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Make WeightOnlyInt8QuantLinear more compatible with torch.compile #19

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Make WeightOnlyInt8QuantLinear more compatible with torch.compile #19

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f17ccff
Make WeightOnlyInt8QuantLinear more compatible with torch.compile
cpuhrsch Dec 3, 2023
235c50d
Merge branch 'main' of github.com:pytorch-labs/ao into wofix1
cpuhrsch Dec 4, 2023
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25 changes: 14 additions & 11 deletions torchao/quantization/weight_only.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,14 +17,17 @@ class WeightOnlyInt8QuantLinear(torch.nn.Linear):
This class is a replacement for `torch.nn.Linear`. It implements a
mixed dtype matmul using int8 symmetric per-channel weight quantization
"""
def __init__(self, *args, **kwargs):
w_int8 = kwargs.pop("w_int8")
scales = kwargs.pop("scales")
super().__init__(*args, **kwargs)
self.w_int8 = w_int8
self.scales = scales
def __init__(
self,
in_features: int,
out_features: int,
bias: bool = True,
) -> None:
super().__init__(in_features, out_features, bias)
# self.w_int8 = w_int8
# self.scales = scales

def forward(self, x):
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Performs the forward pass of the quantized linear layer which consists
ofmixed dtype matmul using int8 symmetric per-channel weight quantization
Expand All @@ -39,15 +42,15 @@ def forward(self, x):
# if len(x.shape)<=2:
# y = torch.mm(x, self.w_int8.to(x.dtype)) * self.scales
# else: # turn x into 2d tensor, then undo it for y
x_view = x.view(-1, x.shape[-1])
x_view = x.reshape(-1, x.shape[-1])
y = torch.mm(x_view, self.w_int8.to(x.dtype)) * self.scales
y = y.reshape(*x.shape[:-1], -1)
if self.bias is not None:
y += self.bias
return y

@classmethod
def from_float(cls, mod):
def from_float(cls, mod: torch.nn.Linear) -> "WeightOnlyInt8QuantLinear":
"""
Converts a `mod` of class `torch.nn.Linear` to the
`WeightOnlyInt8QuantLinear` class
Expand All @@ -69,9 +72,9 @@ def from_float(cls, mod):
fake_in_features,
fake_out_features,
bias=mod.bias is not None,
w_int8=w_int8.t().contiguous(),
scales=scales,
)
new_mod.register_buffer("w_int8", w_int8.t())
new_mod.scales = torch.nn.Parameter(scales)
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probably want requires_grad = False

new_mod.in_features = mod.in_features
new_mod.out_features = mod.out_features
del new_mod.weight
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