/te-ci L1 pytorch

/te-ci pytorch L1

Greptile Summary

Unified quantized tensor creation by migrating Python make_empty implementations to a single C++ create_tensor interface, removing ~287 lines of duplicated Python code across 4 quantizer types while adding device and pin_memory parameter support.

Key changes:

• Added device and pin_memory parameters to all create_tensor C++ method signatures
• Created new create_empty_quantized_tensor function exposed via pybind11
• Replaced abstract Quantizer.make_empty() with concrete implementation calling C++ backend
• Removed redundant Python implementations from Float8Quantizer, Float8CurrentScalingQuantizer, Float8BlockQuantizer, MXFP8Quantizer, and NVFP4Quantizer
• Added device string-to-torch.device conversion in Python wrapper for better API ergonomics

Confidence Score: 5/5

• This PR is safe to merge with minimal risk
• Well-structured refactoring that consolidates duplicate code into a single C++ implementation without changing functionality. The changes are straightforward, removing ~287 lines of duplicated Python code while properly handling device and pin_memory parameters. Device string handling was correctly added to prevent type errors.
• No files require special attention

Important Files Changed

Flowchart

%%{init: {'theme': 'neutral'}}%%
flowchart TD
    A[Python: quantizer.make_empty] --> B[tex.create_empty_quantized_tensor]
    B --> C[C++: create_empty_quantized_tensor]
    C --> D[convert_quantizer]
    C --> E[GetTransformerEngineDType]
    D --> F[quantizer_cpp->create_tensor]
    E --> F
    F --> G{Quantizer Type}
    G --> H[Float8Quantizer::create_tensor]
    G --> I[Float8BlockQuantizer::create_tensor]
    G --> J[MXFP8Quantizer::create_tensor]
    G --> K[NVFP4Quantizer::create_tensor]
    G --> L[NoneQuantizer::create_tensor]
    H --> M[Create Float8Tensor with device/pin_memory]
    I --> N[Create Float8BlockwiseQTensor with device/pin_memory]
    J --> O[Create MXFP8Tensor with device/pin_memory]
    K --> P[Create NVFP4Tensor with device/pin_memory]
    L --> Q[Create unquantized Tensor with device/pin_memory]
    M --> R[Return QuantizedTensor]
    N --> R
    O --> R
    P --> R
    Q --> R
    R --> S[Python: Apply requires_grad if needed]

_{Last reviewed commit: 9cad6d0}