Add test for quantizing models with hierarchies in qat 8da4w (#157)

Summary:
att

Test Plan:
python test/quantization/test_qat.py

Reviewers:

Subscribers:

Tasks:

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Co-authored-by: Jerry Zhang <[email protected]>

test/quantization/test_qat.py

@@ -22,17 +22,30 @@
 
 
 # TODO: put this in a common test utils file
+class Sub(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.linear = torch.nn.Linear(32, 32, bias=False).to(torch.float)
+
+    def example_inputs(self):
+        return (torch.randn(1, 32).to(torch.float),)
+
+    def forward(self, x):
+        return self.linear(x)
+
 class M(torch.nn.Module):
     def __init__(self):
         super().__init__()
         self.linear1 = torch.nn.Linear(64, 32, bias=False).to(torch.float)
+        self.sub = Sub()
         self.linear2 = torch.nn.Linear(32, 64, bias=False).to(torch.float)
 
     def example_inputs(self):
         return (torch.randn(1, 64).to(torch.float),)
 
     def forward(self, x):
         x = self.linear1(x)
+        x = self.sub(x)
         x = self.linear2(x)
         return x
 
@@ -160,6 +173,9 @@ def test_qat_8da4w_quantizer(self):
         self._set_ptq_weight(
             ptq_model.linear1, qat_model.linear1.weight, group_size,
         )
+        self._set_ptq_weight(
+            ptq_model.sub.linear, qat_model.sub.linear.weight, group_size,
+        )
         self._set_ptq_weight(
             ptq_model.linear2, qat_model.linear2.weight, group_size,
         )

torchao/quantization/prototype/qat.py

@@ -24,7 +24,7 @@ class Int8DynActInt4WeightQATQuantizer(TwoStepQuantizer):
         dynamic per token fake quantized activations and int4 fake quantized
         grouped per channel weights.
         """
-    
+
         def __init__(
             self,
             groupsize: int = 256,
@@ -37,7 +37,7 @@ def __init__(
             self.padding_allowed: bool = padding_allowed
             self.precision: torch.dtype = precision
             self.scales_precision: torch.dtype = scales_precision
-    
+
         def prepare(
             self,
             model: torch.nn.Module,
@@ -53,7 +53,7 @@ def prepare(
                 Int8DynActInt4WeightQATLinear,
             )
             return model
-    
+
         def convert(
             self,
             model: torch.nn.Module,
@@ -62,19 +62,19 @@ def convert(
         ) -> torch.nn.Module:
             # TODO: replace Int8DynActInt4WeightQATLinear -> Int8DynActInt4WeightLinear
             pass
-    
-    
+
+
     class Int8DynActInt4WeightQATLinear(torch.nn.Linear):
         """
         This module implements a linear layer with int8 dynamic per token fake
         quantized activations with int4 fake quantized grouped per channel weights.
-    
+
         args:
             groupsize: the number of elements in each quantized group for weights
             precision: precision of weights
             scales_precision: precision of per group scales and zero points
         """
-    
+
         def __init__(
             self,
             in_features: int,
@@ -97,7 +97,7 @@ def __init__(
             assert not bias, "require bias=False"
             self.groupsize = groupsize
             self.scales_precision = scales_precision
-    
+
         def forward(self, x: torch.Tensor) -> torch.Tensor:
             # activations: int8 dynamic asymmetric quant
             (act_qmin, act_qmax) = self._get_qmin_qmax(8)
@@ -107,7 +107,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
             x_fq = fake_quantize_per_token(
                 x, act_scales, act_zp, act_qmin, act_qmax,
             )
-    
+
             # weights: int4 grouped per channel symmetric quant
             (weight_qmin, weight_qmax) = self._get_qmin_qmax(4)
             (weight_scales, weight_zp) = get_group_qparams_symmetric(
@@ -122,7 +122,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
                 self.groupsize,
             )
             return torch.nn.functional.linear(x_fq, w_fq)
-    
+
         def _get_qmin_qmax(self, n_bit: int):
             qmin = -(2 ** (n_bit - 1))
             qmax = 2 ** (n_bit - 1) - 1