vbatts/llama.cpp
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Add remaining time mix parameters

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Layl Bongers 2024-05-13 13:32:41 +02:00 • committed by Molly Sophia
parent dd3aa3d40e
commit b409fd8e11
1 changed files with 57 additions and 22 deletions
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src/llama.cpp
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@ -520,10 +520,17 @@ enum llm_tensor {
LLM_TENSOR_SSM_A, LLM_TENSOR_SSM_A,
LLM_TENSOR_SSM_D, LLM_TENSOR_SSM_D,
LLM_TENSOR_SSM_OUT, LLM_TENSOR_SSM_OUT,
LLM_TENSOR_TIME_MIX_K, LLM_TENSOR_TIME_MIX_LERP_K,
LLM_TENSOR_TIME_MIX_V, LLM_TENSOR_TIME_MIX_LERP_V,
LLM_TENSOR_TIME_MIX_R, LLM_TENSOR_TIME_MIX_LERP_R,
LLM_TENSOR_TIME_MIX_G, LLM_TENSOR_TIME_MIX_LERP_G,
LLM_TENSOR_TIME_MIX_FIRST,
LLM_TENSOR_TIME_MIX_DECAY,
LLM_TENSOR_TIME_MIX_KEY,
LLM_TENSOR_TIME_MIX_VALUE,
LLM_TENSOR_TIME_MIX_RECEPTANCE,
LLM_TENSOR_TIME_MIX_GATE,
LLM_TENSOR_TIME_MIX_LN,
LLM_TENSOR_ATTN_Q_A, LLM_TENSOR_ATTN_Q_A,
LLM_TENSOR_ATTN_Q_B, LLM_TENSOR_ATTN_Q_B,
LLM_TENSOR_ATTN_KV_A_MQA, LLM_TENSOR_ATTN_KV_A_MQA,
@ -1348,16 +1355,23 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ {
LLM_ARCH_RWKV, LLM_ARCH_RWKV,
{ {
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" }, { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" }, { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" }, { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" }, { LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_NORM_2, "blk.%d.attn_norm_2" }, { LLM_TENSOR_ATTN_NORM_2, "blk.%d.attn_norm_2" },
{ LLM_TENSOR_TIME_MIX_K, "blk.%d.time_mix_k" }, { LLM_TENSOR_TIME_MIX_LERP_K, "blk.%d.time_mix.lerp_k" },
{ LLM_TENSOR_TIME_MIX_V, "blk.%d.time_mix_v" }, { LLM_TENSOR_TIME_MIX_LERP_V, "blk.%d.time_mix.lerp_v" },
{ LLM_TENSOR_TIME_MIX_R, "blk.%d.time_mix_r" }, { LLM_TENSOR_TIME_MIX_LERP_R, "blk.%d.time_mix.lerp_r" },
{ LLM_TENSOR_TIME_MIX_G, "blk.%d.time_mix_g" }, { LLM_TENSOR_TIME_MIX_LERP_G, "blk.%d.time_mix.lerp_g" },
{ LLM_TENSOR_TIME_MIX_FIRST, "blk.%d.time_mix.first" },
{ LLM_TENSOR_TIME_MIX_DECAY, "blk.%d.time_mix.decay" },
{ LLM_TENSOR_TIME_MIX_KEY, "blk.%d.time_mix.key" },
{ LLM_TENSOR_TIME_MIX_VALUE, "blk.%d.time_mix.value" },
{ LLM_TENSOR_TIME_MIX_RECEPTANCE, "blk.%d.time_mix.receptance" },
{ LLM_TENSOR_TIME_MIX_GATE, "blk.%d.time_mix.gate" },
{ LLM_TENSOR_TIME_MIX_LN, "blk.%d.time_mix.ln" },
}, },
}, },
{ {
@ -2523,10 +2537,20 @@ struct llama_layer {
struct ggml_tensor * ssm_dt_b; struct ggml_tensor * ssm_dt_b;
// rwkv // rwkv
struct ggml_tensor * time_mix_k; struct ggml_tensor * time_mix_lerp_k;
struct ggml_tensor * time_mix_v; struct ggml_tensor * time_mix_lerp_v;
struct ggml_tensor * time_mix_r; struct ggml_tensor * time_mix_lerp_r;
struct ggml_tensor * time_mix_g; struct ggml_tensor * time_mix_lerp_g;
struct ggml_tensor * time_mix_first;
struct ggml_tensor * time_mix_decay;
struct ggml_tensor * time_mix_key;
struct ggml_tensor * time_mix_value;
struct ggml_tensor * time_mix_receptance;
struct ggml_tensor * time_mix_gate;
struct ggml_tensor * time_mix_ln;
struct ggml_tensor * time_mix_ln_b;
// long rope factors // long rope factors
struct ggml_tensor * rope_long = nullptr; struct ggml_tensor * rope_long = nullptr;
@ -8274,10 +8298,21 @@ static bool llm_load_tensors(
layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}); layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd});
layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}); layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd});
layer.time_mix_k = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_K, "weight", i), {n_embd, 1, 1}); layer.time_mix_lerp_k = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_K, "weight", i), {n_embd, 1, 1});
layer.time_mix_v = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_V, "weight", i), {n_embd, 1, 1}); layer.time_mix_lerp_v = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_V, "weight", i), {n_embd, 1, 1});
layer.time_mix_r = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_R, "weight", i), {n_embd, 1, 1}); layer.time_mix_lerp_r = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_R, "weight", i), {n_embd, 1, 1});
layer.time_mix_g = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_G, "weight", i), {n_embd, 1, 1}); layer.time_mix_lerp_g = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_G, "weight", i), {n_embd, 1, 1});
// TODO: Parametrize hardcoded dimensions for first & decay
layer.time_mix_first = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {64, 32});
layer.time_mix_decay = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {64, 32});
layer.time_mix_key = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {n_embd, n_embd});
layer.time_mix_value = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {n_embd, n_embd});
layer.time_mix_receptance = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {n_embd, n_embd});
layer.time_mix_gate = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_GATE, "weight", i), {n_embd, n_embd});
layer.time_mix_ln = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd});
layer.time_mix_ln_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd});
} }
} }