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* Make sure to set usages for linear op quantizers before forward
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* Avoid unsupported case for fused dbias+quantize kernel

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* Add NVFP4 recipe
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* Add MathDx dependency to GitHub builds
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* Suggestions from GitHub Copilot
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* Move 2x shape logic from core to PyTorch
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* Fix compilation errors with CUDA 12.1
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* SM 70 is not supported in CUDA 13
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* Typo
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* Revert "Move 2x shape logic from core to PyTorch"

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* Added dequantize kernel for FP4
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* Fix linter warning
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* Add NVFP4 support with fusible ops

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* Fix logic for 2x shapes and move to PyTorch
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* Fix CG test model config
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* Debug NVFP4 tensor size function
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* Proper handling of the RNG state
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* Test SR properly
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* Fix workspace size for GEMM heuristic.
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* Fix compile error in C++ NVFP4 test

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* fix distrbuted test problem shape
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* proper assert dim for low precision AG TP
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* `nvte_cublas_gemm_v2` to take alpha pointer (#12)

* make nvte_cublas_gemm_v2 to take alpha/beta pointers
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* users are expected to pass a valid C_tensor
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* typos
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* API to have const float* alpha
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* Minor tweaks

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* Debug IMA with alpha pointer
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* Support fused amax kernels with NVFP4 quantization
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* Disable fused amax with cuDNN LayerNorm kernel
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* Add NVFP4 cases to distributed tests for TE ops
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* Change assert to NVTE_CHECK in the hadamard cast fusion
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* Fix compile error
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* Use global thread IDs for Philox subsequences
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* Add shape checks for NVFP4 cast kernels
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* Do not fuse amax if cuDNN normalization is forced by envvar
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* Replace `is_float8_tensor` with `is_quantized_tensor`

Replace free function `is_float8_tensor` with `is_quantized_tensor` in `_common.py` and use it throughout the `ops` codebase to check if a tensor is a (possibly internal) quantized tensor
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* Pass next and previous op quantizers directly to op_forward and fuser_forward

Change interface of `fuser_forward` and `op_forward` to no longer take preceding and following ops and instead take the following op's input quantizer and preceding op's input gradient's quantizer directly
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* Remove use redundant `detach` in `BasicLinear`

Remove use of `detach` in `BasicLinear` for improved performance (enabled by not passing prev_op to backward)
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* Handle saving internal tensors

Handle saving internal tensors in `_OperationFuserAutogradFunction` using `prepare_for_saving` and `restore_from_saved`
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* Use internal tensors

Enable use of internal tensors in `BasicLinear` quantizers and fix issues resulting from internal tensors not having methods that regular tensors have
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* Apply suggestions from code review
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* Add FP8 current scaling to te.Sequential tests
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* Helper function for test/ref tensors does not produce quantized tensor by default
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* Add FP8 current scaling to distributed te.Sequential tests
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* Add FP8 current scaling to Userbuffers te.Sequential tests
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* Debug MXFP8 tests
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Use ocp fp8.
Workaround: test_cast_float8blockwise.cu link wrong std::max
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* Reorganize PyTorch L1 tests
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* Move ONNX tests to L1
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* Move FA version test to L3
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* Limit parallel build jobs in FA version test
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* Add basic infrastructure for Sequential module
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* Add linear op
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* Add FP8 support in linear op

Runs, but need to validate. Runtime errors with non-FP8 params and FP8 compute, or FP8 params and non-FP8 compute.
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* Add reshape op and unit test
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* Add bias op
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* Add unfused linear op

Test does not pass with FP8.
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* Debug unfused linear op
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* Add test for linear+bias op
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* Add separate abstract classes for unfused and fused ops
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* Consolidate unfused ops in submodule
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* Add linear-bias fused op
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* Use fused cast-transpose in linear ops
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* Disable GEMM+bias fusion with FP32 activations

Not supported by cuBLAS.
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* Add parallel unit test for unfused linear op
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* Refactor parallel tests to reduce job launches
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* Add all-reduce, all-gather, and reduce-scatter ops
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* Remove unused file
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* Debug multi-GPU FP8 test
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* Add support for FP8 scale updates

Still need to implement amax reductions.
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* Add license boilerplate
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* Fuse GEMM+bias in row TP

Add documentation for unfused ops
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* Rename pipeline to fuser

Expand documentation
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* Tweak documentation
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* Preserve cached FP8 transpose between ops
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* Add option for fused wgrad accumulation
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* Directly output FP8 from linear if needed
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* Fix cuDNN front-end commit
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* Use updated FP8 tensor API for transpose caching
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* Use updated API for FP8 scale updates
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* Add tests for non-default FP8 recipes
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* Rename UnfusedOperation to BasicOperation
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* Add unit test to check amax reduction with fusable op
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* Operator autograd state no longer needs to be initialized
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* Initial functional implementation of linear op
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* Debug fused linear+bias op
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* Remove autograd context from functional linear impl
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* Use functional linear impl in fused linear+bias op
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* Rename subdirectory from "fuser" to "ops"

Avoid confusion with kernel fusers and graph compilers.
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* Update with Float8Tensor changes in #820
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* Remove unnecessary CPU overheads
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* Correctly pass FP8 metadata from next op
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* Fix linter errors
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* Add convenience functions to manipulate Sequential class
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* Update name of PyTorch extensions module
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* Clear saved tensor data in linear op after bprop
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* Fix Pylint error
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* Update name of PyTorch extensions module
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* Fix test name in QA script
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* Update name of PyTorch extensions module
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* Run distributed tests even when only 1 GPU is available
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* Only run distributed tests with 2 GPUs if there are >=2 GPUs
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* Review suggestions from @sudhakarsingh27 and @ksivaman

Fix spelling of "fusible". Avoid "input" name in internal APIs.
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* Update transformer_engine/pytorch/ops/__init__.py
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