mirror of
https://github.com/nomic-ai/gpt4all
synced 2024-11-08 07:10:32 +00:00
fix: helly changes
This commit is contained in:
Zach Nussbaum
Adam Treat
parent
d30be81506
commit
61e2aabadb
412
llmodel/mpt.cpp
412
llmodel/mpt.cpp
@ -19,37 +19,31 @@
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static const size_t MB = 1024*1024;
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// default hparams (MPT 7B)
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struct mpt_hparams {
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int32_t n_vocab = 50432;
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int32_t n_ctx = 2048;
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int32_t n_embd = 4096;
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int32_t n_head = 32;
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int32_t n_layer = 32;
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// this isn't used should we remove?
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int32_t n_rot = 0;
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int32_t f16 = 1;
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int32_t n_vocab = 50432;
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int32_t n_ctx = 2048;
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int32_t n_embd = 4096;
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int32_t n_head = 32;
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int32_t n_layer = 32;
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float alibi_bias_max = 8;
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float clip_qkv = 0;
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int32_t expand = 4;
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int32_t f16 = 1;
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};
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struct mpt_layer {
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// normalization
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struct ggml_tensor * norm_1_g;
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struct ggml_tensor * norm_1_b;
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struct ggml_tensor * norm_2_g;
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struct ggml_tensor * norm_2_b;
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struct ggml_tensor * norm_1_w;
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struct ggml_tensor * norm_2_w;
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// attention
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struct ggml_tensor * c_attn_q_proj_w;
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struct ggml_tensor * c_attn_k_proj_w;
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struct ggml_tensor * c_attn_v_proj_w;
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struct ggml_tensor * c_attn_proj_w;
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struct ggml_tensor * attn_Wqkv_w;
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struct ggml_tensor * attn_out_proj_w;
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// ff
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struct ggml_tensor * up_proj_w;
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struct ggml_tensor * up_proj_b;
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struct ggml_tensor * down_proj_w;
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struct ggml_tensor * down_proj_b;
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struct ggml_tensor * ffn_up_proj_w;
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struct ggml_tensor * ffn_down_proj_w;
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};
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struct mpt_buffer {
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@ -90,8 +84,7 @@ struct mpt_model {
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mpt_hparams hparams;
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// normalization
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struct ggml_tensor * ln_f_g;
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struct ggml_tensor * ln_f_b;
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struct ggml_tensor * norm_f_w;
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struct ggml_tensor * wte; // position embedding
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@ -159,10 +152,11 @@ struct mpt_vocab {
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bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & model, mpt_vocab & vocab) {
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printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
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// verify magic
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{
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uint32_t magic;
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fin.read((char *) &magic, sizeof(magic));
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if (magic != 0x67676d6d) {
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if (magic != 0x67676d6d) {
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fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
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return false;
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}
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@ -174,23 +168,26 @@ bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & mod
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fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
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fin.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx));
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fin.read((char *) &hparams.n_embd, sizeof(hparams.n_embd));
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fin.read((char *) &hparams.n_head, sizeof(hparams.n_head));
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fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
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fin.read((char *) &hparams.n_rot, sizeof(hparams.n_rot));
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fin.read((char *) &hparams.f16, sizeof(hparams.f16));
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fin.read((char *) &hparams.n_head, sizeof(hparams.n_head));
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fin.read((char *) &hparams.n_embd, sizeof(hparams.n_embd));
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fin.read((char *) &hparams.alibi_bias_max, sizeof(hparams.alibi_bias_max));
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fin.read((char *) &hparams.clip_qkv, sizeof(hparams.clip_qkv));
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fin.read((char *) &hparams.f16, sizeof(hparams.f16));
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printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
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printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
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printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
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printf("%s: n_head = %d\n", __func__, hparams.n_head);
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printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
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printf("%s: n_rot = %d\n", __func__, hparams.n_rot);
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printf("%s: f16 = %d\n", __func__, hparams.f16);
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printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
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printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
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printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
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printf("%s: n_head = %d\n", __func__, hparams.n_head);
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printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
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printf("%s: alibi_bias_max = %f\n", __func__, hparams.alibi_bias_max);
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printf("%s: clip_qkv = %f\n", __func__, hparams.clip_qkv);
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printf("%s: ftype = %d\n", __func__, hparams.f16);
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}
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// load vocab
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{
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int32_t n_vocab = 0;
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int32_t n_vocab = model.hparams.n_vocab;
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fin.read((char *) &n_vocab, sizeof(n_vocab));
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if (n_vocab != model.hparams.n_vocab) {
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@ -203,14 +200,24 @@ bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & mod
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for (int i = 0; i < n_vocab; i++) {
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uint32_t len;
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fin.read((char *) &len, sizeof(len));
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bool special = false;
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if (len & (1<<31)) {
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len = len &~ (1<<31);
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special = true;
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}
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word.resize(len);
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fin.read((char *) word.data(), len);
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if (len > 0) {
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word.resize(len);
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fin.read((char *) word.data(), len);
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vocab.token_to_id[word] = i;
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vocab.id_to_token[i] = word;
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}
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vocab.token_to_id[word] = i;
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vocab.id_to_token[i] = word;
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// TODO: this only kind-of works, the gpt_tokenize can still incorrectly
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// tokenize special tokens
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}
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}
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// for the big tensors, we have the option to store the data in 16-bit floats or quantized
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// in order to save memory and also to speed up the computation
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ggml_type wtype = GGML_TYPE_COUNT;
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@ -227,8 +234,6 @@ bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & mod
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}
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}
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const ggml_type wtype2 = GGML_TYPE_F32;
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auto & ctx = model.ctx;
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size_t ctx_size = 0;
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@ -240,38 +245,26 @@ bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & mod
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const int n_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int n_vocab = hparams.n_vocab;
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const int expand = hparams.expand;
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ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_g
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ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_b
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ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // wte
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ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_w
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// with weight tying i don't think we need this
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// ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // lmh_g
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// ctx_size += n_vocab*ggml_type_sizef(GGML_TYPE_F32); // lmh_b
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ctx_size += n_embd*n_vocab*ggml_type_sizef(GGML_TYPE_F32); // wte
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ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_g
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ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_b
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ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // norm_1_w
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ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // norm_2_w
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ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_q_proj_w
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ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_k_proj_w
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ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_v_proj_w
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ctx_size += n_layer*(3*n_embd*n_embd*ggml_type_sizef(wtype)); // attn_Wqkv_w
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ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // attn_out_proj_w
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ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_proj_w
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ctx_size += n_layer*(expand*n_embd*n_embd*ggml_type_sizef(wtype)); // ffn_up_proj_w
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ctx_size += n_layer*(expand*n_embd*n_embd*ggml_type_sizef(wtype)); // ffn_down_proj_w
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ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F16); // memory_k
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ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F16); // memory_v
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ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_fc_w
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ctx_size += n_layer*( 4*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_fc_b
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ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_proj_w
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ctx_size += n_layer*( n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_proj_b
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ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_k
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ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v
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ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_g
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ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_b
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// TODO: what is this??
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// TODO probably less now?
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ctx_size += (5 + 10*n_layer)*256; // object overhead
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printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
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@ -282,6 +275,7 @@ bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & mod
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struct ggml_init_params params = {
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.mem_size = ctx_size,
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.mem_buffer = NULL,
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.no_alloc = false,
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};
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model.ctx = ggml_init(params);
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@ -290,7 +284,8 @@ bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & mod
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return false;
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}
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}
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// prepare memory for the weights
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// prepare memory for the weights
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{
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const auto & hparams = model.hparams;
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@ -298,66 +293,40 @@ bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & mod
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const int n_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int n_vocab = hparams.n_vocab;
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const int expand = hparams.expand;
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model.layers.resize(n_layer);
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model.wte = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
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model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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// we don't need this because of weight tying
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// model.lmh_g = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
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// model.lmh_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_vocab);
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model.wte = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
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model.norm_f_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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// map by name
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model.tensors["transformer.wte.weight"] = model.wte;
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model.tensors["transformer.ln_f.weight"] = model.ln_f_g;
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model.tensors["transformer.ln_f.bias"] = model.ln_f_b;
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model.tensors["transformer.norm_f.weight"] = model.norm_f_w;
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = model.layers[i];
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layer.norm_1_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.norm_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.norm_1_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.norm_2_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.norm_2_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.norm_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.c_attn_q_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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layer.c_attn_k_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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layer.c_attn_v_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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layer.c_attn_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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layer.up_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 4*n_embd);
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layer.up_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);
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layer.down_proj_w = ggml_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd);
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layer.down_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.attn_Wqkv_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd * 3);
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layer.attn_out_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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layer.ffn_up_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, expand*n_embd);
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layer.ffn_down_proj_w = ggml_new_tensor_2d(ctx, wtype, expand*n_embd, n_embd);
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// map by name
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model.tensors["transformer.blocks." + std::to_string(i) + ".norm_1.weight"] = layer.norm_1_g;
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model.tensors["transformer.blocks." + std::to_string(i) + ".norm_1.bias"] = layer.norm_1_b;
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model.tensors["transformer.blocks." + std::to_string(i) + ".norm_1.weight"] = layer.norm_1_w;
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model.tensors["transformer.blocks." + std::to_string(i) + ".norm_2.weight"] = layer.norm_2_w;
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model.tensors["transformer.blocks." + std::to_string(i) + ".attn.Wqkv.weight"] = layer.attn_Wqkv_w;
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model.tensors["transformer.blocks." + std::to_string(i) + ".attn.out_proj.weight"] = layer.attn_out_proj_w;
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model.tensors["transformer.blocks." + std::to_string(i) + ".attn.q_proj.weight"] = layer.c_attn_q_proj_w;
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model.tensors["transformer.blocks." + std::to_string(i) + ".attn.k_proj.weight"] = layer.c_attn_k_proj_w;
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model.tensors["transformer.blocks." + std::to_string(i) + ".attn.v_proj.weight"] = layer.c_attn_v_proj_w;
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model.tensors["transformer.blocks." + std::to_string(i) + ".attn.out_proj.weight"] = layer.c_attn_proj_w;
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model.tensors["transformer.blocks." + std::to_string(i) + ".ffn.up_proj.weight"] = layer.up_proj_w;
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model.tensors["transformer.blocks." + std::to_string(i) + ".ffn.up_proj.bias"] = layer.up_proj_b;
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model.tensors["transformer.blocks." + std::to_string(i) + ".ffn.down_proj.weight"] = layer.down_proj_w;
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model.tensors["transformer.blocks." + std::to_string(i) + ".ffn.down_proj.bias"] = layer.down_proj_b;
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model.tensors["transformer.blocks." + std::to_string(i) + ".norm_2.weight"] = layer.norm_2_g;
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model.tensors["transformer.blocks." + std::to_string(i) + ".norm_2.bias"] = layer.norm_2_b;
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model.tensors["transformer.blocks." + std::to_string(i) + ".ffn.up_proj.weight"] = layer.ffn_up_proj_w;
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model.tensors["transformer.blocks." + std::to_string(i) + ".ffn.down_proj.weight"] = layer.ffn_down_proj_w;
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}
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}
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// key + value memory
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// key + value memory
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{
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const auto & hparams = model.hparams;
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@ -377,7 +346,8 @@ bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & mod
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const size_t memory_size = ggml_nbytes(model.kv_self.k) + ggml_nbytes(model.kv_self.v);
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printf("%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
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}
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// load weights
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// load weights
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{
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int n_tensors = 0;
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size_t total_size = 0;
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@ -387,13 +357,11 @@ bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & mod
|
||||
while (true) {
|
||||
int32_t n_dims;
|
||||
int32_t length;
|
||||
int32_t ftype;
|
||||
int32_t ttype;
|
||||
|
||||
fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
|
||||
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
|
||||
fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
|
||||
|
||||
fprintf(stderr, "%s: n_dims = %d, length = %d, ftype = %d\n", __func__, n_dims, length, ftype);
|
||||
fin.read(reinterpret_cast<char *>(&ttype), sizeof(ttype));
|
||||
|
||||
if (fin.eof()) {
|
||||
break;
|
||||
@ -408,7 +376,7 @@ bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & mod
|
||||
|
||||
std::string name(length, 0);
|
||||
fin.read(&name[0], length);
|
||||
fprintf(stderr, "%s: %s\n", __func__, name.data());
|
||||
|
||||
if (model.tensors.find(name.data()) == model.tensors.end()) {
|
||||
fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
|
||||
return false;
|
||||
@ -421,29 +389,17 @@ bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & mod
|
||||
}
|
||||
|
||||
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%lu, %lu], expected [%d, %d]\n",
|
||||
__func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
|
||||
__func__, name.data(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]);
|
||||
return false;
|
||||
}
|
||||
|
||||
// for debugging
|
||||
if (0) {
|
||||
static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
|
||||
printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ftype_str[ftype], ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
|
||||
printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
|
||||
}
|
||||
|
||||
size_t bpe = 0;
|
||||
|
||||
switch (ftype) {
|
||||
case 0: bpe = ggml_type_size(GGML_TYPE_F32); break;
|
||||
case 1: bpe = ggml_type_size(GGML_TYPE_F16); break;
|
||||
case 2: bpe = ggml_type_size(GGML_TYPE_Q4_0); assert(ne[0] % 64 == 0); break;
|
||||
case 3: bpe = ggml_type_size(GGML_TYPE_Q4_1); assert(ne[0] % 64 == 0); break;
|
||||
default:
|
||||
{
|
||||
fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype);
|
||||
return false;
|
||||
}
|
||||
};
|
||||
const size_t bpe = ggml_type_size(ggml_type(ttype));
|
||||
|
||||
if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
|
||||
@ -453,8 +409,7 @@ bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & mod
|
||||
|
||||
fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
|
||||
|
||||
|
||||
fprintf(stderr, "%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
|
||||
//printf("%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ttype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
|
||||
total_size += ggml_nbytes(tensor);
|
||||
if (++n_tensors % 8 == 0) {
|
||||
printf(".");
|
||||
@ -468,7 +423,6 @@ bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & mod
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@ -502,29 +456,30 @@ bool mpt_eval(
|
||||
const int n_ctx = hparams.n_ctx;
|
||||
const int n_head = hparams.n_head;
|
||||
const int n_vocab = hparams.n_vocab;
|
||||
const int n_rot = hparams.n_rot;
|
||||
const int expand = hparams.expand;
|
||||
|
||||
const int d_key = n_embd/n_head;
|
||||
|
||||
static size_t buf_size = 1024u*MB;
|
||||
if (!model.buf.addr || model.buf.size < buf_size)
|
||||
model.buf.resize(buf_size);
|
||||
static size_t buf_size = 256u*1024*1024;
|
||||
static void * buf = malloc(buf_size);
|
||||
|
||||
if (mem_per_token > 0 && mem_per_token*N > model.buf.size) {
|
||||
if (mem_per_token > 0 && mem_per_token*N > buf_size) {
|
||||
const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
|
||||
printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, model.buf.size, buf_size_new);
|
||||
//printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
|
||||
|
||||
// reallocate
|
||||
model.buf.resize(buf_size_new);
|
||||
if (model.buf.addr == nullptr) {
|
||||
fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, model.buf.size);
|
||||
buf_size = buf_size_new;
|
||||
buf = realloc(buf, buf_size);
|
||||
if (buf == nullptr) {
|
||||
fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
struct ggml_init_params params = {
|
||||
.mem_size = model.buf.size,
|
||||
.mem_buffer = model.buf.addr,
|
||||
.mem_size = buf_size,
|
||||
.mem_buffer = buf,
|
||||
.no_alloc = false,
|
||||
};
|
||||
|
||||
struct ggml_context * ctx0 = ggml_init(params);
|
||||
@ -537,27 +492,26 @@ bool mpt_eval(
|
||||
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.wte, embd);
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
struct ggml_tensor * cur;
|
||||
|
||||
// norm
|
||||
{
|
||||
cur = ggml_norm(ctx0, inpL);
|
||||
|
||||
// cur = ln_1_g*cur + ln_1_b
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_mul(ctx0,
|
||||
ggml_repeat(ctx0, model.layers[il].norm_1_g, cur),
|
||||
cur),
|
||||
ggml_repeat(ctx0, model.layers[il].norm_1_b, cur));
|
||||
}
|
||||
|
||||
struct ggml_tensor * inpSA = cur;
|
||||
|
||||
struct ggml_tensor * inpSA = inpL;
|
||||
struct ggml_tensor * cur = inpSA;
|
||||
// self-attention
|
||||
{
|
||||
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].c_attn_q_proj_w, cur);
|
||||
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].c_attn_k_proj_w, cur);
|
||||
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].c_attn_v_proj_w, cur);
|
||||
|
||||
// norm1
|
||||
cur = ggml_norm(ctx0, cur);
|
||||
cur = ggml_mul(ctx0,
|
||||
ggml_repeat(ctx0, model.layers[il].norm_1_w, cur),
|
||||
cur);
|
||||
// compute QKV
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].attn_Wqkv_w,
|
||||
cur);
|
||||
|
||||
// TODO: clip_qkv
|
||||
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*ggml_element_size(cur)*n_embd));
|
||||
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*ggml_element_size(cur)*n_embd));
|
||||
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*ggml_element_size(cur)*n_embd));
|
||||
|
||||
// store key and value to memory
|
||||
if (N >= 1) {
|
||||
@ -567,21 +521,17 @@ bool mpt_eval(
|
||||
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
|
||||
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
|
||||
}
|
||||
|
||||
// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
|
||||
struct ggml_tensor * Q =
|
||||
ggml_permute(ctx0,
|
||||
ggml_cpy(ctx0,
|
||||
Qcur,
|
||||
ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),
|
||||
ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, N),
|
||||
0, 2, 1, 3);
|
||||
|
||||
// K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
|
||||
struct ggml_tensor * K =
|
||||
ggml_permute(ctx0,
|
||||
ggml_reshape_3d(ctx0,
|
||||
ggml_view_1d(ctx0, model.kv_self.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.kv_self.k)*n_embd),
|
||||
n_embd/n_head, n_head, n_past + N),
|
||||
ggml_permute(ctx0,
|
||||
ggml_reshape_3d(ctx0,
|
||||
ggml_view_1d(ctx0, model.kv_self.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.kv_self.k)*n_embd),
|
||||
n_embd/n_head, n_head, n_past + N),
|
||||
0, 2, 1, 3);
|
||||
|
||||
// K * Q
|
||||
@ -594,26 +544,26 @@ bool mpt_eval(
|
||||
ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
|
||||
);
|
||||
|
||||
struct ggml_tensor * KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, n_past, n_head);
|
||||
|
||||
// Alibi
|
||||
struct ggml_tensor * KQ_scaled_biased = ggml_alibi(ctx0, ggml_cont(ctx0, KQ_scaled), n_past, n_head);
|
||||
|
||||
// KQ_masked = mask_past(KQ_scaled)
|
||||
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled_alibi, n_past);
|
||||
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled_biased, n_past);
|
||||
|
||||
// KQ = soft_max(KQ_masked)
|
||||
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
|
||||
|
||||
// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
|
||||
struct ggml_tensor * V_trans =
|
||||
ggml_cpy(ctx0,
|
||||
ggml_permute(ctx0,
|
||||
ggml_reshape_3d(ctx0,
|
||||
ggml_view_1d(ctx0, model.kv_self.v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.kv_self.v)*n_embd),
|
||||
n_embd/n_head, n_head, n_past + N),
|
||||
1, 2, 0, 3),
|
||||
ggml_new_tensor_3d(ctx0, model.kv_self.v->type, n_past + N, n_embd/n_head, n_head));
|
||||
struct ggml_tensor * V =
|
||||
ggml_view_3d(ctx0, model.kv_self.v,
|
||||
n_past + N, n_embd/n_head, n_head,
|
||||
n_ctx*ggml_element_size(model.kv_self.v),
|
||||
n_ctx*ggml_element_size(model.kv_self.v)*n_embd/n_head,
|
||||
il*n_ctx*ggml_element_size(model.kv_self.v)*n_embd);
|
||||
|
||||
// KQV = transpose(V) * KQ_soft_max
|
||||
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
|
||||
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
||||
|
||||
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
||||
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
||||
@ -625,93 +575,55 @@ bool mpt_eval(
|
||||
|
||||
// projection (no bias)
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].c_attn_proj_w,
|
||||
model.layers[il].attn_out_proj_w,
|
||||
cur);
|
||||
}
|
||||
|
||||
// norm 2
|
||||
{
|
||||
cur = ggml_norm(ctx0, cur);
|
||||
|
||||
// cur = ln_1_g*cur + ln_1_b
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_mul(ctx0,
|
||||
ggml_repeat(ctx0, model.layers[il].norm_2_g, cur),
|
||||
cur),
|
||||
ggml_repeat(ctx0, model.layers[il].norm_2_b, cur));
|
||||
}
|
||||
|
||||
struct ggml_tensor * inpFF = cur;
|
||||
|
||||
// residual
|
||||
struct ggml_tensor * resSA = ggml_add(ctx0, cur, inpSA);
|
||||
// feed-forward network
|
||||
// this is independent of the self-attention result, so it could be done in parallel to the self-attention
|
||||
{
|
||||
// note here we pass inpSA instead of cur
|
||||
cur = resSA;
|
||||
// norm2
|
||||
cur = ggml_norm(ctx0, cur);
|
||||
cur = ggml_mul(ctx0,
|
||||
ggml_repeat(ctx0, model.layers[il].norm_2_w, cur),
|
||||
cur);
|
||||
// ffn
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].up_proj_w,
|
||||
inpSA);
|
||||
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_repeat(ctx0, model.layers[il].up_proj_b, cur),
|
||||
model.layers[il].ffn_up_proj_w,
|
||||
cur);
|
||||
|
||||
// RELU activation
|
||||
cur = ggml_relu(ctx0, cur);
|
||||
|
||||
// projection
|
||||
// cur = proj_w*cur + proj_b
|
||||
cur = ggml_gelu(ctx0, cur);
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].down_proj_w,
|
||||
model.layers[il].ffn_down_proj_w,
|
||||
cur);
|
||||
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_repeat(ctx0, model.layers[il].down_proj_b, cur),
|
||||
cur);
|
||||
}
|
||||
|
||||
// self-attention + FF
|
||||
cur = ggml_add(ctx0, cur, inpFF);
|
||||
|
||||
// input for next layer
|
||||
inpL = ggml_add(ctx0, cur, inpL);
|
||||
inpL = ggml_add(ctx0, cur, resSA);
|
||||
}
|
||||
|
||||
// norm
|
||||
struct ggml_tensor * out = inpL;
|
||||
// -> logits
|
||||
{
|
||||
inpL = ggml_norm(ctx0, inpL);
|
||||
|
||||
// inpL = ln_f_g*inpL + ln_f_b
|
||||
inpL = ggml_add(ctx0,
|
||||
ggml_mul(ctx0,
|
||||
ggml_repeat(ctx0, model.ln_f_g, inpL),
|
||||
inpL),
|
||||
ggml_repeat(ctx0, model.ln_f_b, inpL));
|
||||
out = ggml_norm(ctx0, out);
|
||||
out = ggml_mul(ctx0,
|
||||
ggml_repeat(ctx0, model.norm_f_w, out),
|
||||
out);
|
||||
out = ggml_mul_mat(ctx0, model.wte, out);
|
||||
}
|
||||
|
||||
// lm_head with weight tying
|
||||
{
|
||||
inpL = ggml_mul_mat(ctx0, model.wte, inpL);
|
||||
|
||||
}
|
||||
|
||||
// logits -> probs
|
||||
//inpL = ggml_soft_max(ctx0, inpL);
|
||||
|
||||
// run the computation
|
||||
ggml_build_forward_expand(&gf, inpL);
|
||||
ggml_build_forward_expand(&gf, out);
|
||||
ggml_graph_compute (ctx0, &gf);
|
||||
|
||||
//if (n_past%100 == 0) {
|
||||
// ggml_graph_print (&gf);
|
||||
// ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
|
||||
//}
|
||||
|
||||
//embd_w.resize(n_vocab*N);
|
||||
//memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
|
||||
|
||||
// return result for just the last token
|
||||
embd_w.resize(n_vocab);
|
||||
memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
|
||||
memcpy(embd_w.data(), (float *) ggml_get_data(out) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
|
||||
|
||||
if (mem_per_token == 0) {
|
||||
mem_per_token = ggml_used_mem(ctx0)/N;
|
||||
|
||||
Loading…
Reference in New Issue
Block a user