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blepping
2024-08-25 15:32:45 -06:00
parent c1693d0f55 bfecd32f8c
commit df6f1ab39c
1 changed files with 154 additions and 121 deletions
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tools/lcpp.patch
+154 -121
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@@ -1,5 +1,5 @@
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index 1d2a3540..7d354dc6 100644
index 1d2a3540..b1a9ee96 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -230,7 +230,7 @@
@@ -7,12 +7,12 @@ index 1d2a3540..7d354dc6 100644
#define GGML_MAX_SRC 10
#ifndef GGML_MAX_NAME
-#define GGML_MAX_NAME 64
+#define GGML_MAX_NAME 128
+#define GGML_MAX_NAME 128
#endif
#define GGML_MAX_OP_PARAMS 64
#define GGML_DEFAULT_N_THREADS 4
diff --git a/src/llama.cpp b/src/llama.cpp
index 5ab65ea9..6d684fef 100644
index 5ab65ea9..35580d9d 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -212,6 +212,9 @@ enum llm_arch {
@@ -24,7 +24,7 @@ index 5ab65ea9..6d684fef 100644
+ LLM_ARCH_SDXL,
LLM_ARCH_UNKNOWN,
};
@@ -259,6 +262,9 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_JAIS, "jais" },
{ LLM_ARCH_NEMOTRON, "nemotron" },
@@ -34,35 +34,40 @@ index 5ab65ea9..6d684fef 100644
+ { LLM_ARCH_SDXL, "sdxl" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@@ -1337,6 +1343,9 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
+ { LLM_ARCH_FLUX, { {} }, },
+ { LLM_ARCH_SD1, { {} }, },
+ { LLM_ARCH_SDXL, { {} }, },
+ { LLM_ARCH_FLUX, {}},
+ { LLM_ARCH_SD1, {}},
+ { LLM_ARCH_SDXL, {}},
{
LLM_ARCH_UNKNOWN,
{
@@ -4629,6 +4638,13 @@ static void llm_load_hparams(
@@ -4629,6 +4638,12 @@ static void llm_load_hparams(
// get general kv
ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);
+ // Disable LLM metadata for image models
+ if (model.arch == LLM_ARCH_FLUX || model.arch == LLM_ARCH_SD1 || model.arch == LLM_ARCH_SDXL) {
+ model.ftype = ml.ftype;
+ hparams.rope_type = llama_rope_type(&model);
+ return;
+ }
+
// 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);
@@ -15854,26 +15870,40 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
return std::make_pair(i_layer, n_layer);
};
@@ -15827,11 +15842,162 @@ static void llama_tensor_dequantize_internal(
workers.clear();
}
+static ggml_type img_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
+ // Special function for quantizing image model tensors
+ const std::string name = ggml_get_name(tensor);
+ const llm_arch arch = qs.model.arch;
+
+ // Sanity check
+ if (
+ (name.find("model.diffusion_model.") != std::string::npos) ||
+ (name.find("first_stage_model.") != std::string::npos) ||
@@ -71,120 +76,148 @@ index 5ab65ea9..6d684fef 100644
+ throw std::runtime_error("Invalid input GGUF file. This is not a supported UNET model");
+ }
+
// for arches that share the same tensor between the token embeddings and the output, we quantize the token embeddings
// with the quantization of the output tensor
- if (name == tn(LLM_TENSOR_OUTPUT, "weight") || (!qs.has_output && name == tn(LLM_TENSOR_TOKEN_EMBD, "weight"))) {
- if (qs.params->output_tensor_type < GGML_TYPE_COUNT) {
- new_type = qs.params->output_tensor_type;
- } else {
- int nx = tensor->ne[0];
- if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
- new_type = GGML_TYPE_Q8_0;
- }
- else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
- ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ||
- ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
- new_type = GGML_TYPE_Q5_K;
- }
- else if (new_type != GGML_TYPE_Q8_0) {
- new_type = GGML_TYPE_Q6_K;
- }
- }
- } else if (name == "token_embd.weight") {
+ if ( // KEEP IN FP32
+ (name == tn(LLM_TENSOR_OUTPUT, "weight")) ||
+ (!qs.has_output && name == tn(LLM_TENSOR_TOKEN_EMBD, "weight")) ||
+ // Unsupported quant types - exclude all IQ quants for now
+ if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS ||
+ ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ||
+ ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
+ ftype == LLAMA_FTYPE_MOSTLY_IQ1_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL ||
+ ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S ||
+ ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_Q4_0_4_4 ||
+ ftype == LLAMA_FTYPE_MOSTLY_Q4_0_4_8 || ftype == LLAMA_FTYPE_MOSTLY_Q4_0_8_8) {
+ throw std::runtime_error("Invalid quantization type for image model (Not supported)");
+ }
+
+ if ( // Tensors to keep in FP32 precision
+ (arch == LLM_ARCH_FLUX) && (
+ (name.find("img_in.") != std::string::npos) ||
+ (name.find("time_in.in_layer.") != std::string::npos) ||
+ (name.find("vector_in.in_layer.") != std::string::npos) ||
+ (name.find("guidance_in.in_layer.") != std::string::npos) ||
+ (name.find("final_layer.linear.") != std::string::npos)
+ ) {
+ if (qs.params->output_tensor_type < GGML_TYPE_COUNT) {
+ new_type = qs.params->output_tensor_type;
+ } else {
+ new_type = GGML_TYPE_F32;
+ }
+ } else if ( // KEEP IN FP16
+ (name == "token_embd.weight") ||
+ (name.find("time_embedding.") != std::string::npos) ||
+ (name.find("add_embedding.") != std::string::npos) ||
+ ) || (arch == LLM_ARCH_SD1 || arch == LLM_ARCH_SDXL) && (
+ (name.find("conv_in.") != std::string::npos) ||
+ (name.find("conv_out.") != std::string::npos) ||
+ (name == "input_blocks.0.0.weight") ||
+ (name == "out.2.weight")
+ )) {
+ new_type = GGML_TYPE_F32;
+ } else if ( // Tensors to keep in FP16 precision
+ (arch == LLM_ARCH_FLUX) && (
+ (name.find("txt_in.") != std::string::npos) ||
+ (name.find("time_in.") != std::string::npos) ||
+ (name.find("vector_in.") != std::string::npos) ||
+ (name.find("guidance_in.") != std::string::npos) ||
+ (name.find("final_layer.") != std::string::npos)
+ ) {
if (qs.params->token_embedding_type < GGML_TYPE_COUNT) {
new_type = qs.params->token_embedding_type;
} else {
@@ -15891,10 +15921,13 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
new_type == GGML_TYPE_Q4_0_8_8) {
new_type = GGML_TYPE_Q4_0;
}
+ else { // if (ftype == LLAMA_FTYPE_MOSTLY_Q6_K || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) {
+ new_type = GGML_TYPE_F16;
+ ) || (arch == LLM_ARCH_SD1 || arch == LLM_ARCH_SDXL) && (
+ (name.find("class_embedding.") != std::string::npos) ||
+ (name.find("time_embedding.") != std::string::npos) ||
+ (name.find("add_embedding.") != std::string::npos) ||
+ (name.find("time_embed.") != std::string::npos) ||
+ (name.find("label_emb.") != std::string::npos) ||
+ (name.find("proj_in.") != std::string::npos) ||
+ (name.find("proj_out.") != std::string::npos)
+ // (name.find("conv_shortcut.") != std::string::npos) // marginal improvement
+ )) {
+ new_type = GGML_TYPE_F16;
+ } else if ( // Rules for to_v attention
+ (name.find("attn_v.weight") != std::string::npos) ||
+ (name.find(".to_v.weight") != std::string::npos)
+ ){
+ if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
+ new_type = GGML_TYPE_Q3_K;
+ }
}
} else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
- if (name.find("attn_v.weight") != std::string::npos) {
+ if ((name.find("attn_v.weight") != std::string::npos) || (name.find(".to_v.weight") != std::string::npos)) {
if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K;
else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
++qs.i_attention_wv;
@@ -15916,7 +15949,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_S;
}
}
- } else if (name.find("attn_v.weight") != std::string::npos) {
+ } else if ((name.find("attn_v.weight") != std::string::npos) || (name.find(".to_v.weight") != std::string::npos)) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
}
@@ -15954,7 +15987,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
new_type = GGML_TYPE_Q8_0;
}
++qs.i_attention_wv;
- } else if (name.find("attn_k.weight") != std::string::npos) {
+ } else if ((name.find("attn_k.weight") != std::string::npos) || (name.find("to_k.weight") != std::string::npos)) {
if (qs.model.hparams.n_expert == 8) {
// for the 8-expert model, bumping this to Q8_0 trades just ~128MB
// TODO: explore better strategies
@@ -15966,7 +15999,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
new_type = GGML_TYPE_IQ2_S;
}
- } else if (name.find("attn_q.weight") != std::string::npos) {
+ } else if ((name.find("attn_q.weight") != std::string::npos) || (name.find("to_q.weight") != std::string::npos)) {
if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
new_type = GGML_TYPE_IQ3_XXS;
}
@@ -16038,7 +16071,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
}
}
- else if (name.find("attn_qkv.weight") != std::string::npos) {
+ else if ((name.find("attn_qkv.weight") != std::string::npos) || (name.find("attn.qkv.weight") != std::string::npos)) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
new_type = GGML_TYPE_Q4_K;
}
@@ -16105,6 +16138,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break;
default: throw std::runtime_error("\nUnsupported tensor size encountered\n");
}
+ // Force FP16 fallback - needed due to Conv2D
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
+ new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
+ new_type = GGML_TYPE_Q5_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) {
+ new_type = GGML_TYPE_Q6_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) {
+ new_type = GGML_TYPE_Q5_K;
+ }
+ ++qs.i_attention_wv;
+ } else if ( // Rules for fused qkv attention
+ (name.find("attn_qkv.weight") != std::string::npos) ||
+ (name.find("attn.qkv.weight") != std::string::npos)
+ ) {
+ if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
+ new_type = GGML_TYPE_Q4_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
+ new_type = GGML_TYPE_Q5_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) {
+ new_type = GGML_TYPE_Q6_K;
+ }
+ } else if ( // Rules for ffn
+ (name.find("ffn_down") != std::string::npos) ||
+ (name.find("DenseReluDense.wo") != std::string::npos)
+ ) {
+ // TODO: add back `layer_info` with some model specific logic + logic further down
+ if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
+ new_type = GGML_TYPE_Q4_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
+ new_type = GGML_TYPE_Q5_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S) {
+ new_type = GGML_TYPE_Q5_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
+ new_type = GGML_TYPE_Q6_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) {
+ new_type = GGML_TYPE_Q6_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_0) {
+ new_type = GGML_TYPE_Q4_1;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_0) {
+ new_type = GGML_TYPE_Q5_1;
+ }
+ ++qs.i_ffn_down;
+ }
+
+ // Sanity check for row shape
+ bool convert_incompatible_tensor = false;
+ if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K ||
+ new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K) {
+ int nx = tensor->ne[0];
+ int ny = tensor->ne[1];
+ if (nx % QK_K != 0) {
+ LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for %s", __func__, nx, ny, QK_K, ggml_type_name(new_type));
+ convert_incompatible_tensor = true;
+ } else {
+ ++qs.n_k_quantized;
+ }
+ }
+ if (convert_incompatible_tensor) {
+ // TODO: Possibly reenable this in the future
+ // switch (new_type) {
+ // case GGML_TYPE_Q2_K:
+ // case GGML_TYPE_Q3_K:
+ // case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break;
+ // case GGML_TYPE_Q5_K: new_type = GGML_TYPE_Q5_1; break;
+ // case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break;
+ // default: throw std::runtime_error("\nUnsupported tensor size encountered\n");
+ // }
+ new_type = GGML_TYPE_F16;
LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type));
++qs.n_fallback;
}
@@ -17432,6 +17467,9 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
case LLM_ARCH_T5:
case LLM_ARCH_T5ENCODER:
case LLM_ARCH_JAIS:
+ case LLM_ARCH_FLUX:
+ case LLM_ARCH_SD1:
+ case LLM_ARCH_SDXL:
return LLAMA_ROPE_TYPE_NONE;
// use what we call a normal RoPE, operating on pairs of consecutive head values
+ LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type));
+ ++qs.n_fallback;
+ }
+ return new_type;
+}
+
+
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);
// TODO: avoid hardcoded tensor names - use the TN_* constants
const llm_arch arch = qs.model.arch;
+ if (arch == LLM_ARCH_FLUX || arch == LLM_ARCH_SD1 || arch == LLM_ARCH_SDXL) { return img_tensor_get_type(qs, new_type, tensor, ftype); };
const auto tn = LLM_TN(arch);
auto use_more_bits = [](int i_layer, int n_layers) -> bool {