Add op(upsample linear1d) | feat(torchlib)

onnxscript/function_libs/torch_lib/ops/nn.py

@@ -2368,12 +2368,20 @@ def aten_upsample_bilinear2d_backward(
     raise NotImplementedError()
 
 
+@torch_op("aten::upsample_linear1d", trace_only=True)
 def aten_upsample_linear1d(
-    self: TensorType, output_size: INT64, align_corners: bool, scales: Optional[float] = None
-) -> TensorType:
+    self: TReal, output_size: INT64, align_corners: bool, scales: Optional[float] = None
+) -> TReal:
     """upsample_linear1d(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None) -> Tensor"""
-
-    raise NotImplementedError()
+    # FIXME(justinchuby): Support when scales is provided and align_corners is False
+    del scales
+    coordinate_transformation_mode = _get_upsample_align_corners_mode(align_corners)
+    return _aten_upsample_output_size(
+        self,
+        output_size,
+        mode="linear",
+        coordinate_transformation_mode=coordinate_transformation_mode,
+    )
 
 
 def aten_upsample_linear1d_backward(

onnxscript/tests/function_libs/torch_lib/extra_opinfo.py

@@ -1464,9 +1464,7 @@ def shape(size, rank, with_batch_channel=True):
             make_arg(shape(D, rank)), shape(S, rank, False), align_corners
         )
         yield opinfo_core.SampleInput(
-            make_arg(shape(D, rank)),
-            shape(L, rank, False),
-            align_corners,
+            make_arg(shape(D, rank)), shape(L, rank, False), align_corners
         )
         yield opinfo_core.SampleInput(
             make_arg(shape(D, rank)),
@@ -1513,10 +1511,7 @@ def shape(size, rank, with_batch_channel=True):
             make_arg(shape(D, rank)), shape(S, rank, False), align_corners, None
         )
         yield opinfo_core.SampleInput(
-            make_arg(shape(D, rank)),
-            shape(L, rank, False),
-            align_corners,
-            None,
+            make_arg(shape(D, rank)), shape(L, rank, False), align_corners, None
         )
         yield opinfo_core.SampleInput(
             make_arg(shape(D, rank)),
@@ -1544,6 +1539,46 @@ def shape(size, rank, with_batch_channel=True):
         )
 
 
+def sample_inputs_upsample_linear1d(op_info, device, dtype, requires_grad, **kwargs):
+    del op_info
+    del kwargs
+
+    N, C = 2, 3
+    D = 4
+    SS = 3
+    L = 5
+
+    align_corners_options = (True, False)
+    rank = 1
+
+    def shape(size, rank, with_batch_channel=True):
+        if with_batch_channel:
+            return tuple([N, C] + ([size] * rank))
+        return tuple([size] * rank)
+
+    make_arg = functools.partial(
+        torch_testing.make_tensor,
+        device=device,
+        dtype=dtype,
+        requires_grad=requires_grad,
+        low=-1,
+        high=1,
+    )
+
+    yield opinfo_core.SampleInput(make_arg(shape(D, rank)), shape(SS, rank, False), True)
+
+    for align_corners in align_corners_options:
+        yield opinfo_core.SampleInput(
+            make_arg(shape(D, rank)), shape(S, rank, False), align_corners
+        )
+        yield opinfo_core.SampleInput(
+            make_arg(shape(D, rank)), shape(L, rank, False), align_corners
+        )
+        yield opinfo_core.SampleInput(
+            make_arg(shape(D, rank)), shape(L, rank, False), align_corners, scales=4.2
+        )
+
+
 class _TestParamsMaxPoolEmptyStrideBase:
     # Adapted from https://github.com/pytorch/pytorch/blob/d6d55f8590eab05d2536756fb4efcfb2d07eb81a/torch/testing/_internal/common_methods_invocations.py#L3203
     def __init__(self):
@@ -2037,6 +2072,13 @@ def __init__(self):
         sample_inputs_func=sample_inputs_upsample_2d_vec,
         supports_out=False,
     ),
+    opinfo_core.OpInfo(
+        "ops.aten.upsample_linear1d",
+        aten_name="upsample_linear1d",
+        dtypes=common_dtype.floating_types_and(torch.bfloat16),
+        sample_inputs_func=sample_inputs_upsample_linear1d,
+        supports_out=False,
+    ),
     opinfo_core.OpInfo(
         "nn.functional.max_pool1d_with_indices",
         aten_name="max_pool1d_with_indices",

onnxscript/tests/function_libs/torch_lib/ops_test_data.py

@@ -2132,6 +2132,15 @@ def _where_input_wrangler(
         nn_ops.aten_upsample_bicubic2d_vec,
         trace_only=True,
     ),
+    TorchLibOpInfo(
+        "ops.aten.upsample_linear1d",
+        nn_ops.aten_upsample_linear1d,
+        trace_only=True,
+    ).xfail(
+        matcher=lambda sample: sample.args[1] is False
+        and sample.kwargs.get("scales") is not None,
+        reason="fixme: align_corners=False output mismatch when scales are provided",
+    ),
     TorchLibOpInfo(
         "nn.functional.upsample_nearest2d",
         nn_ops.aten_upsample_nearest2d,