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convert : Add support for Microsoft Phi-4 model (#10817)

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* convert : use GPT2 vocab for Phi-4 model

* convert : use null value of sliding_window to distinguish Phi-4 from other PHI3-based models

* llama : do not use sliding window attention mask for Phi-4 model

---------

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
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fairydreaming 2024-12-19 10:37:12 +01:00 committed by GitHub
parent cd920d0ac3
commit 7585edbdeb
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2 changed files with 22 additions and 3 deletions
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15
convert_hf_to_gguf.py
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@ -2200,6 +2200,15 @@ class Phi3MiniModel(Model):
model_arch = gguf.MODEL_ARCH.PHI3 model_arch = gguf.MODEL_ARCH.PHI3
def set_vocab(self): def set_vocab(self):
# Phi-4 model uses GPT2Tokenizer
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)
tokenizer_class = tokenizer_config_json['tokenizer_class']
if tokenizer_class == 'GPT2Tokenizer':
return self._set_vocab_gpt2()
from sentencepiece import SentencePieceProcessor from sentencepiece import SentencePieceProcessor
tokenizer_path = self.dir_model / 'tokenizer.model' tokenizer_path = self.dir_model / 'tokenizer.model'
@ -2316,7 +2325,11 @@ class Phi3MiniModel(Model):
self.gguf_writer.add_rope_dimension_count(rope_dims) self.gguf_writer.add_rope_dimension_count(rope_dims)
self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"])) self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"]))
self.gguf_writer.add_file_type(self.ftype) self.gguf_writer.add_file_type(self.ftype)
self.gguf_writer.add_sliding_window(self.find_hparam(["sliding_window"])) sliding_window = self.hparams.get("sliding_window")
# use zero value of sliding_window to distinguish Phi-4 from other PHI3 models
if sliding_window is None:
sliding_window = 0
self.gguf_writer.add_sliding_window(sliding_window)
def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
n_embd = self.find_hparam(["hidden_size", "n_embd"]) n_embd = self.find_hparam(["hidden_size", "n_embd"])

10
src/llama.cpp
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@ -13333,7 +13333,13 @@ struct llm_build_context {
struct ggml_tensor * inp_pos = build_inp_pos(); struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads) // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask_swa = build_inp_KQ_mask_swa(); struct ggml_tensor * KQ_mask = nullptr;
if (hparams.n_swa == 0) {
// Phi-4 doesn't use sliding window attention
KQ_mask = build_inp_KQ_mask();
} else {
KQ_mask = build_inp_KQ_mask_swa();
}
for (int il = 0; il < n_layer; ++il) { for (int il = 0; il < n_layer; ++il) {
auto residual = inpL; auto residual = inpL;
@ -13391,7 +13397,7 @@ struct llm_build_context {
cur = llm_build_kv(ctx0, lctx, kv_self, gf, cur = llm_build_kv(ctx0, lctx, kv_self, gf,
model.layers[il].wo, model.layers[il].bo, model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask_swa, n_tokens, kv_head, n_kv, 1.0f, cb, il); Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
} }
if (il == n_layer - 1) { if (il == n_layer - 1) {