feat: support cumsum dynamo converter (#2403)

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -736,7 +736,7 @@ def aten_ops_chunk(
     )
 
 
-@dynamo_tensorrt_converter(torch.ops.aten.cumsum.default)
+@dynamo_tensorrt_converter(torch.ops.aten.permute.default)
 @enforce_tensor_types(
     {
         0: (TRTTensor,),

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

@@ -1,10 +1,16 @@
 import math
 from typing import Optional
 
+import numpy as np
+import tensorrt as trt
 from torch.fx.node import Target
 from torch_tensorrt.dynamo._SourceIR import SourceIR
+from torch_tensorrt.dynamo.conversion import impl
 from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
-from torch_tensorrt.dynamo.conversion.converter_utils import get_positive_dim
+from torch_tensorrt.dynamo.conversion.converter_utils import (
+    get_positive_dim,
+    get_trt_tensor,
+)
 from torch_tensorrt.dynamo.conversion.impl.slice.base import slice
 from torch_tensorrt.fx.converters.converter_utils import (
     has_dynamic_shape,
@@ -96,3 +102,98 @@ def expand(
     layer = ctx.net.add_slice(input_t, start=start, shape=shape, stride=stride)
     set_layer_name(layer, target, name, source_ir)
     return layer.get_output(0)
+
+
+def chunk(
+    ctx: ConversionContext,
+    target: Target,
+    source_ir: Optional[SourceIR],
+    name: str,
+    input: TRTTensor,
+    chunks: int,
+    dim: int,
+) -> TRTTensor:
+    if chunks <= 0:
+        raise RuntimeError(
+            f"chunk expects `chunks` to be greater than 0, got: {chunks}"
+        )
+
+    shape = input.shape
+    dim = get_positive_dim(dim, len(shape))
+
+    if dim >= len(shape):
+        raise RuntimeError(
+            f"chunk expects `dim` to be less than the length of input shape, got: {dim}"
+        )
+
+    dynamic_shape = has_dynamic_shape(input.shape)
+    if dynamic_shape > 0:
+        # Check whether slice target dim is dynamic shape dim
+        assert input.shape[dim] != -1, "Can't chunk on dynamic shape dimension!"
+
+    size_dim = shape[dim]
+    chunk_size = math.ceil(size_dim / chunks)
+    result = []
+    start = 0
+    end = min(start + chunk_size, size_dim)
+    cnt = 0
+
+    while start < end:
+        result.append(
+            slice_op(
+                ctx,
+                target,
+                source_ir,
+                f"{name}_slice_{cnt}",
+                input,
+                dim,
+                start,
+                end,
+                1,
+            )
+        )
+        start = end
+        end = min(start + chunk_size, size_dim)
+        cnt += 1
+
+    return result
+
+
+def cumsum(
+    ctx: ConversionContext,
+    target: Target,
+    source_ir: Optional[SourceIR],
+    name: str,
+    input: TRTTensor,
+    dim: int,
+) -> TRTTensor:
+    input_shape = input.shape
+    dim = get_positive_dim(dim, len(input_shape))
+    loop = ctx.net.add_loop()
+    axis = np.array(input_shape[dim])
+    trip_limit = get_trt_tensor(ctx, axis, f"{name}_trip_limit")
+    loop.add_trip_limit(trip_limit, trt.TripLimit.COUNT)
+    iterator = loop.add_iterator(input, dim, reverse=False)
+    data = iterator.get_output(0)
+    new_dims = tuple(data.shape)
+    zeros = np.zeros(new_dims)
+    zero_trttensor = get_trt_tensor(ctx, zeros, f"{name}_initial_value")
+
+    running_sum = loop.add_recurrence(zero_trttensor)
+    set_layer_name(running_sum, target, f"{name}_running_sum", source_ir)
+    running_sum_tensor = running_sum.get_output(0)
+
+    current_sum = impl.elementwise.add(
+        ctx,
+        target,
+        source_ir,
+        f"{name}_elementwise_add",
+        data,
+        running_sum_tensor,
+    )
+    running_sum.set_input(1, current_sum)
+
+    loop_output = loop.add_loop_output(current_sum, trt.LoopOutput.CONCATENATE, dim)
+    set_layer_name(loop_output, target, f"{name}_loop_output", source_ir)
+    loop_output.set_input(1, trip_limit)
+    return loop_output.get_output(0)

tests/py/dynamo/conversion/test_cumsum_aten.py

@@ -0,0 +1,69 @@
+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 TestCumsumConverter(DispatchTestCase):
+    @parameterized.expand(
+        [
+            ((1,), 0),
+            ((2,), 0),
+            ((3,), -1),
+        ]
+    )
+    def test_cumsum_1D(self, shape, dim):
+        class Cumsum(nn.Module):
+            def forward(self, x):
+                return torch.ops.aten.cumsum.default(x, dim)
+
+        inputs = [torch.randn(shape)]
+        self.run_test(
+            Cumsum(),
+            inputs,
+        )
+
+    @parameterized.expand(
+        [
+            ((3, 1), 0),
+            ((3, 1), 1),
+            ((2, 3), -1),
+            ((2, 3), -2),
+        ]
+    )
+    def test_cumsum_2D(self, shape, dims):
+        class Cumsum(nn.Module):
+            def forward(self, x):
+                return torch.ops.aten.cumsum.default(x, dims)
+
+        inputs = [torch.randn(shape)]
+        self.run_test(
+            Cumsum(),
+            inputs,
+        )
+
+    @parameterized.expand(
+        [
+            ((4, 2, 3), 0),
+            ((4, 2, 3), 1),
+            ((1, 2, 3), 2),
+            ((1, 2, 3), -1),
+            ((1, 2, 3), -2),
+        ]
+    )
+    def test_cumsum_3D(self, shape, dims):
+        class Cumsum(nn.Module):
+            def forward(self, x):
+                return torch.ops.aten.cumsum.default(x, dims)
+
+        inputs = [torch.randn(shape)]
+        self.run_test(
+            Cumsum(),
+            inputs,
+        )
+
+
+if __name__ == "__main__":
+    run_tests()