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Attempt at OpenElm

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joshcarp 2024-04-29 14:24:59 -04:00
parent 5539e6fdd1
commit 6e89d82269
5 changed files with 369 additions and 6 deletions
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101
convert-hf-to-gguf.py
View file

@ -56,7 +56,7 @@ class Model(ABC):
self.part_names = self._get_part_names()
self.hparams = Model.load_hparams(self.dir_model)
self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file)
self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_transformer_layers"])
@property
@abstractmethod
@ -2903,6 +2903,105 @@ class OlmoModel(Model):
self.gguf_writer.add_tensor(new_name, data)
@Model.register("OpenELMForCausalLM")
class OpenELM(Model):
model_arch = gguf.MODEL_ARCH.OPENELM
def set_gguf_parameters(self):
self.gguf_writer.add_name("OpenElm")
self.block_count = self.find_hparam(["num_transformer_layers"])
self.gguf_writer.add_layer_norm_eps(1e-5)
self.gguf_writer.add_layer_norm_rms_eps(1e-6) # https://github.com/apple/corenet/blob/0333b1fbb29c31809663c4e6de2654b9ff2d27de/mlx_examples/open_elm/open_elm.py#L20
n_embd = self.find_hparam(["model_dim"])
self.gguf_writer.add_embedding_length(n_embd)
head_dim = self.find_hparam(["head_dim"])
n_head = n_embd // head_dim
rot_pct = 1.0
self.gguf_writer.add_context_length(self.find_hparam(["max_context_length"]))
self.gguf_writer.add_embedding_length(n_embd)
self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_head_count(n_head)
self.gguf_writer.add_head_count_kv(n_head)
self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
self.gguf_writer.add_file_type(self.ftype)
def set_vocab(self):
from sentencepiece import SentencePieceProcessor
tokenizer_path = self.dir_model / 'tokenizer.model'
if not tokenizer_path.is_file():
print(f'Error: Missing {tokenizer_path}', file=sys.stderr)
sys.exit(1)
tokenizer = SentencePieceProcessor(str(tokenizer_path))
vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
scores: list[float] = [-10000.0] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
for token_id in range(tokenizer.vocab_size()):
piece = tokenizer.id_to_piece(token_id)
text = piece.encode("utf-8")
score = tokenizer.get_score(token_id)
toktype = SentencePieceTokenTypes.NORMAL
if tokenizer.is_unknown(token_id):
toktype = SentencePieceTokenTypes.UNKNOWN
elif tokenizer.is_control(token_id):
toktype = SentencePieceTokenTypes.CONTROL
elif tokenizer.is_unused(token_id):
toktype = SentencePieceTokenTypes.UNUSED
elif tokenizer.is_byte(token_id):
toktype = SentencePieceTokenTypes.BYTE
tokens[token_id] = text
scores[token_id] = score
toktypes[token_id] = toktype
added_tokens_file = self.dir_model / 'added_tokens.json'
if added_tokens_file.is_file():
with open(added_tokens_file, "r", encoding="utf-8") as f:
added_tokens_json = json.load(f)
for key in added_tokens_json:
token_id = added_tokens_json[key]
if (token_id >= vocab_size):
print(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
continue
tokens[token_id] = key.encode("utf-8")
scores[token_id] = -1000.0
toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
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)
# Same as super class, but permuting q_proj, k_proj
# Copied from: LlamaModel
def write_tensors(self):
block_count = self.hparams.get("num_transformer_layers", self.hparams.get("num_hidden_layers", self.hparams.get("num_transformer_layers")))
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
n_head = self.hparams.get("model_dim") // self.hparams.get("head_dim") # TODO: propagate this
for name, data_torch in self.get_tensors():
old_dtype = data_torch.dtype
# convert any unsupported data types to float32
if data_torch.dtype not in (torch.float16, torch.float32):
data_torch = data_torch.to(torch.float32)
data = data_torch.numpy()
data = data.squeeze()
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()
new_name += ".weight"
n_dims = len(data.shape)
data_dtype = data.dtype
# if f32 desired, convert any float16 to float32
if self.ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32)
# 1d tensors need to be converted to float32
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
data = data.astype(np.float32)
# if f16 desired, convert any float32 2-dim weight tensors to float16
if self.ftype == 1 and data_dtype == np.float32 and n_dims == 2:
data = data.astype(np.float16)
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
self.gguf_writer.add_tensor(new_name, data)
###### CONVERSION LOGIC ######

14
gguf-py/gguf/constants.py
View file

@ -138,6 +138,7 @@ class MODEL_ARCH(IntEnum):
COMMAND_R = auto()
DBRX = auto()
OLMO = auto()
OPENELM = auto()
class MODEL_TENSOR(IntEnum):
@ -215,6 +216,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.COMMAND_R: "command-r",
MODEL_ARCH.DBRX: "dbrx",
MODEL_ARCH.OLMO: "olmo",
MODEL_ARCH.OPENELM: "openelm",
}
TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@ -725,6 +727,18 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.OPENELM: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_K_NORM,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_NORM,
],
# TODO
}

21
gguf-py/gguf/tensor_mapping.py
View file

@ -24,6 +24,7 @@ class TensorNameMap:
"backbone.embedding", # mamba
"backbone.embeddings", # mamba-hf
"transformer.in_out_embed", # Grok
"transformer.token_embeddings.weight", # openelm
),
# Token type embeddings
@ -36,6 +37,7 @@ class TensorNameMap:
"word_embeddings_layernorm", # bloom
"embeddings.LayerNorm", # bert
"emb_ln", # nomic-bert
"transformer.norm.weight", # openelm
),
# Position embeddings
@ -68,6 +70,7 @@ class TensorNameMap:
"model.norm_f", # mamba-qbert
"backbone.norm_f", # mamba
"transformer.rms_norm", # Grok
"transformer.norm.weight" # openelm
),
# Rope frequencies
@ -97,6 +100,7 @@ class TensorNameMap:
"backbone.layers.{bid}.norm", # mamba
"transformer.decoder_layer.{bid}.rms_norm", # Grok
"transformer.blocks.{bid}.norm_attn_norm.norm_1", # dbrx
"transformer.layers.{bid}.attn_norm.weight" # openelm
),
# Attention norm 2
@ -117,7 +121,8 @@ class TensorNameMap:
"h.{bid}.attn.c_attn", # gpt2
"transformer.h.{bid}.mixer.Wqkv", # phi2
"encoder.layers.{bid}.attn.Wqkv", # nomic-bert
"model.layers.{bid}.self_attn.qkv_proj" # phi3
"model.layers.{bid}.self_attn.qkv_proj", # phi3
"transformer.layers.{bid}.attn.qkv_proj.weight" # openelm
),
# Attention query
@ -128,7 +133,7 @@ class TensorNameMap:
"transformer.h.{bid}.attn.q_proj", # gpt-j
"model.layers.layers.{bid}.self_attn.q_proj", # plamo
"model.layers.{bid}.attention.wq", # internlm2
"transformer.decoder_layer.{bid}.multi_head_attention.query" # Grok
"transformer.decoder_layer.{bid}.multi_head_attention.query",# Grok
),
# Attention key
@ -139,7 +144,8 @@ class TensorNameMap:
"transformer.h.{bid}.attn.k_proj", # gpt-j
"model.layers.layers.{bid}.self_attn.k_proj", # plamo
"model.layers.{bid}.attention.wk", # internlm2
"transformer.decoder_layer.{bid}.multi_head_attention.key" # Grok
"transformer.decoder_layer.{bid}.multi_head_attention.key", # Grok
"transformer.layers.{bid}.attn.k_norm.weight" # openelm
),
# Attention value
@ -150,7 +156,8 @@ class TensorNameMap:
"transformer.h.{bid}.attn.v_proj", # gpt-j
"model.layers.layers.{bid}.self_attn.v_proj", # plamo
"model.layers.{bid}.attention.wv", # internlm2
"transformer.decoder_layer.{bid}.multi_head_attention.value" # Grok
"transformer.decoder_layer.{bid}.multi_head_attention.value",# Grok
),
# Attention output
@ -173,6 +180,7 @@ class TensorNameMap:
"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
"transformer.layers.{bid}.attn.out_proj.weight" # openelm
),
# Attention output norm
@ -204,6 +212,7 @@ class TensorNameMap:
"h.{bid}.ln_2", # gpt2
"model.layers.{bid}.ffn_norm", # internlm2
"transformer.decoder_layer.{bid}.rms_norm_2", # Grok
"transformer.layers.{bid}.ffn_norm.weight", # openelm
),
MODEL_TENSOR.FFN_GATE_INP: (
@ -240,6 +249,7 @@ class TensorNameMap:
"model.layers.{bid}.feed_forward.w3", # internlm2
"encoder.layers.{bid}.mlp.fc11", # nomic-bert
"model.layers.{bid}.mlp.c_fc", # starcoder2
"transformer.layers.{bid}.ffn.proj_1.weight" # openelm
),
MODEL_TENSOR.FFN_UP_EXP: (
@ -299,6 +309,7 @@ class TensorNameMap:
"model.layers.{bid}.feed_forward.w2", # internlm2
"encoder.layers.{bid}.mlp.fc2", # nomic-bert
"model.layers.{bid}.mlp.c_proj", # starcoder2
"transformer.layers.{bid}.ffn.proj_2.weight" # openelm
),
MODEL_TENSOR.FFN_DOWN_EXP: (
@ -317,6 +328,7 @@ class TensorNameMap:
"model.layers.{bid}.self_attn.q_layernorm", # persimmon
"model.layers.{bid}.self_attn.q_norm", # cohere
"transformer.blocks.{bid}.attn.q_ln", # sea-lion
"transformer.layers.{bid}.attn.q_norm.weight" # openelm
),
MODEL_TENSOR.ATTN_K_NORM: (
@ -324,6 +336,7 @@ class TensorNameMap:
"model.layers.{bid}.self_attn.k_layernorm", # persimmon
"model.layers.{bid}.self_attn.k_norm", # cohere
"transformer.blocks.{bid}.attn.k_ln", # sea-lion
"transformer.layers.{bid}.attn.k_norm.weight"
),
MODEL_TENSOR.ROPE_FREQS: (

237
llama.cpp
View file

@ -225,6 +225,7 @@ enum llm_arch {
LLM_ARCH_COMMAND_R,
LLM_ARCH_DBRX,
LLM_ARCH_OLMO,
LLM_ARCH_OPENELM,
LLM_ARCH_UNKNOWN,
};
@ -261,6 +262,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_COMMAND_R, "command-r" },
{ LLM_ARCH_DBRX, "dbrx" },
{ LLM_ARCH_OLMO, "olmo" },
{ LLM_ARCH_OPENELM, "openelm" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@ -1028,6 +1030,21 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_OPENELM,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
},
},
{
LLM_ARCH_UNKNOWN,
{
@ -1775,8 +1792,10 @@ enum e_model {
MODEL_22M,
MODEL_33M,
MODEL_109M,
MODEL_270M,
MODEL_137M,
MODEL_335M,
MODEL_450M,
MODEL_0_5B,
MODEL_1B,
MODEL_2B,
@ -4188,6 +4207,17 @@ static void llm_load_hparams(
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_OPENELM:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 16: model.type = e_model::MODEL_270M; break;
case 20: model.type = e_model::MODEL_450M; break;
case 28: model.type = e_model::MODEL_1B; break;
case 36: model.type = e_model::MODEL_3B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
default: (void)0;
}
@ -4675,6 +4705,22 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
if (vocab.special_eot_id != -1) { LLAMA_LOG_INFO( "%s: EOT token = %d '%s'\n", __func__, vocab.special_eot_id, vocab.id_to_token[vocab.special_eot_id].text.c_str() ); }
}
float make_divisible(
double v,
int divisor = 8,
float min_value = 0.0
) {
if (min_value == 0.0) {
min_value = divisor;
}
float rounded_v = int((v + divisor / 2) / divisor) * divisor;
float new_v = (min_value > rounded_v)? min_value : rounded_v;
if (new_v < 0.9 * v) {
new_v += divisor;
}
return new_v;
}
// Returns false if cancelled by progress_callback
static bool llm_load_tensors(
llama_model_loader & ml,
@ -5924,6 +5970,48 @@ static bool llm_load_tensors(
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
}
} break;
case LLM_ARCH_OPENELM:
{
std::vector<int> num_kv_heads = {3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5};
std::vector<int> num_query_heads = {12, 12, 12, 12, 12, 16, 16, 16, 16, 16, 16, 16, 20, 20, 20, 20};
std::vector<float> ffn_multipliers = {0.5, 0.73, 0.97, 1.2, 1.43, 1.67, 1.9, 2.13, 2.37, 2.6, 2.83, 3.07, 3.3, 3.53, 3.77, 4.0};
llama_hparams modified_hparams(hparams);
const int64_t n_embd_head = hparams.n_embd_head_v;
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab });
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd });
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
ml.n_created--; // artificial tensor
ml.size_data += ggml_nbytes(model.output);
}
for (int i = 0; i < n_layer; ++i) {
const int64_t n_head_k = num_kv_heads[i];
const int64_t n_head_v = num_kv_heads[i];
const int64_t n_head_kv = n_head_k+n_head_v;
const int64_t n_head = n_head_kv+ num_query_heads[i];
const int64_t n_kv = (num_kv_heads[i]+num_kv_heads[i])*n_embd_head;
modified_hparams.n_head = n_head;
modified_hparams.n_head_kv = n_head_kv;
const int64_t n_embd_gqa = n_embd_head * n_head;
const int64_t n_embd_k_gqa = modified_hparams.n_embd_k_gqa();
const int64_t n_embd_v_gqa = modified_hparams.n_embd_v_gqa();
const int64_t ffn_inter = make_divisible(n_embd*ffn_multipliers[i], 256);
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.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head });
layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head });
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd_head*n_head });
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_head_kv*n_embd_head*2, n_embd });
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd });
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * ffn_inter });
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { ffn_inter, n_embd });
}
} break;
default:
throw std::runtime_error("unknown architecture");
}
@ -6849,6 +6937,17 @@ struct llm_build_context {
return lctx.inp_KQ_mask;
}
struct ggml_tensor * build_inp_KQ_mask2(int64_t n_kv, bool causal = true) {
if (causal) {
lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, n_tokens);
} else {
lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens);
}
cb(lctx.inp_KQ_mask, "KQ_mask", -1);
ggml_set_input(lctx.inp_KQ_mask);
return lctx.inp_KQ_mask;
}
struct ggml_tensor * build_inp_KQ_pos() {
lctx.inp_KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_kv);
cb(lctx.inp_KQ_pos, "KQ_pos", -1);
@ -10610,6 +10709,139 @@ struct llm_build_context {
return gf;
}
struct ggml_cgraph * build_openelm() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
const int64_t n_embd_head = hparams.n_embd_head_v;
// TODO: get this from config
std::vector<int> num_kv_heads = {3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5};
std::vector<int> num_query_heads = {12, 12, 12, 12, 12, 16, 16, 16, 16, 16, 16, 16, 20, 20, 20, 20};
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
struct ggml_tensor * inp_pos = build_inp_pos();
llama_hparams modified_hparams(hparams);
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
for (int il = 0; il < n_layer; ++il) {
auto residual = inpL;
// TODO: Want the offsets to be calculated with the num heads at layer level
// This doesn't work at the moment, comment out to test
const int64_t n_head_k = num_kv_heads[il];
const int64_t n_head_v = num_kv_heads[il];
const int64_t n_head_kv = n_head_k+n_head_v;
const int64_t n_head = n_head_kv+ num_query_heads[il];
const int64_t n_kv = (num_kv_heads[il]+num_kv_heads[il])*n_embd_head;
modified_hparams.n_head = n_head;
modified_hparams.n_head_kv = n_head_kv;
const int64_t n_embd_gqa = n_embd_head * n_head;
const int64_t n_embd_k_gqa = modified_hparams.n_embd_k_gqa();
const int64_t n_embd_v_gqa = modified_hparams.n_embd_v_gqa();
// self-attention
{
struct ggml_tensor* attn_norm_output = llm_build_norm(ctx0, inpL, modified_hparams,
model.layers[il].attn_norm,
NULL,
LLM_NORM_RMS, cb, il);
cb(attn_norm_output, "attn_norm", il);
struct ggml_tensor * Qcur = nullptr;
struct ggml_tensor * Kcur = nullptr;
struct ggml_tensor * Vcur = nullptr;
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output);
cb(cur, "wqkv", 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_k_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd)));
Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_v_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_k_gqa)));
// Q/K Layernorm
Qcur = llm_build_norm(ctx0, Qcur, modified_hparams,
model.layers[il].attn_q_norm,
NULL,
LLM_NORM_RMS, cb, il);
cb(Qcur, "Qcur", il);
Kcur = llm_build_norm(ctx0, Kcur, modified_hparams,
model.layers[il].attn_k_norm,
NULL,
LLM_NORM_RMS, cb, il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
struct ggml_tensor * KQ_mask = build_inp_KQ_mask2(n_kv);
Qcur = ggml_rope_custom(
ctx0, Qcur, 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);
Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head)));
cb(Qcur, "Qcur", il);
Kcur = ggml_rope_custom(
ctx0, Kcur, 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, modified_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, cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor* inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
residual = ggml_get_rows(ctx0, residual, inp_out_ids);
}
cur = ggml_add(ctx0, cur, residual);
residual = cur;
cur = llm_build_norm(ctx0, cur, modified_hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
// FF
{
cur = llm_build_norm(ctx0, cur, 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,
NULL, NULL,
model.layers[il].ffn_down, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, residual, cur);
cb(cur, "l_out", il);
inpL = cur;
}
cur = llm_build_norm(ctx0, inpL, modified_hparams,
model.output_norm,
NULL,
LLM_NORM_RMS, cb, -1);
cb(cur, "result_norm", -1);
cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
};
static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
@ -10823,6 +11055,10 @@ static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_olmo();
} break;
case LLM_ARCH_OPENELM:
{
result = llm.build_openelm();
} break;
default:
GGML_ASSERT(false);
}
@ -15679,6 +15915,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
case LLM_ARCH_XVERSE:
case LLM_ARCH_COMMAND_R:
case LLM_ARCH_OLMO:
case LLM_ARCH_OPENELM:
return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2

2
sgemm.cpp
View file

@ -818,7 +818,7 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
assert(m >= 0);
assert(n >= 0);
assert(k >= 0);
assert(lda >= k);
assert(lda >= k); // Currently failing here
assert(ldb >= k);
assert(ldc >= m);
assert(nth > 0);