feat: support _pdist_forward dynamo converter

Author: zewenli98

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -2596,3 +2596,26 @@ def aten_ops_remainder(
         args[0],
         args[1],
     )
+
+
+@dynamo_tensorrt_converter(torch.ops.aten._pdist_forward.default)
+@enforce_tensor_types(
+    {
+        0: (TRTTensor,),
+    }
+)
+def aten_ops_pdist(
+    ctx: ConversionContext,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.normalization.pdist(
+        ctx,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        args_bounds_check(args, 1, 2),
+    )

py/torch_tensorrt/dynamo/conversion/impl/normalization/ops.py

@@ -8,6 +8,7 @@
 from torch_tensorrt.dynamo.conversion import impl
 from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
 from torch_tensorrt.dynamo.conversion.converter_utils import (
+    cast_trt_tensor,
     get_positive_dim,
     get_trt_tensor,
     to_numpy,
@@ -440,3 +441,70 @@ def get_softmax_dim(ndim: int) -> int:
     layer.axes = 1 << dim
     set_layer_name(layer, target, name, source_ir)
     return layer.get_output(0)
+
+
+def pdist(
+    ctx: ConversionContext,
+    target: Target,
+    source_ir: Optional[SourceIR],
+    name: str,
+    input: TRTTensor,
+    p: float = 2,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    shape = input.shape
+    extend_input = impl.shuffle.reshape(
+        ctx,
+        target,
+        source_ir,
+        f"{name}_reshape",
+        input,
+        shape=shape[0:1] + (1,) + shape[1:],
+    )
+    x = impl.elementwise.sub(ctx, target, source_ir, f"{name}_sub", extend_input, input)
+
+    if p == 0:
+        # norm = torch.sum(x!=0, dim=2)
+        nonzero_val = impl.elementwise.ne(ctx, target, source_ir, f"{name}_ne", x, 0)
+        norm = impl.reduce.sum(
+            ctx, target, source_ir, f"{name}_sum", nonzero_val, dim=2, keepdim=False
+        )
+        norm = cast_trt_tensor(
+            ctx, norm, torch.float32, f"{name}_cast", target, source_ir
+        )
+    elif p == 1:
+        # norm = torch.sum(torch.abs(x), dim=2)
+        abs_val = impl.unary.abs(ctx, target, source_ir, f"{name}_abs", x)
+        norm = impl.reduce.sum(
+            ctx, target, source_ir, f"{name}_sum", abs_val, dim=2, keepdim=False
+        )
+    elif 0 < p < 1 or 1 < p < float("inf"):
+        # norm = torch.pow(torch.sum(torch.pow(torch.abs(x), p), dim=2), 1/p)
+        abs_val = impl.unary.abs(ctx, target, source_ir, f"{name}_abs", x)
+        pow_val = impl.elementwise.pow(
+            ctx, target, source_ir, f"{name}_pow1", abs_val, p
+        )
+        sum_val = impl.reduce.sum(
+            ctx, target, source_ir, f"{name}_sum", pow_val, dim=2, keepdim=False
+        )
+        norm = impl.elementwise.pow(
+            ctx, target, source_ir, f"{name}_pow2", sum_val, 1 / p
+        )
+    elif p == float("inf"):
+        # norm = torch.max(torch.abs(x))
+        abs_val = impl.unary.abs(ctx, target, source_ir, f"{name}_abs", x)
+        norm = impl.reduce.max(
+            ctx,
+            target,
+            source_ir,
+            f"{name}_max",
+            abs_val,
+            dim=2,
+            keepdim=False,
+            return_indices=False,
+        )
+    else:
+        raise RuntimeError(
+            f"p should between [0, inf], currently p={p} is not supported!"
+        )
+    indices = np.triu_indices(shape[0], k=1)
+    return impl.select.index(ctx, target, source_ir, f"{name}_index", norm, indices)

tests/py/dynamo/conversion/test_pdist_aten.py

@@ -0,0 +1,36 @@
+import torch
+import torch.nn as nn
+from parameterized import parameterized
+from torch.testing._internal.common_utils import run_tests
+
+from .harness import DispatchTestCase
+
+
+class TestPdistConverter(DispatchTestCase):
+    @parameterized.expand(
+        [
+            ((2, 3), 0),
+            ((2, 3), 0.4),
+            ((2, 3), 1),
+            ((2, 3), 1.5),
+            ((3, 4), 2),
+            ((3, 4), 2.99),
+            ((4, 5), 3),
+            ((4, 5), 3.3),
+            ((5, 6), float("inf")),
+        ]
+    )
+    def test_pdist_float(self, shape, p):
+        class Pdist(nn.Module):
+            def forward(self, input):
+                return torch.ops.aten._pdist_forward.default(input, p)
+
+        inputs = [torch.randn(shape)]
+        self.run_test(
+            Pdist(),
+            inputs,
+        )
+
+
+if __name__ == "__main__":
+    run_tests()