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Cosmos convert logic

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This commit is contained in:
City
2025-06-15 00:09:45 +02:00
parent a2b75978fd
commit b3ec875a68
3 changed files with 66 additions and 22 deletions
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loader.py
+1 -1
View File
@@ -7,7 +7,7 @@ import gguf
from .ops import GGMLTensor
from .dequant import is_quantized, dequantize_tensor
IMG_ARCH_LIST = {"flux", "sd1", "sdxl", "sd3", "aura", "hidream", "ltxv", "hyvid", "wan"}
IMG_ARCH_LIST = {"flux", "sd1", "sdxl", "sd3", "aura", "hidream", "cosmos", "ltxv", "hyvid", "wan"}
TXT_ARCH_LIST = {"t5", "t5encoder", "llama"}
def get_orig_shape(reader, tensor_name):
tools/convert.py
+36 -8
View File
@@ -18,6 +18,7 @@ class ModelTemplate:
keys_detect = [] # list of lists to match in state dict
keys_banned = [] # list of keys that should mark model as invalid for conversion
keys_hiprec = [] # list of keys that need to be kept in fp32 for some reason
keys_ignore = [] # list of strings to ignore keys by when found
def handle_nd_tensor(self, key, data):
raise NotImplementedError(f"Tensor detected that exceeds dims supported by C++ code! ({key} @ {data.shape})")
@@ -60,6 +61,17 @@ class ModelHiDream(ModelTemplate):
"img_emb.emb_pos"
]
class CosmosPredict2(ModelTemplate):
arch = "cosmos"
keys_detect = [
(
"blocks.0.mlp.layer1.weight",
"blocks.0.adaln_modulation_cross_attn.1.weight",
)
]
keys_hiprec = ["pos_embedder"]
keys_ignore = ["_extra_state", "accum_"]
class ModelHyVid(ModelTemplate):
arch = "hyvid"
keys_detect = [
@@ -128,7 +140,7 @@ class ModelSD1(ModelTemplate):
]
# The architectures are checked in order and the first successful match terminates the search.
arch_list = [ModelFlux, ModelSD3, ModelAura, ModelHiDream, ModelLTXV, ModelHyVid, ModelWan, ModelSDXL, ModelSD1]
arch_list = [ModelFlux, ModelSD3, ModelAura, ModelHiDream, CosmosPredict2, ModelLTXV, ModelHyVid, ModelWan, ModelSDXL, ModelSD1]
def is_model_arch(model, state_dict):
# check if model is correct
@@ -163,20 +175,32 @@ def parse_args():
return args
def strip_prefix(state_dict):
# only keep unet with no prefix!
# prefix for mixed state dict
prefix = None
for pfx in ["model.diffusion_model.", "model."]:
if any([x.startswith(pfx) for x in state_dict.keys()]):
prefix = pfx
break
sd = {}
for k, v in state_dict.items():
if prefix and prefix not in k:
continue
if prefix:
# prefix for uniform state dict
if prefix is None:
for pfx in ["net."]:
if all([x.startswith(pfx) for x in state_dict.keys()]):
prefix = pfx
break
# strip prefix if found
if prefix is not None:
logging.info(f"State dict prefix found: '{prefix}'")
sd = {}
for k, v in state_dict.items():
if prefix not in k:
continue
k = k.replace(prefix, "")
sd[k] = v
sd[k] = v
else:
logging.debug("State dict has no prefix")
sd = state_dict
return sd
@@ -209,6 +233,10 @@ def handle_tensors(writer, state_dict, model_arch):
for key, data in tqdm(state_dict.items()):
old_dtype = data.dtype
if any(x in key for x in model_arch.keys_ignore):
tqdm.write(f"Filtering ignored key: '{key}'")
continue
if data.dtype == torch.bfloat16:
data = data.to(torch.float32).numpy()
# this is so we don't break torch 2.0.X
tools/lcpp.patch
+29 -13
View File
@@ -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..39045ca5 100644
index 24e1f1f0..9957ea30 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -205,6 +205,15 @@ enum llm_arch {
@@ -205,6 +205,16 @@ enum llm_arch {
LLM_ARCH_GRANITE,
LLM_ARCH_GRANITE_MOE,
LLM_ARCH_CHAMELEON,
@@ -55,10 +55,11 @@ index 24e1f1f0..39045ca5 100644
+ LLM_ARCH_HYVID,
+ LLM_ARCH_WAN,
+ LLM_ARCH_HIDREAM,
+ LLM_ARCH_COSMOS,
LLM_ARCH_UNKNOWN,
};
@@ -258,6 +267,15 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
@@ -258,6 +268,16 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_GRANITE, "granite" },
{ LLM_ARCH_GRANITE_MOE, "granitemoe" },
{ LLM_ARCH_CHAMELEON, "chameleon" },
@@ -71,10 +72,11 @@ index 24e1f1f0..39045ca5 100644
+ { LLM_ARCH_HYVID, "hyvid" },
+ { LLM_ARCH_WAN, "wan" },
+ { LLM_ARCH_HIDREAM, "hidream" },
+ { LLM_ARCH_COSMOS, "cosmos" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@@ -1531,6 +1549,15 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
@@ -1531,6 +1551,16 @@ 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" },
},
},
@@ -87,10 +89,11 @@ index 24e1f1f0..39045ca5 100644
+ { LLM_ARCH_HYVID, {}},
+ { LLM_ARCH_WAN, {}},
+ { LLM_ARCH_HIDREAM, {}},
+ { LLM_ARCH_COSMOS, {}},
{
LLM_ARCH_UNKNOWN,
{
@@ -5403,6 +5430,23 @@ static void llm_load_hparams(
@@ -5403,6 +5433,24 @@ static void llm_load_hparams(
// get general kv
ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);
@@ -105,6 +108,7 @@ index 24e1f1f0..39045ca5 100644
+ case LLM_ARCH_HYVID:
+ case LLM_ARCH_WAN:
+ case LLM_ARCH_HIDREAM:
+ case LLM_ARCH_COSMOS:
+ model.ftype = ml.ftype;
+ return;
+ default:
@@ -114,7 +118,7 @@ index 24e1f1f0..39045ca5 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 +18060,129 @@ static void llama_tensor_dequantize_internal(
@@ -18016,6 +18064,132 @@ static void llama_tensor_dequantize_internal(
workers.clear();
}
@@ -149,7 +153,8 @@ index 24e1f1f0..39045ca5 100644
+ (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)
+ (name.find(".attn.w2v.weight") != std::string::npos) ||
+ (name.find("_attn.v_proj.weight") != std::string::npos)
+ ){
+ if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
+ new_type = GGML_TYPE_Q3_K;
@@ -184,7 +189,9 @@ index 24e1f1f0..39045ca5 100644
+ (name.find("ffn_down") != std::string::npos) ||
+ ((name.find("experts.") != std::string::npos) && (name.find(".w2.weight") != 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)
+ (name.find(".ff.net.2.weight") != std::string::npos) ||
+ (name.find(".mlp.layer2.weight") != std::string::npos) ||
+ (name.find(".adaln_modulation_mlp.2.weight") != std::string::npos)
+ ) {
+ // TODO: add back `layer_info` with some model specific logic + logic further down
+ if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
@@ -244,7 +251,7 @@ index 24e1f1f0..39045ca5 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 +18680,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
@@ -18513,7 +18687,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
if (llama_model_has_encoder(&model)) {
n_attn_layer *= 3;
}
@@ -255,7 +262,7 @@ index 24e1f1f0..39045ca5 100644
}
size_t total_size_org = 0;
@@ -18547,6 +18716,51 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
@@ -18547,6 +18723,51 @@ 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);
@@ -307,7 +314,7 @@ index 24e1f1f0..39045ca5 100644
}
// Set split info if needed
@@ -18647,6 +18861,92 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
@@ -18647,6 +18868,101 @@ 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;
@@ -392,6 +399,15 @@ index 24e1f1f0..39045ca5 100644
+ quantize &= name.find(".ff_i.gate.weight") == std::string::npos;
+ quantize &= name.find("caption_projection.") == std::string::npos;
+ }
+ if (model.arch == LLM_ARCH_COSMOS) {
+ image_model = true;
+ quantize &= name.find("p_embedder.") == std::string::npos;
+ quantize &= name.find("t_embedder.") == std::string::npos;
+ quantize &= name.find("t_embedding_norm.") == std::string::npos;
+ quantize &= name.find("x_embedder.") == std::string::npos;
+ quantize &= name.find("pos_embedder.") == std::string::npos;
+ quantize &= name.find("final_layer.") == 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;
@@ -400,7 +416,7 @@ index 24e1f1f0..39045ca5 100644
enum ggml_type new_type;
void * new_data;
size_t new_size;
@@ -18655,6 +18955,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
@@ -18655,6 +18971,9 @@ 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.
@@ -410,7 +426,7 @@ index 24e1f1f0..39045ca5 100644
if (!params->pure && ggml_is_quantized(default_type)) {
new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
}
@@ -18664,6 +18967,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
@@ -18664,6 +18983,7 @@ 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;
}