Add backend-agnostic implementation for quantized_linear

Differential Revision: D81363750

Pull Request resolved: #13897

Author: DrJessop

backends/cadence/aot/ref_implementations.py

@@ -6,6 +6,8 @@
 
 # pyre-strict
 
+from typing import Optional
+
 import torch
 from executorch.exir.scalar_type import ScalarType
 from torch.library import impl, Library
@@ -177,6 +179,54 @@ def quantized_add(
     )
 
 
+@impl(m, "quantized_linear")
+def quantized_linear(
+    src: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor,
+    in_zero_point: int,
+    weight_zero_point: torch.Tensor,
+    out_multiplier: torch.Tensor,
+    out_shift: torch.Tensor,
+    out_zero_point: int,
+    offset: Optional[torch.Tensor],
+) -> torch.Tensor:
+    """
+    Quantized linear (transposed matmul) operation.
+
+    Args:
+        - src (Tensor): The activations tensor
+        - weight (Tensor): The weight tensor
+        - bias (Tensor): The bias tensor
+        - in_zero_point (int): The quantized mapping of zero for the input
+        - weight_zero_point (Tensor): The quantized mapping of zero for the weight
+        - out_multiplier (Tensor): The multiplier used to scale the output
+        - out_shift (Tensor): The shift used to scale the output
+        - out_zero_point (int): The quantized mapping of zero for the output
+        - offset (Tensor): Unused
+    """
+    out_scale = -out_multiplier * (1 / (1 << 31)) * (2 ** out_shift[0])
+
+    N, K = weight.shape
+
+    leading_dims = src.shape[:-1]
+    src = src.view(-1, K)
+
+    dtype = src.dtype
+    supported_dtypes = [torch.int8, torch.uint8, torch.int32]
+    if dtype not in supported_dtypes:
+        raise ValueError(
+            f"Unsupported dtype to quantize to. Supported dtypes must be one of {supported_dtypes}"
+        )
+
+    out = torch.nn.functional.linear(
+        src - in_zero_point, weight - weight_zero_point, bias
+    )
+    return quantize_per_tensor(
+        out, out_scale, out_zero_point, -128, 127, dtype
+    ).reshape(*leading_dims, N)
+
+
 @impl(m, "requantize")
 def requantize(
     input: torch.Tensor,

backends/cadence/aot/tests/test_ref_implementations.py

@@ -5,15 +5,17 @@
 # LICENSE file in the root directory of this source tree.
 
 # pyre-strict
-
+import typing
 import unittest
 
+import numpy as np
 import torch
 
 from executorch.backends.cadence.aot.ref_implementations import (
     dequantize_per_tensor,
     quantize_per_tensor,
     quantized_add,
+    quantized_linear,
 )
 from executorch.backends.cadence.aot.typing_stubs import expand
 
@@ -138,3 +140,102 @@ def test_quantized_add(
             torch.equal(output, expected_output),
             f"Values don't match in {name}: got {output}, expected {expected_output}",
         )
+
+    @expand(
+        [
+            # Test case 1: 1x2 input, 1x2 weight (1 output feature)
+            (
+                torch.Size([1, 2]),  # src_shape: 1 sample, 2 input features
+                torch.Size([1, 2]),  # weight_shape: 1 output feature, 2 input features
+                0,  # in_zero_point
+                torch.tensor([0, 0], dtype=torch.int8),  # weight_zero_point
+                torch.tensor(
+                    [1073741824], dtype=torch.int32
+                ),  # out_multiplier (0.5 * 2^31)
+                torch.tensor([0], dtype=torch.int8),  # out_shift
+                0,  # out_zero_point
+                torch.tensor([[-2]], dtype=torch.int8),  # expected_output
+            ),
+            # Test case 2: 1x3 input, 2x3 weight (2 output features)
+            (
+                torch.Size([1, 3]),  # src_shape: 1 sample, 3 input features
+                torch.Size([2, 3]),  # weight_shape: 2 output features, 3 input features
+                0,  # in_zero_point
+                torch.tensor([0, 0, 0], dtype=torch.int8),  # weight_zero_point
+                torch.tensor(
+                    [1073741824], dtype=torch.int32
+                ),  # out_multiplier (0.5 * 2^31)
+                torch.tensor([0], dtype=torch.int8),  # out_shift
+                0,  # out_zero_point
+                torch.tensor([[-10, -30]], dtype=torch.int8),  # expected_output
+            ),
+            # Test case 3: Batch case with different dimensions
+            (
+                torch.Size([1, 2, 2]),  # src_shape: batch=1, seq=2, features=2
+                torch.Size([3, 2]),  # weight_shape: 3 output features, 2 input features
+                0,  # in_zero_point
+                torch.tensor([0, 0], dtype=torch.int8),  # weight_zero_point
+                torch.tensor(
+                    [1073741824], dtype=torch.int32
+                ),  # out_multiplier (0.5 * 2^31)
+                torch.tensor([0], dtype=torch.int8),  # out_shift
+                0,  # out_zero_point
+                torch.tensor(
+                    [[[-2, -8, -14], [-6, -28, -50]]], dtype=torch.int8
+                ),  # expected_output
+            ),
+            # Test case 4: Non-zero zero points
+            (
+                torch.Size([1, 2]),  # src_shape: 1 sample, 2 input features
+                torch.Size([2, 2]),  # weight_shape: 2 output feature, 1 input feature
+                2,  # in_zero_point
+                torch.tensor([1, 1], dtype=torch.int8),  # weight_zero_point
+                torch.tensor(
+                    [268435456], dtype=torch.int32
+                ),  # out_multiplier (1.0 * 2^31)
+                torch.tensor([0]),  # out_shift
+                1,  # out_zero_point
+                torch.tensor([[-15, 25]], dtype=torch.int8),  # expected_output
+            ),
+        ]
+    )
+    def test_quantized_linear(
+        self,
+        src_shape: torch.Size,
+        weight_shape: torch.Size,
+        in_zero_point: int,
+        weight_zero_point: torch.Tensor,
+        out_multiplier: torch.Tensor,
+        out_shift: torch.Tensor,
+        out_zero_point: int,
+        expected_output: torch.Tensor,
+    ) -> None:
+        src = (
+            torch.arange(np.product(src_shape))
+            .reshape(src_shape)
+            .to(expected_output.dtype)
+        )
+        weight = (
+            torch.arange(np.product(weight_shape))
+            .reshape(weight_shape)
+            .to(expected_output.dtype)
+        )
+        bias = torch.arange(weight_shape[0]).to(expected_output.dtype)
+        output = quantized_linear(
+            src,
+            weight,
+            bias,
+            in_zero_point,
+            weight_zero_point,
+            out_multiplier,
+            out_shift,
+            out_zero_point,
+            typing.cast(torch.Tensor, None),
+        )
+
+        self.assertTrue(output.dtype == expected_output.dtype, "Dtype mismatch")
+
+        self.assertTrue(
+            torch.equal(output, expected_output),
+            f"Values don't match: got {output}, expected {expected_output}",
+        )