Support python slicing with tensor inputs.#165074
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laithsakka wants to merge 14 commits intogh/laithsakka/311/basefrom
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Support python slicing with tensor inputs.#165074laithsakka wants to merge 14 commits intogh/laithsakka/311/basefrom
laithsakka wants to merge 14 commits intogh/laithsakka/311/basefrom
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/165074
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laithsakka
changed the title
laithsakka
commented
Oct 9, 2025
allow things like
```
#!/usr/bin/env python
import torch
print("="*60)
print("Testing tensor slicing with torch.compile")
print("="*60)
# Test 1: Simple eager mode
print("\n1. Eager mode test:")
x = torch.randn(10)
idx = torch.tensor(4)
result = x[:idx]
print(f" x[:idx] where idx=4: result.shape = {result.shape}")
assert result.shape[0] == 4
print(" ✓ Eager mode works!")
# Test 2: With torch.compile
print("\n2. Compiled mode test:")
def slice_fn(x, idx):
return x[:idx]
try:
compiled_fn = torch.compile(slice_fn)
x = torch.randn(10)
idx = torch.tensor(4)
result = compiled_fn(x, idx)
print(f" Compiled x[:idx] where idx=4: result.shape = {result.shape}")
assert result.shape[0] == 4
print(" ✓ Compiled mode works!")
except Exception as e:
print(f" ✗ Compiled mode failed: {e}")
import traceback
traceback.print_exc()
# Test 3: With dynamic slicing from sum
print("\n3. Dynamic slicing with sum:")
def dynamic_slice_fn(x, lengths):
idx = lengths.sum()
return x[:idx]
try:
compiled_fn = torch.compile(dynamic_slice_fn)
x = torch.randn(10)
lengths = torch.tensor([1, 1, 1, 1])
result = compiled_fn(x, lengths)
print(f" Compiled x[:lengths.sum()] where sum=4: result.shape = {result.shape}")
assert result.shape[0] == 4
print(" ✓ Dynamic slicing works!")
except Exception as e:
print(f" ✗ Dynamic slicing failed: {e}")
import traceback
traceback.print_exc()
print("\n" + "="*60)
print("SUMMARY: Check results above")
print("="*60)
```
[ghstack-poisoned]
allow things like
```
#!/usr/bin/env python
import torch
print("="*60)
print("Testing tensor slicing with torch.compile")
print("="*60)
# Test 1: Simple eager mode
print("\n1. Eager mode test:")
x = torch.randn(10)
idx = torch.tensor(4)
result = x[:idx]
print(f" x[:idx] where idx=4: result.shape = {result.shape}")
assert result.shape[0] == 4
print(" ✓ Eager mode works!")
# Test 2: With torch.compile
print("\n2. Compiled mode test:")
def slice_fn(x, idx):
return x[:idx]
try:
compiled_fn = torch.compile(slice_fn)
x = torch.randn(10)
idx = torch.tensor(4)
result = compiled_fn(x, idx)
print(f" Compiled x[:idx] where idx=4: result.shape = {result.shape}")
assert result.shape[0] == 4
print(" ✓ Compiled mode works!")
except Exception as e:
print(f" ✗ Compiled mode failed: {e}")
import traceback
traceback.print_exc()
# Test 3: With dynamic slicing from sum
print("\n3. Dynamic slicing with sum:")
def dynamic_slice_fn(x, lengths):
idx = lengths.sum()
return x[:idx]
try:
compiled_fn = torch.compile(dynamic_slice_fn)
x = torch.randn(10)
lengths = torch.tensor([1, 1, 1, 1])
result = compiled_fn(x, lengths)
print(f" Compiled x[:lengths.sum()] where sum=4: result.shape = {result.shape}")
assert result.shape[0] == 4
print(" ✓ Dynamic slicing works!")
except Exception as e:
print(f" ✗ Dynamic slicing failed: {e}")
import traceback
traceback.print_exc()
print("\n" + "="*60)
print("SUMMARY: Check results above")
print("="*60)
```
[ghstack-poisoned]
allow things like
```
#!/usr/bin/env python
import torch
print("="*60)
print("Testing tensor slicing with torch.compile")
print("="*60)
# Test 1: Simple eager mode
print("\n1. Eager mode test:")
x = torch.randn(10)
idx = torch.tensor(4)
result = x[:idx]
print(f" x[:idx] where idx=4: result.shape = {result.shape}")
assert result.shape[0] == 4
print(" ✓ Eager mode works!")
# Test 2: With torch.compile
print("\n2. Compiled mode test:")
def slice_fn(x, idx):
return x[:idx]
try:
compiled_fn = torch.compile(slice_fn)
x = torch.randn(10)
idx = torch.tensor(4)
result = compiled_fn(x, idx)
print(f" Compiled x[:idx] where idx=4: result.shape = {result.shape}")
assert result.shape[0] == 4
print(" ✓ Compiled mode works!")
except Exception as e:
print(f" ✗ Compiled mode failed: {e}")
import traceback
traceback.print_exc()
# Test 3: With dynamic slicing from sum
print("\n3. Dynamic slicing with sum:")
def dynamic_slice_fn(x, lengths):
idx = lengths.sum()
return x[:idx]
try:
compiled_fn = torch.compile(dynamic_slice_fn)
x = torch.randn(10)
lengths = torch.tensor([1, 1, 1, 1])
result = compiled_fn(x, lengths)
print(f" Compiled x[:lengths.sum()] where sum=4: result.shape = {result.shape}")
assert result.shape[0] == 4
print(" ✓ Dynamic slicing works!")
except Exception as e:
print(f" ✗ Dynamic slicing failed: {e}")
import traceback
traceback.print_exc()
print("\n" + "="*60)
print("SUMMARY: Check results above")
print("="*60)
```
[ghstack-poisoned]
allow things like
```
#!/usr/bin/env python
import torch
print("="*60)
print("Testing tensor slicing with torch.compile")
print("="*60)
# Test 1: Simple eager mode
print("\n1. Eager mode test:")
x = torch.randn(10)
idx = torch.tensor(4)
result = x[:idx]
print(f" x[:idx] where idx=4: result.shape = {result.shape}")
assert result.shape[0] == 4
print(" ✓ Eager mode works!")
# Test 2: With torch.compile
print("\n2. Compiled mode test:")
def slice_fn(x, idx):
return x[:idx]
try:
compiled_fn = torch.compile(slice_fn)
x = torch.randn(10)
idx = torch.tensor(4)
result = compiled_fn(x, idx)
print(f" Compiled x[:idx] where idx=4: result.shape = {result.shape}")
assert result.shape[0] == 4
print(" ✓ Compiled mode works!")
except Exception as e:
print(f" ✗ Compiled mode failed: {e}")
import traceback
traceback.print_exc()
# Test 3: With dynamic slicing from sum
print("\n3. Dynamic slicing with sum:")
def dynamic_slice_fn(x, lengths):
idx = lengths.sum()
return x[:idx]
try:
compiled_fn = torch.compile(dynamic_slice_fn)
x = torch.randn(10)
lengths = torch.tensor([1, 1, 1, 1])
result = compiled_fn(x, lengths)
print(f" Compiled x[:lengths.sum()] where sum=4: result.shape = {result.shape}")
assert result.shape[0] == 4
print(" ✓ Dynamic slicing works!")
except Exception as e:
print(f" ✗ Dynamic slicing failed: {e}")
import traceback
traceback.print_exc()
print("\n" + "="*60)
print("SUMMARY: Check results above")
print("="*60)
```
[ghstack-poisoned]
allow things like
```
#!/usr/bin/env python
import torch
print("="*60)
print("Testing tensor slicing with torch.compile")
print("="*60)
# Test 1: Simple eager mode
print("\n1. Eager mode test:")
x = torch.randn(10)
idx = torch.tensor(4)
result = x[:idx]
print(f" x[:idx] where idx=4: result.shape = {result.shape}")
assert result.shape[0] == 4
print(" ✓ Eager mode works!")
# Test 2: With torch.compile
print("\n2. Compiled mode test:")
def slice_fn(x, idx):
return x[:idx]
try:
compiled_fn = torch.compile(slice_fn)
x = torch.randn(10)
idx = torch.tensor(4)
result = compiled_fn(x, idx)
print(f" Compiled x[:idx] where idx=4: result.shape = {result.shape}")
assert result.shape[0] == 4
print(" ✓ Compiled mode works!")
except Exception as e:
print(f" ✗ Compiled mode failed: {e}")
import traceback
traceback.print_exc()
# Test 3: With dynamic slicing from sum
print("\n3. Dynamic slicing with sum:")
def dynamic_slice_fn(x, lengths):
idx = lengths.sum()
return x[:idx]
try:
compiled_fn = torch.compile(dynamic_slice_fn)
x = torch.randn(10)
lengths = torch.tensor([1, 1, 1, 1])
result = compiled_fn(x, lengths)
print(f" Compiled x[:lengths.sum()] where sum=4: result.shape = {result.shape}")
assert result.shape[0] == 4
print(" ✓ Dynamic slicing works!")
except Exception as e:
print(f" ✗ Dynamic slicing failed: {e}")
import traceback
traceback.print_exc()
print("\n" + "="*60)
print("SUMMARY: Check results above")
print("="*60)
```
[ghstack-poisoned]
Contributor
Author
|
great i just need to add unit tests! |
allow things like
```
#!/usr/bin/env python
import torch
print("="*60)
print("Testing tensor slicing with torch.compile")
print("="*60)
# Test 1: Simple eager mode
print("\n1. Eager mode test:")
x = torch.randn(10)
idx = torch.tensor(4)
result = x[:idx]
print(f" x[:idx] where idx=4: result.shape = {result.shape}")
assert result.shape[0] == 4
print(" ✓ Eager mode works!")
# Test 2: With torch.compile
print("\n2. Compiled mode test:")
def slice_fn(x, idx):
return x[:idx]
try:
compiled_fn = torch.compile(slice_fn)
x = torch.randn(10)
idx = torch.tensor(4)
result = compiled_fn(x, idx)
print(f" Compiled x[:idx] where idx=4: result.shape = {result.shape}")
assert result.shape[0] == 4
print(" ✓ Compiled mode works!")
except Exception as e:
print(f" ✗ Compiled mode failed: {e}")
import traceback
traceback.print_exc()
# Test 3: With dynamic slicing from sum
print("\n3. Dynamic slicing with sum:")
def dynamic_slice_fn(x, lengths):
idx = lengths.sum()
return x[:idx]
try:
compiled_fn = torch.compile(dynamic_slice_fn)
x = torch.randn(10)
lengths = torch.tensor([1, 1, 1, 1])
result = compiled_fn(x, lengths)
print(f" Compiled x[:lengths.sum()] where sum=4: result.shape = {result.shape}")
assert result.shape[0] == 4
print(" ✓ Dynamic slicing works!")
except Exception as e:
print(f" ✗ Dynamic slicing failed: {e}")
import traceback
traceback.print_exc()
print("\n" + "="*60)
print("SUMMARY: Check results above")
print("="*60)
```
[ghstack-poisoned]
Lucaskabela
reviewed
Oct 25, 2025
Lucaskabela
reviewed
Oct 25, 2025
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Lucaskabela
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Generally this looks good, to me but needs some cleanup of asserts and I would like to see a test with dynamic=True to make sure the checks before .item() calls have reasonable behavior
Lucaskabela
requested changes
Oct 25, 2025
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Lucaskabela
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Lucaskabela
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See comments - few minor changes suggested but this looks good otherwise
Contributor
Author
ah i see i will just remove those checks as you suggested probably size will be none with the input tensor is dynamic. |
when the slice is tensor, we decompose it to .item() call and pass the unbacked symbol to the slice to avoid DDE. cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames Lucaskabela [ghstack-poisoned]
Contributor
|
Test failures seem legitimate - feel free to re-ping once fixed |
when the slice is tensor, we decompose it to .item() call and pass the unbacked symbol to the slice to avoid DDE. cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames Lucaskabela [ghstack-poisoned]
Lucaskabela
approved these changes
Oct 28, 2025
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Thanks for addressing feedback and fixing the test failures - the dtensor failure seems unrelated to me, so approving
Contributor
Author
|
@pytorchbot merge -i |
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@pytorchbot merge -i |
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Merge startedYour change will be merged while ignoring the following 1 checks: pull / linux-jammy-py3.10-gcc11 / test (distributed, 2, 2, linux.2xlarge) Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
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Stack from ghstack (oldest at bottom):
when the slice is tensor, we decompose it to .item() call and pass the unbacked symbol to the slice to avoid DDE.
the diff also fix an existing bug in codegen_dynamic_slice_size in the cpp wrapper. a +1 should be -1 making it match
python codegen.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov @coconutruben @Lucaskabela