add missing `GGML_TYPE`s
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55dbee2bbe
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@ -480,14 +480,19 @@ static size_t llama_tensor_quantize_impl(enum ggml_type new_type, const float *
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}
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static bool tensor_requires_imatrix(const llama_model_quantize_params * params, const ggml_tensor * t, const ggml_type dst_type) {
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if (dst_type == GGML_TYPE_IQ2_XXS || dst_type == GGML_TYPE_IQ2_XS ||
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dst_type == GGML_TYPE_IQ2_S || dst_type == GGML_TYPE_IQ1_S || (
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if (
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dst_type == GGML_TYPE_IQ2_XXS || dst_type == GGML_TYPE_IQ2_XS ||
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dst_type == GGML_TYPE_IQ3_XXS || dst_type == GGML_TYPE_IQ1_S ||
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dst_type == GGML_TYPE_IQ2_S || dst_type == GGML_TYPE_IQ1_M ||
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dst_type == GGML_TYPE_TQ1_0 || dst_type == GGML_TYPE_TQ2_0 ||
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(
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dst_type == GGML_TYPE_IQ1_M && strcmp(t->name, "token_embd.weight") &&
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strcmp(t->name, "output.weight")
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) || (
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dst_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S &&
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strcmp(t->name, "token_embd.weight") != 0
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)) {
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)
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) {
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return true;
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} else {
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return false;
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@ -979,7 +984,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
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}
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}
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}
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if (tensor_requires_imatrix(params, tensor, new_type) && !imatrix) {
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if (!imatrix && tensor_requires_imatrix(params, tensor, new_type)) {
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LLAMA_LOG_ERROR("\n\n============================================================\n");
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LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name);
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LLAMA_LOG_ERROR("The result will be garbage, so bailing out\n");
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