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Wan2.1 convert logic

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This commit is contained in:
City
2025-02-26 07:06:54 +01:00
parent abdfec54c4
commit 355b43228c
2 changed files with 47 additions and 13 deletions
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tools/lcpp.patch
+35 -11
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@@ -39,10 +39,10 @@ index b16c462f..6d1568f1 100644
const int idx = gguf_find_tensor(ctx, name);
if (idx < 0) {
diff --git a/src/llama.cpp b/src/llama.cpp
index 24e1f1f0..c2142c13 100644
index 24e1f1f0..f90738cf 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -205,6 +205,13 @@ enum llm_arch {
@@ -205,6 +205,14 @@ enum llm_arch {
LLM_ARCH_GRANITE,
LLM_ARCH_GRANITE_MOE,
LLM_ARCH_CHAMELEON,
@@ -53,10 +53,11 @@ index 24e1f1f0..c2142c13 100644
+ LLM_ARCH_AURA,
+ LLM_ARCH_LTXV,
+ LLM_ARCH_HYVID,
+ LLM_ARCH_WAN,
LLM_ARCH_UNKNOWN,
};
@@ -258,6 +265,13 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
@@ -258,6 +266,14 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_GRANITE, "granite" },
{ LLM_ARCH_GRANITE_MOE, "granitemoe" },
{ LLM_ARCH_CHAMELEON, "chameleon" },
@@ -67,10 +68,11 @@ index 24e1f1f0..c2142c13 100644
+ { LLM_ARCH_AURA, "aura" },
+ { LLM_ARCH_LTXV, "ltxv" },
+ { LLM_ARCH_HYVID, "hyvid" },
+ { LLM_ARCH_WAN, "wan" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@@ -1531,6 +1545,13 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
@@ -1531,6 +1547,14 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
},
},
@@ -81,10 +83,11 @@ index 24e1f1f0..c2142c13 100644
+ { LLM_ARCH_AURA, {}},
+ { LLM_ARCH_LTXV, {}},
+ { LLM_ARCH_HYVID,{}},
+ { LLM_ARCH_WAN,{}},
{
LLM_ARCH_UNKNOWN,
{
@@ -5403,6 +5424,21 @@ static void llm_load_hparams(
@@ -5403,6 +5427,22 @@ static void llm_load_hparams(
// get general kv
ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);
@@ -97,6 +100,7 @@ index 24e1f1f0..c2142c13 100644
+ case LLM_ARCH_AURA:
+ case LLM_ARCH_LTXV:
+ case LLM_ARCH_HYVID:
+ case LLM_ARCH_WAN:
+ model.ftype = ml.ftype;
+ return;
+ default:
@@ -106,7 +110,7 @@ index 24e1f1f0..c2142c13 100644
// get hparams kv
ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab);
@@ -18016,6 +18052,126 @@ static void llama_tensor_dequantize_internal(
@@ -18016,6 +18056,128 @@ static void llama_tensor_dequantize_internal(
workers.clear();
}
@@ -139,6 +143,7 @@ index 24e1f1f0..c2142c13 100644
+ if ( // Rules for to_v attention
+ (name.find("attn_v.weight") != std::string::npos) ||
+ (name.find(".to_v.weight") != std::string::npos) ||
+ (name.find(".v.weight") != std::string::npos) ||
+ (name.find(".attn.w1v.weight") != std::string::npos) ||
+ (name.find(".attn.w2v.weight") != std::string::npos)
+ ){
@@ -173,6 +178,7 @@ index 24e1f1f0..c2142c13 100644
+ }
+ } else if ( // Rules for ffn
+ (name.find("ffn_down") != std::string::npos) ||
+ (name.find(".ffn.2.weight") != std::string::npos) || // is this even the right way around?
+ (name.find(".ff.net.2.weight") != std::string::npos)
+ ) {
+ // TODO: add back `layer_info` with some model specific logic + logic further down
@@ -233,7 +239,7 @@ index 24e1f1f0..c2142c13 100644
static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
const std::string name = ggml_get_name(tensor);
@@ -18513,7 +18669,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
@@ -18513,7 +18675,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
if (llama_model_has_encoder(&model)) {
n_attn_layer *= 3;
}
@@ -244,7 +250,7 @@ index 24e1f1f0..c2142c13 100644
}
size_t total_size_org = 0;
@@ -18547,6 +18705,36 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
@@ -18547,6 +18711,45 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
ctx_outs[i_split] = gguf_init_empty();
}
gguf_add_tensor(ctx_outs[i_split], tensor);
@@ -277,11 +283,20 @@ index 24e1f1f0..c2142c13 100644
+ if (name == "img_in.proj.weight" && tensor->ne[5] != 1 ) {
+ throw std::runtime_error("img_in.proj.weight size failed for HyVid");
+ }
+ }
+ // All the modulation layers also have dim1, and I think conv3d fails here too but we segfaul way before that...
+ if (model.arch == LLM_ARCH_WAN) {
+ const std::string name = ggml_get_name(tensor);
+ if (name.find(".modulation") != std::string::npos && tensor->ne[2] == 1) {
+ const int n_dim = 3;
+ gguf_set_tensor_ndim(ctx_outs[i_split], tensor->name, n_dim);
+ LLAMA_LOG_INFO("\n%s: Correcting shape for Wan: [key:%s]\n", __func__, tensor->name);
+ }
+ }
}
// Set split info if needed
@@ -18647,6 +18835,73 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
@@ -18647,6 +18850,82 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
// do not quantize relative position bias (T5)
quantize &= name.find("attn_rel_b.weight") == std::string::npos;
@@ -347,6 +362,15 @@ index 24e1f1f0..c2142c13 100644
+ quantize &= name.find("guidance_in.") == std::string::npos;
+ quantize &= name.find("final_layer.") == std::string::npos;
+ }
+ if (model.arch == LLM_ARCH_WAN) {
+ image_model = true;
+ quantize &= name.find("modulation.") == std::string::npos;
+ quantize &= name.find("patch_embedding.") == std::string::npos;
+ quantize &= name.find("text_embedding.") == std::string::npos;
+ quantize &= name.find("time_projection.") == std::string::npos;
+ quantize &= name.find("time_embedding.") == std::string::npos;
+ quantize &= name.find("head.") == std::string::npos;
+ }
+ // ignore 3D/4D tensors for image models as the code was never meant to handle these
+ if (image_model) {
+ quantize &= ggml_n_dims(tensor) == 2;
@@ -355,7 +379,7 @@ index 24e1f1f0..c2142c13 100644
enum ggml_type new_type;
void * new_data;
size_t new_size;
@@ -18655,6 +18910,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
@@ -18655,6 +18934,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
new_type = default_type;
// get more optimal quantization type based on the tensor shape, layer, etc.
@@ -365,7 +389,7 @@ index 24e1f1f0..c2142c13 100644
if (!params->pure && ggml_is_quantized(default_type)) {
new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
}
@@ -18664,6 +18922,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
@@ -18664,6 +18946,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
if (params->output_tensor_type < GGML_TYPE_COUNT && strcmp(tensor->name, "output.weight") == 0) {
new_type = params->output_tensor_type;
}