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Support Weight-Only quantization on CPU device with QBits backend (#437)
Signed-off-by: Cheng Penghui <[email protected]> Signed-off-by: Cheng, Penghui <[email protected]> Co-authored-by: Casper Hansen <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,6 +1,7 @@ | ||
| from .exllama import WQLinear_Exllama, exllama_post_init | ||
| from .exllamav2 import WQLinear_ExllamaV2, exllamav2_post_init | ||
| from .gemm import WQLinear_GEMM | ||
| from .gemm_qbits import WQLinear_QBits, qbits_post_init | ||
| from .gemv import WQLinear_GEMV | ||
| from .marlin import WQLinear_Marlin, marlin_post_init | ||
| from .gemv_fast import WQLinear_GEMVFast |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,155 @@ | ||
| import torch | ||
| import torch.nn as nn | ||
| from ...utils.packing_utils import reverse_awq_order, unpack_awq | ||
|
|
||
| try: | ||
| from intel_extension_for_transformers import qbits # with QBits kernels () | ||
|
|
||
| QBITS_INSTALLED = True | ||
| except: | ||
| QBITS_INSTALLED = False | ||
|
|
||
| BITS_DTYPE_MAPPING = { | ||
| 4: "int4_clip", | ||
| 8: "int8", | ||
| } | ||
|
|
||
|
|
||
| def convert_dtype_torch2str(dtype): | ||
| if dtype == torch.int8: | ||
| return "int8" | ||
| elif dtype == torch.float: | ||
| return "fp32" | ||
| elif dtype == torch.float16: | ||
| return "fp16" | ||
| elif dtype == torch.bfloat16: | ||
| return "bf16" | ||
| elif isinstance(dtype, str) and dtype in ["int8", "fp32", "fp16", "bf16"]: | ||
| return dtype | ||
| else: | ||
| assert False, "Unsupported pytorch dtype {} to str dtype".format(dtype) | ||
|
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||
|
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| class WQLinear_QBits(nn.Module): | ||
|
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| def __init__(self, w_bit, group_size, in_features, out_features, bias, zero_point, dev): | ||
| super().__init__() | ||
| assert QBITS_INSTALLED, \ | ||
| "Please install ITREX qbits package with `pip install intel-extension-for-transformers`." | ||
|
|
||
| self.use_bf16 = qbits.check_isa_supported("AMX") | ||
|
|
||
| if w_bit not in [2, 3, 4, 8]: | ||
| raise NotImplementedError("Only 2, 3, 4, 8 bits are supported for now.") | ||
|
|
||
| self.in_features = in_features | ||
| self.out_features = out_features | ||
| self.w_bit = w_bit | ||
| self.group_size = group_size if group_size != -1 else in_features | ||
| self.zero_point = zero_point | ||
| self.scale_dtype = torch.float32 | ||
|
|
||
| # quick sanity check (make sure aligment) | ||
| assert self.in_features % self.group_size == 0 | ||
| assert out_features % (32 // self.w_bit) == 0 | ||
| self.pack_num = 32 // self.w_bit | ||
|
|
||
| self.register_buffer( | ||
| "qzeros", | ||
| torch.zeros( | ||
| (in_features // self.group_size, out_features // self.pack_num), | ||
| dtype=torch.int8, | ||
| device=dev, | ||
| ) if self.zero_point else None, | ||
| ) | ||
| self.register_buffer( | ||
| "scales", | ||
| torch.zeros( | ||
| (in_features // self.group_size, out_features), | ||
| dtype=torch.bfloat16 if self.use_bf16 else torch.float32, | ||
| device=dev, | ||
| )) | ||
| if bias: | ||
| self.register_buffer( | ||
| "bias", | ||
| torch.zeros((out_features), dtype=torch.bfloat16 if self.use_bf16 else torch.float32, device=dev), | ||
| ) | ||
| else: | ||
| self.register_buffer( | ||
| "bias", | ||
| None, | ||
| ) | ||
| qweight = torch.zeros((in_features, out_features // self.pack_num), dtype=torch.int32, device=dev) | ||
| self.register_buffer("qweight", qweight) | ||
|
|
||
| def post_init(self): | ||
| assert self.qweight.device.type == "cpu" | ||
|
|
||
| intweight, zeros = unpack_awq(self.qweight, self.qzeros, self.w_bit) # weight: k x n zeros: k / group_size x n | ||
| intweight, zeros = reverse_awq_order(intweight, zeros, self.w_bit) # weight: k x n zeros: k / group_size x n | ||
| if self.zero_point: | ||
| intweight = torch.bitwise_and(intweight, (2**self.w_bit) - 1) - (2**(self.w_bit - 1)) | ||
| zeros = torch.bitwise_and(zeros, (2**self.w_bit) - 1) - (2**(self.w_bit - 1)) | ||
| else: | ||
| intweight = torch.bitwise_and(intweight, (2**self.w_bit) - 1) | ||
| g_idx = torch.empty(0, dtype=torch.int32) | ||
|
|
||
| self.qweight = qbits.repack_quantized_weight(intweight, self.scales.float(), zeros, g_idx, | ||
| BITS_DTYPE_MAPPING[self.w_bit], | ||
| convert_dtype_torch2str(self.scale_dtype), | ||
| convert_dtype_torch2str(self.scales.dtype), self.zero_point, | ||
| self.group_size) | ||
|
|
||
| @classmethod | ||
| def from_linear(cls, linear, w_bit, group_size, init_only=False, scales=None, zeros=None, has_zero_points=False): | ||
| awq_linear = cls( | ||
| w_bit, | ||
| group_size, | ||
| linear.in_features, | ||
| linear.out_features, | ||
| linear.bias is not None, | ||
| has_zero_points, | ||
| linear.weight.device, | ||
| ) | ||
| if init_only: # just prepare for loading sd | ||
| return awq_linear | ||
|
|
||
| raise NotImplementedError("Only inference is supported for Exllama kernels") | ||
|
|
||
| @torch.no_grad() | ||
| def forward(self, x): | ||
| assert QBITS_INSTALLED, ( | ||
| "QBits kernels could not be loaded. " | ||
| "Please install with `pip install intel-extension-for-transformers` and " | ||
| "refer to the detial https://github.com/intel/intel-extension-for-transformers/blob/main/docs/qbits.md") | ||
|
|
||
| input_dtype = x.dtype | ||
| out_shape = x.shape[:-1] + (self.out_features,) | ||
| x = x.view(-1, x.shape[-1]) # convert xd to 2d | ||
| out_2d_shape = x.shape[:-1] + (self.out_features,) | ||
|
|
||
| outputs = torch.zeros(out_2d_shape, dtype=input_dtype) | ||
| bias = self.bias if self.bias is not None else torch.empty( | ||
| 0, dtype=torch.bfloat16 if self.use_bf16 else torch.float32) | ||
|
|
||
| qbits.woq_linear(x, self.qweight, bias, outputs, convert_dtype_torch2str(input_dtype), | ||
| BITS_DTYPE_MAPPING[self.w_bit], convert_dtype_torch2str(self.scale_dtype), self.zero_point) | ||
|
|
||
| return outputs.view(out_shape) | ||
|
|
||
| def extra_repr(self) -> str: | ||
| return ("in_features={}, out_features={}, bias={}, w_bit={}, group_size={}".format( | ||
| self.in_features, | ||
| self.out_features, | ||
| self.bias is not None, | ||
| self.w_bit, | ||
| self.group_size, | ||
| )) | ||
|
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||
|
|
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| def qbits_post_init(model): | ||
| for _, submodule in model.named_modules(): | ||
| if isinstance(submodule, WQLinear_QBits): | ||
| submodule.post_init() | ||
|
|
||
| return model |
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