code..
..
That's it.
but install this
pip install yt-dlp
Thank you!!!
Computer Vision & Machine Learning Research Laboratory
code..
..
That's it.
but install this
pip install yt-dlp
Thank you!!!
toy model
.
configuration
..
in_proj
(ColwiseParallel):out_proj
(RowwiseParallel):Key Corrections and Clarifications:
This corrected diagram and explanation more accurately represent the sequence parallelism process as described in the original comment. It shows how the input is gathered, processed in parallel, and then the output is scattered, allowing for efficient parallel processing of the entire sequence across GPUs.
FSDP and TP are complementary parallelism techniques:
When combined:
Textual Representation:
GPU 1 GPU 2 GPU 3 GPU 4 +--------+ +--------+ +--------+ +--------+ | L1 P1 | | L1 P2 | | L2 P1 | | L2 P2 | | TP1 | | TP2 | | TP1 | | TP2 | +--------+ +--------+ +--------+ +--------+ | | | | +------------+ +------------+ Layer 1 Layer 2 L1, L2: Layers 1 and 2 P1, P2: Parameter shards (FSDP) TP1, TP2: Tensor Parallel splits
Let's use a simplified example with just 2 data points and walk through the process with actual numbers. This will help illustrate how gradients are calculated and accumulated for a batch.
Let's assume we have a very simple model with one parameter w
, currently set to 1.0. Our loss function is the square error, and we're using basic gradient descent with a learning rate of 0.1.
Data points:
Batch size = 2 (both data points in one batch)
Step 1: Forward pass
Step 2: Calculate losses
Step 3: Backward pass (calculate gradients)
Step 4: Accumulate gradients
Step 5: Update weight (once for the batch)
So, after processing this batch of 2 data points:
This process would then repeat for the next batch. In this case, we've processed all our data, so this completes one epoch.
TorchOps.cpp.inc
?torch-mlir
dialect. It is typically generated from .td
(TableGen) files that define the dialect and its operations..td
(TableGen) files describe MLIR operations in a high-level, declarative form, and the cmake
build process automatically generates .cpp.inc
files (like TorchOps.cpp.inc
) from these .td
files.TableGen
tool processes .td
files that define the operations and attributes for the torch
dialect.mlir-tblgen
tool is invoked to generate various .inc
files, including TorchOps.cpp.inc
.The TorchOps.cpp.inc
file is usually generated in the build
directory under the subdirectories for the torch-mlir project. For example:
build/tools/torch-mlir/lib/Dialect/Torch/IR/TorchOps.cpp.inc
This file gets included in the compiled source code to provide the implementation of the Torch dialect operations.
If the file is missing, it's likely because there was an issue in the build process. Here’s how to ensure it’s generated:
Ensure CMake and Ninja Build: Make sure the CMake and Ninja build process is working correctly by following the steps we discussed earlier. You can check that the TorchOps.cpp.inc
file is generated by looking in the build directory:
ls build/tools/torch-mlir/lib/Dialect/Torch/IR/
Check for TableGen Files: Make sure that the .td
files (such as TorchOps.td
) are present in the source directory. These are used by mlir-tblgen
to generate the .cpp.inc
files.
If TorchOps.cpp.inc
or similar files are not generated, ensure:
ninja
or make
.mlir-tblgen
is being invoked during the build process (you should see log messages referencing mlir-tblgen
).