llama : quantize more Mamba tensors
* llama : use f16 as the fallback of fallback quant types
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5c0f108e15
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3491291a32
2 changed files with 4 additions and 19 deletions
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@ -295,6 +295,7 @@ class Model:
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gguf.MODEL_TENSOR.FFN_GATE_INP,
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gguf.MODEL_TENSOR.POS_EMBD,
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gguf.MODEL_TENSOR.TOKEN_TYPES,
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gguf.MODEL_TENSOR.SSM_CONV1D,
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)
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)
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or not name.endswith(".weight")
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@ -2786,23 +2787,6 @@ class MambaModel(Model):
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return [(new_name, data_torch)]
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def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
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if bid is not None and new_name in (
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self.format_tensor_name(
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n, bid, ".weight" if name.endswith(".weight") else ""
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)
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for n in [
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gguf.MODEL_TENSOR.SSM_CONV1D,
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gguf.MODEL_TENSOR.SSM_X,
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gguf.MODEL_TENSOR.SSM_DT,
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gguf.MODEL_TENSOR.SSM_A,
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gguf.MODEL_TENSOR.SSM_D,
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]
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):
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return gguf.GGMLQuantizationType.F32
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return super().tensor_force_quant(name, new_name, bid, n_dims)
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@Model.register("CohereForCausalLM")
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class CommandR2Model(Model):
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@ -16122,6 +16122,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
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case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break;
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default: throw std::runtime_error("\nUnsupported tensor size encountered\n");
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}
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if (tensor->ne[0] % ggml_blck_size(new_type) != 0) {
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new_type = GGML_TYPE_F16;
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}
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LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type));
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++qs.n_fallback;
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}
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@ -16450,8 +16453,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
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// do not quantize Mamba's small yet 2D weights
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// NOTE: can't use LLM_TN here because the layer number is not known
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quantize &= name.find("ssm_conv1d.weight") == std::string::npos;
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quantize &= name.find("ssm_x.weight") == std::string::npos;
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quantize &= name.find("ssm_dt.weight") == std::string::npos;
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// do not quantize relative position bias (T5)
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quantize &= name.find("attn_rel_b.weight") == std::string::npos;
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