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Add Nemotron/Minitron GGUF Conversion & Inference Support #8922

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merged 10 commits into from
Aug 16, 2024
Merged

Add Nemotron/Minitron GGUF Conversion & Inference Support #8922

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aa2f4a7
Add nemotron GGUF conversion & inference support
suhara Aug 8, 2024
45e9d16
Fix formatting issues
suhara Aug 8, 2024
147cdf6
Remove unnecessary write_tensors()
suhara Aug 8, 2024
b841554
Update convert_hf_to_gguf.py
suhara Aug 9, 2024
092382f
Update src/llama.cpp
suhara Aug 9, 2024
6f369f3
Address comments by @compilade
suhara Aug 9, 2024
ae86b5e
Replace ggml_mul_mat()->llm_build_lora_mm()
suhara Aug 11, 2024
bd76198
Remove mutable variable
suhara Aug 11, 2024
e4bb91b
Use for bias tensors
suhara Aug 13, 2024
0645adc
Cover corner case for role_scaling not in config.json
suhara Aug 14, 2024
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41 changes: 41 additions & 0 deletions convert_hf_to_gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3734,6 +3734,47 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
name = name.removeprefix("transformer.")
return [(self.map_tensor_name(name), data_torch)]


@Model.register("NemotronForCausalLM")
class NemotronModel(Model):
model_arch = gguf.MODEL_ARCH.NEMOTRON

def set_vocab(self):
self._set_vocab_sentencepiece()
self.gguf_writer.add_pad_token_id(0)
self.gguf_writer.add_unk_token_id(1)

def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
self.gguf_writer.add_vocab_size(hparams["vocab_size"])

f_norm_eps = self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon", "norm_eps"])
self.gguf_writer.add_layer_norm_eps(f_norm_eps)

# * Partial RoPE
rot_pct = self.find_hparam(["partial_rotary_factor", "rope_pct", "rope_percent"])
n_embd = self.find_hparam(["hidden_size", "n_embd"])
n_head = self.find_hparam(["num_attention_heads", "n_head"])
self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)

# * RopeScaling for Nemotron
if self.hparams["rope_scaling"] is None:
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
else:
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
self.gguf_writer.add_rope_scaling_factor(self.hparams["factor"])

def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# * Adding +1 to LayerNorm's weights here to implement layernorm1p w/o changing anything on the GGML engine side
# model.layers.{l}.input_layernorm.weight
# model.layers.{l}.post_attention_layernorm.weight
# model.norm.weight
if name.endswith("norm.weight"):
data_torch = data_torch + 1

return [(self.map_tensor_name(name), data_torch)]

###### CONVERSION LOGIC ######


Expand Down
21 changes: 21 additions & 0 deletions gguf-py/gguf/constants.py
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Expand Up @@ -219,6 +219,7 @@ class MODEL_ARCH(IntEnum):
T5 = auto()
T5ENCODER = auto()
JAIS = auto()
NEMOTRON = auto()


class MODEL_TENSOR(IntEnum):
Expand Down Expand Up @@ -347,6 +348,7 @@ class MODEL_TENSOR(IntEnum):
MODEL_ARCH.T5: "t5",
MODEL_ARCH.T5ENCODER: "t5encoder",
MODEL_ARCH.JAIS: "jais",
MODEL_ARCH.NEMOTRON: "nemotron",
}

TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
Expand Down Expand Up @@ -1065,6 +1067,21 @@ class MODEL_TENSOR(IntEnum):
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.NEMOTRON: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_ROT_EMBD,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
# TODO
}

Expand Down Expand Up @@ -1105,6 +1122,10 @@ class MODEL_TENSOR(IntEnum):
MODEL_ARCH.CHATGLM: [
MODEL_TENSOR.ROPE_FREQS,
],
MODEL_ARCH.NEMOTRON: [
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
}

#
Expand Down
21 changes: 11 additions & 10 deletions gguf-py/gguf/tensor_mapping.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@ class TensorNameMap:
"transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx jais
"transformer.word_embeddings", # falcon
"word_embeddings", # bloom
"model.embed_tokens", # llama-hf
"model.embed_tokens", # llama-hf nemotron
"tok_embeddings", # llama-pth
"embeddings.word_embeddings", # bert nomic-bert
"language_model.embedding.word_embeddings", # persimmon
Expand Down Expand Up @@ -52,7 +52,7 @@ class TensorNameMap:
# Output
MODEL_TENSOR.OUTPUT: (
"embed_out", # gptneox
"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais
"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais nemotron
"output", # llama-pth bloom internlm2
"word_embeddings_for_head", # persimmon
"lm_head.linear", # phi2
Expand All @@ -75,6 +75,7 @@ class TensorNameMap:
"transformer.rms_norm", # Grok
"encoder.final_layernorm", # chatglm
"transformer.norm", # openelm
"model.norm", # nemotron
),

# Rope frequencies
Expand All @@ -93,7 +94,7 @@ class TensorNameMap:
"transformer.h.{bid}.input_layernorm", # falcon7b
"h.{bid}.input_layernorm", # bloom
"transformer.h.{bid}.ln_mlp", # falcon40b
"model.layers.{bid}.input_layernorm", # llama-hf
"model.layers.{bid}.input_layernorm", # llama-hf nemotron
"layers.{bid}.attention_norm", # llama-pth
"language_model.encoder.layers.{bid}.input_layernorm", # persimmon
"model.layers.{bid}.ln1", # yi
Expand Down Expand Up @@ -135,7 +136,7 @@ class TensorNameMap:

# Attention query
MODEL_TENSOR.ATTN_Q: (
"model.layers.{bid}.self_attn.q_proj", # llama-hf
"model.layers.{bid}.self_attn.q_proj", # llama-hf nemotron
"layers.{bid}.attention.wq", # llama-pth
"encoder.layer.{bid}.attention.self.query", # bert
"transformer.h.{bid}.attn.q_proj", # gpt-j
Expand All @@ -146,7 +147,7 @@ class TensorNameMap:

# Attention key
MODEL_TENSOR.ATTN_K: (
"model.layers.{bid}.self_attn.k_proj", # llama-hf
"model.layers.{bid}.self_attn.k_proj", # llama-hf nemotron
"layers.{bid}.attention.wk", # llama-pth
"encoder.layer.{bid}.attention.self.key", # bert
"transformer.h.{bid}.attn.k_proj", # gpt-j
Expand All @@ -158,7 +159,7 @@ class TensorNameMap:

# Attention value
MODEL_TENSOR.ATTN_V: (
"model.layers.{bid}.self_attn.v_proj", # llama-hf
"model.layers.{bid}.self_attn.v_proj", # llama-hf nemotron
"layers.{bid}.attention.wv", # llama-pth
"encoder.layer.{bid}.attention.self.value", # bert
"transformer.h.{bid}.attn.v_proj", # gpt-j
Expand All @@ -175,7 +176,7 @@ class TensorNameMap:
"transformer.blocks.{bid}.attn.out_proj", # mpt
"transformer.h.{bid}.self_attention.dense", # falcon
"h.{bid}.self_attention.dense", # bloom
"model.layers.{bid}.self_attn.o_proj", # llama-hf
"model.layers.{bid}.self_attn.o_proj", # llama-hf nemotron
"layers.{bid}.attention.wo", # llama-pth
"encoder.layer.{bid}.attention.output.dense", # bert
"transformer.h.{bid}.attn.out_proj", # gpt-j
Expand Down Expand Up @@ -218,7 +219,7 @@ class TensorNameMap:
"transformer.h.{bid}.ln_2", # gpt2 refact qwen jais
"h.{bid}.post_attention_layernorm", # bloom
"transformer.blocks.{bid}.norm_2", # mpt
"model.layers.{bid}.post_attention_layernorm", # llama-hf
"model.layers.{bid}.post_attention_layernorm", # llama-hf nemotron
"layers.{bid}.ffn_norm", # llama-pth
"language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon
"model.layers.{bid}.ln2", # yi
Expand Down Expand Up @@ -258,7 +259,7 @@ class TensorNameMap:
"transformer.blocks.{bid}.ffn.up_proj", # mpt
"transformer.h.{bid}.mlp.dense_h_to_4h", # falcon
"h.{bid}.mlp.dense_h_to_4h", # bloom
"model.layers.{bid}.mlp.up_proj", # llama-hf refact
"model.layers.{bid}.mlp.up_proj", # llama-hf refact nemotron
"layers.{bid}.feed_forward.w3", # llama-pth
"encoder.layer.{bid}.intermediate.dense", # bert
"transformer.h.{bid}.mlp.fc_in", # gpt-j
Expand Down Expand Up @@ -329,7 +330,7 @@ class TensorNameMap:
"transformer.blocks.{bid}.ffn.down_proj", # mpt
"transformer.h.{bid}.mlp.dense_4h_to_h", # falcon
"h.{bid}.mlp.dense_4h_to_h", # bloom
"model.layers.{bid}.mlp.down_proj", # llama-hf
"model.layers.{bid}.mlp.down_proj", # llama-hf nemotron
"layers.{bid}.feed_forward.w2", # llama-pth
"encoder.layer.{bid}.output.dense", # bert
"transformer.h.{bid}.mlp.fc_out", # gpt-j
Expand Down
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