_token_types(toktypes)
+
+ special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+ special_vocab.add_to_gguf(self.gguf_writer)
+
class GPTNeoXModel(Model):
def set_gguf_parameters(self):
self._set_vocab_sentencepiece()
+class MiniCPMModel(Model):
+ def set_gguf_parameters(self):
+ block_count = self.hparams["num_hidden_layers"]
+ self.gguf_writer.add_name("MiniCPM")
+ self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
+ self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
+ self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
+ self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
+ self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
+ self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
+ self.gguf_writer.add_file_type(self.ftype)
+ self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
+
+ def set_vocab(self):
+ self._set_vocab_hf()
+
+
class QwenModel(Model):
@staticmethod
def token_bytes_to_string(b):
LLM_ARCH_CODESHELL,
LLM_ARCH_ORION,
LLM_ARCH_INTERNLM2,
+ LLM_ARCH_MINICPM,
LLM_ARCH_UNKNOWN,
};
{ LLM_ARCH_CODESHELL, "codeshell" },
{ LLM_ARCH_ORION, "orion" },
{ LLM_ARCH_INTERNLM2, "internlm2" },
+ { LLM_ARCH_MINICPM, "minicpm" },
};
enum llm_kv {
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
+ {
+ LLM_ARCH_MINICPM,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
+ { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
+ { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
+ { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ { LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" },
+ { LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
+ { LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
+ },
+ },
{
LLM_ARCH_UNKNOWN,
{
MODEL_UNKNOWN,
MODEL_0_5B,
MODEL_1B,
+ MODEL_2B,
MODEL_3B,
MODEL_4B,
MODEL_7B,
static const char * llama_model_type_name(e_model type) {
switch (type) {
case MODEL_1B: return "1B";
+ case MODEL_2B: return "2B";
case MODEL_3B: return "3B";
case MODEL_7B: return "7B";
case MODEL_8B: return "8B";
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
+ case LLM_ARCH_MINICPM:
+ {
+ switch (hparams.n_layer) {
+ case 40: model.type = e_model::MODEL_2B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
case LLM_ARCH_FALCON:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
switch (model.arch) {
case LLM_ARCH_LLAMA:
case LLM_ARCH_REFACT:
+ case LLM_ARCH_MINICPM:
{
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});
+ if (model.arch != LLM_ARCH_MINICPM){
+ 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) {
return gf;
}
+ // ref: https://arxiv.org/abs/2203.03466
+ // https://github.com/ggerganov/llama.cpp/issues/5276#issuecomment-1925774738
+ // based on the original build_llama() function
+ struct ggml_cgraph * build_minicpm() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+ GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+ const int64_t n_embd = hparams.n_embd;
+ //TODO: if the model varies, these parameters need to be read from the model
+ const int64_t n_embd_base = 256;
+ const float scale_embd = 12.0f;
+ const float scale_depth = 1.4f;
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
+ cb(inpL, "inp_embd", -1);
+
+ // scale the input embeddings
+ inpL = ggml_scale(ctx0, inpL, scale_embd);
+ cb(inpL, "inp_scaled", -1);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
+ cb(inp_pos, "inp_pos", -1);
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
+ cb(KQ_mask, "KQ_mask", -1);
+
+ // shift the entire K-cache if needed
+ if (do_rope_shift) {
+ llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb);
+ }
+
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * inpSA = inpL;
+
+ // norm
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+ if (model.layers[il].bq) {
+ Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+ cb(Qcur, "Qcur", il);
+ }
+
+ struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+ if (model.layers[il].bk) {
+ Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+ cb(Kcur, "Kcur", il);
+ }
+
+ struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+ if (model.layers[il].bv) {
+ Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+ cb(Vcur, "Vcur", il);
+ }
+
+ Qcur = ggml_rope_custom(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
+ hparams.n_rot, 0, 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,
+ hparams.n_rot, 0, 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, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ cb(cur, "kqv_out", il);
+ }
+
+ // scale_res - scale the hidden states for residual connection
+ const float scale_res = scale_depth/sqrtf(float(n_layer));
+ cur = ggml_scale(ctx0, cur, scale_res);
+ cb(cur, "hidden_scaled", -1);
+
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward network
+ {
+ cur = llm_build_norm(ctx0, ffn_inp, hparams,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_ffn(ctx0, cur,
+ model.layers[il].ffn_up, NULL,
+ model.layers[il].ffn_gate, NULL,
+ model.layers[il].ffn_down, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ // scale the hidden states for residual connection
+ cur = ggml_scale(ctx0, cur, scale_res);
+ cb(cur, "hidden_scaled_ffn", -1);
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head scaling
+ const float scale_lmhead = float(n_embd_base)/float(n_embd);
+ cur = ggml_scale(ctx0, cur, scale_lmhead);
+ cb(cur, "lmhead_scaling", -1);
+
+ // lm_head
+ cur = ggml_mul_mat(ctx0, model.tok_embd, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
};
static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_internlm2();
} break;
+ case LLM_ARCH_MINICPM:
+ {
+ result = llm.build_minicpm();
+ } break;
default:
GGML_ASSERT(false);
}
overview / pkg / ggml / sources / llama.cpp / commitdiff