Numpy changes for aten::index converter #2396
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There are some changes that do not conform to Python style guidelines:
--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/converter_utils.py 2023-10-13 16:23:31.589620+00:00
+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/converter_utils.py 2023-10-13 16:26:06.957081+00:00
@@ -178,12 +178,11 @@
rhs_val = cast_trt_tensor(ctx, rhs_val, trt.float32, name)
return [lhs_val, rhs_val]
def broadcastable(
- a: Union[TRTTensor, np.ndarray],
- b: Union[TRTTensor, np.ndarray]
+ a: Union[TRTTensor, np.ndarray], b: Union[TRTTensor, np.ndarray]
) -> bool:
"Check if two tensors are broadcastable according to torch rules"
a_shape = tuple(a.shape)
b_shape = tuple(b.shape)
gs-olive
reviewed
Oct 20, 2023
| @@ -127,6 +140,7 @@ def index( | |||
| rank = len(input_shape) | |||
| adv_indx_count = len(adv_indx_indices) | |||
| dim_tensor_list = [] | |||
| dim_list = [] | |||
There was a problem hiding this comment.
If this is unused, dim_list = [] can be removed
gs-olive
approved these changes
Oct 20, 2023
gs-olive
requested changes
Oct 23, 2023
apbose
commented
Oct 23, 2023
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Hi @gs-olive. Regarding adding of the test for this feature, the earlier tests were all numpy indices, where is_numpy is set as True. So those tests should be good for this. Is there any way so that tests can be added so that the indices are ITensors?
|
Yes, there is. You would just need to pass the indices as inputs to the |
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There are some changes that do not conform to Python style guidelines:
--- /home/runner/work/TensorRT/TensorRT/tests/py/dynamo/conversion/test_index_aten.py 2023-10-30 19:32:26.833431+00:00
+++ /home/runner/work/TensorRT/TensorRT/tests/py/dynamo/conversion/test_index_aten.py 2023-10-30 19:35:28.665002+00:00
@@ -24,11 +24,11 @@
input = [torch.randn(2, 2)]
self.run_test(
TestModule(),
input,
)
-
+
def test_index_zero_two_dim_ITensor(self):
class TestModule(nn.Module):
def forward(self, x, index0):
indices = [None, index0]
out = torch.ops.aten.index.Tensor(x, indices)
@@ -56,25 +56,22 @@
input = [torch.randn(2, 2, 2)]
self.run_test(
TestModule(),
input,
)
-
+
def test_index_zero_index_three_dim_ITensor(self):
class TestModule(nn.Module):
def forward(self, x, index0):
indices = [None, index0, None]
out = torch.ops.aten.index.Tensor(x, indices)
return out
input = torch.randn(2, 2, 2)
index0 = torch.randint(0, 1, (1, 1))
index0 = index0.to(torch.int32)
- self.run_test(
- TestModule(),
- [input, index0]
- )
+ self.run_test(TestModule(), [input, index0])
def test_index_zero_index_one_index_two_three_dim(self):
class TestModule(nn.Module):
def __init__(self):
self.index0 = torch.randint(0, 1, (1, 1))
gs-olive
reviewed
Oct 30, 2023
| @@ -2,11 +2,10 @@ | |||
|
|
|||
| import torch | |||
| import torch.nn as nn | |||
| from harness import DispatchTestCase | |||
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Requires switch back to .harness to pass CI
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Ok corrected, wil wait for CI then will merge.
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This PR addresses the changes for the cases where indices are list of numpy arrays or numpy for aten::index. #2394