support op dispatch#93

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
zhaoguochun1995 wants to merge 7 commits into
base:main
Choose a base branch
from
Open

support op dispatch #93

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
7 commits
Select commit Hold shift + click to select a range
cf4b44f
support op dispatch
zhaoguochun1995 Apr 11, 2024
d0c203a
support op dispatch
zhaoguochun1995 Apr 15, 2024
94b2600
add some op
zhaoguochun1995 Apr 15, 2024
a1b1db3
add rms_norm op
zhaoguochun1995 Apr 16, 2024
30dc807
auto format
zhaoguochun1995 Apr 16, 2024
2e4b897
add apply_rotary
zhaoguochun1995 Apr 16, 2024
bd17565
backup
zhaoguochun1995 Apr 16, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
136 changes: 136 additions & 0 deletions csrc/extensions.cpp
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -1,10 +1,12 @@
// Copyright (c) 2023, DeepLink.

#include <cstdint>
#include <iostream>
#include <string>
#include <tuple>
#include <utility>

#include "torch/library.h"
#include <ATen/core/ATen_fwd.h>
#include <ATen/core/Generator.h>
#include <ATen/core/TensorBody.h>
Expand DownExpand Up@@ -363,4 +365,138 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
}
}

std::tuple<at::Tensor&, at::Tensor&, at::Tensor&> adamw(
at::Tensor& param, at::Tensor& exp_avg, at::Tensor& exp_avg_sq,
const c10::optional<at::Tensor>& max_exp_avg_sq_opt, const at::Tensor& grad,
double lr, double beta1, double beta2, double epsilon, double weight_decay,
int64_t step, bool amsgrad) {
// the diopiAdamW func has no "maximize" param
at::Tensor& grad_ref =
const_cast<at::Tensor&>(grad); // todo: grad is const value
at::Tensor max_exp_avg_sq_opt_value =
max_exp_avg_sq_opt.value_or(at::Tensor());
callDiopi(diopiAdamW, param, grad_ref, exp_avg, exp_avg_sq,
max_exp_avg_sq_opt_value, lr, beta1, beta2, epsilon, weight_decay,
step, amsgrad);
return std::tie(param, exp_avg, exp_avg_sq);
}

at::Tensor& apply_penalty(at::Tensor& logits,
const at::Tensor& presence_penalty,
const at::Tensor& frequency_penalty,
const at::Tensor& p_token_ids,
const at::Tensor& p_token_counts,
const at::Tensor& p_cumsum_seq_len,
int64_t p_max_len_in_batch) {
callDiopi(diopiApplyPenalty, logits, presence_penalty, frequency_penalty,
p_token_ids, p_token_counts, p_cumsum_seq_len, p_max_len_in_batch);
return logits;
}

at::Tensor& dest_index_copy_kv(const at::Tensor& k, const at::Tensor& dest_loc,
at::Tensor& out) {
callDiopi(diopiDestIndexCopyKV, out, k, dest_loc);
return out;
}

std::tuple<at::Tensor&, at::Tensor&> rms_norm(
at::Tensor& output, at::Tensor& inv_rms, const at::Tensor& input,
const OptionalIntArray& normalized_shape, const at::Tensor& weight,
const c10::optional<at::Tensor>& bias_opt, double eps) {
callDiopi(diopiRMSNorm, output, inv_rms, input, normalized_shape, weight,
bias_opt, eps);
return std::tie(output, inv_rms);
}

std::tuple<at::Tensor&, at::Tensor&, at::Tensor&> rms_norm_backward(
at::Tensor& grad_input, at::Tensor& grad_weight, at::Tensor& grad_bias_opt,
const at::Tensor& grad_output, const at::Tensor& input,
const at::Tensor& weight, const c10::optional<at::Tensor>& bias_opt,
const at::Tensor& inv_rms, const OptionalIntArray& normalized_shape,
double eps) {
callDiopi(diopiRMSNormBackward, grad_input, grad_weight, grad_bias_opt,
grad_output, input, weight, bias_opt, inv_rms, normalized_shape,
eps);
return std::tie(grad_input, grad_weight, grad_bias_opt);
}

at::Tensor& apply_rotary(at::Tensor& output, const at::Tensor& input,
const at::Tensor& cos, const at::Tensor& sin,
const bool conj, const bool interleaved) {
callDiopi(diopiRotaryEmbedding, output, input, cos, sin, conj, interleaved);
return output;
}

at::Tensor& example_for_all_backend(at::Tensor& inout) {
std::cout << __FUNCTION__ << ": " << inout.options() << "\n";
return inout;
}

at::Tensor& example_only_for_xpu(at::Tensor& inout) {
std::cout << __FUNCTION__ << ": " << inout.options() << "\n";
return inout;
}

// By default, all backends (XPU, AutocastXPU, AutoGradXPU, CUDA, PrivateUse1,
// AutogradPrivateUse1 etc) are registered. If you need to register separately
// for a certain backend, separate registration for a certain backend is also
// supported.
TORCH_LIBRARY(deeplink_ext_, m) {
if (&diopiAdamW != nullptr) {
m.def(
"adamw(Tensor(a!) param, Tensor(b!) exp_avg, Tensor(c!) exp_avg_sq, "
"Tensor? max_exp_avg_sq_opt, Tensor grad, float lr, float beta1, float "
"beta2, float epsilon, float weight_decay, int step, bool "
"amsgrad)->(Tensor(a!), Tensor(b!), Tensor(c!))",
adamw);
}
if (&diopiApplyPenalty != nullptr) {
m.def(
"apply_penalty(Tensor(a!) logits, Tensor presence_penalty, Tensor "
"frequency_penalty, Tensor p_token_ids, Tensor p_token_counts, Tensor "
"p_cumsum_seq_len, int p_max_len_in_batch)->Tensor(a!)",
apply_penalty);
}
if (&diopiDestIndexCopyKV != nullptr) {
m.def(
"dest_index_copy_kv(Tensor(a!) out, Tensor k, Tensor "
"dest_loc)->Tensor(a!)",
dest_index_copy_kv);
}
if (&diopiDestIndexCopyKV != nullptr) {
m.def(
"rms_norm(Tensor(a!) output, Tensor(b!) inv_rms, Tensor input, int[]? "
"normalized_shape, Tensor weight, Tensor? bias_opt, float eps) -> "
"(Tensor(a!), Tensor(b!))",
rms_norm);
}

if (&diopiRMSNormBackward != nullptr) {
m.def(
"rms_norm_backward(Tensor(a!) grad_input, Tensor(b!) grad_weight, "
"Tensor(c!) grad_bias_opt, Tensor grad_output, Tensor input, Tensor "
"weight, Tensor? bias_opt, Tensor inv_rms, int[]? normalized_shape, "
"float eps) -> (Tensor(a!), Tensor(b!), Tensor(c!))",
rms_norm_backward);
}
if (&diopiRotaryEmbedding != nullptr) {
m.def(
"apply_rotary(Tensor(a!) output, Tensor input, Tensor cos, Tensor sin, "
"bool conj, bool interleaved) -> Tensor(a!)",
apply_rotary);
}

m.def("example(Tensor(a!) inout)->Tensor(a!)", example_for_all_backend);
}

// only impl for dipu
TORCH_LIBRARY_IMPL(deeplink_ext_, XPU, m) {
// m.impl("example", example_only_for_xpu);
}

int n = [](){
std::cout << "deeplink_ext_ loaded" << std::endl;
return 0;
}();

} // namespace dipu::dipu_ext
16 changes: 15 additions & 1 deletion setup.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -1,7 +1,8 @@
# Copyright (c) 2024, DeepLink.

from setuptools import find_packages, setup, Extension
from torch.utils.cpp_extension import BuildExtension, include_paths, library_paths
from torch.utils.cpp_extension import BuildExtension, CppExtension, include_paths, library_paths

import glob
import os
import subprocess
Expand DownExpand Up@@ -86,3 +87,16 @@ def get_ext():
cmdclass={"build_ext": BuildExtensionWithCompdb},
install_requires=["einops"],
)


setup(

@mrdanielwmrdanielwMay 6, 2024

Copy link
Copy Markdown
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

请教下 这个位置引入cppextension 是为了使用”TORCH_LIBRARY“吗 我记得BuildExtension和CppExtension 是在cuda 编译才用到， 如果在其他设备上用这种方式编译会不会有问题？

@zhaoguochun1995zhaoguochun1995May 7, 2024

Copy link
Copy Markdown
ContributorAuthor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

setup里面的只是个草稿，还有待完善。cppextension不光光可以编译cuda kernel,也可以只编译cpp文件的。

name='deeplink_ext_ops',
ext_modules=[
CppExtension(
name='deeplink_ext_ops',
sources=glob.glob("./csrc/*.cpp"),
extra_compile_args=[' -g ']),
],
cmdclass={
'build_ext': BuildExtension
})
26 changes: 26 additions & 0 deletions test_dispatch.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,26 @@
import torch
import torch_dipu
import deeplink_ext

so_path = deeplink_ext.__path__[0] + "/cpp_extensions.cpython-39-x86_64-linux-gnu.so"
torch.ops.load_library(so_path)
print(f"torch.ops.loaded_libraries:{torch.ops.loaded_libraries}")

#print(torch.ops.deeplink_ext_.dest_index_copy_kv)

def code_to_profile():
x = torch.randn(3,4)
y = torch.ops.deeplink_ext_.example(x)
y = torch.ops.deeplink_ext_.example(x.cuda())


with torch.profiler.profile(
activities=[
torch.profiler.ProfilerActivity.CPU,
torch.profiler.ProfilerActivity.CUDA,
]
) as p:
code_to_profile()
print(p.key_averages().table(
sort_by="self_cuda_time_total", row_limit=-1))

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
support op dispatch#93

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Open

zhaoguochun1995
wants to merge
7
commits into

base:main
Choose a base branch

from

 Open


support op dispatch
#93
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


7 commits


Select commit
Hold shift + click to select a range
cf4b44f

support op dispatch


zhaoguochun1995 Apr 11, 2024
d0c203a

support op dispatch


zhaoguochun1995 Apr 15, 2024
94b2600

add some op


zhaoguochun1995 Apr 15, 2024
a1b1db3

add rms_norm op


zhaoguochun1995 Apr 16, 2024
30dc807

auto format


zhaoguochun1995 Apr 16, 2024
2e4b897

add apply_rotary


zhaoguochun1995 Apr 16, 2024
bd17565

backup


zhaoguochun1995 Apr 16, 2024

File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


136 changes: 136 additions & 0 deletions
csrc/extensions.cpp

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,10 +1,12 @@
// Copyright (c) 2023, DeepLink.

#include <cstdint>
#include <iostream>
#include <string>
#include <tuple>
#include <utility>

#include "torch/library.h"
#include <ATen/core/ATen_fwd.h>
#include <ATen/core/Generator.h>
#include <ATen/core/TensorBody.h>
Expand DownExpand Up@@ -363,4 +365,138 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
 }
}

std::tuple<at::Tensor&, at::Tensor&, at::Tensor&> adamw(
 at::Tensor& param, at::Tensor& exp_avg, at::Tensor& exp_avg_sq,
 const c10::optional<at::Tensor>& max_exp_avg_sq_opt, const at::Tensor& grad,
 double lr, double beta1, double beta2, double epsilon, double weight_decay,
 int64_t step, bool amsgrad) {
 // the diopiAdamW func has no "maximize" param
 at::Tensor& grad_ref =
 const_cast<at::Tensor&>(grad); // todo: grad is const value
 at::Tensor max_exp_avg_sq_opt_value =
 max_exp_avg_sq_opt.value_or(at::Tensor());
 callDiopi(diopiAdamW, param, grad_ref, exp_avg, exp_avg_sq,
 max_exp_avg_sq_opt_value, lr, beta1, beta2, epsilon, weight_decay,
 step, amsgrad);
 return std::tie(param, exp_avg, exp_avg_sq);
}

at::Tensor& apply_penalty(at::Tensor& logits,
 const at::Tensor& presence_penalty,
 const at::Tensor& frequency_penalty,
 const at::Tensor& p_token_ids,
 const at::Tensor& p_token_counts,
 const at::Tensor& p_cumsum_seq_len,
 int64_t p_max_len_in_batch) {
 callDiopi(diopiApplyPenalty, logits, presence_penalty, frequency_penalty,
 p_token_ids, p_token_counts, p_cumsum_seq_len, p_max_len_in_batch);
 return logits;
}

at::Tensor& dest_index_copy_kv(const at::Tensor& k, const at::Tensor& dest_loc,
 at::Tensor& out) {
 callDiopi(diopiDestIndexCopyKV, out, k, dest_loc);
 return out;
}

std::tuple<at::Tensor&, at::Tensor&> rms_norm(
 at::Tensor& output, at::Tensor& inv_rms, const at::Tensor& input,
 const OptionalIntArray& normalized_shape, const at::Tensor& weight,
 const c10::optional<at::Tensor>& bias_opt, double eps) {
 callDiopi(diopiRMSNorm, output, inv_rms, input, normalized_shape, weight,
 bias_opt, eps);
 return std::tie(output, inv_rms);
}

std::tuple<at::Tensor&, at::Tensor&, at::Tensor&> rms_norm_backward(
 at::Tensor& grad_input, at::Tensor& grad_weight, at::Tensor& grad_bias_opt,
 const at::Tensor& grad_output, const at::Tensor& input,
 const at::Tensor& weight, const c10::optional<at::Tensor>& bias_opt,
 const at::Tensor& inv_rms, const OptionalIntArray& normalized_shape,
 double eps) {
 callDiopi(diopiRMSNormBackward, grad_input, grad_weight, grad_bias_opt,
 grad_output, input, weight, bias_opt, inv_rms, normalized_shape,
 eps);
 return std::tie(grad_input, grad_weight, grad_bias_opt);
}

at::Tensor& apply_rotary(at::Tensor& output, const at::Tensor& input,
 const at::Tensor& cos, const at::Tensor& sin,
 const bool conj, const bool interleaved) {
 callDiopi(diopiRotaryEmbedding, output, input, cos, sin, conj, interleaved);
 return output;
}

at::Tensor& example_for_all_backend(at::Tensor& inout) {
 std::cout << __FUNCTION__ << ": " << inout.options() << "\n";
 return inout;
}

at::Tensor& example_only_for_xpu(at::Tensor& inout) {
 std::cout << __FUNCTION__ << ": " << inout.options() << "\n";
 return inout;
}

// By default, all backends (XPU, AutocastXPU, AutoGradXPU, CUDA, PrivateUse1,
// AutogradPrivateUse1 etc) are registered. If you need to register separately
// for a certain backend, separate registration for a certain backend is also
// supported.
TORCH_LIBRARY(deeplink_ext_, m) {
 if (&diopiAdamW != nullptr) {
 m.def(
 "adamw(Tensor(a!) param, Tensor(b!) exp_avg, Tensor(c!) exp_avg_sq, "
 "Tensor? max_exp_avg_sq_opt, Tensor grad, float lr, float beta1, float "
 "beta2, float epsilon, float weight_decay, int step, bool "
 "amsgrad)->(Tensor(a!), Tensor(b!), Tensor(c!))",
 adamw);
 }
 if (&diopiApplyPenalty != nullptr) {
 m.def(
 "apply_penalty(Tensor(a!) logits, Tensor presence_penalty, Tensor "
 "frequency_penalty, Tensor p_token_ids, Tensor p_token_counts, Tensor "
 "p_cumsum_seq_len, int p_max_len_in_batch)->Tensor(a!)",
 apply_penalty);
 }
 if (&diopiDestIndexCopyKV != nullptr) {
 m.def(
 "dest_index_copy_kv(Tensor(a!) out, Tensor k, Tensor "
 "dest_loc)->Tensor(a!)",
 dest_index_copy_kv);
 }
 if (&diopiDestIndexCopyKV != nullptr) {
 m.def(
 "rms_norm(Tensor(a!) output, Tensor(b!) inv_rms, Tensor input, int[]? "
 "normalized_shape, Tensor weight, Tensor? bias_opt, float eps) -> "
 "(Tensor(a!), Tensor(b!))",
 rms_norm);
 }

 if (&diopiRMSNormBackward != nullptr) {
 m.def(
 "rms_norm_backward(Tensor(a!) grad_input, Tensor(b!) grad_weight, "
 "Tensor(c!) grad_bias_opt, Tensor grad_output, Tensor input, Tensor "
 "weight, Tensor? bias_opt, Tensor inv_rms, int[]? normalized_shape, "
 "float eps) -> (Tensor(a!), Tensor(b!), Tensor(c!))",
 rms_norm_backward);
 }
 if (&diopiRotaryEmbedding != nullptr) {
 m.def(
 "apply_rotary(Tensor(a!) output, Tensor input, Tensor cos, Tensor sin, "
 "bool conj, bool interleaved) -> Tensor(a!)",
 apply_rotary);
 }

 m.def("example(Tensor(a!) inout)->Tensor(a!)", example_for_all_backend);
}

// only impl for dipu
TORCH_LIBRARY_IMPL(deeplink_ext_, XPU, m) {
 // m.impl("example", example_only_for_xpu);
}

int n = [](){
 std::cout << "deeplink_ext_ loaded" << std::endl;
 return 0;
}();

} // namespace dipu::dipu_ext

16 changes: 15 additions & 1 deletion
setup.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,7 +1,8 @@
# Copyright (c) 2024, DeepLink.

from setuptools import find_packages, setup, Extension
from torch.utils.cpp_extension import BuildExtension, include_paths, library_paths
from torch.utils.cpp_extension import BuildExtension, CppExtension, include_paths, library_paths

import glob
import os
import subprocess
Expand DownExpand Up@@ -86,3 +87,16 @@ def get_ext():
 cmdclass={"build_ext": BuildExtensionWithCompdb},
 install_requires=["einops"],
)


setup(
@mrdanielwmrdanielwMay 6, 2024

Copy link


Copy Markdown

Collaborator
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

请教下 这个位置引入cppextension 是为了使用”TORCH_LIBRARY“吗 我记得BuildExtension和CppExtension 是在cuda 编译才用到， 如果在其他设备上用这种方式编译会不会有问题？
@zhaoguochun1995zhaoguochun1995May 7, 2024

Copy link


Copy Markdown

ContributorAuthor
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

setup里面的只是个草稿，还有待完善。cppextension不光光可以编译cuda kernel,也可以只编译cpp文件的。
 name='deeplink_ext_ops',
 ext_modules=[
 CppExtension(
 name='deeplink_ext_ops',
 sources=glob.glob("./csrc/*.cpp"),
 extra_compile_args=[' -g ']),
 ],
 cmdclass={
 'build_ext': BuildExtension
 })

26 changes: 26 additions & 0 deletions
test_dispatch.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,26 @@
import torch
import torch_dipu
import deeplink_ext

so_path = deeplink_ext.__path__[0] + "/cpp_extensions.cpython-39-x86_64-linux-gnu.so"
torch.ops.load_library(so_path)
print(f"torch.ops.loaded_libraries:{torch.ops.loaded_libraries}")

#print(torch.ops.deeplink_ext_.dest_index_copy_kv)

def code_to_profile():
 x = torch.randn(3,4)
 y = torch.ops.deeplink_ext_.example(x)
 y = torch.ops.deeplink_ext_.example(x.cuda())


with torch.profiler.profile(
 activities=[
 torch.profiler.ProfilerActivity.CPU,
 torch.profiler.ProfilerActivity.CUDA,
 ]
) as p:
 code_to_profile()
print(p.key_averages().table(
 sort_by="self_cuda_time_total", row_limit=-1))


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
support op dispatch#93

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Open

zhaoguochun1995
wants to merge
7
commits into

base:main
Choose a base branch

from

 Open


support op dispatch
#93
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


7 commits


Select commit
Hold shift + click to select a range
cf4b44f

support op dispatch


zhaoguochun1995 Apr 11, 2024
d0c203a

support op dispatch


zhaoguochun1995 Apr 15, 2024
94b2600

add some op


zhaoguochun1995 Apr 15, 2024
a1b1db3

add rms_norm op


zhaoguochun1995 Apr 16, 2024
30dc807

auto format


zhaoguochun1995 Apr 16, 2024
2e4b897

add apply_rotary


zhaoguochun1995 Apr 16, 2024
bd17565

backup


zhaoguochun1995 Apr 16, 2024

File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


136 changes: 136 additions & 0 deletions
csrc/extensions.cpp

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,10 +1,12 @@
// Copyright (c) 2023, DeepLink.

#include <cstdint>
#include <iostream>
#include <string>
#include <tuple>
#include <utility>

#include "torch/library.h"
#include <ATen/core/ATen_fwd.h>
#include <ATen/core/Generator.h>
#include <ATen/core/TensorBody.h>
Expand DownExpand Up@@ -363,4 +365,138 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
 }
}

std::tuple<at::Tensor&, at::Tensor&, at::Tensor&> adamw(
 at::Tensor& param, at::Tensor& exp_avg, at::Tensor& exp_avg_sq,
 const c10::optional<at::Tensor>& max_exp_avg_sq_opt, const at::Tensor& grad,
 double lr, double beta1, double beta2, double epsilon, double weight_decay,
 int64_t step, bool amsgrad) {
 // the diopiAdamW func has no "maximize" param
 at::Tensor& grad_ref =
 const_cast<at::Tensor&>(grad); // todo: grad is const value
 at::Tensor max_exp_avg_sq_opt_value =
 max_exp_avg_sq_opt.value_or(at::Tensor());
 callDiopi(diopiAdamW, param, grad_ref, exp_avg, exp_avg_sq,
 max_exp_avg_sq_opt_value, lr, beta1, beta2, epsilon, weight_decay,
 step, amsgrad);
 return std::tie(param, exp_avg, exp_avg_sq);
}

at::Tensor& apply_penalty(at::Tensor& logits,
 const at::Tensor& presence_penalty,
 const at::Tensor& frequency_penalty,
 const at::Tensor& p_token_ids,
 const at::Tensor& p_token_counts,
 const at::Tensor& p_cumsum_seq_len,
 int64_t p_max_len_in_batch) {
 callDiopi(diopiApplyPenalty, logits, presence_penalty, frequency_penalty,
 p_token_ids, p_token_counts, p_cumsum_seq_len, p_max_len_in_batch);
 return logits;
}

at::Tensor& dest_index_copy_kv(const at::Tensor& k, const at::Tensor& dest_loc,
 at::Tensor& out) {
 callDiopi(diopiDestIndexCopyKV, out, k, dest_loc);
 return out;
}

std::tuple<at::Tensor&, at::Tensor&> rms_norm(
 at::Tensor& output, at::Tensor& inv_rms, const at::Tensor& input,
 const OptionalIntArray& normalized_shape, const at::Tensor& weight,
 const c10::optional<at::Tensor>& bias_opt, double eps) {
 callDiopi(diopiRMSNorm, output, inv_rms, input, normalized_shape, weight,
 bias_opt, eps);
 return std::tie(output, inv_rms);
}

std::tuple<at::Tensor&, at::Tensor&, at::Tensor&> rms_norm_backward(
 at::Tensor& grad_input, at::Tensor& grad_weight, at::Tensor& grad_bias_opt,
 const at::Tensor& grad_output, const at::Tensor& input,
 const at::Tensor& weight, const c10::optional<at::Tensor>& bias_opt,
 const at::Tensor& inv_rms, const OptionalIntArray& normalized_shape,
 double eps) {
 callDiopi(diopiRMSNormBackward, grad_input, grad_weight, grad_bias_opt,
 grad_output, input, weight, bias_opt, inv_rms, normalized_shape,
 eps);
 return std::tie(grad_input, grad_weight, grad_bias_opt);
}

at::Tensor& apply_rotary(at::Tensor& output, const at::Tensor& input,
 const at::Tensor& cos, const at::Tensor& sin,
 const bool conj, const bool interleaved) {
 callDiopi(diopiRotaryEmbedding, output, input, cos, sin, conj, interleaved);
 return output;
}

at::Tensor& example_for_all_backend(at::Tensor& inout) {
 std::cout << __FUNCTION__ << ": " << inout.options() << "\n";
 return inout;
}

at::Tensor& example_only_for_xpu(at::Tensor& inout) {
 std::cout << __FUNCTION__ << ": " << inout.options() << "\n";
 return inout;
}

// By default, all backends (XPU, AutocastXPU, AutoGradXPU, CUDA, PrivateUse1,
// AutogradPrivateUse1 etc) are registered. If you need to register separately
// for a certain backend, separate registration for a certain backend is also
// supported.
TORCH_LIBRARY(deeplink_ext_, m) {
 if (&diopiAdamW != nullptr) {
 m.def(
 "adamw(Tensor(a!) param, Tensor(b!) exp_avg, Tensor(c!) exp_avg_sq, "
 "Tensor? max_exp_avg_sq_opt, Tensor grad, float lr, float beta1, float "
 "beta2, float epsilon, float weight_decay, int step, bool "
 "amsgrad)->(Tensor(a!), Tensor(b!), Tensor(c!))",
 adamw);
 }
 if (&diopiApplyPenalty != nullptr) {
 m.def(
 "apply_penalty(Tensor(a!) logits, Tensor presence_penalty, Tensor "
 "frequency_penalty, Tensor p_token_ids, Tensor p_token_counts, Tensor "
 "p_cumsum_seq_len, int p_max_len_in_batch)->Tensor(a!)",
 apply_penalty);
 }
 if (&diopiDestIndexCopyKV != nullptr) {
 m.def(
 "dest_index_copy_kv(Tensor(a!) out, Tensor k, Tensor "
 "dest_loc)->Tensor(a!)",
 dest_index_copy_kv);
 }
 if (&diopiDestIndexCopyKV != nullptr) {
 m.def(
 "rms_norm(Tensor(a!) output, Tensor(b!) inv_rms, Tensor input, int[]? "
 "normalized_shape, Tensor weight, Tensor? bias_opt, float eps) -> "
 "(Tensor(a!), Tensor(b!))",
 rms_norm);
 }

 if (&diopiRMSNormBackward != nullptr) {
 m.def(
 "rms_norm_backward(Tensor(a!) grad_input, Tensor(b!) grad_weight, "
 "Tensor(c!) grad_bias_opt, Tensor grad_output, Tensor input, Tensor "
 "weight, Tensor? bias_opt, Tensor inv_rms, int[]? normalized_shape, "
 "float eps) -> (Tensor(a!), Tensor(b!), Tensor(c!))",
 rms_norm_backward);
 }
 if (&diopiRotaryEmbedding != nullptr) {
 m.def(
 "apply_rotary(Tensor(a!) output, Tensor input, Tensor cos, Tensor sin, "
 "bool conj, bool interleaved) -> Tensor(a!)",
 apply_rotary);
 }

 m.def("example(Tensor(a!) inout)->Tensor(a!)", example_for_all_backend);
}

// only impl for dipu
TORCH_LIBRARY_IMPL(deeplink_ext_, XPU, m) {
 // m.impl("example", example_only_for_xpu);
}

int n = [](){
 std::cout << "deeplink_ext_ loaded" << std::endl;
 return 0;
}();

} // namespace dipu::dipu_ext

16 changes: 15 additions & 1 deletion
setup.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,7 +1,8 @@
# Copyright (c) 2024, DeepLink.

from setuptools import find_packages, setup, Extension
from torch.utils.cpp_extension import BuildExtension, include_paths, library_paths
from torch.utils.cpp_extension import BuildExtension, CppExtension, include_paths, library_paths

import glob
import os
import subprocess
Expand DownExpand Up@@ -86,3 +87,16 @@ def get_ext():
 cmdclass={"build_ext": BuildExtensionWithCompdb},
 install_requires=["einops"],
)


setup(
@mrdanielwmrdanielwMay 6, 2024

Copy link


Copy Markdown

Collaborator
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

请教下 这个位置引入cppextension 是为了使用”TORCH_LIBRARY“吗 我记得BuildExtension和CppExtension 是在cuda 编译才用到， 如果在其他设备上用这种方式编译会不会有问题？
@zhaoguochun1995zhaoguochun1995May 7, 2024

Copy link


Copy Markdown

ContributorAuthor
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

setup里面的只是个草稿，还有待完善。cppextension不光光可以编译cuda kernel,也可以只编译cpp文件的。
 name='deeplink_ext_ops',
 ext_modules=[
 CppExtension(
 name='deeplink_ext_ops',
 sources=glob.glob("./csrc/*.cpp"),
 extra_compile_args=[' -g ']),
 ],
 cmdclass={
 'build_ext': BuildExtension
 })

26 changes: 26 additions & 0 deletions
test_dispatch.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,26 @@
import torch
import torch_dipu
import deeplink_ext

so_path = deeplink_ext.__path__[0] + "/cpp_extensions.cpython-39-x86_64-linux-gnu.so"
torch.ops.load_library(so_path)
print(f"torch.ops.loaded_libraries:{torch.ops.loaded_libraries}")

#print(torch.ops.deeplink_ext_.dest_index_copy_kv)

def code_to_profile():
 x = torch.randn(3,4)
 y = torch.ops.deeplink_ext_.example(x)
 y = torch.ops.deeplink_ext_.example(x.cuda())


with torch.profiler.profile(
 activities=[
 torch.profiler.ProfilerActivity.CPU,
 torch.profiler.ProfilerActivity.CUDA,
 ]
) as p:
 code_to_profile()
print(p.key_averages().table(
 sort_by="self_cuda_time_total", row_limit=-1))


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
support op dispatch#93

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Open

zhaoguochun1995
wants to merge
7
commits into

base:main
Choose a base branch

from

 Open


support op dispatch
#93
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


7 commits


Select commit
Hold shift + click to select a range
cf4b44f

support op dispatch


zhaoguochun1995 Apr 11, 2024
d0c203a

support op dispatch


zhaoguochun1995 Apr 15, 2024
94b2600

add some op


zhaoguochun1995 Apr 15, 2024
a1b1db3

add rms_norm op


zhaoguochun1995 Apr 16, 2024
30dc807

auto format


zhaoguochun1995 Apr 16, 2024
2e4b897

add apply_rotary


zhaoguochun1995 Apr 16, 2024
bd17565

backup


zhaoguochun1995 Apr 16, 2024

File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


136 changes: 136 additions & 0 deletions
csrc/extensions.cpp

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,10 +1,12 @@
// Copyright (c) 2023, DeepLink.

#include <cstdint>
#include <iostream>
#include <string>
#include <tuple>
#include <utility>

#include "torch/library.h"
#include <ATen/core/ATen_fwd.h>
#include <ATen/core/Generator.h>
#include <ATen/core/TensorBody.h>
Expand DownExpand Up@@ -363,4 +365,138 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
 }
}

std::tuple<at::Tensor&, at::Tensor&, at::Tensor&> adamw(
 at::Tensor& param, at::Tensor& exp_avg, at::Tensor& exp_avg_sq,
 const c10::optional<at::Tensor>& max_exp_avg_sq_opt, const at::Tensor& grad,
 double lr, double beta1, double beta2, double epsilon, double weight_decay,
 int64_t step, bool amsgrad) {
 // the diopiAdamW func has no "maximize" param
 at::Tensor& grad_ref =
 const_cast<at::Tensor&>(grad); // todo: grad is const value
 at::Tensor max_exp_avg_sq_opt_value =
 max_exp_avg_sq_opt.value_or(at::Tensor());
 callDiopi(diopiAdamW, param, grad_ref, exp_avg, exp_avg_sq,
 max_exp_avg_sq_opt_value, lr, beta1, beta2, epsilon, weight_decay,
 step, amsgrad);
 return std::tie(param, exp_avg, exp_avg_sq);
}

at::Tensor& apply_penalty(at::Tensor& logits,
 const at::Tensor& presence_penalty,
 const at::Tensor& frequency_penalty,
 const at::Tensor& p_token_ids,
 const at::Tensor& p_token_counts,
 const at::Tensor& p_cumsum_seq_len,
 int64_t p_max_len_in_batch) {
 callDiopi(diopiApplyPenalty, logits, presence_penalty, frequency_penalty,
 p_token_ids, p_token_counts, p_cumsum_seq_len, p_max_len_in_batch);
 return logits;
}

at::Tensor& dest_index_copy_kv(const at::Tensor& k, const at::Tensor& dest_loc,
 at::Tensor& out) {
 callDiopi(diopiDestIndexCopyKV, out, k, dest_loc);
 return out;
}

std::tuple<at::Tensor&, at::Tensor&> rms_norm(
 at::Tensor& output, at::Tensor& inv_rms, const at::Tensor& input,
 const OptionalIntArray& normalized_shape, const at::Tensor& weight,
 const c10::optional<at::Tensor>& bias_opt, double eps) {
 callDiopi(diopiRMSNorm, output, inv_rms, input, normalized_shape, weight,
 bias_opt, eps);
 return std::tie(output, inv_rms);
}

std::tuple<at::Tensor&, at::Tensor&, at::Tensor&> rms_norm_backward(
 at::Tensor& grad_input, at::Tensor& grad_weight, at::Tensor& grad_bias_opt,
 const at::Tensor& grad_output, const at::Tensor& input,
 const at::Tensor& weight, const c10::optional<at::Tensor>& bias_opt,
 const at::Tensor& inv_rms, const OptionalIntArray& normalized_shape,
 double eps) {
 callDiopi(diopiRMSNormBackward, grad_input, grad_weight, grad_bias_opt,
 grad_output, input, weight, bias_opt, inv_rms, normalized_shape,
 eps);
 return std::tie(grad_input, grad_weight, grad_bias_opt);
}

at::Tensor& apply_rotary(at::Tensor& output, const at::Tensor& input,
 const at::Tensor& cos, const at::Tensor& sin,
 const bool conj, const bool interleaved) {
 callDiopi(diopiRotaryEmbedding, output, input, cos, sin, conj, interleaved);
 return output;
}

at::Tensor& example_for_all_backend(at::Tensor& inout) {
 std::cout << __FUNCTION__ << ": " << inout.options() << "\n";
 return inout;
}

at::Tensor& example_only_for_xpu(at::Tensor& inout) {
 std::cout << __FUNCTION__ << ": " << inout.options() << "\n";
 return inout;
}

// By default, all backends (XPU, AutocastXPU, AutoGradXPU, CUDA, PrivateUse1,
// AutogradPrivateUse1 etc) are registered. If you need to register separately
// for a certain backend, separate registration for a certain backend is also
// supported.
TORCH_LIBRARY(deeplink_ext_, m) {
 if (&diopiAdamW != nullptr) {
 m.def(
 "adamw(Tensor(a!) param, Tensor(b!) exp_avg, Tensor(c!) exp_avg_sq, "
 "Tensor? max_exp_avg_sq_opt, Tensor grad, float lr, float beta1, float "
 "beta2, float epsilon, float weight_decay, int step, bool "
 "amsgrad)->(Tensor(a!), Tensor(b!), Tensor(c!))",
 adamw);
 }
 if (&diopiApplyPenalty != nullptr) {
 m.def(
 "apply_penalty(Tensor(a!) logits, Tensor presence_penalty, Tensor "
 "frequency_penalty, Tensor p_token_ids, Tensor p_token_counts, Tensor "
 "p_cumsum_seq_len, int p_max_len_in_batch)->Tensor(a!)",
 apply_penalty);
 }
 if (&diopiDestIndexCopyKV != nullptr) {
 m.def(
 "dest_index_copy_kv(Tensor(a!) out, Tensor k, Tensor "
 "dest_loc)->Tensor(a!)",
 dest_index_copy_kv);
 }
 if (&diopiDestIndexCopyKV != nullptr) {
 m.def(
 "rms_norm(Tensor(a!) output, Tensor(b!) inv_rms, Tensor input, int[]? "
 "normalized_shape, Tensor weight, Tensor? bias_opt, float eps) -> "
 "(Tensor(a!), Tensor(b!))",
 rms_norm);
 }

 if (&diopiRMSNormBackward != nullptr) {
 m.def(
 "rms_norm_backward(Tensor(a!) grad_input, Tensor(b!) grad_weight, "
 "Tensor(c!) grad_bias_opt, Tensor grad_output, Tensor input, Tensor "
 "weight, Tensor? bias_opt, Tensor inv_rms, int[]? normalized_shape, "
 "float eps) -> (Tensor(a!), Tensor(b!), Tensor(c!))",
 rms_norm_backward);
 }
 if (&diopiRotaryEmbedding != nullptr) {
 m.def(
 "apply_rotary(Tensor(a!) output, Tensor input, Tensor cos, Tensor sin, "
 "bool conj, bool interleaved) -> Tensor(a!)",
 apply_rotary);
 }

 m.def("example(Tensor(a!) inout)->Tensor(a!)", example_for_all_backend);
}

// only impl for dipu
TORCH_LIBRARY_IMPL(deeplink_ext_, XPU, m) {
 // m.impl("example", example_only_for_xpu);
}

int n = [](){
 std::cout << "deeplink_ext_ loaded" << std::endl;
 return 0;
}();

} // namespace dipu::dipu_ext

16 changes: 15 additions & 1 deletion
setup.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,7 +1,8 @@
# Copyright (c) 2024, DeepLink.

from setuptools import find_packages, setup, Extension
from torch.utils.cpp_extension import BuildExtension, include_paths, library_paths
from torch.utils.cpp_extension import BuildExtension, CppExtension, include_paths, library_paths

import glob
import os
import subprocess
Expand DownExpand Up@@ -86,3 +87,16 @@ def get_ext():
 cmdclass={"build_ext": BuildExtensionWithCompdb},
 install_requires=["einops"],
)


setup(
@mrdanielwmrdanielwMay 6, 2024

Copy link


Copy Markdown

Collaborator
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

请教下 这个位置引入cppextension 是为了使用”TORCH_LIBRARY“吗 我记得BuildExtension和CppExtension 是在cuda 编译才用到， 如果在其他设备上用这种方式编译会不会有问题？
@zhaoguochun1995zhaoguochun1995May 7, 2024

Copy link


Copy Markdown

ContributorAuthor
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

setup里面的只是个草稿，还有待完善。cppextension不光光可以编译cuda kernel,也可以只编译cpp文件的。
 name='deeplink_ext_ops',
 ext_modules=[
 CppExtension(
 name='deeplink_ext_ops',
 sources=glob.glob("./csrc/*.cpp"),
 extra_compile_args=[' -g ']),
 ],
 cmdclass={
 'build_ext': BuildExtension
 })

26 changes: 26 additions & 0 deletions
test_dispatch.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,26 @@
import torch
import torch_dipu
import deeplink_ext

so_path = deeplink_ext.__path__[0] + "/cpp_extensions.cpython-39-x86_64-linux-gnu.so"
torch.ops.load_library(so_path)
print(f"torch.ops.loaded_libraries:{torch.ops.loaded_libraries}")

#print(torch.ops.deeplink_ext_.dest_index_copy_kv)

def code_to_profile():
 x = torch.randn(3,4)
 y = torch.ops.deeplink_ext_.example(x)
 y = torch.ops.deeplink_ext_.example(x.cuda())


with torch.profiler.profile(
 activities=[
 torch.profiler.ProfilerActivity.CPU,
 torch.profiler.ProfilerActivity.CUDA,
 ]
) as p:
 code_to_profile()
print(p.key_averages().table(
 sort_by="self_cuda_time_total", row_limit=-1))


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
support op dispatch#93

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Open

zhaoguochun1995
wants to merge
7
commits into

base:main
Choose a base branch

from

 Open


support op dispatch
#93
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


7 commits


Select commit
Hold shift + click to select a range
cf4b44f

support op dispatch


zhaoguochun1995 Apr 11, 2024
d0c203a

support op dispatch


zhaoguochun1995 Apr 15, 2024
94b2600

add some op


zhaoguochun1995 Apr 15, 2024
a1b1db3

add rms_norm op


zhaoguochun1995 Apr 16, 2024
30dc807

auto format


zhaoguochun1995 Apr 16, 2024
2e4b897

add apply_rotary


zhaoguochun1995 Apr 16, 2024
bd17565

backup


zhaoguochun1995 Apr 16, 2024

File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


136 changes: 136 additions & 0 deletions
csrc/extensions.cpp

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,10 +1,12 @@
// Copyright (c) 2023, DeepLink.

#include <cstdint>
#include <iostream>
#include <string>
#include <tuple>
#include <utility>

#include "torch/library.h"
#include <ATen/core/ATen_fwd.h>
#include <ATen/core/Generator.h>
#include <ATen/core/TensorBody.h>
Expand DownExpand Up@@ -363,4 +365,138 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
 }
}

std::tuple<at::Tensor&, at::Tensor&, at::Tensor&> adamw(
 at::Tensor& param, at::Tensor& exp_avg, at::Tensor& exp_avg_sq,
 const c10::optional<at::Tensor>& max_exp_avg_sq_opt, const at::Tensor& grad,
 double lr, double beta1, double beta2, double epsilon, double weight_decay,
 int64_t step, bool amsgrad) {
 // the diopiAdamW func has no "maximize" param
 at::Tensor& grad_ref =
 const_cast<at::Tensor&>(grad); // todo: grad is const value
 at::Tensor max_exp_avg_sq_opt_value =
 max_exp_avg_sq_opt.value_or(at::Tensor());
 callDiopi(diopiAdamW, param, grad_ref, exp_avg, exp_avg_sq,
 max_exp_avg_sq_opt_value, lr, beta1, beta2, epsilon, weight_decay,
 step, amsgrad);
 return std::tie(param, exp_avg, exp_avg_sq);
}

at::Tensor& apply_penalty(at::Tensor& logits,
 const at::Tensor& presence_penalty,
 const at::Tensor& frequency_penalty,
 const at::Tensor& p_token_ids,
 const at::Tensor& p_token_counts,
 const at::Tensor& p_cumsum_seq_len,
 int64_t p_max_len_in_batch) {
 callDiopi(diopiApplyPenalty, logits, presence_penalty, frequency_penalty,
 p_token_ids, p_token_counts, p_cumsum_seq_len, p_max_len_in_batch);
 return logits;
}

at::Tensor& dest_index_copy_kv(const at::Tensor& k, const at::Tensor& dest_loc,
 at::Tensor& out) {
 callDiopi(diopiDestIndexCopyKV, out, k, dest_loc);
 return out;
}

std::tuple<at::Tensor&, at::Tensor&> rms_norm(
 at::Tensor& output, at::Tensor& inv_rms, const at::Tensor& input,
 const OptionalIntArray& normalized_shape, const at::Tensor& weight,
 const c10::optional<at::Tensor>& bias_opt, double eps) {
 callDiopi(diopiRMSNorm, output, inv_rms, input, normalized_shape, weight,
 bias_opt, eps);
 return std::tie(output, inv_rms);
}

std::tuple<at::Tensor&, at::Tensor&, at::Tensor&> rms_norm_backward(
 at::Tensor& grad_input, at::Tensor& grad_weight, at::Tensor& grad_bias_opt,
 const at::Tensor& grad_output, const at::Tensor& input,
 const at::Tensor& weight, const c10::optional<at::Tensor>& bias_opt,
 const at::Tensor& inv_rms, const OptionalIntArray& normalized_shape,
 double eps) {
 callDiopi(diopiRMSNormBackward, grad_input, grad_weight, grad_bias_opt,
 grad_output, input, weight, bias_opt, inv_rms, normalized_shape,
 eps);
 return std::tie(grad_input, grad_weight, grad_bias_opt);
}

at::Tensor& apply_rotary(at::Tensor& output, const at::Tensor& input,
 const at::Tensor& cos, const at::Tensor& sin,
 const bool conj, const bool interleaved) {
 callDiopi(diopiRotaryEmbedding, output, input, cos, sin, conj, interleaved);
 return output;
}

at::Tensor& example_for_all_backend(at::Tensor& inout) {
 std::cout << __FUNCTION__ << ": " << inout.options() << "\n";
 return inout;
}

at::Tensor& example_only_for_xpu(at::Tensor& inout) {
 std::cout << __FUNCTION__ << ": " << inout.options() << "\n";
 return inout;
}

// By default, all backends (XPU, AutocastXPU, AutoGradXPU, CUDA, PrivateUse1,
// AutogradPrivateUse1 etc) are registered. If you need to register separately
// for a certain backend, separate registration for a certain backend is also
// supported.
TORCH_LIBRARY(deeplink_ext_, m) {
 if (&diopiAdamW != nullptr) {
 m.def(
 "adamw(Tensor(a!) param, Tensor(b!) exp_avg, Tensor(c!) exp_avg_sq, "
 "Tensor? max_exp_avg_sq_opt, Tensor grad, float lr, float beta1, float "
 "beta2, float epsilon, float weight_decay, int step, bool "
 "amsgrad)->(Tensor(a!), Tensor(b!), Tensor(c!))",
 adamw);
 }
 if (&diopiApplyPenalty != nullptr) {
 m.def(
 "apply_penalty(Tensor(a!) logits, Tensor presence_penalty, Tensor "
 "frequency_penalty, Tensor p_token_ids, Tensor p_token_counts, Tensor "
 "p_cumsum_seq_len, int p_max_len_in_batch)->Tensor(a!)",
 apply_penalty);
 }
 if (&diopiDestIndexCopyKV != nullptr) {
 m.def(
 "dest_index_copy_kv(Tensor(a!) out, Tensor k, Tensor "
 "dest_loc)->Tensor(a!)",
 dest_index_copy_kv);
 }
 if (&diopiDestIndexCopyKV != nullptr) {
 m.def(
 "rms_norm(Tensor(a!) output, Tensor(b!) inv_rms, Tensor input, int[]? "
 "normalized_shape, Tensor weight, Tensor? bias_opt, float eps) -> "
 "(Tensor(a!), Tensor(b!))",
 rms_norm);
 }

 if (&diopiRMSNormBackward != nullptr) {
 m.def(
 "rms_norm_backward(Tensor(a!) grad_input, Tensor(b!) grad_weight, "
 "Tensor(c!) grad_bias_opt, Tensor grad_output, Tensor input, Tensor "
 "weight, Tensor? bias_opt, Tensor inv_rms, int[]? normalized_shape, "
 "float eps) -> (Tensor(a!), Tensor(b!), Tensor(c!))",
 rms_norm_backward);
 }
 if (&diopiRotaryEmbedding != nullptr) {
 m.def(
 "apply_rotary(Tensor(a!) output, Tensor input, Tensor cos, Tensor sin, "
 "bool conj, bool interleaved) -> Tensor(a!)",
 apply_rotary);
 }

 m.def("example(Tensor(a!) inout)->Tensor(a!)", example_for_all_backend);
}

// only impl for dipu
TORCH_LIBRARY_IMPL(deeplink_ext_, XPU, m) {
 // m.impl("example", example_only_for_xpu);
}

int n = [](){
 std::cout << "deeplink_ext_ loaded" << std::endl;
 return 0;
}();

} // namespace dipu::dipu_ext

16 changes: 15 additions & 1 deletion
setup.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,7 +1,8 @@
# Copyright (c) 2024, DeepLink.

from setuptools import find_packages, setup, Extension
from torch.utils.cpp_extension import BuildExtension, include_paths, library_paths
from torch.utils.cpp_extension import BuildExtension, CppExtension, include_paths, library_paths

import glob
import os
import subprocess
Expand DownExpand Up@@ -86,3 +87,16 @@ def get_ext():
 cmdclass={"build_ext": BuildExtensionWithCompdb},
 install_requires=["einops"],
)


setup(
@mrdanielwmrdanielwMay 6, 2024

Copy link


Copy Markdown

Collaborator
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

请教下 这个位置引入cppextension 是为了使用”TORCH_LIBRARY“吗 我记得BuildExtension和CppExtension 是在cuda 编译才用到， 如果在其他设备上用这种方式编译会不会有问题？
@zhaoguochun1995zhaoguochun1995May 7, 2024

Copy link


Copy Markdown

ContributorAuthor
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

setup里面的只是个草稿，还有待完善。cppextension不光光可以编译cuda kernel,也可以只编译cpp文件的。
 name='deeplink_ext_ops',
 ext_modules=[
 CppExtension(
 name='deeplink_ext_ops',
 sources=glob.glob("./csrc/*.cpp"),
 extra_compile_args=[' -g ']),
 ],
 cmdclass={
 'build_ext': BuildExtension
 })

26 changes: 26 additions & 0 deletions
test_dispatch.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,26 @@
import torch
import torch_dipu
import deeplink_ext

so_path = deeplink_ext.__path__[0] + "/cpp_extensions.cpython-39-x86_64-linux-gnu.so"
torch.ops.load_library(so_path)
print(f"torch.ops.loaded_libraries:{torch.ops.loaded_libraries}")

#print(torch.ops.deeplink_ext_.dest_index_copy_kv)

def code_to_profile():
 x = torch.randn(3,4)
 y = torch.ops.deeplink_ext_.example(x)
 y = torch.ops.deeplink_ext_.example(x.cuda())


with torch.profiler.profile(
 activities=[
 torch.profiler.ProfilerActivity.CPU,
 torch.profiler.ProfilerActivity.CUDA,
 ]
) as p:
 code_to_profile()
print(p.key_averages().table(
 sort_by="self_cuda_time_total", row_limit=-1))


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
support op dispatch#93

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Open

zhaoguochun1995
wants to merge
7
commits into

base:main
Choose a base branch

from

 Open


support op dispatch
#93
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


7 commits


Select commit
Hold shift + click to select a range
cf4b44f

support op dispatch


zhaoguochun1995 Apr 11, 2024
d0c203a

support op dispatch


zhaoguochun1995 Apr 15, 2024
94b2600

add some op


zhaoguochun1995 Apr 15, 2024
a1b1db3

add rms_norm op


zhaoguochun1995 Apr 16, 2024
30dc807

auto format


zhaoguochun1995 Apr 16, 2024
2e4b897

add apply_rotary


zhaoguochun1995 Apr 16, 2024
bd17565

backup


zhaoguochun1995 Apr 16, 2024

File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


136 changes: 136 additions & 0 deletions
csrc/extensions.cpp

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,10 +1,12 @@
// Copyright (c) 2023, DeepLink.

#include <cstdint>
#include <iostream>
#include <string>
#include <tuple>
#include <utility>

#include "torch/library.h"
#include <ATen/core/ATen_fwd.h>
#include <ATen/core/Generator.h>
#include <ATen/core/TensorBody.h>
Expand DownExpand Up@@ -363,4 +365,138 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
 }
}

std::tuple<at::Tensor&, at::Tensor&, at::Tensor&> adamw(
 at::Tensor& param, at::Tensor& exp_avg, at::Tensor& exp_avg_sq,
 const c10::optional<at::Tensor>& max_exp_avg_sq_opt, const at::Tensor& grad,
 double lr, double beta1, double beta2, double epsilon, double weight_decay,
 int64_t step, bool amsgrad) {
 // the diopiAdamW func has no "maximize" param
 at::Tensor& grad_ref =
 const_cast<at::Tensor&>(grad); // todo: grad is const value
 at::Tensor max_exp_avg_sq_opt_value =
 max_exp_avg_sq_opt.value_or(at::Tensor());
 callDiopi(diopiAdamW, param, grad_ref, exp_avg, exp_avg_sq,
 max_exp_avg_sq_opt_value, lr, beta1, beta2, epsilon, weight_decay,
 step, amsgrad);
 return std::tie(param, exp_avg, exp_avg_sq);
}

at::Tensor& apply_penalty(at::Tensor& logits,
 const at::Tensor& presence_penalty,
 const at::Tensor& frequency_penalty,
 const at::Tensor& p_token_ids,
 const at::Tensor& p_token_counts,
 const at::Tensor& p_cumsum_seq_len,
 int64_t p_max_len_in_batch) {
 callDiopi(diopiApplyPenalty, logits, presence_penalty, frequency_penalty,
 p_token_ids, p_token_counts, p_cumsum_seq_len, p_max_len_in_batch);
 return logits;
}

at::Tensor& dest_index_copy_kv(const at::Tensor& k, const at::Tensor& dest_loc,
 at::Tensor& out) {
 callDiopi(diopiDestIndexCopyKV, out, k, dest_loc);
 return out;
}

std::tuple<at::Tensor&, at::Tensor&> rms_norm(
 at::Tensor& output, at::Tensor& inv_rms, const at::Tensor& input,
 const OptionalIntArray& normalized_shape, const at::Tensor& weight,
 const c10::optional<at::Tensor>& bias_opt, double eps) {
 callDiopi(diopiRMSNorm, output, inv_rms, input, normalized_shape, weight,
 bias_opt, eps);
 return std::tie(output, inv_rms);
}

std::tuple<at::Tensor&, at::Tensor&, at::Tensor&> rms_norm_backward(
 at::Tensor& grad_input, at::Tensor& grad_weight, at::Tensor& grad_bias_opt,
 const at::Tensor& grad_output, const at::Tensor& input,
 const at::Tensor& weight, const c10::optional<at::Tensor>& bias_opt,
 const at::Tensor& inv_rms, const OptionalIntArray& normalized_shape,
 double eps) {
 callDiopi(diopiRMSNormBackward, grad_input, grad_weight, grad_bias_opt,
 grad_output, input, weight, bias_opt, inv_rms, normalized_shape,
 eps);
 return std::tie(grad_input, grad_weight, grad_bias_opt);
}

at::Tensor& apply_rotary(at::Tensor& output, const at::Tensor& input,
 const at::Tensor& cos, const at::Tensor& sin,
 const bool conj, const bool interleaved) {
 callDiopi(diopiRotaryEmbedding, output, input, cos, sin, conj, interleaved);
 return output;
}

at::Tensor& example_for_all_backend(at::Tensor& inout) {
 std::cout << __FUNCTION__ << ": " << inout.options() << "\n";
 return inout;
}

at::Tensor& example_only_for_xpu(at::Tensor& inout) {
 std::cout << __FUNCTION__ << ": " << inout.options() << "\n";
 return inout;
}

// By default, all backends (XPU, AutocastXPU, AutoGradXPU, CUDA, PrivateUse1,
// AutogradPrivateUse1 etc) are registered. If you need to register separately
// for a certain backend, separate registration for a certain backend is also
// supported.
TORCH_LIBRARY(deeplink_ext_, m) {
 if (&diopiAdamW != nullptr) {
 m.def(
 "adamw(Tensor(a!) param, Tensor(b!) exp_avg, Tensor(c!) exp_avg_sq, "
 "Tensor? max_exp_avg_sq_opt, Tensor grad, float lr, float beta1, float "
 "beta2, float epsilon, float weight_decay, int step, bool "
 "amsgrad)->(Tensor(a!), Tensor(b!), Tensor(c!))",
 adamw);
 }
 if (&diopiApplyPenalty != nullptr) {
 m.def(
 "apply_penalty(Tensor(a!) logits, Tensor presence_penalty, Tensor "
 "frequency_penalty, Tensor p_token_ids, Tensor p_token_counts, Tensor "
 "p_cumsum_seq_len, int p_max_len_in_batch)->Tensor(a!)",
 apply_penalty);
 }
 if (&diopiDestIndexCopyKV != nullptr) {
 m.def(
 "dest_index_copy_kv(Tensor(a!) out, Tensor k, Tensor "
 "dest_loc)->Tensor(a!)",
 dest_index_copy_kv);
 }
 if (&diopiDestIndexCopyKV != nullptr) {
 m.def(
 "rms_norm(Tensor(a!) output, Tensor(b!) inv_rms, Tensor input, int[]? "
 "normalized_shape, Tensor weight, Tensor? bias_opt, float eps) -> "
 "(Tensor(a!), Tensor(b!))",
 rms_norm);
 }

 if (&diopiRMSNormBackward != nullptr) {
 m.def(
 "rms_norm_backward(Tensor(a!) grad_input, Tensor(b!) grad_weight, "
 "Tensor(c!) grad_bias_opt, Tensor grad_output, Tensor input, Tensor "
 "weight, Tensor? bias_opt, Tensor inv_rms, int[]? normalized_shape, "
 "float eps) -> (Tensor(a!), Tensor(b!), Tensor(c!))",
 rms_norm_backward);
 }
 if (&diopiRotaryEmbedding != nullptr) {
 m.def(
 "apply_rotary(Tensor(a!) output, Tensor input, Tensor cos, Tensor sin, "
 "bool conj, bool interleaved) -> Tensor(a!)",
 apply_rotary);
 }

 m.def("example(Tensor(a!) inout)->Tensor(a!)", example_for_all_backend);
}

// only impl for dipu
TORCH_LIBRARY_IMPL(deeplink_ext_, XPU, m) {
 // m.impl("example", example_only_for_xpu);
}

int n = [](){
 std::cout << "deeplink_ext_ loaded" << std::endl;
 return 0;
}();

} // namespace dipu::dipu_ext

16 changes: 15 additions & 1 deletion
setup.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,7 +1,8 @@
# Copyright (c) 2024, DeepLink.

from setuptools import find_packages, setup, Extension
from torch.utils.cpp_extension import BuildExtension, include_paths, library_paths
from torch.utils.cpp_extension import BuildExtension, CppExtension, include_paths, library_paths

import glob
import os
import subprocess
Expand DownExpand Up@@ -86,3 +87,16 @@ def get_ext():
 cmdclass={"build_ext": BuildExtensionWithCompdb},
 install_requires=["einops"],
)


setup(
@mrdanielwmrdanielwMay 6, 2024

Copy link


Copy Markdown

Collaborator
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

请教下 这个位置引入cppextension 是为了使用”TORCH_LIBRARY“吗 我记得BuildExtension和CppExtension 是在cuda 编译才用到， 如果在其他设备上用这种方式编译会不会有问题？
@zhaoguochun1995zhaoguochun1995May 7, 2024

Copy link


Copy Markdown

ContributorAuthor
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

setup里面的只是个草稿，还有待完善。cppextension不光光可以编译cuda kernel,也可以只编译cpp文件的。
 name='deeplink_ext_ops',
 ext_modules=[
 CppExtension(
 name='deeplink_ext_ops',
 sources=glob.glob("./csrc/*.cpp"),
 extra_compile_args=[' -g ']),
 ],
 cmdclass={
 'build_ext': BuildExtension
 })

26 changes: 26 additions & 0 deletions
test_dispatch.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,26 @@
import torch
import torch_dipu
import deeplink_ext

so_path = deeplink_ext.__path__[0] + "/cpp_extensions.cpython-39-x86_64-linux-gnu.so"
torch.ops.load_library(so_path)
print(f"torch.ops.loaded_libraries:{torch.ops.loaded_libraries}")

#print(torch.ops.deeplink_ext_.dest_index_copy_kv)

def code_to_profile():
 x = torch.randn(3,4)
 y = torch.ops.deeplink_ext_.example(x)
 y = torch.ops.deeplink_ext_.example(x.cuda())


with torch.profiler.profile(
 activities=[
 torch.profiler.ProfilerActivity.CPU,
 torch.profiler.ProfilerActivity.CUDA,
 ]
) as p:
 code_to_profile()
print(p.key_averages().table(
 sort_by="self_cuda_time_total", row_limit=-1))


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
support op dispatch#93

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Open

zhaoguochun1995
wants to merge
7
commits into

base:main
Choose a base branch

from

 Open


support op dispatch
#93
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


7 commits


Select commit
Hold shift + click to select a range
cf4b44f

support op dispatch


zhaoguochun1995 Apr 11, 2024
d0c203a

support op dispatch


zhaoguochun1995 Apr 15, 2024
94b2600

add some op


zhaoguochun1995 Apr 15, 2024
a1b1db3

add rms_norm op


zhaoguochun1995 Apr 16, 2024
30dc807

auto format


zhaoguochun1995 Apr 16, 2024
2e4b897

add apply_rotary


zhaoguochun1995 Apr 16, 2024
bd17565

backup


zhaoguochun1995 Apr 16, 2024

File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


136 changes: 136 additions & 0 deletions
csrc/extensions.cpp

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,10 +1,12 @@
// Copyright (c) 2023, DeepLink.

#include <cstdint>
#include <iostream>
#include <string>
#include <tuple>
#include <utility>

#include "torch/library.h"
#include <ATen/core/ATen_fwd.h>
#include <ATen/core/Generator.h>
#include <ATen/core/TensorBody.h>
Expand DownExpand Up@@ -363,4 +365,138 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
 }
}

std::tuple<at::Tensor&, at::Tensor&, at::Tensor&> adamw(
 at::Tensor& param, at::Tensor& exp_avg, at::Tensor& exp_avg_sq,
 const c10::optional<at::Tensor>& max_exp_avg_sq_opt, const at::Tensor& grad,
 double lr, double beta1, double beta2, double epsilon, double weight_decay,
 int64_t step, bool amsgrad) {
 // the diopiAdamW func has no "maximize" param
 at::Tensor& grad_ref =
 const_cast<at::Tensor&>(grad); // todo: grad is const value
 at::Tensor max_exp_avg_sq_opt_value =
 max_exp_avg_sq_opt.value_or(at::Tensor());
 callDiopi(diopiAdamW, param, grad_ref, exp_avg, exp_avg_sq,
 max_exp_avg_sq_opt_value, lr, beta1, beta2, epsilon, weight_decay,
 step, amsgrad);
 return std::tie(param, exp_avg, exp_avg_sq);
}

at::Tensor& apply_penalty(at::Tensor& logits,
 const at::Tensor& presence_penalty,
 const at::Tensor& frequency_penalty,
 const at::Tensor& p_token_ids,
 const at::Tensor& p_token_counts,
 const at::Tensor& p_cumsum_seq_len,
 int64_t p_max_len_in_batch) {
 callDiopi(diopiApplyPenalty, logits, presence_penalty, frequency_penalty,
 p_token_ids, p_token_counts, p_cumsum_seq_len, p_max_len_in_batch);
 return logits;
}

at::Tensor& dest_index_copy_kv(const at::Tensor& k, const at::Tensor& dest_loc,
 at::Tensor& out) {
 callDiopi(diopiDestIndexCopyKV, out, k, dest_loc);
 return out;
}

std::tuple<at::Tensor&, at::Tensor&> rms_norm(
 at::Tensor& output, at::Tensor& inv_rms, const at::Tensor& input,
 const OptionalIntArray& normalized_shape, const at::Tensor& weight,
 const c10::optional<at::Tensor>& bias_opt, double eps) {
 callDiopi(diopiRMSNorm, output, inv_rms, input, normalized_shape, weight,
 bias_opt, eps);
 return std::tie(output, inv_rms);
}

std::tuple<at::Tensor&, at::Tensor&, at::Tensor&> rms_norm_backward(
 at::Tensor& grad_input, at::Tensor& grad_weight, at::Tensor& grad_bias_opt,
 const at::Tensor& grad_output, const at::Tensor& input,
 const at::Tensor& weight, const c10::optional<at::Tensor>& bias_opt,
 const at::Tensor& inv_rms, const OptionalIntArray& normalized_shape,
 double eps) {
 callDiopi(diopiRMSNormBackward, grad_input, grad_weight, grad_bias_opt,
 grad_output, input, weight, bias_opt, inv_rms, normalized_shape,
 eps);
 return std::tie(grad_input, grad_weight, grad_bias_opt);
}

at::Tensor& apply_rotary(at::Tensor& output, const at::Tensor& input,
 const at::Tensor& cos, const at::Tensor& sin,
 const bool conj, const bool interleaved) {
 callDiopi(diopiRotaryEmbedding, output, input, cos, sin, conj, interleaved);
 return output;
}

at::Tensor& example_for_all_backend(at::Tensor& inout) {
 std::cout << __FUNCTION__ << ": " << inout.options() << "\n";
 return inout;
}

at::Tensor& example_only_for_xpu(at::Tensor& inout) {
 std::cout << __FUNCTION__ << ": " << inout.options() << "\n";
 return inout;
}

// By default, all backends (XPU, AutocastXPU, AutoGradXPU, CUDA, PrivateUse1,
// AutogradPrivateUse1 etc) are registered. If you need to register separately
// for a certain backend, separate registration for a certain backend is also
// supported.
TORCH_LIBRARY(deeplink_ext_, m) {
 if (&diopiAdamW != nullptr) {
 m.def(
 "adamw(Tensor(a!) param, Tensor(b!) exp_avg, Tensor(c!) exp_avg_sq, "
 "Tensor? max_exp_avg_sq_opt, Tensor grad, float lr, float beta1, float "
 "beta2, float epsilon, float weight_decay, int step, bool "
 "amsgrad)->(Tensor(a!), Tensor(b!), Tensor(c!))",
 adamw);
 }
 if (&diopiApplyPenalty != nullptr) {
 m.def(
 "apply_penalty(Tensor(a!) logits, Tensor presence_penalty, Tensor "
 "frequency_penalty, Tensor p_token_ids, Tensor p_token_counts, Tensor "
 "p_cumsum_seq_len, int p_max_len_in_batch)->Tensor(a!)",
 apply_penalty);
 }
 if (&diopiDestIndexCopyKV != nullptr) {
 m.def(
 "dest_index_copy_kv(Tensor(a!) out, Tensor k, Tensor "
 "dest_loc)->Tensor(a!)",
 dest_index_copy_kv);
 }
 if (&diopiDestIndexCopyKV != nullptr) {
 m.def(
 "rms_norm(Tensor(a!) output, Tensor(b!) inv_rms, Tensor input, int[]? "
 "normalized_shape, Tensor weight, Tensor? bias_opt, float eps) -> "
 "(Tensor(a!), Tensor(b!))",
 rms_norm);
 }

 if (&diopiRMSNormBackward != nullptr) {
 m.def(
 "rms_norm_backward(Tensor(a!) grad_input, Tensor(b!) grad_weight, "
 "Tensor(c!) grad_bias_opt, Tensor grad_output, Tensor input, Tensor "
 "weight, Tensor? bias_opt, Tensor inv_rms, int[]? normalized_shape, "
 "float eps) -> (Tensor(a!), Tensor(b!), Tensor(c!))",
 rms_norm_backward);
 }
 if (&diopiRotaryEmbedding != nullptr) {
 m.def(
 "apply_rotary(Tensor(a!) output, Tensor input, Tensor cos, Tensor sin, "
 "bool conj, bool interleaved) -> Tensor(a!)",
 apply_rotary);
 }

 m.def("example(Tensor(a!) inout)->Tensor(a!)", example_for_all_backend);
}

// only impl for dipu
TORCH_LIBRARY_IMPL(deeplink_ext_, XPU, m) {
 // m.impl("example", example_only_for_xpu);
}

int n = [](){
 std::cout << "deeplink_ext_ loaded" << std::endl;
 return 0;
}();

} // namespace dipu::dipu_ext

16 changes: 15 additions & 1 deletion
setup.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,7 +1,8 @@
# Copyright (c) 2024, DeepLink.

from setuptools import find_packages, setup, Extension
from torch.utils.cpp_extension import BuildExtension, include_paths, library_paths
from torch.utils.cpp_extension import BuildExtension, CppExtension, include_paths, library_paths

import glob
import os
import subprocess
Expand DownExpand Up@@ -86,3 +87,16 @@ def get_ext():
 cmdclass={"build_ext": BuildExtensionWithCompdb},
 install_requires=["einops"],
)


setup(
@mrdanielwmrdanielwMay 6, 2024

Copy link


Copy Markdown

Collaborator
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

请教下 这个位置引入cppextension 是为了使用”TORCH_LIBRARY“吗 我记得BuildExtension和CppExtension 是在cuda 编译才用到， 如果在其他设备上用这种方式编译会不会有问题？
@zhaoguochun1995zhaoguochun1995May 7, 2024

Copy link


Copy Markdown

ContributorAuthor
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

setup里面的只是个草稿，还有待完善。cppextension不光光可以编译cuda kernel,也可以只编译cpp文件的。
 name='deeplink_ext_ops',
 ext_modules=[
 CppExtension(
 name='deeplink_ext_ops',
 sources=glob.glob("./csrc/*.cpp"),
 extra_compile_args=[' -g ']),
 ],
 cmdclass={
 'build_ext': BuildExtension
 })

26 changes: 26 additions & 0 deletions
test_dispatch.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,26 @@
import torch
import torch_dipu
import deeplink_ext

so_path = deeplink_ext.__path__[0] + "/cpp_extensions.cpython-39-x86_64-linux-gnu.so"
torch.ops.load_library(so_path)
print(f"torch.ops.loaded_libraries:{torch.ops.loaded_libraries}")

#print(torch.ops.deeplink_ext_.dest_index_copy_kv)

def code_to_profile():
 x = torch.randn(3,4)
 y = torch.ops.deeplink_ext_.example(x)
 y = torch.ops.deeplink_ext_.example(x.cuda())


with torch.profiler.profile(
 activities=[
 torch.profiler.ProfilerActivity.CPU,
 torch.profiler.ProfilerActivity.CUDA,
 ]
) as p:
 code_to_profile()
print(p.key_averages().table(
 sort_by="self_cuda_time_total", row_limit=-1))


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
support op dispatch#93

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Open

zhaoguochun1995
wants to merge
7
commits into

base:main
Choose a base branch

from

 Open


support op dispatch
#93
Show file tree
Hide file tree
 Changes from all commits
Commits

Show all changes


7 commits


Select commit
Hold shift + click to select a range
cf4b44f

support op dispatch


zhaoguochun1995 Apr 11, 2024
d0c203a

support op dispatch


zhaoguochun1995 Apr 15, 2024
94b2600

add some op


zhaoguochun1995 Apr 15, 2024
a1b1db3

add rms_norm op


zhaoguochun1995 Apr 16, 2024
30dc807

auto format


zhaoguochun1995 Apr 16, 2024
2e4b897

add apply_rotary


zhaoguochun1995 Apr 16, 2024
bd17565

backup


zhaoguochun1995 Apr 16, 2024

File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


136 changes: 136 additions & 0 deletions
csrc/extensions.cpp

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,10 +1,12 @@
// Copyright (c) 2023, DeepLink.

#include <cstdint>
#include <iostream>
#include <string>
#include <tuple>
#include <utility>

#include "torch/library.h"
#include <ATen/core/ATen_fwd.h>
#include <ATen/core/Generator.h>
#include <ATen/core/TensorBody.h>
Expand DownExpand Up@@ -363,4 +365,138 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
 }
}

std::tuple<at::Tensor&, at::Tensor&, at::Tensor&> adamw(
 at::Tensor& param, at::Tensor& exp_avg, at::Tensor& exp_avg_sq,
 const c10::optional<at::Tensor>& max_exp_avg_sq_opt, const at::Tensor& grad,
 double lr, double beta1, double beta2, double epsilon, double weight_decay,
 int64_t step, bool amsgrad) {
 // the diopiAdamW func has no "maximize" param
 at::Tensor& grad_ref =
 const_cast<at::Tensor&>(grad); // todo: grad is const value
 at::Tensor max_exp_avg_sq_opt_value =
 max_exp_avg_sq_opt.value_or(at::Tensor());
 callDiopi(diopiAdamW, param, grad_ref, exp_avg, exp_avg_sq,
 max_exp_avg_sq_opt_value, lr, beta1, beta2, epsilon, weight_decay,
 step, amsgrad);
 return std::tie(param, exp_avg, exp_avg_sq);
}

at::Tensor& apply_penalty(at::Tensor& logits,
 const at::Tensor& presence_penalty,
 const at::Tensor& frequency_penalty,
 const at::Tensor& p_token_ids,
 const at::Tensor& p_token_counts,
 const at::Tensor& p_cumsum_seq_len,
 int64_t p_max_len_in_batch) {
 callDiopi(diopiApplyPenalty, logits, presence_penalty, frequency_penalty,
 p_token_ids, p_token_counts, p_cumsum_seq_len, p_max_len_in_batch);
 return logits;
}

at::Tensor& dest_index_copy_kv(const at::Tensor& k, const at::Tensor& dest_loc,
 at::Tensor& out) {
 callDiopi(diopiDestIndexCopyKV, out, k, dest_loc);
 return out;
}

std::tuple<at::Tensor&, at::Tensor&> rms_norm(
 at::Tensor& output, at::Tensor& inv_rms, const at::Tensor& input,
 const OptionalIntArray& normalized_shape, const at::Tensor& weight,
 const c10::optional<at::Tensor>& bias_opt, double eps) {
 callDiopi(diopiRMSNorm, output, inv_rms, input, normalized_shape, weight,
 bias_opt, eps);
 return std::tie(output, inv_rms);
}

std::tuple<at::Tensor&, at::Tensor&, at::Tensor&> rms_norm_backward(
 at::Tensor& grad_input, at::Tensor& grad_weight, at::Tensor& grad_bias_opt,
 const at::Tensor& grad_output, const at::Tensor& input,
 const at::Tensor& weight, const c10::optional<at::Tensor>& bias_opt,
 const at::Tensor& inv_rms, const OptionalIntArray& normalized_shape,
 double eps) {
 callDiopi(diopiRMSNormBackward, grad_input, grad_weight, grad_bias_opt,
 grad_output, input, weight, bias_opt, inv_rms, normalized_shape,
 eps);
 return std::tie(grad_input, grad_weight, grad_bias_opt);
}

at::Tensor& apply_rotary(at::Tensor& output, const at::Tensor& input,
 const at::Tensor& cos, const at::Tensor& sin,
 const bool conj, const bool interleaved) {
 callDiopi(diopiRotaryEmbedding, output, input, cos, sin, conj, interleaved);
 return output;
}

at::Tensor& example_for_all_backend(at::Tensor& inout) {
 std::cout << __FUNCTION__ << ": " << inout.options() << "\n";
 return inout;
}

at::Tensor& example_only_for_xpu(at::Tensor& inout) {
 std::cout << __FUNCTION__ << ": " << inout.options() << "\n";
 return inout;
}

// By default, all backends (XPU, AutocastXPU, AutoGradXPU, CUDA, PrivateUse1,
// AutogradPrivateUse1 etc) are registered. If you need to register separately
// for a certain backend, separate registration for a certain backend is also
// supported.
TORCH_LIBRARY(deeplink_ext_, m) {
 if (&diopiAdamW != nullptr) {
 m.def(
 "adamw(Tensor(a!) param, Tensor(b!) exp_avg, Tensor(c!) exp_avg_sq, "
 "Tensor? max_exp_avg_sq_opt, Tensor grad, float lr, float beta1, float "
 "beta2, float epsilon, float weight_decay, int step, bool "
 "amsgrad)->(Tensor(a!), Tensor(b!), Tensor(c!))",
 adamw);
 }
 if (&diopiApplyPenalty != nullptr) {
 m.def(
 "apply_penalty(Tensor(a!) logits, Tensor presence_penalty, Tensor "
 "frequency_penalty, Tensor p_token_ids, Tensor p_token_counts, Tensor "
 "p_cumsum_seq_len, int p_max_len_in_batch)->Tensor(a!)",
 apply_penalty);
 }
 if (&diopiDestIndexCopyKV != nullptr) {
 m.def(
 "dest_index_copy_kv(Tensor(a!) out, Tensor k, Tensor "
 "dest_loc)->Tensor(a!)",
 dest_index_copy_kv);
 }
 if (&diopiDestIndexCopyKV != nullptr) {
 m.def(
 "rms_norm(Tensor(a!) output, Tensor(b!) inv_rms, Tensor input, int[]? "
 "normalized_shape, Tensor weight, Tensor? bias_opt, float eps) -> "
 "(Tensor(a!), Tensor(b!))",
 rms_norm);
 }

 if (&diopiRMSNormBackward != nullptr) {
 m.def(
 "rms_norm_backward(Tensor(a!) grad_input, Tensor(b!) grad_weight, "
 "Tensor(c!) grad_bias_opt, Tensor grad_output, Tensor input, Tensor "
 "weight, Tensor? bias_opt, Tensor inv_rms, int[]? normalized_shape, "
 "float eps) -> (Tensor(a!), Tensor(b!), Tensor(c!))",
 rms_norm_backward);
 }
 if (&diopiRotaryEmbedding != nullptr) {
 m.def(
 "apply_rotary(Tensor(a!) output, Tensor input, Tensor cos, Tensor sin, "
 "bool conj, bool interleaved) -> Tensor(a!)",
 apply_rotary);
 }

 m.def("example(Tensor(a!) inout)->Tensor(a!)", example_for_all_backend);
}

// only impl for dipu
TORCH_LIBRARY_IMPL(deeplink_ext_, XPU, m) {
 // m.impl("example", example_only_for_xpu);
}

int n = [](){
 std::cout << "deeplink_ext_ loaded" << std::endl;
 return 0;
}();

} // namespace dipu::dipu_ext

16 changes: 15 additions & 1 deletion
setup.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,7 +1,8 @@
# Copyright (c) 2024, DeepLink.

from setuptools import find_packages, setup, Extension
from torch.utils.cpp_extension import BuildExtension, include_paths, library_paths
from torch.utils.cpp_extension import BuildExtension, CppExtension, include_paths, library_paths

import glob
import os
import subprocess
Expand DownExpand Up@@ -86,3 +87,16 @@ def get_ext():
 cmdclass={"build_ext": BuildExtensionWithCompdb},
 install_requires=["einops"],
)


setup(
@mrdanielwmrdanielwMay 6, 2024

Copy link


Copy Markdown

Collaborator
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

请教下 这个位置引入cppextension 是为了使用”TORCH_LIBRARY“吗 我记得BuildExtension和CppExtension 是在cuda 编译才用到， 如果在其他设备上用这种方式编译会不会有问题？
@zhaoguochun1995zhaoguochun1995May 7, 2024

Copy link


Copy Markdown

ContributorAuthor
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

setup里面的只是个草稿，还有待完善。cppextension不光光可以编译cuda kernel,也可以只编译cpp文件的。
 name='deeplink_ext_ops',
 ext_modules=[
 CppExtension(
 name='deeplink_ext_ops',
 sources=glob.glob("./csrc/*.cpp"),
 extra_compile_args=[' -g ']),
 ],
 cmdclass={
 'build_ext': BuildExtension
 })

26 changes: 26 additions & 0 deletions
test_dispatch.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,26 @@
import torch
import torch_dipu
import deeplink_ext

so_path = deeplink_ext.__path__[0] + "/cpp_extensions.cpython-39-x86_64-linux-gnu.so"
torch.ops.load_library(so_path)
print(f"torch.ops.loaded_libraries:{torch.ops.loaded_libraries}")

#print(torch.ops.deeplink_ext_.dest_index_copy_kv)

def code_to_profile():
 x = torch.randn(3,4)
 y = torch.ops.deeplink_ext_.example(x)
 y = torch.ops.deeplink_ext_.example(x.cuda())


with torch.profiler.profile(
 activities=[
 torch.profiler.ProfilerActivity.CPU,
 torch.profiler.ProfilerActivity.CUDA,
 ]
) as p:
 code_to_profile()
print(p.key_averages().table(
 sort_by="self_cuda_time_total", row_limit=-1))