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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
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* Fix bugs uncovered by test
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* Fix reshaping columnwise_data of MXFP8Tensor
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* Fix taking dtype from weight or grad_output in BasicLinear._functional_backward
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* Added MCore fsdp support for TE
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* Refactored based on new MCore FSDP
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* Code cleanup and extended across modules
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* Added FSDP suport
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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`

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* Handle saving internal tensors

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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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* Optimize _common.reshape by removing redundant operations
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* Use view instead of reshape when possible
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* Simplify convert_tensor (requires testing)
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* Remove reshape
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* Refactor existing code to use maybe_quantize where possible
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* Check if tensor is any kind of quantized tensor
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* Revert "Check if tensor is any kind of quantized tensor"

This reverts commit cf09d61ffe41f38720d820ddc4f011f9dc1fb56e.
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* Add FP8 current scaling to te.Sequential tests
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* Debug MXFP8 tests
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* Flatten basic op params during fuser init
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(cherry picked from commit 949abe97070721b1da5117903067608250f5fb61)

* Add caching for is_non_tn_fp8_gemm_supported
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(cherry picked from commit fd830ae24ffbd2d0727010b1a8a119ca72f61ce5)

* Pass fuser to _OperationFuserAutogradFunction.forward and moving computation to __init__
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* Pass basic_op_kwargs and is_grad_enabled as parameters rather than in fuser
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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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Activation ops support fusing backward pass with quantize
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* Update LayerNormLinear
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