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Add test for `LayerNormMLP` implementation using `te.ops.Sequential` to `test_fusible_ops.py` (#1924)

* Add e2e test for LayerNormMLP implemented with te.Sequential
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Fix bugs uncovered by test
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

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* Fix reshaping columnwise_data of MXFP8Tensor
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Fix taking dtype from weight or grad_output in BasicLinear._functional_backward
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

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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
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* 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
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* 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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* Support L2Norm basic op
Signed-off-by: Evgeny <etsykunov@nvidia.com>

* Add L2Norm module wrapper
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* Expose qk_norm to MHA nd transformer laayer
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* Move tests into separate file
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* fix pass
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* Add license
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* Remove  module
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* Resollve comments
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* Linear op avoids saving input tensor if weight grad is not needed
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* 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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* Add support for fused dSwiGLU-cast-transpose
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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

Rename "CastFloat8" op to "Quantize". Add more fine-grained control for SM margin. Add docs for legacy sequence_parallel kwarg.
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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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* 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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* 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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* Remove unused file
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* Add license boilerplate
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* Fuse GEMM+bias in row TP

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* Rename pipeline to fuser

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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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* Initial functional implementation of linear 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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* 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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