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						156af15d1e
					
				
					 2 changed files with 7 additions and 5 deletions
				
			
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			@ -30,7 +30,8 @@ ggml_tensor_qtype = {"sym_int4": 2,   # q4_0 in ggml
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                     "sym_int5": 6,   # q5_0 in ggml
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                     "asym_int5": 7,  # q5_1 in ggml
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                     "sym_int8": 8,   # q8_0 in ggml
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                     "nf4": 10}
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                     "nf4": 10,
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                     "nf3": 11}
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_llama_quantize_type = {"q4_0": 2,
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                        "q4_1": 3,
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			@ -62,6 +62,7 @@ TORCH_LINEAR_THRESHOLD = 96
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SYM_INT4 = ggml_tensor_qtype["sym_int4"]
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SYM_INT8 = ggml_tensor_qtype["sym_int8"]
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NF4 = ggml_tensor_qtype["nf4"]
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NF3 = ggml_tensor_qtype["nf3"]
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def ggml_convert_qtype(tensor: torch.Tensor, qtype: int,
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			@ -101,7 +102,7 @@ def ggml_q_format_convet_cpu2xpu(tensor: torch.Tensor, num_elem: int, qtype: int
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    src = ctypes.c_void_p(tensor.data.data_ptr())
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    if qtype in [SYM_INT4, SYM_INT8, NF4]:
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    if qtype in [SYM_INT4, SYM_INT8, NF4, NF3]:
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        dst_tensor = torch.empty_like(tensor)
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    elif qtype == ggml_tensor_qtype["sym_int5"]:
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        QK = ggml.ggml_qk_size(qtype)
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			@ -126,7 +127,7 @@ def ggml_q_format_convet_xpu2cpu(tensor: torch.Tensor, num_elem: int, qtype: int
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    src = ctypes.c_void_p(tensor.data.data_ptr())
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    if qtype in [SYM_INT4, SYM_INT8, NF4]:
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    if qtype in [SYM_INT4, SYM_INT8, NF4, NF3]:
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        dst_tensor = torch.empty_like(tensor)
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    elif qtype == ggml_tensor_qtype["sym_int5"]:
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        QK = ggml.ggml_qk_size(ggml_tensor_qtype["asym_int5"])
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			@ -363,8 +364,8 @@ class LowBitLinear(nn.Linear):
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        else:
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            # CPU logic
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            # todo may need to set a different number on different platforms
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            invalidInputError(self.qtype != ggml_tensor_qtype["nf4"],
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                              "NF4 quantization is currently not supported on CPU")
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            invalidInputError(self.qtype != NF3 and self.qtype != NF4,
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                              "NF3 and NF4 quantization are currently not supported on CPU")
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            if IS_SERVER and (not IS_SPR) and \
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                    self.qtype == SYM_INT4 and x_2d.shape[0] >= TORCH_LINEAR_THRESHOLD:
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                x0_fp32 = ggml_int4_convert_fp32(x0, self.weight_shape, self.weight_length)
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