GitLab

* Check tensor-recipe compatibility
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* Tensor class in recipe, checking for *Base
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* Warn about recipe change
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* Disable warning for recipe wrappers
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* Test recipe change
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* Use QuantizedTensorBase
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* Fix circular import
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* Revert previous circular import fix
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* Fix pytorch imports in common
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* Let quantizer know about the recipe
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* Fix imports
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* check in per-tensor current scaling full recipe
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setup basics of current scaling quantizer in python level
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add test case for current scaling dequantize
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finish linear layer fwd bwd test, determined error with bf16
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achieved zero tolerance for Linear by specify gemm use_split_accumulator config
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enable layernormlinear with current scaling, pass bitwise test
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refactor test case code
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make current scaling quantizers distrbuted, pass distributed linear&layernormlinear tests
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bug fix: use cached fp8 recipe in backward
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fix layernorm_mlp with current scaling, fix activation_helper with current scaling
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support detailed numerical settings from recipe to quantization kernel
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resolving MR comments
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recipe naming
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* resolve mr comments, remove IS_CURRENT_SCALING template from kernels
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* resolve mr comments, make current scaling c++ test cases
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* add current scaling to test_numerics.py, skip act recomp and grouped linear
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* add benchmark for quantizer
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* add benchmarks for linear layer
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* bug fix, typo
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* resolve more mr comments
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* avoid potential race condition by not using from_blob to construct amax tensor in C++
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* resolve more comments
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* Debug linter warnings and license check
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* Debug import error in FP8 tensor test
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* Debug compilation error with CUDA 12.1 for Turing
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* resolve mr comments, fix activation cast fusion
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* resolve comments, add NVTEQuantizationParams for compute scale
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* remove is_current_scaling check totally from common folder
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* remove benchmarks, will contribute in another repo
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* adjust cs default recipe config
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* adjust comments in test
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* Remove current scaling mode from core lib
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* Refactor current-scaling-specific logic in core C++ lib

Move amax and scale update functions out of casting functions, and put into dedicated current-scaling source file. Add general API for accessing quantization config object.
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* Add missing header in C++ tests
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* Disable test config with FP8 transpose on Blackwell
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* Fix compilation error in C++ 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

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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

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* Debug unfused linear op
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* Consolidate unfused ops in submodule
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* Disable GEMM+bias fusion with FP32 activations

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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"

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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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* 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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* Review suggestions from @sudhakarsingh27 and @ksivaman

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* Update transformer_engine/pytorch/ops/__init__.py
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* Apply formatting
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* Make update kernel in-place for PyTorch
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