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* Linear op avoids saving input tensor if weight grad is not needed
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Linear op forward avoids producing quantized tensors with unnecessary usages
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* Fix linter warnings
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* Avoid unnecessary usages in fused linear ops
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* Add option to cache activation input in FP8
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* Avoid casting to FP8 transpose
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* Skip input caching if device is not supported
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* Add documentation that FP8 input caching is experimental
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* Debug checkpointing with te.Sequential
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Explicitly specify quantized tensor usages needed for linear op backward
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* Add activation ops
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* Fix lint warnings
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* Fix linter warning
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* Update to use QuantizedTensor
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* Respect PyTorch autograd dtype
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* Rename CastFloat8 op to Quantize
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Signed-off-by: Tim Moon <tmoon@nvidia.com>
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* Remove manual FP8 scale update for FP8 params
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* lint
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* Add layer norm op
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* Add FP8 cast op
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* Add tests for linear and layernorm with FP8 output
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* RMSNorm op
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* Fix linter warnings
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* Replace LayerNorm module with LayerNorm op
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* Replace RMSNorm module with RMSNorm op
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* Add AMP support
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* Do not save autograd context if grad mode is disabled

Debugging ONNX export tests.
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* Forward args in pre_forward func to base op class
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* Update to use QuantizedTensor class
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* Apply suggestions from code review
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* Review suggestions from @ptrendx

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* Fix linter warnings
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* Use weight dtype as default compute dtype
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* Fix linter warnings
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* Handle Float8Tensor when casting module dtype

Keep data in Float8Tensor and only change nominal dtype. Monkey-patch PyTorch module casting functions to handle Float8Tensor. Add tests.
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* Respect autocast dtype in linear op
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* Suppress linter warning
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* Suppress linter warning
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* Tweak comments

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* Add base class for tensor proxies
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* Move tensor detaching logic to tensor proxy base class
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* Use Python wrappers to PyTorch extensions
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* Include transpose caching logic in proxy encode function
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* Debug dimension mismatch with amax history
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* Move dequantize logic to proxy_decode func
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* Rename to "QuantizedTensor"
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* Rename "proxy_detach" to "detach"
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* Include transpose cache in detach and clone funcs
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* Fix linter warnings
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* Update FP8 workspaces with QuantizedTensor functions
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* Move logic for FP8 transpose cache in FP8 workspaces to base class
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* Remove cast-transpose logic from linear op
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* Remove unnecessary args for Float8Tensor when using FP8 attr dict
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* Remove __torch_function__ to QuantizedTensor
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* Fix linter warnings
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* Update tests/pytorch/test_float8tensor.py
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* Debug FP8 transpose test
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* Debug cast functions
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* Add op for in-place add
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* Add op for in-place add
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* Add op that adds extra output to fuser
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* Add fused op for GEMM+bias+add
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* Add fused op for dgrad+add
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* Add documentation
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* Fix linter warnings
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* Review suggestions from @ptrendx

Output tensor dtype and device take precedence over weight tensor in linear functional API. Move some index calculation to fuser constructor. Avoid some unnecessary dereferences.
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* Debug test failures
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* Update transformer_engine/pytorch/ops/fuser.py
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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

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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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* Clear saved tensor data in linear op after bprop
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* Fix Pylint error
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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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---------
Signed-off-by: Tim Moon <tmoon@nvidia.com>
Signed-off-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
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