feat: support adaptive avg pool 2d and 3d dynamo converters

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -2185,7 +2185,12 @@ def aten_ops_avg_pool(
 
 
 @dynamo_tensorrt_converter(torch.ops.aten.adaptive_avg_pool1d.default)
-def aten_ops_adaptive_avg_pool(
+@enforce_tensor_types(
+    {
+        0: (TRTTensor,),
+    }
+)
+def aten_ops_adaptive_avg_pool1d(
     ctx: ConversionContext,
     target: Target,
     args: Tuple[Argument, ...],
@@ -2202,6 +2207,32 @@ def aten_ops_adaptive_avg_pool(
     )
 
 
+@dynamo_tensorrt_converter(torch.ops.aten.adaptive_avg_pool2d.default)
+@dynamo_tensorrt_converter(torch.ops.aten._adaptive_avg_pool2d.default)
+@dynamo_tensorrt_converter(torch.ops.aten.adaptive_avg_pool3d.default)
+@dynamo_tensorrt_converter(torch.ops.aten._adaptive_avg_pool3d.default)
+@enforce_tensor_types(
+    {
+        0: (TRTTensor,),
+    }
+)
+def aten_ops_adaptive_avg_poolNd(
+    ctx: ConversionContext,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.pool.adaptive_avg_poolNd(
+        ctx,
+        target,
+        source_ir=SourceIR.ATEN,
+        name=name,
+        input=args[0],
+        output_size=args[1],
+    )
+
+
 def max_pool_param_validator(pool_node: Node) -> bool:
     dilation = args_bounds_check(pool_node.args, 4, 1)
     ceil_mode = args_bounds_check(pool_node.args, 5, False)

py/torch_tensorrt/dynamo/conversion/impl/pool.py

@@ -6,7 +6,10 @@
 from torch.fx.node import Target
 from torch_tensorrt.dynamo._SourceIR import SourceIR
 from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
-from torch_tensorrt.dynamo.conversion.converter_utils import extend_attr_to_tuple
+from torch_tensorrt.dynamo.conversion.converter_utils import (
+    extend_attr_to_tuple,
+    get_positive_dim,
+)
 from torch_tensorrt.fx.converters.converter_utils import (
     has_dynamic_shape,
     set_layer_name,
@@ -169,3 +172,228 @@ def end_index(idx: int, out_dim: int, in_dim: int) -> int:
 
     output = impl.cat.cat(ctx, target, source_ir, f"{name}_cat", output_list, dim=-1)
     return output
+
+
+def adaptive_avg_poolNd(
+    ctx: ConversionContext,
+    target: Union[Target, str],
+    source_ir: Optional[SourceIR],
+    name: str,
+    input: TRTTensor,
+    output_size: Sequence[int],
+) -> TRTTensor:
+    input_shape = input.shape
+    input_rank = len(input_shape)
+    output_rank = len(output_size)
+    need_reshape_back = False
+
+    if input_rank == output_rank + 1:  # reshape to 4D/5D for TRT pooling
+        input = impl.shuffle.reshape(
+            ctx, target, source_ir, f"{name}_reshape", input, (1, *input.shape)
+        )
+        need_reshape_back = True
+        input_shape = input.shape
+        input_rank = len(input_shape)
+
+    extend_len = len(output_size)
+    output_size = list(output_size)
+    original_input = input
+
+    # repeat_interleave the input if the dim of output is larger than input
+    insert_axises = []
+    for axis in range(1, extend_len + 1):
+        axis = -axis
+        positive_axis = get_positive_dim(
+            axis, input_rank
+        )  # convert to positive axis, which is for calculating new shapes below
+        input_dim = input_shape[axis]
+        output_dim = output_size[axis]
+        diff = output_dim - input_dim
+        if diff > 0:  # the dim of output is larger than input
+            times = output_dim // input_dim
+            remainder = output_dim % input_dim
+            if (
+                diff == 2 and remainder == 2
+            ):  # case 1: output_dim - input_dim == 2 and is not an integral multiple
+                insert_axises.append(axis)
+                remainder -= 1
+                output_size[axis] -= 1
+
+            if (
+                remainder + 1 == input_dim
+            ):  # case 2: remainder + 1 == input_dim, we will repeat_interleave the whole input
+                remainder = 0
+                times += 1
+
+            flags = []  # record the axis that needs to be repeated
+            concat_list = []
+            for j in range(
+                input_dim
+            ):  # iterate the input dim to see which dim needs to be repeated or not
+                single_elem = impl.select.select(
+                    ctx, target, source_ir, f"{name}_select_{axis}_{j}", input, axis, j
+                )
+                new_shape = list(single_elem.shape)
+                new_shape.insert(positive_axis, 1)
+                single_elem = impl.shuffle.reshape(
+                    ctx,
+                    target,
+                    source_ir,
+                    f"{name}_reshape_{axis}_{j}",
+                    single_elem,
+                    new_shape,
+                )
+                if remainder > 0 or j in flags:
+                    concat_list.extend([single_elem] * (times + 1))
+                    remainder -= 2
+                    flags.append(input_dim - j - 1)
+                else:
+                    concat_list.extend([single_elem] * times)
+                out = impl.cat.cat(
+                    ctx, target, source_ir, f"{name}_cat_{axis}_{j}", concat_list, axis
+                )
+            input = out
+
+    stride = tuple(
+        input.shape[-extend_len + i] // output_size[i] for i in range(extend_len)
+    )
+    kernel_size = tuple(
+        input.shape[-extend_len + i] - (output_size[i] - 1) * stride[i]
+        for i in range(extend_len)
+    )
+
+    # Don't have to pool, directly return
+    if all(s == 1 for s in stride) and all(k == 1 for k in kernel_size):
+        if need_reshape_back:  # reshape back
+            input = impl.shuffle.reshape(
+                ctx,
+                target,
+                source_ir,
+                f"{name}_reshape_back",
+                input,
+                (*input.shape[1:],),
+            )
+        return input
+
+    layer = ctx.net.add_pooling_nd(
+        input=input, type=trt.PoolingType.AVERAGE, window_size=kernel_size
+    )
+    layer.stride_nd = stride
+    set_layer_name(layer, target, f"{name}_pooling_{extend_len}d", source_ir)
+
+    output = layer.get_output(0)
+
+    # For case 1, we need to split the output and insert the mid of input
+    for axis in insert_axises:
+        positive_axis = get_positive_dim(axis, input_rank)
+        input_dim = input_shape[axis]
+        output_dim = output_size[axis]
+        if input_dim % 2 == 1:
+            prev_one = impl.select.select(
+                ctx,
+                target,
+                source_ir,
+                f"{name}_select_prev_one_{axis}",
+                output,
+                axis,
+                output_dim // 2 - 1,
+            )
+            extend_shape = list(prev_one.shape)
+            extend_shape.insert(positive_axis, 1)
+            prev_one = impl.shuffle.reshape(
+                ctx,
+                target,
+                source_ir,
+                f"{name}_reshape_extend_shape_{axis}",
+                prev_one,
+                extend_shape,
+            )
+            prev_two = impl.select.select(
+                ctx,
+                target,
+                source_ir,
+                f"{name}_select_prev_two_{axis}",
+                output,
+                axis,
+                output_dim // 2 - 2,
+            )
+            prev_two = impl.shuffle.reshape(
+                ctx,
+                target,
+                source_ir,
+                f"{name}_two_shape_reshape_{axis}",
+                prev_two,
+                extend_shape,
+            )
+            prev_one_two_diff = impl.elementwise.sub(
+                ctx,
+                target,
+                source_ir,
+                f"{name}_prev_one_two_diff_{axis}",
+                prev_one,
+                prev_two,
+            )
+
+            mid = impl.elementwise.add(
+                ctx,
+                target,
+                source_ir,
+                f"{name}_mid_{axis}",
+                prev_one,
+                prev_one_two_diff,
+            )
+            split_output = impl.split.split(
+                ctx, target, source_ir, f"{name}_split_{axis}", output, 2, axis
+            )
+            split_output.insert(1, mid)
+            output = impl.cat.cat(
+                ctx, target, source_ir, f"{name}_cat_{axis}", split_output, axis
+            )
+        else:
+            mid1 = impl.select.select(
+                ctx,
+                target,
+                source_ir,
+                f"{name}_select_{axis}",
+                original_input,
+                axis,
+                input_dim // 2 - 1,
+            )
+            new_shape = list(mid1.shape)
+            new_shape.insert(positive_axis, 1)
+            mid1 = impl.shuffle.reshape(
+                ctx, target, source_ir, f"{name}_reshape_{axis}", mid1, new_shape
+            )
+            mid2 = impl.select.select(
+                ctx,
+                target,
+                source_ir,
+                f"{name}_select_{axis}",
+                original_input,
+                axis,
+                input_dim // 2,
+            )
+            mid2 = impl.shuffle.reshape(
+                ctx, target, source_ir, f"{name}_reshape_{axis}", mid2, new_shape
+            )
+            split_output = impl.split.split(
+                ctx,
+                target,
+                source_ir,
+                f"{name}_split_{axis}",
+                output,
+                [output_dim // 2, 1, output_dim // 2],
+                axis,
+            )
+            split_output[1] = mid1
+            split_output.insert(2, mid2)
+            output = impl.cat.cat(
+                ctx, target, source_ir, f"{name}_cat_{axis}", split_output, axis
+            )
+
+    if need_reshape_back:  # reshape back
+        output = impl.shuffle.reshape(
+            ctx, target, source_ir, f"{name}_reshape_back", output, (*output.shape[1:],)
+        )
+
+    return output