diff options
Diffstat (limited to 'src')
| -rw-r--r-- | src/llama.cpp | 16 |
1 files changed, 12 insertions, 4 deletions
diff --git a/src/llama.cpp b/src/llama.cpp index 943b945a..117f59be 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -3955,6 +3955,7 @@ struct llama_model_loader { case GGML_TYPE_IQ3_XXS_R4: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4; break; case GGML_TYPE_IQ1_S: ftype = LLAMA_FTYPE_MOSTLY_IQ1_S; break; case GGML_TYPE_IQ1_S_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ1_S_R4;break; + case GGML_TYPE_IQ1_M_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ1_M_R4;break; case GGML_TYPE_IQ1_M: ftype = LLAMA_FTYPE_MOSTLY_IQ1_M; break; case GGML_TYPE_IQ1_BN: ftype = LLAMA_FTYPE_MOSTLY_IQ1_BN; break; case GGML_TYPE_IQ2_BN: ftype = LLAMA_FTYPE_MOSTLY_IQ2_BN; break; @@ -4690,6 +4691,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { case LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4: return "IQ3_XXS_R4 - 3.0625 bpw"; case LLAMA_FTYPE_MOSTLY_IQ1_S: return "IQ1_S - 1.5625 bpw"; case LLAMA_FTYPE_MOSTLY_IQ1_S_R4: return "IQ1_S_R4 - 1.5 bpw"; + case LLAMA_FTYPE_MOSTLY_IQ1_M_R4: return "IQ1_M_R4 - 1.75 bpw"; case LLAMA_FTYPE_MOSTLY_IQ1_M: return "IQ1_M - 1.75 bpw"; case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw"; case LLAMA_FTYPE_MOSTLY_IQ4_NL_R4:return "IQ4_NL_R4 - 4.5 bpw"; @@ -15969,7 +15971,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n ftype == LLAMA_FTYPE_MOSTLY_IQ1_M || ftype == LLAMA_FTYPE_MOSTLY_IQ2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K || ftype == LLAMA_FTYPE_MOSTLY_IQ2_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_K_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ2_K_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4 || - ftype == LLAMA_FTYPE_MOSTLY_IQ2_M_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S_R4) { + ftype == LLAMA_FTYPE_MOSTLY_IQ2_M_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S_R4 || + ftype == LLAMA_FTYPE_MOSTLY_IQ1_M_R4) { new_type = !qs.has_output ? GGML_TYPE_IQ4_K : GGML_TYPE_Q5_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS_R4) { @@ -15991,7 +15994,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS_R4 || - ftype == LLAMA_FTYPE_MOSTLY_IQ1_S_R4) { + ftype == LLAMA_FTYPE_MOSTLY_IQ1_S_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M_R4) { new_type = GGML_TYPE_Q2_K; } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M_R4) { @@ -16068,7 +16071,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n new_type = GGML_TYPE_BF16; } } - } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S_R4) { + } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S_R4 || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M_R4) { if (name.find("attn_v.weight") != std::string::npos) { if (qs.model.hparams.n_expert >= 4 || qs.model.hparams.n_gqa() >= 4) new_type = GGML_TYPE_IQ4_K_R4; else if (qs.model.hparams.n_gqa() >= 2) new_type = GGML_TYPE_IQ3_K_R4; @@ -16134,7 +16137,6 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n new_type = GGML_TYPE_Q5_K; } else { if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ2_K; - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S_R4) new_type = GGML_TYPE_IQ2_K_R4; else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || is_iq2_m) new_type = GGML_TYPE_IQ3_S; } } @@ -16580,6 +16582,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s case LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4: default_type = GGML_TYPE_IQ3_XXS_R4; break; case LLAMA_FTYPE_MOSTLY_IQ1_S: default_type = GGML_TYPE_IQ1_S; break; case LLAMA_FTYPE_MOSTLY_IQ1_S_R4:default_type = GGML_TYPE_IQ1_S_R4;break; + case LLAMA_FTYPE_MOSTLY_IQ1_M_R4:default_type = GGML_TYPE_IQ1_M_R4;break; case LLAMA_FTYPE_MOSTLY_IQ1_M: default_type = GGML_TYPE_IQ1_M; break; case LLAMA_FTYPE_MOSTLY_IQ1_BN: default_type = GGML_TYPE_IQ1_BN; break; case LLAMA_FTYPE_MOSTLY_IQ2_BN: default_type = GGML_TYPE_IQ2_BN; break; @@ -16934,6 +16937,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s new_type == GGML_TYPE_IQ2_S_R4|| new_type == GGML_TYPE_IQ1_S || new_type == GGML_TYPE_IQ1_S_R4|| + new_type == GGML_TYPE_IQ1_M_R4|| (new_type == GGML_TYPE_IQ1_M && strcmp(tensor->name, "token_embd.weight") && strcmp(tensor->name, "output.weight")) || (new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0))) { LLAMA_LOG_ERROR("\n\n============================================================\n"); @@ -17057,6 +17061,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ1_S; else chunk_size_multiplier = 4; } + else if (new_type == GGML_TYPE_IQ1_M_R4) { + if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ1_M; + else chunk_size_multiplier = 4; + } else if (new_type == GGML_TYPE_BF16_R16) { if (tensor->ne[1] % 16 != 0) new_type = GGML_TYPE_BF16; else chunk_size_multiplier = 16; |