Adds `c_shim_aten.{h/cpp}` and use this for `fill_`
This is the generated `c_shim_aten.cpp` for reference
```cpp
// WARNING: THIS FILE IS AUTOGENERATED BY torchgen. DO NOT MODIFY BY HAND.
// See 7e86a7c015/torchgen/gen.py (L2424-L2436) for details
// This file corresponds to the aten_shimified_ops list in torchgen/aoti/fallback_ops.py
#include <torch/csrc/inductor/aoti_torch/generated/c_shim_aten.h>
#include <torch/csrc/inductor/aoti_torch/utils.h>
#ifndef AT_PER_OPERATOR_HEADERS
#include <ATen/Functions.h>
#include <ATen/CompositeExplicitAutogradFunctions.h>
#include <ATen/CompositeExplicitAutogradNonFunctionalFunctions.h>
#include <ATen/CompositeImplicitAutogradFunctions.h>
#else
#include <ATen/ops/fill.h>
#endif // AT_PER_OPERATOR_HEADERS
using namespace torch::aot_inductor;
AOTITorchError aoti_torch_aten_fill__Scalar(AtenTensorHandle self, double value) {
AOTI_TORCH_CONVERT_EXCEPTION_TO_ERROR_CODE({
at::fill_(
*tensor_handle_to_tensor_pointer(self), value
);
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158974
Approved by: https://github.com/albanD, https://github.com/janeyx99
The op test__weight_int4pack_mm_with_scales_and_zeros is for Intel GPU. It is functionally equivalent to the CUDA/CPU op test__weight_int4pack_mm (with the constraint that oneDNN only supports integer zero points, which is why we need this API). Since test__weight_int4pack_mm is already included in AOTI's fallback list, this PR adds support for XPU.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155780
Approved by: https://github.com/jansel
Fixes #ISSUE_NUMBER
## Background
Task: [T222738229](https://www.internalfb.com/intern/tasks/?t=222738229)
It's the first starter task on the project **_Enabling TorchNative Standalone on Whisper_**. We are using cshim to create a layer of abstraction between _**libtorch**_ and **_AOTInductor generated artifacts_**.
So we needed to add an entry in the cshim for every API surface in libtorch. And we only care about operators that AOTInductor does not handle. And for this task, we only wanted to add it for the following ops.
## What I've done?
4 new fallback ops are added that show up in the Whisper model. (torchgen/aoti/fallback_ops.py)
- aten.permute (default)
- aten.squueze (dim)
- aten.abs (default)
- aten.hann_window (default)
Then I ran the below command to generate new header C shim header files. As it says [here](7e86a7c015/torchgen/gen.py (L2424-L2436%20for%20details))
`python torchgen/gen.py --update-aoti-c-shim`
Then, `python setup.py develop` to rebuild PyTorch
## Testing
Also 4 new tests have been added on test/inductor/test_aot_inductor.py
- test_proxy_executor_permute
- test_proxy_executor_abs
- test_proxy_executor_squeeze
- test_proxy_executor_hann
I ran these commands to test it (inside local pytorch root folder):
`python test/inductor/test_aot_inductor.py -k test_proxy_executor_permute`
`python test/inductor/test_aot_inductor.py -k test_proxy_executor_abs`
`python test/inductor/test_aot_inductor.py -k test_proxy_executor_squeeze`
`python test/inductor/test_aot_inductor.py -k test_proxy_executor_hann`
## NOTE:
I didn't see any order between the tests inside _test/inductor/test_aot_inductor.py_. That's why, I added new tests just after the test given in the example.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154251
Approved by: https://github.com/angelayi
Motivation
===
This PR is part of the plan of OneDNN Upstreaming, as #114848 [(comment)](https://github.com/pytorch/pytorch/issues/114848#issuecomment-2451553203) stated. The support of SDPA is via the overridable variance on XPU backend. Beside the added `Attention.cpp` file, `Graph.h` is added to hold utils for OneDNN graph including those for kernel/compile graph caching. In addition, a selection of testcases in `test/test_transformers.py` are copied into the new `test/xpu/test_transformers.py` and modified accordingly to provide additional tests beyond `./third_party/torch-xpu-ops/test/xpu/test_ops_xpu.py`.
Depends on OneDNN version v3.7 upgrade in #147498
Depends on BUILD_GRAPH switch in #147608
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147614
Approved by: https://github.com/jansel, https://github.com/EikanWang
Adds a C-shim fallback for `set_.source_Tensor`, which is effectively required by `ir.SetSourceTensorKernel`. As a necessary prerequisite to use that IR node, updates `CppWrapperCpu` to handle in-place returns in C-shim ops (the arguments for those returns are silently dropped by `torchgen`).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145654
Approved by: https://github.com/desertfire
ghstack dependencies: #145095
Removes 4 fallback ops that are currently not possible to codegen, which does not break ABI-compatibility.
1. `_cudnn_rnn_backward` and `_histogramdd_bin_edges` both return `Tensor[]`, which we cannot codegen with the current design.
2. `_sparse_coo_tensor_with_dims_and_tensors` only supplies a Sparse operator, which we don't support.
3. `zeros.names` requires a `Dimname` input, which we can't currently codegen.
Removing these ops from the list will improve test performance, since the fallback op generation will use the Python proxy executor instead of calling non-existent C functions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143421
Approved by: https://github.com/desertfire
ghstack dependencies: #141371, #143223
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
Fixes the error of running WOQ-INT8 LLaMA:
```
E In file included from /home/user/inductor/pytorch/torch/include/torch/csrc/inductor/aoti_runtime/arrayref_tensor.h:3,
E from /tmp/torchinductor_user/sw/csw5gfmlzp5iooqvfwl2gwn574frwdpmtrx2y6nu2m6x76d3xcux.cpp:4:
E /tmp/torchinductor_user/sw/csw5gfmlzp5iooqvfwl2gwn574frwdpmtrx2y6nu2m6x76d3xcux.cpp: In function ‘void inductor_entry_impl(AtenTensorOpaque**, AtenTensorOpaque**)’:
E /tmp/torchinductor_user/sw/csw5gfmlzp5iooqvfwl2gwn574frwdpmtrx2y6nu2m6x76d3xcux.cpp:117:33: error: ‘aoti_torch_cpu__weight_int8pack_mm’ was not declared in this scope
E 117 | AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_cpu__weight_int8pack_mm(convert_arrayref_tensor_to_tensor(arg8_1), _frozen_param0, _frozen_param1, &buf0_handle));
E | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138691
Approved by: https://github.com/leslie-fang-intel, https://github.com/jgong5, https://github.com/desertfire
Looks like one of the first failures seen is `test_causal_variants_compile_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` when `test_causal_variants_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` passes.
What seems interesting here is that the `torch.compile` version fails while the eager version passes. Not sure what the difference would be here...
Nevertheless, is there a recommended mechanism to skip cuDNN SDPA as a backend for this test? CC @drisspg
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125343
Approved by: https://github.com/Skylion007
Looks like one of the first failures seen is `test_causal_variants_compile_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` when `test_causal_variants_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` passes.
What seems interesting here is that the `torch.compile` version fails while the eager version passes. Not sure what the difference would be here...
Nevertheless, is there a recommended mechanism to skip cuDNN SDPA as a backend for this test? CC @drisspg
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125343
Approved by: https://github.com/Skylion007
