Enable arithmetic operations between device tensors/batches and scalars #6143

rostan-t · 2025-12-19T09:55:23Z

Category: Bug fix (non-breaking change which fixes an issue)

Description:

This PR enables arithmetic operations between tensors/batches and scalars. Previously, x + n worked if x was a CPU tensor and n a scalar, but not if x was a GPU tensor.

Arithmetic operations between a GPU tensor/batch and a Python list or tuple are also supported. Tensor types need to be explicitly copied.

Additional information:

Affected modules and functionalities:

Dynamic mode tensors and batches.

Key points relevant for the review:

Do arithmetic operations work the way we intend to?

Tests:

Checklist

Documentation

DALI team only

Requirements

Implements new requirements
Affects existing requirements
N/A

REQ IDs: N/A

JIRA TASK: DALI-4545

dali/python/nvidia/dali/experimental/dynamic/_arithmetic.py

dali/python/nvidia/dali/experimental/dynamic/_batch.py

dali/python/nvidia/dali/experimental/dynamic/_tensor.py

greptile-apps · 2025-12-19T09:59:44Z

Greptile Summary

This PR fixes a critical bug that prevented arithmetic operations between GPU tensors/batches and scalars, and refactors the arithmetic operation handling logic for better maintainability.

Key Changes:

Fixed TensorListGPU.broadcast method in C++ backend that was incorrectly using CPUBackend types instead of GPUBackend types
Extracted arithmetic operation logic into a new _arithmetic.py module, removing duplication from _tensor.py and _batch.py
Enhanced device handling to properly convert scalars to GPU tensors when operating with GPU tensors
Added explicit validation for implicitly convertible types (numbers, lists, tuples) when targeting GPU
Improved error messages for mixed CPU/GPU operations
Added comprehensive tests covering scalar operations on both CPU and GPU tensors/batches, plus device compatibility checks

Confidence Score: 5/5

This PR is safe to merge - it fixes critical bugs, improves code organization, and includes thorough test coverage
The changes are well-structured bug fixes with comprehensive tests. The C++ fix corrects a clear type mismatch bug, and the Python refactoring improves code organization while maintaining backward compatibility. All changes follow existing patterns and include extensive test coverage for the new functionality.
No files require special attention

Important Files Changed

Filename	Overview
dali/python/backend_impl.cc	Fixed critical bug in `TensorListGPU.broadcast` where CPU backend types were incorrectly used instead of GPU backend types
dali/python/nvidia/dali/experimental/dynamic/_arithmetic.py	New module extracting arithmetic operation logic with improved device handling and explicit scalar-to-tensor conversion
dali/test/python/experimental_mode/test_arithm_ops.py	Added comprehensive tests for scalar arithmetic operations on GPU/CPU tensors and batches, plus device compatibility validation

Sequence Diagram

sequenceDiagram
    participant User
    participant Tensor/Batch
    participant _arithm_op
    participant as_tensor
    participant _arithmetic_generic_op

    User->>Tensor/Batch: x + scalar (e.g., gpu_tensor + 5)
    Tensor/Batch->>_arithm_op: __add__(self, scalar)
    
    Note over _arithm_op: Detect device type from<br/>Tensor/Batch arguments
    _arithm_op->>_arithm_op: gpu = any(arg.device == "gpu")
    
    alt scalar argument (not Tensor/Batch)
        _arithm_op->>_arithm_op: Check _implicitly_convertible(scalar)
        alt gpu is True and not implicitly convertible
            _arithm_op-->>User: ValueError: Not implicitly copyable to GPU
        else convertible
            _arithm_op->>as_tensor: as_tensor(scalar, device="gpu" if gpu else None)
            as_tensor-->>_arithm_op: Tensor on correct device
        end
    end
    
    _arithm_op->>_arithm_op: Validate all args on same device
    alt mixed GPU/CPU inputs
        _arithm_op-->>User: ValueError: Cannot mix GPU and CPU inputs
    else all same device
        _arithm_op->>_arithmetic_generic_op: Call with tensor args
        _arithmetic_generic_op-->>User: Result tensor
    end

greptile-apps

Additional Comments (3)

dali/python/nvidia/dali/experimental/dynamic/_arithmetic.py, line 36 (link)

syntax: Typo: "implictly" should be "implicitly"
dali/python/nvidia/dali/experimental/dynamic/_arithmetic.py, line 30-46 (link)

logic: Logic bug: when a scalar appears before a GPU tensor (e.g., 3 + gpu_tensor), the scalar gets converted to CPU before detecting the GPU tensor, causing the final check to fail with "Cannot mix GPU and CPU inputs."

The algorithm needs two passes:
1. First pass: scan all args to detect if any GPU tensor exists
2. Second pass: convert scalars to appropriate device
Example failure case:
```
gpu_tensor = ndd.tensor([1, 2, 3], device="gpu")
result = 5 + gpu_tensor  # Will raise ValueError
```

dali/test/python/experimental_mode/test_arithm_ops.py, line 128 (link)

style: Test only covers tensor + scalar but not scalar + tensor. Add reverse operation tests:

# Also test scalar + tensor
result_reversed = ndd.as_tensor(apply_bin_op(op, scalar, x))
ref_reversed = apply_bin_op(op, scalar, tensor)
if not np.allclose(result_reversed.cpu(), ref_reversed):
    msg = f"{scalar} {op} {tensor} = \n{result_reversed}\n!=\n{ref_reversed}"
    raise AssertionError(msg)

_{5 files reviewed, 3 comments}

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dali/python/nvidia/dali/experimental/dynamic/_arithmetic.py

greptile-apps

Additional Comments (1)

dali/python/nvidia/dali/experimental/dynamic/_arithmetic.py, line 20 (link)

syntax: using | operator in isinstance() requires Python 3.10+, but pyproject.toml targets Python 3.8+. Use tuple syntax instead for compatibility.

_{5 files reviewed, 1 comment}

_{Edit Code Review Agent Settings | Greptile}

mzient · 2025-12-30T13:05:54Z

dali/python/nvidia/dali/experimental/dynamic/_arithmetic.py

+
+
+def _implicitly_convertible(value: Any):
+    return isinstance(value, (SupportsInt, SupportsFloat, list, tuple))


Hmmm.... I don't think this'll work:

>>> isinstance(cp.array([0,0]), typing.SupportsFloat) # CuPy, not a scalar True >>> isinstance(np.array([0,0]), typing.SupportsFloat) # NumPy, not a scalar True >>> isinstance(np.array(["a","b"]), typing.SupportsFloat) # NumPy, not a scalar, not even a numeric type True

Fixed by 9319fa9. Now:

>>> isinstance(cp.array([0, 0]), numbers.Real) False >>> isinstance(np.array([0, 0]), numbers.Real) False >>> isinstance(np.int64(0), numbers.Real) True

Signed-off-by: Rostan Tabet <[email protected]>

rostan-t · 2026-01-02T18:44:24Z

!build

dali-automaton · 2026-01-02T18:50:41Z

CI MESSAGE: [41073770]: BUILD STARTED

dali-automaton · 2026-01-02T20:28:56Z

CI MESSAGE: [41073770]: BUILD PASSED

mzient · 2026-01-09T15:44:22Z

dali/python/nvidia/dali/experimental/dynamic/_arithmetic.py

+            if gpu and not _implicitly_convertible(arg):
+                raise ValueError(f"Type {type(arg)} is not implicitly copyable to the GPU.")


What happens if arg is a CuPy array or a torch cuda tensor? Normally they would get converted to a GPU Tensor, but now we'd throw.

Fixed by bcefb0d. This also raises an exception earlier in cases like (ndd GPU tensor) + (PyTorch CPU tensor).

greptile-apps

Greptile Overview

Greptile Summary

Overview

This PR addresses a critical bug preventing arithmetic operations between GPU tensors/batches and scalars. It refactors arithmetic handling into a dedicated module and fixes a backend implementation bug.

Key Changes

1. Critical C++ Backend Fix (backend_impl.cc)

Fixed TensorListGPU.broadcast() method which was incorrectly using CPUBackend type parameters instead of GPUBackend. This prevented GPU tensor broadcasting from working at all. The fix is minimal and correct.

2. New _arithmetic.py Module

Extracted arithmetic operation logic into a centralized module to reduce duplication. The module implements:

_implicitly_convertible(): Validates scalar types (numbers, lists, tuples)
_arithm_op(): Handles argument conversion and device consistency checking

3. Refactored _tensor.py and _batch.py

Both files now delegate arithmetic operations to the new _arithm_op() function instead of inline implementations.

4. Test Coverage

Added comprehensive tests (test_binary_scalars) covering:

Scalar operations on both CPU and GPU tensors
Scalar operations on GPU batches with different batch sizes
Device mixing detection and error handling
PyTorch GPU tensor interoperability

Critical Issue Found

Logic Bug in _arithmetic.py (Lines 34-38)

The scalar-to-tensor conversion logic contains a critical structural flaw:

if not isinstance(arg, (Tensor, Batch)):
    if gpu and _implicitly_convertible(arg):
        arg = as_tensor(arg, device="gpu")
    arg = as_tensor(arg)  # ← ALWAYS EXECUTES (wrong!)

Line 38 should be in an else clause. Currently, every scalar is converted twice - once to the correct device, then again to default device. The code works by accident because as_tensor() preserves device for existing Tensor objects, but this is fragile and violates the principle of least surprise.

Positive Aspects

C++ backend fix is correct and necessary
Refactoring improves code maintainability
Test coverage for new functionality is good
Device validation logic correctly prevents mixed CPU/GPU operations
Batch broadcasting now works correctly for GPU tensors

Recommendations

The _arithmetic.py logic should be corrected to explicitly handle the scalar conversion decision, making the intent clear and eliminating accidental reliance on as_tensor() device preservation semantics.

Confidence Score: 2/5

This PR should NOT be merged in its current state due to a critical logic bug in _arithmetic.py that violates code quality standards, despite the bug working accidentally in practice.
The C++ backend fix (TensorListGPU.broadcast) is correct and critical. However, the _arithmetic.py module contains a structural logic error where line 38 always executes regardless of the condition at line 36. This means scalars are converted twice - once to the correct device, then again unconditionally. While this works due to as_tensor() preserving device for existing Tensor objects, it's fragile, poorly written, and could break if as_tensor() behavior changes. The code violates Python style expectations and makes the intent unclear. This is a moderate issue that doesn't cause runtime failures in current code but represents poor engineering practice and technical debt. The test coverage added is good, but doesn't validate the implementation quality.
dali/python/nvidia/dali/experimental/dynamic/_arithmetic.py - Lines 34-38 need restructuring to clarify intent and eliminate accidental reliance on as_tensor() device preservation behavior

Important Files Changed

File Analysis

Filename	Score	Overview
dali/python/nvidia/dali/experimental/dynamic/_arithmetic.py	2/5	New module that centralizes arithmetic operation logic. Contains a critical logic bug at line 38 where scalar conversion happens twice due to incorrect indentation - line 38 should be in an else clause. The code works by accident due to as_tensor() preserving device for existing Tensor objects, but is fragile and error-prone.
dali/python/backend_impl.cc	5/5	Critical bug fix in TensorListGPU.broadcast method (lines 1612-1613). Corrected type parameters from CPUBackend to GPUBackend, allowing GPU tensor broadcasting to work correctly. Fix is correct and minimal.
dali/python/nvidia/dali/experimental/dynamic/_tensor.py	5/5	Updated to import and use the new _arithmetic module for all arithmetic operations. Changes are clean refactoring from inline implementation to delegating to _arithm_op. No new issues introduced.
dali/python/nvidia/dali/experimental/dynamic/_batch.py	5/5	Updated to import and use the new _arithmetic module for all arithmetic operations. The broadcast method correctly detects device type and uses appropriate TensorList backend. Changes are clean refactoring with no new issues.
dali/test/python/experimental_mode/test_arithm_ops.py	4/5	New tests added for scalar arithmetic operations on both CPU and GPU tensors/batches, and device compatibility checks. Tests are comprehensive but could benefit from additional edge case coverage (e.g., complex numbers, boolean operations with scalars).

Sequence Diagram

sequenceDiagram
    participant User
    participant Tensor as GPU Tensor
    participant _arithm_op as _arithm_op()
    participant as_tensor as as_tensor()
    participant Backend as Backend

    User->>Tensor: tensor + 3 (scalar)
    Tensor->>_arithm_op: __add__(self, 3)
    
    _arithm_op->>_arithm_op: gpu = True (detect GPU)
    _arithm_op->>_arithm_op: arg = 3 (scalar)
    
    Note over _arithm_op: Check: gpu and<br/>_implicitly_convertible(3)?
    _arithm_op->>as_tensor: as_tensor(3, device='gpu')
    as_tensor-->>_arithm_op: GPU Tensor
    
    Note over _arithm_op: BUG: Line 38<br/>ALWAYS executes
    _arithm_op->>as_tensor: as_tensor(GPU_Tensor)
    as_tensor-->>_arithm_op: GPU Tensor (device preserved)
    
    _arithm_op->>_arithm_op: Validate: GPU == GPU ✓
    _arithm_op->>Backend: _arithmetic_generic_op(tensor, scalar)
    Backend-->>_arithm_op: Result Tensor
    _arithm_op-->>Tensor: Result
    Tensor-->>User: GPU Tensor result
    
    rect rgb(255, 200, 200)
        Note over _arithm_op: Works by accident:<br/>as_tensor() preserves<br/>device for Tensor objects
    end

dali/python/nvidia/dali/experimental/dynamic/_arithmetic.py

greptile-apps

Greptile Overview

Greptile Summary

This PR successfully enables arithmetic operations between GPU tensors/batches and scalars, fixing a critical bug and improving code organization.

Key Changes:

Fixed critical bug in C++ backend where TensorListGPU.broadcast incorrectly used CPUBackend types
Extracted arithmetic operation logic into new _arithmetic.py module, eliminating code duplication between _tensor.py and _batch.py
Enhanced device handling to properly detect GPU context and convert scalars/lists/tuples to GPU tensors when operating with GPU data
Added comprehensive test coverage for scalar operations on both CPU and GPU, plus device compatibility validation

Improvements Over Previous Implementation:
The new _arithm_op function correctly handles the case where scalars appear before GPU tensors in operations (e.g., 5 + gpu_tensor). The old implementation would convert the scalar to CPU before detecting the GPU tensor, leading to device mismatch errors or incorrect behavior.

Confidence Score: 5/5

This PR is safe to merge - it fixes critical bugs, improves code organization, and has comprehensive test coverage
The changes fix a clear copy-paste bug in the C++ backend, successfully refactor duplicated Python code into a shared module, and properly handle GPU/CPU device detection. The only minor issue is a small code structure inefficiency that doesn't affect correctness. Comprehensive tests validate the new functionality.
No files require special attention - all changes are well-implemented

Important Files Changed

File Analysis

Filename	Score	Overview
dali/python/backend_impl.cc	5/5	Fixed critical bug in `TensorListGPU.broadcast` that incorrectly used `CPUBackend` types instead of `GPUBackend` types
dali/python/nvidia/dali/experimental/dynamic/_arithmetic.py	4/5	New module extracting arithmetic logic; correctly handles GPU/CPU device detection and scalar-to-tensor conversion, with minor code structure inefficiency
dali/python/nvidia/dali/experimental/dynamic/_batch.py	5/5	Refactored to use centralized `_arithm_op` from `_arithmetic` module, removing duplicated code
dali/python/nvidia/dali/experimental/dynamic/_tensor.py	5/5	Refactored to use centralized `_arithm_op` from `_arithmetic` module, removing duplicated code
dali/test/python/experimental_mode/test_arithm_ops.py	5/5	Added comprehensive tests for scalar operations on GPU/CPU tensors and batches, plus device compatibility validation

Sequence Diagram

sequenceDiagram
    participant User
    participant Tensor/Batch
    participant _arithm_op
    participant as_tensor
    participant _arithmetic_generic_op
    participant Backend

    User->>Tensor/Batch: gpu_tensor + scalar
    Tensor/Batch->>_arithm_op: __add__(scalar)
    
    Note over _arithm_op: Detect GPU from args
    _arithm_op->>_arithm_op: Check if any arg is GPU tensor
    _arithm_op->>_arithm_op: gpu = True
    
    Note over _arithm_op: Convert scalar to tensor
    _arithm_op->>as_tensor: as_tensor(scalar, device="gpu")
    as_tensor->>Backend: TensorListGPU.broadcast(tensor, 1)
    Backend-->>as_tensor: GPU tensor
    as_tensor-->>_arithm_op: GPU scalar tensor
    
    _arithm_op->>_arithm_op: Validate devices match
    _arithm_op->>_arithmetic_generic_op: Execute operation
    _arithmetic_generic_op->>Backend: Run GPU arithmetic op
    Backend-->>_arithmetic_generic_op: Result tensor
    _arithmetic_generic_op-->>User: GPU result tensor

dali/python/nvidia/dali/experimental/dynamic/_arithmetic.py

Signed-off-by: Rostan Tabet <[email protected]>

greptile-apps

Greptile Overview

Greptile Summary

This PR fixes a critical bug preventing arithmetic operations between GPU tensors/batches and scalars, and refactors the arithmetic operation logic for better maintainability.

Key Changes:

Fixed TensorListGPU.broadcast in C++ backend that incorrectly used CPUBackend types instead of GPUBackend types, which was the root cause preventing GPU tensor operations
Extracted arithmetic operation handling into new _arithmetic.py module, eliminating code duplication between _tensor.py and _batch.py
Implemented smart device detection: when any operand is a GPU tensor, implicitly convertible scalars (numbers, lists, tuples) are automatically converted to GPU tensors
Added validation to prevent mixing CPU and GPU operands, providing clear error messages
Added comprehensive test coverage for scalar operations on both devices and device compatibility checks

Issues Found:

One syntax error in _tensor.py line 71: missing f prefix for f-string formatting

Confidence Score: 4/5

This PR is safe to merge after fixing the f-string syntax error - it addresses a critical bug and improves code quality
Score of 4 reflects one syntax error that must be fixed before merging. The core logic is sound: the C++ backend fix is correct, the arithmetic refactoring properly handles device placement, and tests comprehensively cover the new functionality. The syntax error on line 71 is in an error path that likely wasn't hit by tests.
Fix the f-string syntax error in dali/python/nvidia/dali/experimental/dynamic/_tensor.py line 71 before merging

Important Files Changed

File Analysis

Filename	Score	Overview
dali/python/backend_impl.cc	5/5	Critical bug fix: corrected `TensorListGPU.broadcast` to use `GPUBackend` instead of `CPUBackend` - this was preventing GPU tensor broadcasting from working
dali/python/nvidia/dali/experimental/dynamic/_arithmetic.py	5/5	New module extracting arithmetic operation logic with proper GPU/CPU device handling and implicit scalar-to-tensor conversion
dali/python/nvidia/dali/experimental/dynamic/_tensor.py	4/5	Refactored to use shared `_arithm_op` from `_arithmetic.py`; contains one f-string syntax error on line 71
dali/python/nvidia/dali/experimental/dynamic/_batch.py	5/5	Refactored to use shared `_arithm_op` from `_arithmetic.py`, removing duplicate code and improving maintainability
dali/test/python/experimental_mode/test_arithm_ops.py	5/5	Added comprehensive tests for scalar operations with tensors/batches on both CPU and GPU, plus device compatibility validation tests

Sequence Diagram

sequenceDiagram
    participant User
    participant Tensor/Batch
    participant _arithm_op
    participant as_tensor
    participant _arithmetic_generic_op
    participant Backend

    User->>Tensor/Batch: gpu_tensor + scalar
    Tensor/Batch->>_arithm_op: __add__(scalar)
    
    Note over _arithm_op: Detect GPU device from<br/>existing Tensor/Batch args
    
    _arithm_op->>_arithm_op: Check if scalar is<br/>implicitly_convertible<br/>(Real, list, tuple)
    
    alt Scalar is implicitly convertible & GPU detected
        _arithm_op->>as_tensor: Convert scalar to GPU tensor
        as_tensor-->>_arithm_op: GPU Tensor
    else Otherwise
        _arithm_op->>as_tensor: Convert scalar to CPU tensor
        as_tensor-->>_arithm_op: CPU Tensor
    end
    
    _arithm_op->>_arithm_op: Validate all args<br/>are on same device
    
    alt Device mismatch
        _arithm_op-->>User: ValueError: Cannot mix GPU and CPU
    else All devices match
        _arithm_op->>_arithmetic_generic_op: Execute operation
        _arithmetic_generic_op->>Backend: TensorListGPU.broadcast<br/>(now uses GPUBackend)
        Backend-->>_arithmetic_generic_op: Result
        _arithmetic_generic_op-->>User: Tensor/Batch result
    end

greptile-apps · 2026-01-09T17:00:59Z

dali/python/nvidia/dali/experimental/dynamic/_tensor.py

        return arr.astype(np.int32), nvidia.dali.types.IMAGE_TYPE
    else:
-        raise TypeError(f"Unexpected element type {type(item)}")
+        raise TypeError("Unexpected element type f{type(item)}")


Missing f prefix for f-string.

Suggested change

raise TypeError("Unexpected element type f{type(item)}")

raise TypeError(f"Unexpected element type {type(item)}")

This is not related to this PR and I fixed it in another PR already.

rostan-t · 2026-01-09T19:01:43Z

!build

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rostan-t force-pushed the ndd-gpu-arithmetic branch from c0cc95a to 2265d73 Compare December 19, 2025 12:19

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rostan-t added 5 commits January 2, 2026 18:30

Fix input device of TensorListGPU.broadcast

b113ab0

Signed-off-by: Rostan Tabet <[email protected]>

Fix arithmetic operations with GPU tensors and scalars

dd60cf9

Signed-off-by: Rostan Tabet <[email protected]>

Add a test for arithmetic ops with incompatible devices

8507567

Signed-off-by: Rostan Tabet <[email protected]>

Fix case where scalar is the first arg and make isinstance check faster

dcc9463

Signed-off-by: Rostan Tabet <[email protected]>

Fix criteria used to detect scalars

9319fa9

Signed-off-by: Rostan Tabet <[email protected]>

rostan-t force-pushed the ndd-gpu-arithmetic branch from 8d39fde to 9319fa9 Compare January 2, 2026 18:36

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dali/python/nvidia/dali/experimental/dynamic/_arithmetic.py Outdated Show resolved Hide resolved

Properly handle data that's already on the GPU

bcefb0d

Signed-off-by: Rostan Tabet <[email protected]>

rostan-t force-pushed the ndd-gpu-arithmetic branch from fcf79a4 to bcefb0d Compare January 9, 2026 16:55

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		def _implicitly_convertible(value: Any):
		return isinstance(value, (SupportsInt, SupportsFloat, list, tuple))

		if gpu and not _implicitly_convertible(arg):
		raise ValueError(f"Type {type(arg)} is not implicitly copyable to the GPU.")

	raise TypeError("Unexpected element type f{type(item)}")
	raise TypeError(f"Unexpected element type {type(item)}")

Enable arithmetic operations between device tensors/batches and scalars #6143

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Enable arithmetic operations between device tensors/batches and scalars #6143

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rostan-t commented Dec 19, 2025

Category: Bug fix (non-breaking change which fixes an issue)

Description:

Additional information:

Affected modules and functionalities:

Key points relevant for the review:

Tests:

Checklist

Documentation

DALI team only

Requirements

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Greptile Summary

Confidence Score: 5/5

Important Files Changed

Sequence Diagram

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Greptile Overview

Greptile Summary

Overview

Key Changes

1. Critical C++ Backend Fix (backend_impl.cc)

2. New _arithmetic.py Module

3. Refactored _tensor.py and _batch.py

4. Test Coverage

Critical Issue Found

Logic Bug in _arithmetic.py (Lines 34-38)

Positive Aspects

Recommendations

Confidence Score: 2/5

Important Files Changed

Sequence Diagram

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greptile-apps bot commented Dec 19, 2025 •

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