register quantized_linear.per_tensor in lib (#6563)

Summary:

cont of previous diff

Reviewed By: hsharma35

Differential Revision: D65104400

Author: zonglinpeng

backends/cadence/aot/ops_registrations.py

@@ -50,7 +50,11 @@
     "quantized_linear.out(Tensor src, Tensor weight, Tensor bias, int src_zero_point, Tensor weight_zero_point, Tensor out_multiplier, Tensor out_shift, int out_zero_point, Tensor? offset, *, Tensor(a!) out) ->  Tensor(a!)"
 )
 lib.define(
-    "cadence::quantized_linear.per_tensor_out(Tensor src, Tensor weight, Tensor bias, SymInt src_zero_point, SymInt weight_zero_point, SymInt out_multiplier, SymInt out_shift, SymInt out_zero_point, Tensor? offset, *, Tensor(a!) out) -> Tensor(a!)"
+    "quantized_linear.per_tensor_out(Tensor src, Tensor weight, Tensor bias, SymInt src_zero_point, SymInt weight_zero_point, SymInt out_multiplier, SymInt out_shift, SymInt out_zero_point, Tensor? offset, *, Tensor(a!) out) -> Tensor(a!)"
+)
+lib.define(
+    "quantized_linear.per_tensor(Tensor src, Tensor weight, Tensor bias, SymInt src_zero_point, "
+    "SymInt weight_zero_point, SymInt out_multiplier, SymInt out_shift, SymInt out_zero_point, Tensor? offset) -> Tensor"
 )
 
 lib.define(
@@ -123,6 +127,28 @@ def quantized_linear_meta(
     return src.new_empty(out_size, dtype=src.dtype)
 
 
+@register_fake("cadence::quantized_linear.per_tensor")
+def quantized_linear_per_tensor_meta(
+    src: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor,
+    in_zero_point: torch.SymInt,
+    weight_zero_point: torch.SymInt,
+    out_multiplier: torch.SymInt,
+    out_shift: torch.SymInt,
+    out_zero_point: torch.SymInt,
+    offset: Optional[torch.Tensor],
+) -> torch.Tensor:
+    # src comes in shape [leading_dims, in_dim]
+    # weight comes in shape [out_dim, in_dim]
+    # output comes in empty with shape [leading_dims, out_dim]
+    out_size = list(src.size())
+    weight_size = list(weight.size())
+    assert len(weight_size) == 2
+    out_size[-1] = weight_size[0]
+    return src.new_empty(out_size, dtype=src.dtype)
+
+
 @register_fake("cadence::quantized_conv")
 def quantized_conv_meta(
     input: torch.Tensor,