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Revert "[Common] PDL for Quantization Kernels (#2001)"

This reverts commit bfab8c67

.
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Fix incorrect version checks for atomic GEMM
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Fix typo
Signed-off-by: Tim Moon <tmoon@nvidia.com>

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

* Add BackwardAddRMSNorm fused operation
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Try to optimize register usage in kernels
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Add separate BackwardAdd stage for the fused backward add
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

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

* Support use of `alpha` and `beta` in `tex.generic_gemm`
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Support use of `alpha` and `beta` in `general_gemm`
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Support use of `alpha` and `beta` in `BasicLinear._functional_forward` and `BasicLinear._functional_backward`
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Add `ForwardLinearScaleAdd` fusion
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Add `BackwardLinearScale` fusion
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Apply suggestions from code review
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* Remove calls to `validate_gemm_scale` from `BasicLinear`
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

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Add launch bounds to swizzle kernel, use empty scale inv
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* reduce driver calls
Signed-off-by: Xin Yao <xiny@nvidia.com>

* reduce driver calls
Signed-off-by: Xin Yao <xiny@nvidia.com>

* adjust tests to capture this
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* fix: update grad_output quant to avoid redundant work
Signed-off-by: kshitij12345 <kshitijkalambarkar@gmail.com>

* add test
Signed-off-by: kshitij12345 <kshitijkalambarkar@gmail.com>

* don't keep only columnwise quant if requires_dgrad=False
Signed-off-by: kshitij12345 <kshitijkalambarkar@gmail.com>

* fix stray merge
Signed-off-by: kshitij12345 <kshitijkalambarkar@gmail.com>

* fix for ctx.use_bias is True case
Signed-off-by: kshitij12345 <kshitijkalambarkar@gmail.com>

* Skip if FP8 not available
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

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* enable PDL for blockwise qunatization kernels
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* add comment
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* Compute amax in normalization forward in current scaling in untuned kernels
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* fix: Add stream synchronization before destroying MPI communicator (#1979)
Signed-off-by: djns99 <40156487+djns99@users.noreply.github.com>

* feat: Implement column-wise userbuffer overlap for comm+GEMM operations

Add support for overlapping column-wise allgather communication with GEMM
operations to improve training performance:

* **Core infrastructure changes:**
  - Update bulk_overlap_columnwise_ag() to accept explicit stream parameter
  - Modify userbuffers send/recv loops to use rank-ordered iteration
  - Add userbuffers_send_all/recv_all function declarations

* **Python integration:**
  - Add bulk_overlap_ag_with_external_gemm() C++ extension function
  - Expose new overlap function via pybind11 bindings
  - Update overlap method configurations to include more ring_exchange ops

* **LayerNorm MLP optimization:**
  - Enable column-wise quantization for FC2 gradient output
  - Implement overlap of allgather communication with FC2 DGRAD GEMM
  - Use fill_userbuffers_buffer_for_all_gather for efficient buffering

This optimization allows overlapping communication and computation phases
more effectively, reducing training wall-clock time by hiding allgather
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* fix: Working userbuffer overlapping API
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* fix: Fix overwriting bulk overlap UB object for layernormLinear
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* fix: Update external overlap to use tp size instead of nvsize to determine number of copies
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* fix: Fix linter error
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* fix: Explanatory comments of overlap logic
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* fix: Fix the UB fused ops tests
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* PDL for MXFP8 Quantize
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* for loop
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* bulk alloc
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* multi-tensor swizzle
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* pad zeros in swizzle kernels
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* unify single- and multi-tensor swizzle
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* fix empty tensor list
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* fix bug for col swizzle
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* check context & fix signifiers
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* Add basic kernel for swapping first two tensor dims
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* Add NVRTC kernel for swapping first dims
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* Tune kernel
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* Make sure writes are contiguous
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Fix tensor numel() return dtype

The original dytpe int would be an issue if the tensor element numbers are larger than int32's range (which is not a super large number in real workloads)
Signed-off-by: Shang Zhang <shangz@nvidia.com>

* fix bug if all values<0
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* minor fix
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* Compute amax in normalization kernels as long as the pointer is provided, even if using non quantized output
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* Fuse amax computation into normalization forward
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* Compute amax in activation kernels when the output pointer is provided, even for non-fp8 outputs
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(cherry picked from commit 9f13fe2fefc58cae93bc467d87d01ecf792a0381)

* Initialize metatensor values
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* Fuse computation of amax into the activation kernel for fp8 current scaling
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* Zero out amax in `create_hp_tensor_with_amax` instead of relying on `Float8CurrentScalingQuantizer.__init__` to zero-initialize it
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* remove reciprocal op
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* Fix bug when constructing FP8 tensor
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* Add quantize function to C++ quantizers
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* Prototype function to coerce Python quantized tensors to match quantizer
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* Use quantizer class in tex.quantize
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* Add FP8 current scaling support for activation backward
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* Disable quantized GEMM output with FP8 current scaling
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