Add quantized max_pool2d operator

backends/cadence/aot/functions.yaml

@@ -309,6 +309,11 @@
     - arg_meta: null
       kernel_name: impl::generic::quantized_relu_asym8u_asym8u_per_tensor_out
 
+- func: cadence::quantized_max_pool2d.out(Tensor input, int[] kernel_size, int[] stride, int[] padding, int[] dilation, bool ceil_mode, *, Tensor(a!) out) -> Tensor(a!)
+  kernels:
+    - arg_meta: null
+      kernel_name: impl::generic::quantized_max_pool2d_out
+
 - func: cadence::quantized_add.per_tensor_out(Tensor X, float X_scale, int X_zero_point, Tensor Y, float Y_scale, int Y_zero_point, float out_scale, int out_zero_point, *, Tensor(a!) out) -> Tensor(a!)
   kernels:
     - arg_meta: null

backends/cadence/aot/ops_registrations.py

@@ -213,6 +213,13 @@ def register_fake(
     "quantized_relu.out(Tensor X, Tensor X_zero_point, int out_zero_point, Tensor out_multiplier, Tensor out_shift, *, Tensor(a!) out) -> Tensor (a!)"
 )
 
+lib.define(
+    "quantized_max_pool2d(Tensor input, int[] kernel_size, int[] stride, int[] padding, int[] dilation, bool ceil_mode) -> Tensor"
+)
+lib.define(
+    "quantized_max_pool2d.out(Tensor input, int[] kernel_size, int[] stride, int[] padding, int[] dilation, bool ceil_mode, *, Tensor(a!) out) -> Tensor(a!)"
+)
+
 lib.define(
     "quantized_conv2d_nhwc(Tensor input, Tensor weight, Tensor bias, int[] stride, SymInt[] padding, int[] dilation, int groups, int input_zero_point, Tensor weight_zero_point, Tensor bias_scale, float out_scale, int out_zero_point, Tensor out_multiplier, Tensor out_shift) -> (Tensor Z)"
 )
@@ -2270,6 +2277,46 @@ def quantized_relu_asym8u_asym8u_per_tensor_meta(
     return input.new_empty(input.size(), dtype=input.dtype)
 
 
+@register_fake("cadence::quantized_max_pool2d")
+def quantized_max_pool2d_meta(
+    input: torch.Tensor,
+    kernel_size: list[int],
+    stride: list[int],
+    padding: list[int],
+    dilation: list[int],
+    ceil_mode: bool,
+) -> torch.Tensor:
+    # Compute output size using standard max_pool2d formula
+    # Output = floor((Input + 2*Padding - Dilation*(Kernel-1) - 1) / Stride + 1)
+    batch = input.size(0)
+    channels = input.size(1)
+    height_in = input.size(2)
+    width_in = input.size(3)
+
+    if ceil_mode:
+        import math
+
+        height_out = math.ceil(
+            (height_in + 2 * padding[0] - dilation[0] * (kernel_size[0] - 1) - 1)
+            / stride[0]
+            + 1
+        )
+        width_out = math.ceil(
+            (width_in + 2 * padding[1] - dilation[1] * (kernel_size[1] - 1) - 1)
+            / stride[1]
+            + 1
+        )
+    else:
+        height_out = (
+            height_in + 2 * padding[0] - dilation[0] * (kernel_size[0] - 1) - 1
+        ) // stride[0] + 1
+        width_out = (
+            width_in + 2 * padding[1] - dilation[1] * (kernel_size[1] - 1) - 1
+        ) // stride[1] + 1
+
+    return input.new_empty([batch, channels, height_out, width_out], dtype=input.dtype)
+
+
 @register_fake("cadence::fully_connected")
 def fully_connected_meta(
     src: torch.Tensor,

backends/cadence/aot/quantizer/fusion_pass.py

@@ -1,3 +1,3 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # All rights reserved.
 #
@@ -6,6 +6,7 @@
 
 # pyre-strict
 
+import operator as op_module
 from typing import Any, cast, Dict, List, Tuple
 
 import torch
@@ -24,6 +25,8 @@
     LayerNormPattern,
     LinearPattern,
     MatmulPattern,
+    MaxPool2dPattern,
+    MaxPool2dWithoutIndicesPattern,
     MixedW8A32ConvPattern,
     MixedW8A32GruPattern,
     MixedW8A32LinearPattern,
@@ -457,6 +460,34 @@
     return args, kwargs
 
 
+def get_args_and_kwargs_max_pool2d(
+    inputs_inputs: List[fx.Node],
+    op_node: fx.Node,
+) -> Tuple[Tuple[ArgsType, ...], Dict[str, ArgsType]]:
+    """
+    Returns the args and kwargs for the max_pool2d replacement op.
+
+    Max pooling is order-preserving, so we can perform the max operation
+    directly on quantized values without any requantization.
+    """
+    # Get the pooling parameters from the original op node
+    kernel_size = op_node.args[1] if len(op_node.args) > 1 else [1, 1]
+    stride = op_node.args[2] if len(op_node.args) > 2 else kernel_size
+    padding = op_node.args[3] if len(op_node.args) > 3 else [0, 0]
+    dilation = op_node.args[4] if len(op_node.args) > 4 else [1, 1]
+    ceil_mode = op_node.args[5] if len(op_node.args) > 5 else False
+
+    args = (inputs_inputs[0],)
+    kwargs = {
+        "kernel_size": kernel_size,
+        "stride": stride,
+        "padding": padding,
+        "dilation": dilation,
+        "ceil_mode": ceil_mode,
+    }
+    return args, kwargs
+
+
 def get_args_and_kwargs_mixed_w8a32_gru(
     graph_module: GraphModule,
     other_inputs: List[fx.Node],
@@ -549,6 +580,10 @@
 
                 assert op_node is not None, "op_node is None"
                 quant_node = list(op_node.users.keys())[0]
+                # For ops that return tuples (e.g., max_pool2d_with_indices),
+                # traverse through the getitem to find the actual quant node
+                if quant_node.target is op_module.getitem:
+                    quant_node = list(quant_node.users.keys())[0]
 
                 with graph_module.graph.inserting_after(op_node):
                     args = tuple(
@@ -697,6 +732,11 @@
                             dequants_biases,
                             op_node,
                         )
+                    elif isinstance(pattern, (MaxPool2dPattern, MaxPool2dWithoutIndicesPattern)):
+                        args, kwargs = get_args_and_kwargs_max_pool2d(
+                            inputs_inputs,
+                            op_node,
+                        )
 
                     fused = graph_module.graph.call_function(
                         pattern.replacement_op(),

backends/cadence/aot/quantizer/patterns.py

@@ -417,6 +417,90 @@ def replacement_op(self) -> OpOverload:
         return torch.ops.cadence.quantized_matmul.default
 
 
+class MaxPool2dPattern(QuantizationPattern):
+    """
+    Pattern for quantized max pooling (with indices variant).
+
+    Max pooling is order-preserving, so max(a, b) in the quantized domain gives
+    the same result as quantizing max(dequant(a), dequant(b)) when using the same
+    scale/zero_point. This means we can perform max pooling directly on quantized
+    values without any requantization.
+
+    The input and output share quantization parameters.
+    """
+
+    def partition_types(self) -> List[OpOverload]:
+        return [torch.ops.aten.max_pool2d_with_indices.default]
+
+    def get_anchors(
+        self, gm: fx.GraphModule, fused_partition: List[fx.GraphModule]
+    ) -> Tuple[PartitionAnchors, fx.Node]:
+        # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C.TensorBase.__ge...
+        max_pool_node = fused_partition[0].nodes[-1]
+
+        # Input and output share quantization parameters since max is order-preserving
+        return (
+            PartitionAnchors(
+                inputs=[(max_pool_node, 0)],
+                weights=[],
+                biases=[],
+                # kernel_size, stride, padding, dilation, ceil_mode are literals
+                literals=[
+                    (max_pool_node, i) for i in range(1, len(max_pool_node.args))
+                ],
+                output=[
+                    (
+                        max_pool_node,
+                        SharedQuantizationSpec((max_pool_node.args[0], max_pool_node)),
+                    )
+                ],
+            ),
+            max_pool_node,
+        )
+
+    def replacement_op(self) -> OpOverload:
+        return torch.ops.cadence.quantized_max_pool2d.default
+
+
+class MaxPool2dWithoutIndicesPattern(QuantizationPattern):
+    """
+    Pattern for quantized max pooling (without indices variant).
+
+    Same as MaxPool2dPattern but matches aten.max_pool2d.default which returns
+    a single tensor instead of a tuple (values, indices).
+    """
+
+    def partition_types(self) -> List[OpOverload]:
+        return [torch.ops.aten.max_pool2d.default]
+
+    def get_anchors(
+        self, gm: fx.GraphModule, fused_partition: List[fx.GraphModule]
+    ) -> Tuple[PartitionAnchors, fx.Node]:
+        # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C.TensorBase.__ge...
+        max_pool_node = fused_partition[0].nodes[-1]
+
+        return (
+            PartitionAnchors(
+                inputs=[(max_pool_node, 0)],
+                weights=[],
+                biases=[],
+                literals=[
+                    (max_pool_node, i) for i in range(1, len(max_pool_node.args))
+                ],
+                output=[
+                    (
+                        max_pool_node,
+                        SharedQuantizationSpec((max_pool_node.args[0], max_pool_node)),
+                    )
+                ],
+            ),
+            max_pool_node,
+        )
+
+    def replacement_op(self) -> OpOverload:
+        return torch.ops.cadence.quantized_max_pool2d.default
+
+
 # This is a base class for ReLU, since it can be used with two different aten ops
 class ReluBasePattern(QuantizationPattern):
     @abstractmethod

backends/cadence/aot/quantizer/quantizer.py

@@ -24,6 +24,8 @@
     LayerNormPattern,
     LinearPattern,
     MatmulPattern,
+    MaxPool2dPattern,
+    MaxPool2dWithoutIndicesPattern,
     MixedW8A32ConvPattern,
     MixedW8A32GruPattern,
     MixedW8A32LinearPattern,
@@ -227,6 +229,8 @@ def get_cadence_default_quantizers() -> List[Quantizer]:
         CadenceAtenQuantizer(Conv2dPattern(), qconfig_A8W8sym),
         CadenceAtenQuantizer(LinearPattern(), qconfig_A8W8),
         CadenceAtenQuantizer(MatmulPattern(), qconfig_A8W8),
+        CadenceAtenQuantizer(MaxPool2dPattern(), qconfig_A8W8),
+        CadenceAtenQuantizer(MaxPool2dWithoutIndicesPattern(), qconfig_A8W8),
         CadenceAtenQuantizer(ReluPattern0(), qconfig_A8W8),
         CadenceAtenQuantizer(ReluPattern1(), qconfig_A8W8),
     ]

backends/cadence/aot/ref_implementations.py

@@ -1868,6 +1868,35 @@ def rms_norm(
     return W * nn.RMSNorm(list(normalized_shape), eps=eps, dtype=X.dtype)(X)
 
 
+@impl_tracked(m, "quantized_max_pool2d")
+def quantized_max_pool2d(
+    input: torch.Tensor,
+    kernel_size: list[int],
+    stride: list[int],
+    padding: list[int],
+    dilation: list[int],
+    ceil_mode: bool,
+) -> torch.Tensor:
+    """
+    Quantized max pooling operation.
+
+    Max pooling is order-preserving, so max(a, b) in the quantized domain gives
+    the same result as quantizing max(dequant(a), dequant(b)) when using the same
+    scale/zero_point. This means we can perform max pooling directly on quantized
+    integer values without dequantization/requantization.
+    """
+    # Directly apply max_pool2d on quantized values
+    # Since max is order-preserving, the result is correct without any dequant/requant
+    return F.max_pool2d(
+        input,
+        kernel_size=kernel_size,
+        stride=stride,
+        padding=padding,
+        dilation=dilation,
+        ceil_mode=ceil_mode,
+    )
+
+
 @impl_tracked(m, "where_Scalar")
 def where_Scalar(
     condition: torch.Tensor,