65 lines
2.3 KiB
Python
65 lines
2.3 KiB
Python
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from typing import Optional, TypeVar, Union, overload
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import torch
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import torch.nn.functional as F
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from torch import Tensor, device, dtype, nn
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import bitsandbytes as bnb
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from bitsandbytes.optim import GlobalOptimManager
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from bitsandbytes.utils import OutlierTracer, find_outlier_dims
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T = TypeVar("T", bound="torch.nn.Module")
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class LinearFP8Mixed(nn.Linear):
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def __init__(self, input_features, output_features, bias=True):
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super().__init__(input_features, output_features, bias)
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self.bw_code = None
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self.fw_code = None
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array = [4096, 2048, 1024, 512, 256, 128, 64, 0]
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for i, k in enumerate(array):
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if input_features > array[i + 1]:
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self.bsz = k
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break
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for i, k in enumerate(array):
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if output_features > array[i + 1]:
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self.bsz2 = k
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break
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def forward(self, x: torch.Tensor):
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if self.fw_code is None:
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self.bw_code = bnb.functional.create_fp8_map(True, 5, 2, 8).to(x.device)
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self.fw_code = bnb.functional.create_fp8_map(True, 4, 3, 8).to(x.device)
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out = bnb.research.matmul_fp8_mixed(x, self.weight.t(), fw_code=self.fw_code, bw_code=self.bw_code, bsz=self.bsz, bsz2=self.bsz2)
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if self.bias is not None:
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out += self.bias
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return out
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class LinearFP8Global(nn.Linear):
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def __init__(self, input_features, output_features, bias=True):
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super().__init__(input_features, output_features, bias)
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self.bw_code = None
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self.fw_code = None
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array = [4096, 2048, 1024, 512, 256, 128, 64, 0]
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for i, k in enumerate(array):
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if input_features > array[i + 1]:
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self.bsz = k
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break
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for i, k in enumerate(array):
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if output_features > array[i + 1]:
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self.bsz2 = k
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break
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def forward(self, x: torch.Tensor):
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if self.fw_code is None:
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self.bw_code = bnb.functional.create_fp8_map(True, 5, 2, 8).to(x.device)
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self.fw_code = bnb.functional.create_fp8_map(True, 4, 3, 8).to(x.device)
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out = bnb.matmul_fp8_global(x, self.weight.t(), fw_code=self.fw_code, bw_code=self.bw_code, bsz=self.bsz, bsz2=self.bsz2)
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if self.bias is not None:
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out += self.bias
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return out
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