vbatts/llama.cpp
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q/k ln and pos_embd only if required

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bryan 2024-04-03 10:50:59 +08:00
parent b69e6c0a0f
commit d9e48194e4
3 changed files with 8 additions and 7 deletions
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1
README.md
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@ -117,6 +117,7 @@ Typically finetunes of the base models below are supported as well.
- [x] [Mamba](https://github.com/state-spaces/mamba) - [x] [Mamba](https://github.com/state-spaces/mamba)
- [x] [Xverse](https://huggingface.co/models?search=xverse) - [x] [Xverse](https://huggingface.co/models?search=xverse)
- [x] [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01) - [x] [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
- [x] [SEA-LION](https://huggingface.co/models?search=sea-lion)
**Multimodal models:** **Multimodal models:**

4
gguf-py/gguf/tensor_mapping.py
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@ -289,13 +289,13 @@ class TensorNameMap:
MODEL_TENSOR.ATTN_Q_NORM: ( MODEL_TENSOR.ATTN_Q_NORM: (
"language_model.encoder.layers.{bid}.self_attention.q_layernorm", "language_model.encoder.layers.{bid}.self_attention.q_layernorm",
"model.layers.{bid}.self_attn.q_layernorm", # persimmon "model.layers.{bid}.self_attn.q_layernorm", # persimmon
"transformer.blocks.{bid}.attn.q_ln", "transformer.blocks.{bid}.attn.q_ln", # sea-lion
), ),
MODEL_TENSOR.ATTN_K_NORM: ( MODEL_TENSOR.ATTN_K_NORM: (
"language_model.encoder.layers.{bid}.self_attention.k_layernorm", "language_model.encoder.layers.{bid}.self_attention.k_layernorm",
"model.layers.{bid}.self_attn.k_layernorm", # persimmon "model.layers.{bid}.self_attn.k_layernorm", # persimmon
"transformer.blocks.{bid}.attn.k_ln", "transformer.blocks.{bid}.attn.k_ln", # sea-lion
), ),
MODEL_TENSOR.ROPE_FREQS: ( MODEL_TENSOR.ROPE_FREQS: (

10
llama.cpp
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@ -4746,7 +4746,7 @@ static bool llm_load_tensors(
case LLM_ARCH_MPT: case LLM_ARCH_MPT:
{ {
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}); model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, false);
// output // output
{ {
@ -4785,11 +4785,11 @@ static bool llm_load_tensors(
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});
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, false); layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, false);
layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}); layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, false);
layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}); layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, false);
layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}); layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, false);
layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}); layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, false);
// AWQ ScaleActivation layer // AWQ ScaleActivation layer
layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, false); layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, false);