Replace (more) exact calculation with hardware approximation.
Benefits:
Reduced code size.
Improved performance for certain scenarios.
Experiments show low reduction in precision.
Experiments show no significant performance regressions. bfloat16 as well as float16 related calculations may benefit largely from this change.
Co-author: @mhalk @amd-hhashemi
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165589
Approved by: https://github.com/jeffdaily
----
- `cmake_dependent_option` condition should be `USE_ROCM OR (USE_CUDA AND NOT MSVC)` (similar to the one for flash attention)
- Default settings should be user overridable, i.e. even if one builds for SM_10, they should be able to pass `USE_FBGEMM_GENAI=0` and skip the build
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164165
Approved by: https://github.com/Skylion007
Note. This is a replica PR of #155901 which will be closed. I had to create a new PR in order to add it into my ghstack as there are some later commits which depend on it.
### Summary
🚀 This PR moves the prioritized text linker optimization from setup.py to cmake ( and enables by default on Linux aarch64 systems )
This change consolidates what was previously manual CI logic into a single location (cmake), ensuring consistent behavior across local builds, CI pipelines, and developer environments.
### Motivation
Prioritized text layout has measurable performance benefits on Arm systems by reducing code padding and improving cache utilization. This optimization was previously triggered manually via CI scripts (.ci/aarch64_linux/aarch64_ci_build.sh) or user-set environment variables. By detecting the target architecture within setup.py, this change enables the optimization automatically where applicable, improving maintainability and usability.
Note:
Due to ninja/cmake graph generation issues we cannot apply the linker file globally to all targets to the targets must be manually defined. See CMakeLists.txt the main libraries torch_python, torch, torch_cpu, torch_cuda, torch_xpu have been targetted which should be enough to maintain the performance benefits outlined above.
Co-authored-by: Usamah Zaheer <usamah.zaheer@arm.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160078
Approved by: https://github.com/seemethere
Note. This is a replica PR of #155901 which will be closed. I had to create a new PR in order to add it into my ghstack as there are some later commits which depend on it.
### Summary
🚀 This PR moves the prioritized text linker optimization from setup.py to cmake ( and enables by default on Linux aarch64 systems )
This change consolidates what was previously manual CI logic into a single location (cmake), ensuring consistent behavior across local builds, CI pipelines, and developer environments.
### Motivation
Prioritized text layout has measurable performance benefits on Arm systems by reducing code padding and improving cache utilization. This optimization was previously triggered manually via CI scripts (.ci/aarch64_linux/aarch64_ci_build.sh) or user-set environment variables. By detecting the target architecture within setup.py, this change enables the optimization automatically where applicable, improving maintainability and usability.
Note:
Due to ninja/cmake graph generation issues we cannot apply the linker file globally to all targets to the targets must be manually defined. See CMakeLists.txt the main libraries torch_python, torch, torch_cpu, torch_cuda, torch_xpu have been targetted which should be enough to maintain the performance benefits outlined above.
Co-authored-by: Usamah Zaheer <usamah.zaheer@arm.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160078
Approved by: https://github.com/seemethere
This pull request adds the following ops for sparse matrices using Eigen library:
```python
add(a_csr, b_csr)
add(a_csc, b_csc)
addmm(c_csr, a_csr, b_csr)
addmm(c_csr, a_csr, b_csc)
addmm(c_csr, a_csc, b_csc)
addmm(c_csr, a_csc, b_csr)
addmm(c_csc, a_csr, b_csr)
addmm(c_csc, a_csr, b_csc)
addmm(c_csc, a_csc, b_csc)
addmm(c_csc, a_csc, b_csr)
```
Currently, the operations for sparse matrices on CPU are available through MKL only. The non-existence of MKL on `aarch64` causes the unavailability of these ops on any machines with ARM based CPUs, including Apple Silicon, AWS Graviton and NVIDIA Grace. This PR addresses this issue by using Eigen as a backend for the above ops.
This is a re-factored version of my previous PR #101814. The main difference with the old one, this does not enable Eigen by default.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155357
Approved by: https://github.com/pearu, https://github.com/eqy
Co-authored-by: Eli Uriegas <eliuriegas@meta.com>
This pull request adds the following ops for sparse matrices using Eigen library:
```python
add(a_csr, b_csr)
add(a_csc, b_csc)
addmm(c_csr, a_csr, b_csr)
addmm(c_csr, a_csr, b_csc)
addmm(c_csr, a_csc, b_csc)
addmm(c_csr, a_csc, b_csr)
addmm(c_csc, a_csr, b_csr)
addmm(c_csc, a_csr, b_csc)
addmm(c_csc, a_csc, b_csc)
addmm(c_csc, a_csc, b_csr)
```
Currently, the operations for sparse matrices on CPU are available through MKL only. The non-existence of MKL on `aarch64` causes the unavailability of these ops on any machines with ARM based CPUs, including Apple Silicon, AWS Graviton and NVIDIA Grace. This PR addresses this issue by using Eigen as a backend for the above ops.
This is a re-factored version of my previous PR #101814. The main difference with the old one, this does not enable Eigen by default.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155357
Approved by: https://github.com/pearu, https://github.com/eqy
Merge the recent commits of FBGEMM and remove unnecessary CMake code.
Specifically, we
1. enable `fbgemm_autovec` since the target is now correctly handled.
2. remove option `USE_FAKELOWP` which is not used.
3. remove `CAFFE2_COMPILER_SUPPORTS_AVX512_EXTENSIONS` check.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158210
Approved by: https://github.com/q10
Refactors how the enablement/disablement of CK Gemms and SDPA works.
- Adds USE_ROCM_CK_GEMM compile flag for enabling CK gemms.
- USE_ROCM_CK_GEMM is set to True by default on Linux
- Updates USE_CK_FLASH_ATTENTION to USE_ROCM_CK_SDPA.
- USE_ROCM_CK_SDPA is set to False by default
- (USE_CK_FLASH_ATTENTION still works for now, but will be deprecated in a future release)
- Prevents these CK libraries from being used unless pytorch has been built specifically with the functionality AND is running on a system architecture that supports it.
- the getters for these library backends will also do some validity checking in case the user used an environment variable to change the backend. If invalid, (i.e. one of the cases mentioned above is false) the backend will be set as the current non-CK default
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152951
Approved by: https://github.com/eqy, https://github.com/jeffdaily, https://github.com/m-gallus
Co-authored-by: Jeff Daily <jeff.daily@amd.com>
Co-authored-by: Jithun Nair <jithun.nair@amd.com>
Co-authored-by: Jane (Yuan) Xu <31798555+janeyx99@users.noreply.github.com>
This PR uses `find_library` to replace `find_path`.
It also searches for NVSHMEM host lib and device lib separately.
Tested against system install location: /usr/local/lib and /usr/local/include.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157695
Approved by: https://github.com/Skylion007
ghstack dependencies: #157513
This PR uses `find_library` to replace `find_path`.
It also searches for NVSHMEM host lib and device lib separately.
Tested against system install location: /usr/local/lib and /usr/local/include.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157695
Approved by: https://github.com/Skylion007
ghstack dependencies: #157513
Replace https://github.com/pytorch/pytorch/pull/138947 for re-import.
Replaces https://github.com/ROCm/pytorch/pull/1592
This PR contains the initial implementation of SDPA with composable_kernel backend. The CK path can be forced by simply calling torch.backends.cuda.preferred_rocm_fa_library("ck"). Similarly, you can force the incumbent aotriton implementation by passing in "aotriton" or "default". As you'd expect, not setting this option will result in aotriton to be used as the backend. In the case of CK, if pytorch deems flash attention usable, then it will use the CK path in all the same places aotriton would have been used. This PR makes no changes to the heuristics which select which attention scheme to use (i.e. flash attention vs memory efficient attention vs math etc etc). It only gets called when flash attention is both enabled (via USE_FLASH_ATTENTION) and is selected at runtime by the existing heuristics.
Files located in pytorch/aten/src/ATen/native/transformers/hip/flash_attn/ck/mha* have been pulled from https://github.com/Dao-AILab/flash-attention courtesy of @tridao's hard work who is the co-author
NOTE: In order to use this backend, the user MUST set USE_CK_FLASH_ATTENTION=1 in their environment when they build PyTorch.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143695
Approved by: https://github.com/malfet
Co-authored-by: Andy Lugo <Andy.LugoReyes@amd.com>
Co-authored-by: Jithun Nair <jithun.nair@amd.com>
Description:
1. Quantize Linear Layer Weights to 4-bits:
Quantize the weights of the Linear layer to 4 bits, using symmetric quantization.
Pack two 4-bit weights into one uint8 container.
Choose a quantization scheme (channel-wise or group-wise), with the group size being a multiple of 32.
2. Prepare Quantized Weights, Scales, and Optional Bias:
After quantizing, obtain the quantized_weights, scales, and groupsize.
If the original Linear layer has a bias, prepare it as well.
3. Pack the Weights Efficiently:
Use torch.ops.aten._dyn_quant_pack_4bit_weight to optimally pack the weights, scales, and optional bias.
```python
packed_weights = torch.ops.aten._dyn_quant_pack_4bit_weight(weight, scales_and_zeros, bias, groupsize, in_features, out_features)
```
Input parameters should include:
in_features and out_features (the same as the Linear layer’s corresponding parameters).
4. Perform Dynamic Quantized Matrix Multiplication:
Use torch.ops.aten._dyn_quant_matmul_4bit to perform matrix multiplication with quantized weights.
```python
output = torch.ops.aten._dyn_quant_matmul_4bit(input, packed_weights, groupsize, in_features, out_features)
```
Inputs required include:
The input tensor, packed_weights , groupsize, and the in_features and out_features.
API Usage: https://github.com/pytorch/pytorch/issues/143289
Model Perf :
7B Transformer model:
Prefill : 340 t/s
Decode : 40 t/s
2B Transformer model
Prefill : 747 t/s
Decode : 80 t/s
Tests:
python test/test_linalg.py -k test__dyn_quant_pack_4bit_weight
Ran 1 test in 0.016s
OK
python test/test_linalg.py -k test__dyn_quant_matmul_4bit
Ran 8 tests in 0.077s
OK
python test/test_linalg.py -k test_compile_dyn_quant_matmul_4bit
Ran 8 tests in 11.454s
Change-Id: Ia1672bad5e6ec94e64d8bb1971395d60f4b3a452
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134124
Approved by: https://github.com/digantdesai, https://github.com/malfet
Description:
1. Quantize Linear Layer Weights to 4-bits:
Quantize the weights of the Linear layer to 4 bits, using symmetric quantization.
Pack two 4-bit weights into one uint8 container.
Choose a quantization scheme (channel-wise or group-wise), with the group size being a multiple of 32.
2. Prepare Quantized Weights, Scales, and Optional Bias:
After quantizing, obtain the quantized_weights, scales, and groupsize.
If the original Linear layer has a bias, prepare it as well.
3. Pack the Weights Efficiently:
Use torch.ops.aten._dyn_quant_pack_4bit_weight to optimally pack the weights, scales, and optional bias.
```python
packed_weights = torch.ops.aten._dyn_quant_pack_4bit_weight(weight, scales_and_zeros, bias, groupsize, in_features, out_features)
```
Input parameters should include:
in_features and out_features (the same as the Linear layer’s corresponding parameters).
4. Perform Dynamic Quantized Matrix Multiplication:
Use torch.ops.aten._dyn_quant_matmul_4bit to perform matrix multiplication with quantized weights.
```python
output = torch.ops.aten._dyn_quant_matmul_4bit(input, packed_weights, groupsize, in_features, out_features)
```
Inputs required include:
The input tensor, packed_weights , groupsize, and the in_features and out_features.
API Usage: https://github.com/pytorch/pytorch/issues/143289
Model Perf :
7B Transformer model:
Prefill : 340 t/s
Decode : 40 t/s
2B Transformer model
Prefill : 747 t/s
Decode : 80 t/s
Tests:
python test/test_linalg.py -k test__dyn_quant_pack_4bit_weight
Ran 1 test in 0.016s
OK
python test/test_linalg.py -k test__dyn_quant_matmul_4bit
Ran 8 tests in 0.077s
OK
python test/test_linalg.py -k test_compile_dyn_quant_matmul_4bit
Ran 8 tests in 11.454s
Change-Id: Ia1672bad5e6ec94e64d8bb1971395d60f4b3a452
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134124
Approved by: https://github.com/digantdesai, https://github.com/malfet
Description:
1. Quantize Linear Layer Weights to 4-bits:
Quantize the weights of the Linear layer to 4 bits, using symmetric quantization.
Pack two 4-bit weights into one uint8 container.
Choose a quantization scheme (channel-wise or group-wise), with the group size being a multiple of 32.
2. Prepare Quantized Weights, Scales, and Optional Bias:
After quantizing, obtain the quantized_weights, scales, and groupsize.
If the original Linear layer has a bias, prepare it as well.
3. Pack the Weights Efficiently:
Use torch.ops.aten._dyn_quant_pack_4bit_weight to optimally pack the weights, scales, and optional bias.
```python
packed_weights = torch.ops.aten._dyn_quant_pack_4bit_weight(weight, scales_and_zeros, bias, groupsize, in_features, out_features)
```
Input parameters should include:
in_features and out_features (the same as the Linear layer’s corresponding parameters).
4. Perform Dynamic Quantized Matrix Multiplication:
Use torch.ops.aten._dyn_quant_matmul_4bit to perform matrix multiplication with quantized weights.
```python
output = torch.ops.aten._dyn_quant_matmul_4bit(input, packed_weights, groupsize, in_features, out_features)
```
Inputs required include:
The input tensor, packed_weights , groupsize, and the in_features and out_features.
API Usage: https://github.com/pytorch/pytorch/issues/143289
Model Perf :
7B Transformer model:
Prefill : 340 t/s
Decode : 40 t/s
2B Transformer model
Prefill : 747 t/s
Decode : 80 t/s
Tests:
python test/test_linalg.py -k test__dyn_quant_pack_4bit_weight
Ran 1 test in 0.016s
OK
python test/test_linalg.py -k test__dyn_quant_matmul_4bit
Ran 8 tests in 0.077s
OK
python test/test_linalg.py -k test_compile_dyn_quant_matmul_4bit
Ran 8 tests in 11.454s
Change-Id: Ia1672bad5e6ec94e64d8bb1971395d60f4b3a452
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134124
Approved by: https://github.com/digantdesai, https://github.com/malfet
# Motivation
This PR aims to maintain backward compatibility when building PyTorch XPU with the old and new compilers.
# Additional Context
The details are described here. The new compiler (2025.0.0) has some breaking changes compared with the old compiler(2024.1), for examples:
1. On Windows, sycl library is named `sycl7.lib` in the old compiler but is named `sycl.lib` in the new compiler.
2. On Linux, in order to support ABI=0, we have to link `libsycl-preview.so` in the old compiler but we could link `libsycl.so` in the new compiler to have the same ABI compatibility.
3. We added a macro `SYCL_COMPILER_VERSION` to support our new code has good backward compatibility with the old compiler. Now the new feature(Event elapsed_time, memory summary, and device architecture property) introduced by the new compiler will be controlled within the macro `SYCL_COMPILER_VERSION`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139258
Approved by: https://github.com/EikanWang, https://github.com/atalman, https://github.com/gujinghui
