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Merged
ggerganov merged 6 commits into from
Mar 23, 2024
+384 −20
Merged

Add grok-1 support #6204

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59b389f
Add support for Grok model architecture
arki05 Mar 21, 2024
9561254
Revert convert-hf-to-gguf to default options
arki05 Mar 21, 2024
6052e3b
Fixed f_norm_rms_eps bug
arki05 Mar 21, 2024
81ce9df
Fix whitespaces
arki05 Mar 21, 2024
abdc8ea
llama : fix grok rope type
ggerganov Mar 22, 2024
9a9e6cd
llama : minor
ggerganov Mar 23, 2024
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Fix whitespaces
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@arki05
arki05 committed Mar 21, 2024
commit 81ce9df3ee03d68f857d53a200b83b7269d695b1
8 changes: 3 additions & 5 deletions convert-hf-to-gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1068,16 +1068,14 @@ class GrokModel(Model):

def set_vocab(self):
self._set_vocab_sentencepiece()

def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)

def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_name("Grok")





@Model.register("MiniCPMForCausalLM")
class MiniCPMModel(Model):
Expand Down
66 changes: 32 additions & 34 deletions gguf-py/gguf/tensor_mapping.py
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Expand Up @@ -23,7 +23,7 @@ class TensorNameMap:
"model.embedding", # mamba-qbert
"backbone.embedding", # mamba
"backbone.embeddings", # mamba-hf
"transformer.in_out_embed", # Grok
"transformer.in_out_embed", # Grok
),

# Token type embeddings
Expand Down Expand Up @@ -67,7 +67,7 @@ class TensorNameMap:
"lm_head.ln", # phi2
"model.norm_f", # mamba-qbert
"backbone.norm_f", # mamba
"transformer.rms_norm", # Grok
"transformer.rms_norm", # Grok
),

# Rope frequencies
Expand Down Expand Up @@ -95,7 +95,7 @@ class TensorNameMap:
"model.layers.{bid}.attention_norm", # internlm2
"model.layers.{bid}.norm", # mamba-qbert
"backbone.layers.{bid}.norm", # mamba
"transformer.decoder_layer.{bid}.rms_norm", # Grok
"transformer.decoder_layer.{bid}.rms_norm", # Grok
),

# Attention norm 2
Expand All @@ -119,34 +119,34 @@ class TensorNameMap:

# Attention query
MODEL_TENSOR.ATTN_Q: (
"model.layers.{bid}.self_attn.q_proj", # llama-hf
"layers.{bid}.attention.wq", # llama-pth
"encoder.layer.{bid}.attention.self.query", # bert
"transformer.h.{bid}.attn.q_proj", # gpt-j
"model.layers.layers.{bid}.self_attn.q_proj", # plamo
"model.layers.{bid}.attention.wq", # internlm2
"model.layers.{bid}.self_attn.q_proj", # llama-hf
"layers.{bid}.attention.wq", # llama-pth
"encoder.layer.{bid}.attention.self.query", # bert
"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
),

# Attention key
MODEL_TENSOR.ATTN_K: (
"model.layers.{bid}.self_attn.k_proj", # llama-hf
"layers.{bid}.attention.wk", # llama-pth
"encoder.layer.{bid}.attention.self.key", # bert
"transformer.h.{bid}.attn.k_proj", # gpt-j
"model.layers.layers.{bid}.self_attn.k_proj", # plamo
"model.layers.{bid}.attention.wk", # internlm2
"model.layers.{bid}.self_attn.k_proj", # llama-hf
"layers.{bid}.attention.wk", # llama-pth
"encoder.layer.{bid}.attention.self.key", # bert
"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
),

# Attention value
MODEL_TENSOR.ATTN_V: (
"model.layers.{bid}.self_attn.v_proj", # llama-hf
"layers.{bid}.attention.wv", # llama-pth
"encoder.layer.{bid}.attention.self.value", # bert
"transformer.h.{bid}.attn.v_proj", # gpt-j
"model.layers.layers.{bid}.self_attn.v_proj", # plamo
"model.layers.{bid}.attention.wv", # internlm2
"model.layers.{bid}.self_attn.v_proj", # llama-hf
"layers.{bid}.attention.wv", # llama-pth
"encoder.layer.{bid}.attention.self.value", # bert
"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
),

Expand All @@ -168,14 +168,14 @@ class TensorNameMap:
"model.layers.layers.{bid}.self_attn.o_proj", # plamo
"model.layers.{bid}.attention.wo", # internlm2
"encoder.layers.{bid}.attn.out_proj", # nomic-bert
"transformer.decoder_layer.{bid}.multi_head_attention.linear" # Grok
"transformer.decoder_layer.{bid}.multi_head_attention.linear"# Grok
),

# Attention output norm
MODEL_TENSOR.ATTN_OUT_NORM: (
"encoder.layer.{bid}.attention.output.LayerNorm", # bert
"encoder.layers.{bid}.norm1", # nomic-bert
"transformer.decoder_layer.{bid}.rms_norm_1", # Grok
"transformer.decoder_layer.{bid}.rms_norm_1", # Grok
),

# Rotary embeddings
Expand All @@ -198,15 +198,13 @@ class TensorNameMap:
"model.layers.{bid}.ln2", # yi
"h.{bid}.ln_2", # gpt2
"model.layers.{bid}.ffn_norm", # internlm2

"transformer.decoder_layer.{bid}.rms_norm_2", # Grok

"transformer.decoder_layer.{bid}.rms_norm_2", # Grok
),

MODEL_TENSOR.FFN_GATE_INP: (
"layers.{bid}.feed_forward.gate", # mixtral
"model.layers.{bid}.block_sparse_moe.gate", # mixtral
"transformer.decoder_layer.{bid}.router" # Grok
"transformer.decoder_layer.{bid}.router" # Grok
),

# Feed-forward up
Expand Down Expand Up @@ -234,8 +232,8 @@ class TensorNameMap:

MODEL_TENSOR.FFN_UP_EXP: (
"layers.{bid}.feed_forward.experts.{xid}.w3", # mixtral
"model.layers.{bid}.block_sparse_moe.experts.{xid}.w3", # mixtral
"transformer.decoder_layer.{bid}.moe.{xid}.linear_v", # Grok
"model.layers.{bid}.block_sparse_moe.experts.{xid}.w3", # mixtral
"transformer.decoder_layer.{bid}.moe.{xid}.linear_v", # Grok
),

# AWQ-activation gate
Expand All @@ -256,7 +254,7 @@ class TensorNameMap:
MODEL_TENSOR.FFN_GATE_EXP: (
"layers.{bid}.feed_forward.experts.{xid}.w1", # mixtral
"model.layers.{bid}.block_sparse_moe.experts.{xid}.w1", # mixtral
"transformer.decoder_layer.{bid}.moe.{xid}.linear" # Grok
"transformer.decoder_layer.{bid}.moe.{xid}.linear" # Grok
),

# Feed-forward down
Expand Down Expand Up @@ -284,7 +282,7 @@ class TensorNameMap:
MODEL_TENSOR.FFN_DOWN_EXP: (
"layers.{bid}.feed_forward.experts.{xid}.w2", # mixtral
"model.layers.{bid}.block_sparse_moe.experts.{xid}.w2", # mixtral
"transformer.decoder_layer.{bid}.moe.{xid}.linear_1", # Grok
"transformer.decoder_layer.{bid}.moe.{xid}.linear_1", # Grok

),

Expand All @@ -303,9 +301,9 @@ class TensorNameMap:
),

MODEL_TENSOR.LAYER_OUT_NORM: (
"encoder.layer.{bid}.output.LayerNorm", # bert
"encoder.layers.{bid}.norm2", # nomic-bert
"transformer.decoder_layer.{bid}.rms_norm_3", # Grok
"encoder.layer.{bid}.output.LayerNorm", # bert
"encoder.layers.{bid}.norm2", # nomic-bert
"transformer.decoder_layer.{bid}.rms_norm_3", # Grok
),

MODEL_TENSOR.SSM_IN: (
Expand Down
8 changes: 3 additions & 5 deletions llama.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -4330,7 +4330,7 @@ static bool llm_load_tensors(
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});

layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});

layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});

layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd}, false);
Expand All @@ -4345,8 +4345,6 @@ static bool llm_load_tensors(
layer.ffn_down_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd});
layer.ffn_up_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), {n_embd, n_ff});
}



layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
}
Expand Down Expand Up @@ -6480,7 +6478,7 @@ struct llm_build_context {
}

cur = moe_out;


// Grok
// if layer_out_norm is present then apply it before adding the input
Expand Down Expand Up @@ -6515,7 +6513,7 @@ struct llm_build_context {

// lm_head
cur = ggml_mul_mat(ctx0, model.output, cur);


// Grok
// multiply logits by output_multiplier_scale of 0.5773502691896257
Expand Down