Summary:
Currently `get_c2_fbandroid_xplat_compiler_flags()` is reading the `caffe2.strip_glog` buckconfig which we want to get rid of.
This diff removes the `fbandroid_compiler_flags` arg and merges it with compiler_flags with a nested select and the select version of the method
The goal is to get rid of all the usages of `get_c2_fbandroid_xplat_compiler_flags()` so that we can get rid of the `caffe2.strip_glog` buckconfig
Test Plan: CI
bifferential Revision: D84626885
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165558
Approved by: https://github.com/malfet
Summary:
Currently `get_c2_fbandroid_xplat_compiler_flags()` is reading the `caffe2.strip_glog` buckconfig which we want to get rid of.
This diff removes the `fbandroid_compiler_flags` arg and merges it with compiler_flags with a nested select and the select version of the method
The goal is to get rid of all the usages of `get_c2_fbandroid_xplat_compiler_flags()` so that we can get rid of the `caffe2.strip_glog` buckconfig
Test Plan: CI
Differential Revision: D84626885
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165558
Approved by: https://github.com/malfet
Summary:
Previously, many arvr targets transitively depended on c10, not c10_ovrsource,
because they either explicitly depended on c10 (because they didn't know
better) or they depended on legacy Caffe2, which never got the ovrsource
treatment. So we found all these spots (driven by D82283623) and forced them
to query arvr mode to figure out which one they should use. The goal is you
NEVER have both targets in the same build rule at the same time.
This diff could be reverted if D82224960 works out but I haven't gotten it to work yet.
Test Plan: sandcastle
Reviewed By: EscapeZero
Differential Revision: D82390436
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164128
Approved by: https://github.com/albanD, https://github.com/malfet
Summary: Platform args was a buck1 concept that we decided to port over to buck2 in order to make the migration easier. However, platforms args existing in the repo blocks some buck modernization like modefile free efforts, so we're trying to get rid of the usage.
Test Plan:
CI
Rollback Plan:
Differential Revision: D82470032
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163086
Approved by: https://github.com/malfet, https://github.com/8Keep
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>
Meta:
`fbsource//xplat/caffe2:gen_torch_vulkan_spv_cpp` takes on average 2 min to build and is one of topmost slow targets in fbandroid.
See: https://fb.workplace.com/groups/2840058936242210/posts/4067730240141734
This target hat to run locally because it uses manifold backend for dotslash. This diff moves the `glslc` to cas backend so that it can run on RE.
Here are commands executed:
```
% manifold get dotslash_glslc/flat/glslc-linux-x86_64.tar.gz
% manifold get dotslash_glslc/flat/glslc-macos-v2024_4.tar.gz
% manifold get dotslash_glslc/flat/glslc-windows-v2024_3.tar
% ls
-rw-r--r-- 1 navidq staff 2.0M Jun 12 10:02 glslc-linux-x86_64.tar.gz
-rw-r--r-- 1 navidq staff 4.7M Jun 12 10:03 glslc-macos-v2024_4.tar.gz
-rw-r--r-- 1 navidq staff 4.4M Jun 12 10:03 glslc-windows-v2024_3.tar
% frecli --use-case dotslash cas upload-blob --skip-find-missing glslc-linux-x86_64.tar.gz
ea5d674e0e7e9782be3f5c309e3484732e5b3a331cbe3258f3e929002811627b:2072937
% frecli --use-case dotslash cas upload-blob --skip-find-missing glslc-macos-v2024_4.tar.gz
1331dc691835e4676832b7c21ef669083a3acc8856981583d0698192f466c51a:4898649
% frecli --use-case dotslash cas upload-blob --skip-find-missing glslc-windows-v2024_3.tar
76181fbb1ce5c62d0c905db26df3a64e999d0baff2e93270775921daa91e3a1a:4585984
```
Differential Revision: [D76513735](https://our.internmc.facebook.com/intern/diff/D76513735/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155832
Approved by: https://github.com/GregoryComer
Summary: The goal of this PR and future follow-up PRs is to group a set of header files required by AOTInductor Standalone in a separate directory, ensuring they are implemented in a header-only manner.
Test Plan: CI
Bifferential Revision: D75756619
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154850
Approved by: https://github.com/janeyx99
Summary:
CUDA Post: https://fb.workplace.com/groups/ai.efficiency.tools.users/permalink/2020094788475989/
# Context
In this diff, we want to enable the on-demand mode of memory snapshot to allow user to trace any remote process via dyno command line.
# Design decision
**How do we send on-demand signal to remote process**
We leverage the dyno-Kineto approach.
Since dyno is running on all machine in Meta, it can send a request to the remote machine to start the Kineto.
Kineto will start another thread for memoryProfiler (https://fburl.com/code/dxsmmrok)
**why we use different approach as CUDA**
On CUDA side, we are using pybind to load torch Module and invoke the python api to start/stop the profiling. However, this requires us to compile the whole torch binary in the predictor which is not recommended by runtime(andruwang)
Thus, we decide to use the CPP api directly to avoid un-necessary dependency
**why the snapshot is saved as json string directly instead of pickle**
Pickle is primarily designed for use with Python and doesn't have well support in cpp. Also, it is hard for user to download the snapshot file and open locally.
Due to the dependency issue, it is hard to import the gzip/pickle library to decode the data. Thus, let's use JSON for now. I will work on the visualizer to fasten the render and support other format later.
**Plan**:
* Now, we will encoded file into gz for MTIA ondemand only and update the visualizer to support both type.
* Update auto-trace and CUDA side to encode in gzip as well
* Fully remove pickle dependency.
Test Plan:
# Remote cogwheel test
Servicelab: https://fburl.com/servicelab/pckux7a3
snapshot file manifold: https://fburl.com/manifold/fnotk18c
snapshot file in pastry: P1805522232
Visualization on D74399684
{F1977786422}
# Local Predictor Test
url: https://fburl.com/pytorch_memory_visualizer/y06kskkm
{F1977787329}
Differential Revision: D74179606
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153171
Approved by: https://github.com/sraikund16
Summary:
Profiler side of memory snapshot.
1. Add API to actually do snapshot when client interface is called
2. Add ifdefs to builds so that kineto hooks snapshot correctly.
Design Philosophy: There is one interesting part of this implementation and it is during export. For export we are callign the python impl of the export rather than CPP even though we are already in CPP. This is because it is better to simply have one path of export rather than 2. Personally, I want there to be parity between auto-trace and on-demand so it if we can limit the side paths then we will have an easier time maintaining this relationship
Test Plan: {F1976563426}
Reviewed By: sanrise
Differential Revision: D70733247
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150559
Approved by: https://github.com/sanrise
Summary:
`--no-as-needed` is not available in ld64.lld
Applying this on all macos is potentially too broad? I am not sure if `fbcode//mode/mac` uses a different linker, but arvr mode for sure uses ld64.lld.
Test Plan: CI / used for a macOS build on top of the stack.
Differential Revision: D71315125
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149421
Approved by: https://github.com/colesbury
Summary:
X-link: https://github.com/pytorch/executorch/pull/7040
Accomplished by importing relevant files from c10 into
executorch/runtime/core/portable_type/c10, and then using `using` in
the top-level ExecuTorch headers. This approach should keep the
ExecuTorch build hermetic for embedded use cases. In the future, we
should add a CI job to ensure the c10 files stay identical to the
PyTorch ones.
ghstack-source-id: 260047850
exported-using-ghexport
Test Plan: builds
Differential Revision: D66106969
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144111
Approved by: 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
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
