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llama : support small Granite models (#7481)

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* Add optional MLP bias for Granite models

Add optional MLP bias for ARCH_LLAMA to support Granite models.
Partially addresses ggerganov/llama.cpp/issues/7116
Still needs some more changes to properly support Granite.

* llama: honor add_space_prefix from the model configuration

propagate the add_space_prefix configuration from the HF model
configuration to the gguf file and honor it with the gpt2 tokenizer.

Signed-off-by: Giuseppe Scrivano <gscrivan@redhat.com>

* llama: add support for small granite models

it works only for the small models 3b and 8b.

The convert-hf-to-gguf.py script uses the vocabulary size of the
granite models to detect granite and set the correct configuration.

Signed-off-by: Giuseppe Scrivano <gscrivan@redhat.com>

---------

Signed-off-by: Giuseppe Scrivano <gscrivan@redhat.com>
Co-authored-by: Steffen Roecker <sroecker@redhat.com>
This commit is contained in:
Giuseppe Scrivano 2024-05-28 20:49:49 +02:00 committed by GitHub
parent 56411a950f
commit 5442939fcc
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2 changed files with 34 additions and 8 deletions
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15
convert-hf-to-gguf.py
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@ -1317,6 +1317,17 @@ class LlamaModel(Model):
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
if tokenizer_config_file.is_file():
with open(tokenizer_config_file, "r", encoding="utf-8") as f:
tokenizer_config_json = json.load(f)
if "add_prefix_space" in tokenizer_config_json:
self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
# Apply to granite small models only
if self.hparams.get("vocab_size", 32000) == 49152:
self.gguf_writer.add_add_bos_token(False)
@staticmethod @staticmethod
def permute(weights: Tensor, n_head: int, n_head_kv: int | None): def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
if n_head_kv is not None and n_head != n_head_kv: if n_head_kv is not None and n_head != n_head_kv:
@ -1331,9 +1342,9 @@ class LlamaModel(Model):
n_head = self.hparams["num_attention_heads"] n_head = self.hparams["num_attention_heads"]
n_kv_head = self.hparams.get("num_key_value_heads") n_kv_head = self.hparams.get("num_key_value_heads")
if name.endswith("q_proj.weight"): if name.endswith(("q_proj.weight", "q_proj.bias")):
data_torch = LlamaModel.permute(data_torch, n_head, n_head) data_torch = LlamaModel.permute(data_torch, n_head, n_head)
if name.endswith("k_proj.weight"): if name.endswith(("k_proj.weight", "k_proj.bias")):
data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
# process the experts separately # process the experts separately

27
llama.cpp
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@ -2028,8 +2028,9 @@ struct llama_layer {
struct ggml_tensor * ffn_up_shexp; struct ggml_tensor * ffn_up_shexp;
// ff bias // ff bias
struct ggml_tensor * ffn_down_b; // b2 struct ggml_tensor * ffn_gate_b = nullptr;
struct ggml_tensor * ffn_up_b; // b3 struct ggml_tensor * ffn_down_b = nullptr; // b2
struct ggml_tensor * ffn_up_b = nullptr; // b3
struct ggml_tensor * ffn_act; struct ggml_tensor * ffn_act;
// mamba proj // mamba proj
@ -4058,7 +4059,9 @@ static void llm_load_hparams(
switch (hparams.n_layer) { switch (hparams.n_layer) {
case 22: model.type = e_model::MODEL_1B; break; case 22: model.type = e_model::MODEL_1B; break;
case 26: model.type = e_model::MODEL_3B; break; case 26: model.type = e_model::MODEL_3B; break;
case 32: model.type = hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B; break; // granite uses a vocab with len 49152
case 32: model.type = hparams.n_vocab == 49152 ? e_model::MODEL_3B : (hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B); break;
case 36: model.type = e_model::MODEL_8B; break; // granite
case 40: model.type = e_model::MODEL_13B; break; case 40: model.type = e_model::MODEL_13B; break;
case 48: model.type = e_model::MODEL_34B; break; case 48: model.type = e_model::MODEL_34B; break;
case 60: model.type = e_model::MODEL_30B; break; case 60: model.type = e_model::MODEL_30B; break;
@ -4328,6 +4331,8 @@ static void llm_load_hparams(
case 30: model.type = e_model::MODEL_3B; break; case 30: model.type = e_model::MODEL_3B; break;
case 32: model.type = e_model::MODEL_7B; break; case 32: model.type = e_model::MODEL_7B; break;
case 40: model.type = e_model::MODEL_15B; break; case 40: model.type = e_model::MODEL_15B; break;
case 52: model.type = e_model::MODEL_20B; break; // granite
case 88: model.type = e_model::MODEL_34B; break; // granite
default: model.type = e_model::MODEL_UNKNOWN; default: model.type = e_model::MODEL_UNKNOWN;
} }
} break; } break;
@ -4590,6 +4595,11 @@ static void llm_load_vocab(
} else { } else {
if (tokenizer_model == "gpt2") { if (tokenizer_model == "gpt2") {
vocab.type = LLAMA_VOCAB_TYPE_BPE; vocab.type = LLAMA_VOCAB_TYPE_BPE;
const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
if (add_space_prefix_keyidx != -1) {
vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
}
} else { } else {
LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_model.c_str()); LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_model.c_str());
LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__); LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__);
@ -5211,6 +5221,11 @@ static bool llm_load_tensors(
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
// optional MLP bias
layer.ffn_gate_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_down_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_up_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
} else { } else {
layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
@ -7483,9 +7498,9 @@ struct llm_build_context {
cb(cur, "ffn_norm", il); cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, cur, cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, NULL, model.layers[il].ffn_up, model.layers[il].ffn_up_b,
model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate, model.layers[il].ffn_gate_b,
model.layers[il].ffn_down, NULL, model.layers[il].ffn_down, model.layers[il].ffn_down_b,
NULL, NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il); LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
cb(cur, "ffn_out", il); cb(cur, "ffn_out", il);