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

This reverts commit bfab8c67

.
Signed-off-by: Jeremy Berchtold <jberchtold@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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* 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
latency behind GEMM execution.
Signed-off-by: djns99 <40156487+djns99@users.noreply.github.com>

* fix: Working userbuffer overlapping API
Signed-off-by: djns99 <40156487+djns99@users.noreply.github.com>

* fix: Fix overwriting bulk overlap UB object for layernormLinear
Signed-off-by: djns99 <40156487+djns99@users.noreply.github.com>

* 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
Signed-off-by: djns99 <40156487+djns99@users.noreply.github.com>

* fix: Fix the UB fused ops tests
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* fix: Fix linter errors
Signed-off-by: djns99 <40156487+djns99@users.noreply.github.com>

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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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* Compute amax in activation kernels when the output pointer is provided, even for non-fp8 outputs
Signed-off-by: Jan Bielak <jbielak@nvidia.com>
(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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* Refactor Quantizer::create_tensor function
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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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* Add coerce_tensor functions for MXFP8 and DSv3
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* Avoid quantizing empty tensors
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* Use consistent shapes for FP8 transposes
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* In attention impl, construct FP8 tensors with pre-initialized scale-invs
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* Initialize MXFP8 scales to zero
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* Store copy of quantizer when creating quantized tensors
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* Fix linter warnings
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* Make sure quantized tensors have private quantizer

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* Fixed integer overflow when computing offsets
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* Fixed conflicts
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* Fixed dBias accumulation error due to initialization. Minor code refactoring
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* Fixed rowwise dbias error
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* Changed ptx API
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* Added a struct for two packed FP8 values
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* Rolled back to scalar code for columnwise scaling due to its better performance
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* Minor corrections
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* Rebased on main
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* Fixes per code review
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* Computed activations are now numerically truncated to InputType before scaling. Improved test suite.
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* Minor refactoring
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* Modified mismatches checks of MXFP8 to address FP8 numerics
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* Add run-time version checks in cuBLAS GEMM wrapper
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* Fix clearing tensor data in backward removing is_first_op
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* [PyTorch|common] Implement unpadding kernel for FP8

1. Add multi-tensor unpadding kernel
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* Support MXFP8 and handle empty matrices
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[PyTorch] FP8 Subchannel Recipe With FP8 Gather And Configurable Scaling Factor Tensor Swizzling (#1707)

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for more information, see https://pre-commit.ci



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Signed-off-by: Alp Dener <adener@nvidia.com>

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for more information, see https://pre-commit.ci



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for more information, see https://pre-commit.ci



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for more information, see https://pre-commit.ci



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Signed-off-by: Alp Dener <adener@nvidia.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

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Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>