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Add Nemotron/Minitron GGUF Conversion & Inference Support (#8922)
* Add nemotron GGUF conversion & inference support * Fix formatting issues * Remove unnecessary write_tensors() * Update convert_hf_to_gguf.py Co-authored-by: compilade <git@compilade.net> * Update src/llama.cpp Co-authored-by: compilade <git@compilade.net> * Address comments by @compilade * Replace ggml_mul_mat()->llm_build_lora_mm() * Remove mutable variable * Use for bias tensors * Cover corner case for role_scaling not in config.json --------- Co-authored-by: compilade <git@compilade.net>
This commit is contained in:
Yoshi Suhara
GitHub
parent
e3f6fd56b1
commit
2a24c8caa6
@ -3740,6 +3740,47 @@ class ChatGLMModel(Model):
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name = name.removeprefix("transformer.")
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return [(self.map_tensor_name(name), data_torch)]
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@Model.register("NemotronForCausalLM")
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class NemotronModel(Model):
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model_arch = gguf.MODEL_ARCH.NEMOTRON
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def set_vocab(self):
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self._set_vocab_sentencepiece()
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self.gguf_writer.add_pad_token_id(0)
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self.gguf_writer.add_unk_token_id(1)
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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hparams = self.hparams
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self.gguf_writer.add_vocab_size(hparams["vocab_size"])
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f_norm_eps = self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon", "norm_eps"])
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self.gguf_writer.add_layer_norm_eps(f_norm_eps)
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# * Partial RoPE
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rot_pct = self.find_hparam(["partial_rotary_factor", "rope_pct", "rope_percent"])
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n_embd = self.find_hparam(["hidden_size", "n_embd"])
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n_head = self.find_hparam(["num_attention_heads", "n_head"])
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self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
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# * RopeScaling for Nemotron
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if "rope_scaling" not in self.hparams or self.hparams["rope_scaling"] is None:
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self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
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else:
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self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
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self.gguf_writer.add_rope_scaling_factor(self.hparams["factor"])
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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# * Adding +1 to LayerNorm's weights here to implement layernorm1p w/o changing anything on the GGML engine side
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# model.layers.{l}.input_layernorm.weight
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# model.layers.{l}.post_attention_layernorm.weight
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# model.norm.weight
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if name.endswith("norm.weight"):
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data_torch = data_torch + 1
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return [(self.map_tensor_name(name), data_torch)]
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###### CONVERSION LOGIC ######
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@ -219,6 +219,7 @@ class MODEL_ARCH(IntEnum):
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T5 = auto()
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T5ENCODER = auto()
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JAIS = auto()
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NEMOTRON = auto()
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class MODEL_TENSOR(IntEnum):
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@ -347,6 +348,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.T5: "t5",
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MODEL_ARCH.T5ENCODER: "t5encoder",
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MODEL_ARCH.JAIS: "jais",
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MODEL_ARCH.NEMOTRON: "nemotron",
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}
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TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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@ -1065,6 +1067,21 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_GATE,
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MODEL_TENSOR.FFN_UP,
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],
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MODEL_ARCH.NEMOTRON: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_K,
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MODEL_TENSOR.ATTN_V,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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],
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# TODO
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}
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@ -1105,6 +1122,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_ARCH.CHATGLM: [
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MODEL_TENSOR.ROPE_FREQS,
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],
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MODEL_ARCH.NEMOTRON: [
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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],
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}
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#
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@ -13,7 +13,7 @@ class TensorNameMap:
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"transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx jais
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"transformer.word_embeddings", # falcon
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"word_embeddings", # bloom
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"model.embed_tokens", # llama-hf
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"model.embed_tokens", # llama-hf nemotron
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"tok_embeddings", # llama-pth
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"embeddings.word_embeddings", # bert nomic-bert
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"language_model.embedding.word_embeddings", # persimmon
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@ -52,7 +52,7 @@ class TensorNameMap:
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# Output
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MODEL_TENSOR.OUTPUT: (
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"embed_out", # gptneox
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"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais
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"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais nemotron
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"output", # llama-pth bloom internlm2
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"word_embeddings_for_head", # persimmon
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"lm_head.linear", # phi2
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@ -75,6 +75,7 @@ class TensorNameMap:
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"transformer.rms_norm", # Grok
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"encoder.final_layernorm", # chatglm
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"transformer.norm", # openelm
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"model.norm", # nemotron
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),
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# Rope frequencies
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@ -93,7 +94,7 @@ class TensorNameMap:
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"transformer.h.{bid}.input_layernorm", # falcon7b
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"h.{bid}.input_layernorm", # bloom
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"transformer.h.{bid}.ln_mlp", # falcon40b
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"model.layers.{bid}.input_layernorm", # llama-hf
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"model.layers.{bid}.input_layernorm", # llama-hf nemotron
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"layers.{bid}.attention_norm", # llama-pth
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"language_model.encoder.layers.{bid}.input_layernorm", # persimmon
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"model.layers.{bid}.ln1", # yi
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@ -135,7 +136,7 @@ class TensorNameMap:
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# Attention query
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MODEL_TENSOR.ATTN_Q: (
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"model.layers.{bid}.self_attn.q_proj", # llama-hf
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"model.layers.{bid}.self_attn.q_proj", # llama-hf nemotron
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"layers.{bid}.attention.wq", # llama-pth
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"encoder.layer.{bid}.attention.self.query", # bert
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"transformer.h.{bid}.attn.q_proj", # gpt-j
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@ -146,7 +147,7 @@ class TensorNameMap:
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# Attention key
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MODEL_TENSOR.ATTN_K: (
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"model.layers.{bid}.self_attn.k_proj", # llama-hf
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"model.layers.{bid}.self_attn.k_proj", # llama-hf nemotron
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"layers.{bid}.attention.wk", # llama-pth
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"encoder.layer.{bid}.attention.self.key", # bert
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"transformer.h.{bid}.attn.k_proj", # gpt-j
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@ -158,7 +159,7 @@ class TensorNameMap:
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# Attention value
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MODEL_TENSOR.ATTN_V: (
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"model.layers.{bid}.self_attn.v_proj", # llama-hf
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"model.layers.{bid}.self_attn.v_proj", # llama-hf nemotron
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"layers.{bid}.attention.wv", # llama-pth
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"encoder.layer.{bid}.attention.self.value", # bert
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"transformer.h.{bid}.attn.v_proj", # gpt-j
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@ -175,7 +176,7 @@ class TensorNameMap:
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"transformer.blocks.{bid}.attn.out_proj", # mpt
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"transformer.h.{bid}.self_attention.dense", # falcon
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"h.{bid}.self_attention.dense", # bloom
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"model.layers.{bid}.self_attn.o_proj", # llama-hf
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"model.layers.{bid}.self_attn.o_proj", # llama-hf nemotron
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"layers.{bid}.attention.wo", # llama-pth
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"encoder.layer.{bid}.attention.output.dense", # bert
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"transformer.h.{bid}.attn.out_proj", # gpt-j
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@ -218,7 +219,7 @@ class TensorNameMap:
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"transformer.h.{bid}.ln_2", # gpt2 refact qwen jais
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"h.{bid}.post_attention_layernorm", # bloom
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"transformer.blocks.{bid}.norm_2", # mpt
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"model.layers.{bid}.post_attention_layernorm", # llama-hf
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"model.layers.{bid}.post_attention_layernorm", # llama-hf nemotron
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"layers.{bid}.ffn_norm", # llama-pth
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"language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon
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"model.layers.{bid}.ln2", # yi
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@ -258,7 +259,7 @@ class TensorNameMap:
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"transformer.blocks.{bid}.ffn.up_proj", # mpt
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"transformer.h.{bid}.mlp.dense_h_to_4h", # falcon
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"h.{bid}.mlp.dense_h_to_4h", # bloom
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"model.layers.{bid}.mlp.up_proj", # llama-hf refact
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"model.layers.{bid}.mlp.up_proj", # llama-hf refact nemotron
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"layers.{bid}.feed_forward.w3", # llama-pth
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"encoder.layer.{bid}.intermediate.dense", # bert
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"transformer.h.{bid}.mlp.fc_in", # gpt-j
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@ -329,7 +330,7 @@ class TensorNameMap:
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"transformer.blocks.{bid}.ffn.down_proj", # mpt
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"transformer.h.{bid}.mlp.dense_4h_to_h", # falcon
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"h.{bid}.mlp.dense_4h_to_h", # bloom
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"model.layers.{bid}.mlp.down_proj", # llama-hf
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"model.layers.{bid}.mlp.down_proj", # llama-hf nemotron
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"layers.{bid}.feed_forward.w2", # llama-pth
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"encoder.layer.{bid}.output.dense", # bert
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"transformer.h.{bid}.mlp.fc_out", # gpt-j
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199
src/llama.cpp
199
src/llama.cpp
@ -210,6 +210,7 @@ enum llm_arch {
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LLM_ARCH_T5,
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LLM_ARCH_T5ENCODER,
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LLM_ARCH_JAIS,
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LLM_ARCH_NEMOTRON,
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LLM_ARCH_UNKNOWN,
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};
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@ -255,6 +256,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_T5, "t5" },
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{ LLM_ARCH_T5ENCODER, "t5encoder" },
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{ LLM_ARCH_JAIS, "jais" },
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{ LLM_ARCH_NEMOTRON, "nemotron" },
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{ LLM_ARCH_UNKNOWN, "(unknown)" },
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};
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@ -1296,6 +1298,24 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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},
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},
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{
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LLM_ARCH_NEMOTRON,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_OUTPUT, "output" },
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{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_UNKNOWN,
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{
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@ -5235,6 +5255,14 @@ static void llm_load_hparams(
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_NEMOTRON:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
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switch (hparams.n_layer) {
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case 32: model.type = e_model::MODEL_4B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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default: (void)0;
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}
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@ -7568,6 +7596,48 @@ static bool llm_load_tensors(
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layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
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}
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} break;
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case LLM_ARCH_NEMOTRON:
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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// output
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{
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model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
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model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
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model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
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}
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for (int i = 0; i < n_layer; ++i) {
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ggml_context * ctx_layer = ctx_for_layer(i);
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ggml_context * ctx_split = ctx_for_layer_split(i);
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auto & layer = model.layers[i];
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layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
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layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
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layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
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layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
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layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
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layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
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// optional bias tensors
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layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
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layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
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layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
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layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
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// optional MLP bias
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layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
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}
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} break;
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default:
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throw std::runtime_error("unknown architecture");
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}
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@ -8254,7 +8324,7 @@ static struct ggml_tensor * llm_build_kqv(
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struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
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cb(kq, "kq", il);
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if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX || model.arch == LLM_ARCH_QWEN2) {
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if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX || model.arch == LLM_ARCH_QWEN2 || model.arch == LLM_ARCH_NEMOTRON) {
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// for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
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// ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
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ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
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@ -13755,6 +13825,128 @@ struct llm_build_context {
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return gf;
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}
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struct ggml_cgraph * build_nemotron() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
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const int64_t n_embd_head = hparams.n_embd_head_v;
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
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//GGML_ASSERT(n_embd_head == hparams.n_rot);
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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
||||
|
||||
// inp_pos - contains the positions
|
||||
struct ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
||||
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
||||
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
||||
|
||||
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,
|
||||
model.layers[il].attn_norm_b,
|
||||
LLM_NORM, cb, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
// self-attention
|
||||
{
|
||||
// compute Q and K and RoPE them
|
||||
struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, 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 = llm_build_lora_mm(lctx, 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 = llm_build_lora_mm(lctx, 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_ext(
|
||||
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
Kcur = ggml_rope_ext(
|
||||
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
cur = llm_build_kv(ctx0, lctx, kv_self, gf,
|
||||
model.layers[il].wo, model.layers[il].bo,
|
||||
Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), 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);
|
||||
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||
}
|
||||
|
||||
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,
|
||||
model.layers[il].ffn_norm_b,
|
||||
LLM_NORM, cb, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
cur = llm_build_ffn(ctx0, lctx, cur,
|
||||
model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
|
||||
NULL, NULL, NULL,
|
||||
model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
|
||||
NULL,
|
||||
LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il);
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(cur, "ffn_out", il);
|
||||
|
||||
cur = lctx.cvec.apply_to(ctx0, cur, il);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
cur = llm_build_norm(ctx0, cur, hparams,
|
||||
model.output_norm, model.output_norm_b,
|
||||
LLM_NORM, cb, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
|
||||
// lm_head
|
||||
cur = llm_build_lora_mm(lctx, 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) {
|
||||
@ -14010,6 +14202,10 @@ static struct ggml_cgraph * llama_build_graph(
|
||||
{
|
||||
result = llm.build_jais();
|
||||
} break;
|
||||
case LLM_ARCH_NEMOTRON:
|
||||
{
|
||||
result = llm.build_nemotron();
|
||||
} break;
|
||||
default:
|
||||
GGML_ABORT("fatal error");
|
||||
}
|
||||
@ -17080,6 +17276,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
|
||||
case LLM_ARCH_OPENELM:
|
||||
case LLM_ARCH_GPTNEOX:
|
||||
case LLM_ARCH_CODESHELL:
|
||||
case LLM_ARCH_NEMOTRON:
|
||||
return LLAMA_ROPE_TYPE_NEOX;
|
||||
|
||||
// all model arches should be listed explicitly here
|
||||
|
||||
Loading…
Reference in New Issue
Block a user