Implement aten::index | feat(torchlib) (#862) by justinchuby · Pull Request #883 · microsoft/onnxscript

justinchuby · 2023-07-17T17:13:13Z

Stack from ghstack (oldest at bottom):

-> Implement aten::index | feat(torchlib) (#862) #883

This change implements the logic for aten::index and adds tests for different nd index combinations and permutations.

Understanding `aten::index`

For arg0 with shape [7, 3, 4, 5, 6]
The indexing operation arg0[0, :, 1:2, tensor([[4,5]])] will be translated to

+>  select: i64[3, 4, 5, 6] = torch.ops.aten.select.int(arg0, 0, 0);
+>  slice_1: i64[3, 4, 5, 6] = torch.ops.aten.slice.Tensor(select, 0, 0, 9223372036854775807);
+>  slice_2: i64[3, 1, 5, 6] = torch.ops.aten.slice.Tensor(slice_1, 1, 1, 2);
+>  index: i64[3, 1, 1, 2, 6] = torch.ops.aten.index.Tensor(slice_2, [None, None, arg1]);

Here,

indices = [None, None, arg1] is equivalent to indices = [None, None, arg1, None]
The operation arg0[0, :, 1:2, tensor([[4,5]])] is equivalent to arg0[0, :, 1:2, tensor([[4,5]]), :]
None in indices are like fillers for dimensions that cannot be removed in the process.

Gather op reference

Co-authored-by: BowenBao bowbao@microsoft.com

[ghstack-poisoned]

Remove the duplicate check_model message when displaying [ghstack-poisoned]

WIP: need more tests Gather op: - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbao@microsoft.com> [ghstack-poisoned]

WIP: need more tests Gather op: - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> ghstack-source-id: a991236 Pull Request resolved: #883

Gather op: - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> [ghstack-poisoned]

WIP: need more tests Gather op: - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> ghstack-source-id: 8755417 Pull Request resolved: #883

Gather op: - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> [ghstack-poisoned]

WIP: need more tests Gather op: - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> ghstack-source-id: 078cf56 Pull Request resolved: #883 Signed-off-by: Justin Chu <justinchu@microsoft.com>

Gather op: - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> [ghstack-poisoned]

WIP: need more tests Gather op: - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> ghstack-source-id: e641533 Pull Request resolved: #883 Signed-off-by: Justin Chu <justinchu@microsoft.com>

--- **This change implements the logic for `aten::index` and adds tests for different nd index combinations and permutations.** ## Understanding `aten::index` For `arg0` with shape `[7, 3, 4, 5, 6]` The indexing operation `arg0[0, :, 1:2, tensor([[4,5]])]` will be translated to ``` +> select: i64[3, 4, 5, 6] = torch.ops.aten.select.int(arg0, 0, 0); +> slice_1: i64[3, 4, 5, 6] = torch.ops.aten.slice.Tensor(select, 0, 0, 9223372036854775807); +> slice_2: i64[3, 1, 5, 6] = torch.ops.aten.slice.Tensor(slice_1, 1, 1, 2); +> index: i64[3, 1, 1, 2, 6] = torch.ops.aten.index.Tensor(slice_2, [None, None, arg1]); ``` Here, - `indices = [None, None, arg1]` is equivalent to `indices = [None, None, arg1, None]` - The operation `arg0[0, :, 1:2, tensor([[4,5]])]` is equivalent to `arg0[0, :, 1:2, tensor([[4,5]]), :]` None in `indices` are like fillers for dimensions that cannot be removed in the process. ## Gather op reference - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> [ghstack-poisoned]

WIP: need more tests Gather op: - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> ghstack-source-id: 194baee Pull Request resolved: #883 Signed-off-by: Justin Chu <justinchu@microsoft.com>

BowenBao

🎉 LGTM. Do you plan to add bool mask indices support in this or another PR?

BowenBao · 2023-07-17T18:00:38Z

onnxscript/tests/function_libs/torch_lib/ops_test_common.py

                        sequence_input.append(input)
                        ort_inputs[input_name] = subarg
+                    else:
+                        sequence_input.append(subarg)


Let's put comment here explaining why this is needed in case we forget?

Should we consider elif None? It's easier to catch what we are not expecting.

Good point. I wonder if there are things we don't expect that can sneak in? Nested lists?

No idea. Just in case.

SG. Created #892 for now.

justinchuby · 2023-07-17T18:06:19Z

🎉 LGTM. Do you plan to add bool mask indices support in this or another PR?

I plan to do it in another PR to keep this one manageable.

codecov · 2023-07-17T18:07:39Z

Codecov Report

Merging #883 (169debc) into main (bbdbf1e) will decrease coverage by 0.09%.
The diff coverage is n/a.

❗ Current head 169debc differs from pull request most recent head 9d941c5. Consider uploading reports for the commit 9d941c5 to get more accurate results

@@            Coverage Diff             @@
##             main     #883      +/-   ##
==========================================
- Coverage   76.59%   76.51%   -0.09%     
==========================================
  Files         112      112              
  Lines       13408    13408              
  Branches     1348     1348              
==========================================
- Hits        10270    10259      -11     
- Misses       2801     2812      +11     
  Partials      337      337

see 1 file with indirect coverage changes

onnxscript/tests/function_libs/torch_lib/ops_test_common.py

) Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom): * #883 * #882 * __->__ #881 The torchscript ONNX graph generator creates numeric value names by default (`0`, `1`). These are not legal ONNX tensor names, since ONNX requires the names to be valid C variable names. This change updates the names by prepending a prefix `_val_` or `_const_` to make them valid ONNX names. It also improves readability by making the names less likely to be confused with shape values. I decided to use the `_` prefix to reduce the chance of name collision with FX names. After: ``` < ir_version: 8, opset_import: ["" : 18], producer_name: "pytorch", producer_version: "2.1.0" > torch_jit (float[5,5,5,5] input_0, int64[2] input_1_3) => (float[5,5,5,2] _val_10) { _val_2 = Transpose <perm = [0, 1, 2, 3]> (input_0) _val_3 = Max (input_1_3) _val_4 = Shape <start = 0> (_val_3) _val_5 = Expand (input_1_3, _val_4) _const_6 = Constant <value = int64 {-1}> () _val_7 = Unsqueeze (_val_5, _const_6) _val_8 = Concat <axis = -1> (_val_7) _val_9 = GatherND <batch_dims = 0> (_val_2, _val_8) _val_10 = Transpose <perm = [0, 1, 2, 3]> (_val_9) } ``` Before: ``` < ir_version: 8, opset_import: ["" : 18], producer_name: "pytorch", producer_version: "2.1.0" > torch_jit (float[5,5,5,5] input_0, int64[2] input_1_3) => (float[5,5,5,2] 10) { 2 = Transpose <perm = [0, 1, 2, 3]> (input_0) 3 = Max (input_1_3) 4 = Shape <start = 0> (3) 5 = Expand (input_1_3, 4) 6 = Constant <value = int64 {-1}> () 7 = Unsqueeze (5, 6) 8 = Concat <axis = -1> (7) 9 = GatherND <batch_dims = 0> (2, 8) 10 = Transpose <perm = [0, 1, 2, 3]> (9) } ```

Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom): * #883 * __->__ #882 * #881 Remove the duplicate check_model message when displaying

…)" --- **This change implements the logic for `aten::index` and adds tests for different nd index combinations and permutations.** ## Understanding `aten::index` For `arg0` with shape `[7, 3, 4, 5, 6]` The indexing operation `arg0[0, :, 1:2, tensor([[4,5]])]` will be translated to ``` +> select: i64[3, 4, 5, 6] = torch.ops.aten.select.int(arg0, 0, 0); +> slice_1: i64[3, 4, 5, 6] = torch.ops.aten.slice.Tensor(select, 0, 0, 9223372036854775807); +> slice_2: i64[3, 1, 5, 6] = torch.ops.aten.slice.Tensor(slice_1, 1, 1, 2); +> index: i64[3, 1, 1, 2, 6] = torch.ops.aten.index.Tensor(slice_2, [None, None, arg1]); ``` Here, - `indices = [None, None, arg1]` is equivalent to `indices = [None, None, arg1, None]` - The operation `arg0[0, :, 1:2, tensor([[4,5]])]` is equivalent to `arg0[0, :, 1:2, tensor([[4,5]]), :]` None in `indices` are like fillers for dimensions that cannot be removed in the process. ## Gather op reference - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> [ghstack-poisoned]

--- **This change implements the logic for `aten::index` and adds tests for different nd index combinations and permutations.** ## Understanding `aten::index` For `arg0` with shape `[7, 3, 4, 5, 6]` The indexing operation `arg0[0, :, 1:2, tensor([[4,5]])]` will be translated to ``` +> select: i64[3, 4, 5, 6] = torch.ops.aten.select.int(arg0, 0, 0); +> slice_1: i64[3, 4, 5, 6] = torch.ops.aten.slice.Tensor(select, 0, 0, 9223372036854775807); +> slice_2: i64[3, 1, 5, 6] = torch.ops.aten.slice.Tensor(slice_1, 1, 1, 2); +> index: i64[3, 1, 1, 2, 6] = torch.ops.aten.index.Tensor(slice_2, [None, None, arg1]); ``` Here, - `indices = [None, None, arg1]` is equivalent to `indices = [None, None, arg1, None]` - The operation `arg0[0, :, 1:2, tensor([[4,5]])]` is equivalent to `arg0[0, :, 1:2, tensor([[4,5]]), :]` None in `indices` are like fillers for dimensions that cannot be removed in the process. ## Gather op reference - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> [ghstack-poisoned]

WIP: need more tests Gather op: - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> ghstack-source-id: 4b050c3 Pull Request resolved: #883 Signed-off-by: Justin Chu <justinchu@microsoft.com>

…)" --- **This change implements the logic for `aten::index` and adds tests for different nd index combinations and permutations.** ## Understanding `aten::index` For `arg0` with shape `[7, 3, 4, 5, 6]` The indexing operation `arg0[0, :, 1:2, tensor([[4,5]])]` will be translated to ``` +> select: i64[3, 4, 5, 6] = torch.ops.aten.select.int(arg0, 0, 0); +> slice_1: i64[3, 4, 5, 6] = torch.ops.aten.slice.Tensor(select, 0, 0, 9223372036854775807); +> slice_2: i64[3, 1, 5, 6] = torch.ops.aten.slice.Tensor(slice_1, 1, 1, 2); +> index: i64[3, 1, 1, 2, 6] = torch.ops.aten.index.Tensor(slice_2, [None, None, arg1]); ``` Here, - `indices = [None, None, arg1]` is equivalent to `indices = [None, None, arg1, None]` - The operation `arg0[0, :, 1:2, tensor([[4,5]])]` is equivalent to `arg0[0, :, 1:2, tensor([[4,5]]), :]` None in `indices` are like fillers for dimensions that cannot be removed in the process. ## Gather op reference - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> [ghstack-poisoned]

--- **This change implements the logic for `aten::index` and adds tests for different nd index combinations and permutations.** ## Understanding `aten::index` For `arg0` with shape `[7, 3, 4, 5, 6]` The indexing operation `arg0[0, :, 1:2, tensor([[4,5]])]` will be translated to ``` +> select: i64[3, 4, 5, 6] = torch.ops.aten.select.int(arg0, 0, 0); +> slice_1: i64[3, 4, 5, 6] = torch.ops.aten.slice.Tensor(select, 0, 0, 9223372036854775807); +> slice_2: i64[3, 1, 5, 6] = torch.ops.aten.slice.Tensor(slice_1, 1, 1, 2); +> index: i64[3, 1, 1, 2, 6] = torch.ops.aten.index.Tensor(slice_2, [None, None, arg1]); ``` Here, - `indices = [None, None, arg1]` is equivalent to `indices = [None, None, arg1, None]` - The operation `arg0[0, :, 1:2, tensor([[4,5]])]` is equivalent to `arg0[0, :, 1:2, tensor([[4,5]]), :]` None in `indices` are like fillers for dimensions that cannot be removed in the process. ## Gather op reference - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> [ghstack-poisoned]

WIP: need more tests Gather op: - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> ghstack-source-id: b22c3f8 Pull Request resolved: #883 Signed-off-by: Justin Chu <justinchu@microsoft.com>

…)" --- **This change implements the logic for `aten::index` and adds tests for different nd index combinations and permutations.** ## Understanding `aten::index` For `arg0` with shape `[7, 3, 4, 5, 6]` The indexing operation `arg0[0, :, 1:2, tensor([[4,5]])]` will be translated to ``` +> select: i64[3, 4, 5, 6] = torch.ops.aten.select.int(arg0, 0, 0); +> slice_1: i64[3, 4, 5, 6] = torch.ops.aten.slice.Tensor(select, 0, 0, 9223372036854775807); +> slice_2: i64[3, 1, 5, 6] = torch.ops.aten.slice.Tensor(slice_1, 1, 1, 2); +> index: i64[3, 1, 1, 2, 6] = torch.ops.aten.index.Tensor(slice_2, [None, None, arg1]); ``` Here, - `indices = [None, None, arg1]` is equivalent to `indices = [None, None, arg1, None]` - The operation `arg0[0, :, 1:2, tensor([[4,5]])]` is equivalent to `arg0[0, :, 1:2, tensor([[4,5]]), :]` None in `indices` are like fillers for dimensions that cannot be removed in the process. ## Gather op reference - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> [ghstack-poisoned]

--- **This change implements the logic for `aten::index` and adds tests for different nd index combinations and permutations.** ## Understanding `aten::index` For `arg0` with shape `[7, 3, 4, 5, 6]` The indexing operation `arg0[0, :, 1:2, tensor([[4,5]])]` will be translated to ``` +> select: i64[3, 4, 5, 6] = torch.ops.aten.select.int(arg0, 0, 0); +> slice_1: i64[3, 4, 5, 6] = torch.ops.aten.slice.Tensor(select, 0, 0, 9223372036854775807); +> slice_2: i64[3, 1, 5, 6] = torch.ops.aten.slice.Tensor(slice_1, 1, 1, 2); +> index: i64[3, 1, 1, 2, 6] = torch.ops.aten.index.Tensor(slice_2, [None, None, arg1]); ``` Here, - `indices = [None, None, arg1]` is equivalent to `indices = [None, None, arg1, None]` - The operation `arg0[0, :, 1:2, tensor([[4,5]])]` is equivalent to `arg0[0, :, 1:2, tensor([[4,5]]), :]` None in `indices` are like fillers for dimensions that cannot be removed in the process. ## Gather op reference - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> [ghstack-poisoned]

WIP: need more tests Gather op: - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> ghstack-source-id: 1c8f0e2 Pull Request resolved: #883 Signed-off-by: Justin Chu <justinchu@microsoft.com>

thiagocrepaldi

LGTM

thiagocrepaldi · 2023-07-17T18:30:51Z

onnxscript/tests/function_libs/torch_lib/extra_opinfo.py

+    index_1d = common_methods_invocations.index_variable(2, s, device=device)
+    index_2d = common_methods_invocations.index_variable((s + 1, 2), s, device=device)
+    index_3d = common_methods_invocations.index_variable((s + 2, s + 1, 2), s, device=device)
+    test_args = [


would itertools.{product,permutation,combination} help in ensuring all combinations are covered and make the code shorter?

Good point. Let me try that

So I tested with itertools but realized some combinations are invalid so we cannot enumerate them all using itertools. For the sake of clarity I propose that we keep the current explicit tests. I also added more test cases and comments

thiagocrepaldi · 2023-07-17T18:38:02Z

onnxscript/tests/function_libs/torch_lib/ops_test_common.py

 def convert_kwargs_for_onnx(kwargs: dict[str, Any]) -> dict[str, Any]:
-    """Converts kwargs to be compatible with ONNX Runtime.
-
-    ONNX Runtime doesn't support torch.bool, so we convert them to torch.uint8.


for my knowledge, does ort support torch.bool now? or was this docstring outdated already?

I actually don't know if ORT supports bool (it should?), but I think this message was a mistake by copilot because we don't actually have this conversion logic as code. If we see issues with ORT I will make adjustments.

thiagocrepaldi · 2023-07-17T18:39:26Z

onnxscript/tests/function_libs/torch_lib/extra_opinfo.py

+    index_1d = common_methods_invocations.index_variable(2, s, device=device)
+    index_2d = common_methods_invocations.index_variable((s + 1, 2), s, device=device)
+    index_3d = common_methods_invocations.index_variable((s + 2, s + 1, 2), s, device=device)
+    test_args = [


could itertools.product (or friends) with length 1 to 4 could shorten this listen and ensure no combination is left out?

As above. Turns out some combinations are invalid to torch and it may be better to specify explicitly.

…)" --- **This change implements the logic for `aten::index` and adds tests for different nd index combinations and permutations.** ## Understanding `aten::index` For `arg0` with shape `[7, 3, 4, 5, 6]` The indexing operation `arg0[0, :, 1:2, tensor([[4,5]])]` will be translated to ``` +> select: i64[3, 4, 5, 6] = torch.ops.aten.select.int(arg0, 0, 0); +> slice_1: i64[3, 4, 5, 6] = torch.ops.aten.slice.Tensor(select, 0, 0, 9223372036854775807); +> slice_2: i64[3, 1, 5, 6] = torch.ops.aten.slice.Tensor(slice_1, 1, 1, 2); +> index: i64[3, 1, 1, 2, 6] = torch.ops.aten.index.Tensor(slice_2, [None, None, arg1]); ``` Here, - `indices = [None, None, arg1]` is equivalent to `indices = [None, None, arg1, None]` - The operation `arg0[0, :, 1:2, tensor([[4,5]])]` is equivalent to `arg0[0, :, 1:2, tensor([[4,5]]), :]` None in `indices` are like fillers for dimensions that cannot be removed in the process. ## Gather op reference - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> [ghstack-poisoned]

--- **This change implements the logic for `aten::index` and adds tests for different nd index combinations and permutations.** ## Understanding `aten::index` For `arg0` with shape `[7, 3, 4, 5, 6]` The indexing operation `arg0[0, :, 1:2, tensor([[4,5]])]` will be translated to ``` +> select: i64[3, 4, 5, 6] = torch.ops.aten.select.int(arg0, 0, 0); +> slice_1: i64[3, 4, 5, 6] = torch.ops.aten.slice.Tensor(select, 0, 0, 9223372036854775807); +> slice_2: i64[3, 1, 5, 6] = torch.ops.aten.slice.Tensor(slice_1, 1, 1, 2); +> index: i64[3, 1, 1, 2, 6] = torch.ops.aten.index.Tensor(slice_2, [None, None, arg1]); ``` Here, - `indices = [None, None, arg1]` is equivalent to `indices = [None, None, arg1, None]` - The operation `arg0[0, :, 1:2, tensor([[4,5]])]` is equivalent to `arg0[0, :, 1:2, tensor([[4,5]]), :]` None in `indices` are like fillers for dimensions that cannot be removed in the process. ## Gather op reference - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> [ghstack-poisoned]

WIP: need more tests Gather op: - https://github.com/openxla/xla/blob/main/docs/operation_semantics.md?rgh-link-date=2023-07-13T01%3A09%3A16Z#gather - https://www.pathpartnertech.com/gather-scatter-operation-in-deep-learning-framework/ --------- Co-authored-by: BowenBao <bowbaomicrosoft.com> ghstack-source-id: 4e36b48 Pull Request resolved: #883 Signed-off-by: Justin Chu <justinchu@microsoft.com>

justinchuby added 3 commits July 17, 2023 17:09

Rename intermediate values to _val and _const | feat(torchlib)

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Simplify model error message in test | test(torchlib)

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Remove the duplicate check_model message when displaying [ghstack-poisoned]

This was referenced Jul 17, 2023

Rename intermediate values to _val and _const | feat(torchlib) #881

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Simplify model error message in test | test(torchlib) #882

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Implement aten::index | feat(torchlib) #862

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justinchuby marked this pull request as ready for review July 17, 2023 17:47

justinchuby added module: torchlib Related to the torch/aten function lib in development change base before merge Remember to change the merge base to main when the PR is ready to merge labels Jul 17, 2023

justinchuby requested review from fatcat-z and xiaowuhu July 17, 2023 18:00

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ATen ops list #258

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justinchuby added 2 commits July 17, 2023 21:40

justinchuby merged commit 2e0aee7 into main Jul 17, 2023

justinchuby deleted the gh/justinchuby/33/head branch July 17, 2023 21:52

This was referenced Jul 17, 2023

[torchlib] Explicitly check for None in sequence inputs #892

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[OpInfo] index.Tensor pytorch/pytorch#105483

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Understanding `aten::index`

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