[ET-VK] Introduce graph runtime shader library that enables dynamic shapes - #2366

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[ET-VK] Introduce graph runtime shader library that enables dynamic shapes#2366
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Stack from ghstack (oldest at bottom):

Context

pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.

The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:

layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}

Shaders will accept separate UBOs for each piece of tensor metadata:

layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;

Each UBO will be owned and maintained by the corresponding vTensor instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the tensor.virtual_resize(new_sizes) call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.

This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.

Considerations

Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?

The primary motivation is code quality.

First, having vTensor supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc Params structs to organize arguments to write into a api::UniformParamsBuffer.

Constructing an ExecuteNode for binary operators is now

 graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))

instead of

ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));

Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.

Differential Revision: D54754545

@SS-JIA
[ET-VK] Introduce graph runtime shader library that enables dynamic s…
b24a594
…hapes
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@pytorch-bot

pytorch-botBot commented Mar 12, 2024•
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🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/2366

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@facebook-github-botfacebook-github-bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Mar 12, 2024
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This pull request was exported from Phabricator. Differential Revision: D54754545

This was referenced Mar 12, 2024
Closed
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@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
9be2c32
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot

facebook-github-bot commented Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545

@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
5bb0389
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot

facebook-github-bot commented Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545

@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
5cdc6cd
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot

facebook-github-bot commented Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545

SS-JIA added a commit that referenced this pull request Mar 13, 2024
@SS-JIA
[ET-VK] Introduce graph runtime shader library that enables dynamic s…
add1f02
…hapes
Pull Request resolved: #2366
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
ghstack-source-id: 218421178
@exported-using-ghexport
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
@facebook-github-bot

facebook-github-bot commented Mar 13, 2024

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This pull request has been merged in 835279e.

junpi3 pushed a commit that referenced this pull request Mar 13, 2024
Jorge Pineda
[ET-VK][EZ] Include alpha to fix aten.sub.Tensor
ae0776f
Missed this in #2366
Differential Revision: [D54880024](https://our.internmc.facebook.com/intern/diff/D54880024/)
[ghstack-poisoned]
@junpi3junpi3 mentioned this pull request Mar 13, 2024
Closed
junpi3 pushed a commit that referenced this pull request Mar 13, 2024
Jorge Pineda
[ET-VK][EZ] Include alpha to fix aten.sub.Tensor
fe1491e
Missed this in #2366
Differential Revision: [D54880024](https://our.internmc.facebook.com/intern/diff/D54880024/)
ghstack-source-id: 218593311
Pull Request resolved: #2418
facebook-github-bot pushed a commit that referenced this pull request Mar 14, 2024
Jorge Pineda@facebook-github-bot
Include alpha to fix aten.sub.Tensor (#2418)
a5faf03
Summary:
Pull Request resolved: #2418
Missed this in #2366
ghstack-source-id: 218593311
exported-using-ghexport
bypass-github-export-checks
Reviewed By: SS-JIA
Differential Revision: D54880024
fbshipit-source-id: c4e19d8fefbb9d2fc4547ec2edc236060638da5e
@SS-JIA
SS-JIA deleted the gh/SS-JIA/11/head branch January 24, 2025 19:40
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Stack from ghstack (oldest at bottom):
Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}

Shaders will accept separate UBOs for each piece of tensor metadata:
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;

Each UBO will be owned and maintained by the corresponding vTensor instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the tensor.virtual_resize(new_sizes) call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having vTensor supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc Params structs to organize arguments to write into a api::UniformParamsBuffer.
Constructing an ExecuteNode for binary operators is now
 graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))

instead of
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));

Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: D54754545
@SS-JIA
[ET-VK] Introduce graph runtime shader library that enables dynamic s…
b24a594
…hapes
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@pytorch-bot
pytorch-botBot
commented
Mar 12, 2024•

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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/2366
Note: Links to docs will display an error until the docs builds have been completed.
❌ 2 New Failures
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This pull request was exported from Phabricator. Differential Revision: D54754545

This was referenced Mar 12, 2024
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@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
9be2c32
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
5bb0389
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
5cdc6cd
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
SS-JIA added a commit
that referenced
this pull request
Mar 13, 2024
@SS-JIA
[ET-VK] Introduce graph runtime shader library that enables dynamic s…
add1f02
…hapes
Pull Request resolved: #2366
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
ghstack-source-id: 218421178
@exported-using-ghexport
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
@facebook-github-bot
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Mar 13, 2024

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This pull request has been merged in 835279e.
junpi3 pushed a commit
that referenced
this pull request
Mar 13, 2024
Jorge Pineda
[ET-VK][EZ] Include alpha to fix aten.sub.Tensor
ae0776f
Missed this in #2366
Differential Revision: [D54880024](https://our.internmc.facebook.com/intern/diff/D54880024/)
[ghstack-poisoned]
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mentioned this pull request
Mar 13, 2024
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junpi3 pushed a commit
that referenced
this pull request
Mar 13, 2024
Jorge Pineda
[ET-VK][EZ] Include alpha to fix aten.sub.Tensor
fe1491e
Missed this in #2366
Differential Revision: [D54880024](https://our.internmc.facebook.com/intern/diff/D54880024/)
ghstack-source-id: 218593311
Pull Request resolved: #2418
facebook-github-bot pushed a commit
that referenced
this pull request
Mar 14, 2024
Jorge Pineda@facebook-github-bot
Include alpha to fix aten.sub.Tensor (#2418)
a5faf03
Summary:
Pull Request resolved: #2418
Missed this in #2366
ghstack-source-id: 218593311
exported-using-ghexport
bypass-github-export-checks
Reviewed By: SS-JIA
Differential Revision: D54880024
fbshipit-source-id: c4e19d8fefbb9d2fc4547ec2edc236060638da5e
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Stack from ghstack (oldest at bottom):
Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}

Shaders will accept separate UBOs for each piece of tensor metadata:
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;

Each UBO will be owned and maintained by the corresponding vTensor instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the tensor.virtual_resize(new_sizes) call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having vTensor supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc Params structs to organize arguments to write into a api::UniformParamsBuffer.
Constructing an ExecuteNode for binary operators is now
 graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))

instead of
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));

Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: D54754545
@SS-JIA
[ET-VK] Introduce graph runtime shader library that enables dynamic s…
b24a594
…hapes
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
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Mar 12, 2024•

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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/2366
Note: Links to docs will display an error until the docs builds have been completed.
❌ 2 New Failures
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This pull request was exported from Phabricator. Differential Revision: D54754545

This was referenced Mar 12, 2024
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@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
9be2c32
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
5bb0389
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
5cdc6cd
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
SS-JIA added a commit
that referenced
this pull request
Mar 13, 2024
@SS-JIA
[ET-VK] Introduce graph runtime shader library that enables dynamic s…
add1f02
…hapes
Pull Request resolved: #2366
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
ghstack-source-id: 218421178
@exported-using-ghexport
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
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Mar 13, 2024

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This pull request has been merged in 835279e.
junpi3 pushed a commit
that referenced
this pull request
Mar 13, 2024
Jorge Pineda
[ET-VK][EZ] Include alpha to fix aten.sub.Tensor
ae0776f
Missed this in #2366
Differential Revision: [D54880024](https://our.internmc.facebook.com/intern/diff/D54880024/)
[ghstack-poisoned]
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mentioned this pull request
Mar 13, 2024
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junpi3 pushed a commit
that referenced
this pull request
Mar 13, 2024
Jorge Pineda
[ET-VK][EZ] Include alpha to fix aten.sub.Tensor
fe1491e
Missed this in #2366
Differential Revision: [D54880024](https://our.internmc.facebook.com/intern/diff/D54880024/)
ghstack-source-id: 218593311
Pull Request resolved: #2418
facebook-github-bot pushed a commit
that referenced
this pull request
Mar 14, 2024
Jorge Pineda@facebook-github-bot
Include alpha to fix aten.sub.Tensor (#2418)
a5faf03
Summary:
Pull Request resolved: #2418
Missed this in #2366
ghstack-source-id: 218593311
exported-using-ghexport
bypass-github-export-checks
Reviewed By: SS-JIA
Differential Revision: D54880024
fbshipit-source-id: c4e19d8fefbb9d2fc4547ec2edc236060638da5e
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Stack from ghstack (oldest at bottom):
Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}

Shaders will accept separate UBOs for each piece of tensor metadata:
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;

Each UBO will be owned and maintained by the corresponding vTensor instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the tensor.virtual_resize(new_sizes) call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having vTensor supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc Params structs to organize arguments to write into a api::UniformParamsBuffer.
Constructing an ExecuteNode for binary operators is now
 graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))

instead of
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));

Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: D54754545
@SS-JIA
[ET-VK] Introduce graph runtime shader library that enables dynamic s…
b24a594
…hapes
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
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Mar 12, 2024•

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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/2366
Note: Links to docs will display an error until the docs builds have been completed.
❌ 2 New Failures
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This pull request was exported from Phabricator. Differential Revision: D54754545

This was referenced Mar 12, 2024
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@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
9be2c32
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
5bb0389
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
5cdc6cd
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
SS-JIA added a commit
that referenced
this pull request
Mar 13, 2024
@SS-JIA
[ET-VK] Introduce graph runtime shader library that enables dynamic s…
add1f02
…hapes
Pull Request resolved: #2366
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
ghstack-source-id: 218421178
@exported-using-ghexport
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
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Mar 13, 2024

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This pull request has been merged in 835279e.
junpi3 pushed a commit
that referenced
this pull request
Mar 13, 2024
Jorge Pineda
[ET-VK][EZ] Include alpha to fix aten.sub.Tensor
ae0776f
Missed this in #2366
Differential Revision: [D54880024](https://our.internmc.facebook.com/intern/diff/D54880024/)
[ghstack-poisoned]
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mentioned this pull request
Mar 13, 2024
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junpi3 pushed a commit
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Mar 13, 2024
Jorge Pineda
[ET-VK][EZ] Include alpha to fix aten.sub.Tensor
fe1491e
Missed this in #2366
Differential Revision: [D54880024](https://our.internmc.facebook.com/intern/diff/D54880024/)
ghstack-source-id: 218593311
Pull Request resolved: #2418
facebook-github-bot pushed a commit
that referenced
this pull request
Mar 14, 2024
Jorge Pineda@facebook-github-bot
Include alpha to fix aten.sub.Tensor (#2418)
a5faf03
Summary:
Pull Request resolved: #2418
Missed this in #2366
ghstack-source-id: 218593311
exported-using-ghexport
bypass-github-export-checks
Reviewed By: SS-JIA
Differential Revision: D54880024
fbshipit-source-id: c4e19d8fefbb9d2fc4547ec2edc236060638da5e
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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");
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Stack from ghstack (oldest at bottom):
Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}

Shaders will accept separate UBOs for each piece of tensor metadata:
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;

Each UBO will be owned and maintained by the corresponding vTensor instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the tensor.virtual_resize(new_sizes) call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having vTensor supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc Params structs to organize arguments to write into a api::UniformParamsBuffer.
Constructing an ExecuteNode for binary operators is now
 graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))

instead of
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));

Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: D54754545
@SS-JIA
[ET-VK] Introduce graph runtime shader library that enables dynamic s…
b24a594
…hapes
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/2366
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This pull request was exported from Phabricator. Differential Revision: D54754545

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@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
9be2c32
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
5bb0389
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
5cdc6cd
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
SS-JIA added a commit
that referenced
this pull request
Mar 13, 2024
@SS-JIA
[ET-VK] Introduce graph runtime shader library that enables dynamic s…
add1f02
…hapes
Pull Request resolved: #2366
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
ghstack-source-id: 218421178
@exported-using-ghexport
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
@facebook-github-bot
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Mar 13, 2024

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This pull request has been merged in 835279e.
junpi3 pushed a commit
that referenced
this pull request
Mar 13, 2024
Jorge Pineda
[ET-VK][EZ] Include alpha to fix aten.sub.Tensor
ae0776f
Missed this in #2366
Differential Revision: [D54880024](https://our.internmc.facebook.com/intern/diff/D54880024/)
[ghstack-poisoned]
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Mar 13, 2024
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junpi3 pushed a commit
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Mar 13, 2024
Jorge Pineda
[ET-VK][EZ] Include alpha to fix aten.sub.Tensor
fe1491e
Missed this in #2366
Differential Revision: [D54880024](https://our.internmc.facebook.com/intern/diff/D54880024/)
ghstack-source-id: 218593311
Pull Request resolved: #2418
facebook-github-bot pushed a commit
that referenced
this pull request
Mar 14, 2024
Jorge Pineda@facebook-github-bot
Include alpha to fix aten.sub.Tensor (#2418)
a5faf03
Summary:
Pull Request resolved: #2418
Missed this in #2366
ghstack-source-id: 218593311
exported-using-ghexport
bypass-github-export-checks
Reviewed By: SS-JIA
Differential Revision: D54880024
fbshipit-source-id: c4e19d8fefbb9d2fc4547ec2edc236060638da5e
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Stack from ghstack (oldest at bottom):
Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}

Shaders will accept separate UBOs for each piece of tensor metadata:
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;

Each UBO will be owned and maintained by the corresponding vTensor instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the tensor.virtual_resize(new_sizes) call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having vTensor supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc Params structs to organize arguments to write into a api::UniformParamsBuffer.
Constructing an ExecuteNode for binary operators is now
 graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))

instead of
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));

Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: D54754545
@SS-JIA
[ET-VK] Introduce graph runtime shader library that enables dynamic s…
b24a594
…hapes
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
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Mar 12, 2024•

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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/2366
Note: Links to docs will display an error until the docs builds have been completed.
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This pull request was exported from Phabricator. Differential Revision: D54754545

This was referenced Mar 12, 2024
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@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
9be2c32
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
5bb0389
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
5cdc6cd
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
SS-JIA added a commit
that referenced
this pull request
Mar 13, 2024
@SS-JIA
[ET-VK] Introduce graph runtime shader library that enables dynamic s…
add1f02
…hapes
Pull Request resolved: #2366
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
ghstack-source-id: 218421178
@exported-using-ghexport
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
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Mar 13, 2024

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This pull request has been merged in 835279e.
junpi3 pushed a commit
that referenced
this pull request
Mar 13, 2024
Jorge Pineda
[ET-VK][EZ] Include alpha to fix aten.sub.Tensor
ae0776f
Missed this in #2366
Differential Revision: [D54880024](https://our.internmc.facebook.com/intern/diff/D54880024/)
[ghstack-poisoned]
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mentioned this pull request
Mar 13, 2024
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junpi3 pushed a commit
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Mar 13, 2024
Jorge Pineda
[ET-VK][EZ] Include alpha to fix aten.sub.Tensor
fe1491e
Missed this in #2366
Differential Revision: [D54880024](https://our.internmc.facebook.com/intern/diff/D54880024/)
ghstack-source-id: 218593311
Pull Request resolved: #2418
facebook-github-bot pushed a commit
that referenced
this pull request
Mar 14, 2024
Jorge Pineda@facebook-github-bot
Include alpha to fix aten.sub.Tensor (#2418)
a5faf03
Summary:
Pull Request resolved: #2418
Missed this in #2366
ghstack-source-id: 218593311
exported-using-ghexport
bypass-github-export-checks
Reviewed By: SS-JIA
Differential Revision: D54880024
fbshipit-source-id: c4e19d8fefbb9d2fc4547ec2edc236060638da5e
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Stack from ghstack (oldest at bottom):
Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}

Shaders will accept separate UBOs for each piece of tensor metadata:
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;

Each UBO will be owned and maintained by the corresponding vTensor instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the tensor.virtual_resize(new_sizes) call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having vTensor supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc Params structs to organize arguments to write into a api::UniformParamsBuffer.
Constructing an ExecuteNode for binary operators is now
 graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))

instead of
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));

Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: D54754545
@SS-JIA
[ET-VK] Introduce graph runtime shader library that enables dynamic s…
b24a594
…hapes
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
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pytorch-botBot
commented
Mar 12, 2024•

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This pull request was exported from Phabricator. Differential Revision: D54754545

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@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
9be2c32
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
5bb0389
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
5cdc6cd
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
SS-JIA added a commit
that referenced
this pull request
Mar 13, 2024
@SS-JIA
[ET-VK] Introduce graph runtime shader library that enables dynamic s…
add1f02
…hapes
Pull Request resolved: #2366
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
ghstack-source-id: 218421178
@exported-using-ghexport
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
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Mar 13, 2024

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This pull request has been merged in 835279e.
junpi3 pushed a commit
that referenced
this pull request
Mar 13, 2024
Jorge Pineda
[ET-VK][EZ] Include alpha to fix aten.sub.Tensor
ae0776f
Missed this in #2366
Differential Revision: [D54880024](https://our.internmc.facebook.com/intern/diff/D54880024/)
[ghstack-poisoned]
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mentioned this pull request
Mar 13, 2024
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junpi3 pushed a commit
that referenced
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Mar 13, 2024
Jorge Pineda
[ET-VK][EZ] Include alpha to fix aten.sub.Tensor
fe1491e
Missed this in #2366
Differential Revision: [D54880024](https://our.internmc.facebook.com/intern/diff/D54880024/)
ghstack-source-id: 218593311
Pull Request resolved: #2418
facebook-github-bot pushed a commit
that referenced
this pull request
Mar 14, 2024
Jorge Pineda@facebook-github-bot
Include alpha to fix aten.sub.Tensor (#2418)
a5faf03
Summary:
Pull Request resolved: #2418
Missed this in #2366
ghstack-source-id: 218593311
exported-using-ghexport
bypass-github-export-checks
Reviewed By: SS-JIA
Differential Revision: D54880024
fbshipit-source-id: c4e19d8fefbb9d2fc4547ec2edc236060638da5e
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Stack from ghstack (oldest at bottom):
Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}

Shaders will accept separate UBOs for each piece of tensor metadata:
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;

Each UBO will be owned and maintained by the corresponding vTensor instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the tensor.virtual_resize(new_sizes) call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having vTensor supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc Params structs to organize arguments to write into a api::UniformParamsBuffer.
Constructing an ExecuteNode for binary operators is now
 graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))

instead of
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));

Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: D54754545
@SS-JIA
[ET-VK] Introduce graph runtime shader library that enables dynamic s…
b24a594
…hapes
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/2366
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This pull request was exported from Phabricator. Differential Revision: D54754545

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@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
9be2c32
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
5bb0389
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
@SS-JIA
Update on "[ET-VK] Introduce graph runtime shader library that enable…
5cdc6cd
…s dynamic shapes"
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
[ghstack-poisoned]
@facebook-github-bot
facebook-github-bot
commented
Mar 12, 2024

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This pull request was exported from Phabricator. Differential Revision: D54754545
SS-JIA added a commit
that referenced
this pull request
Mar 13, 2024
@SS-JIA
[ET-VK] Introduce graph runtime shader library that enables dynamic s…
add1f02
…hapes
Pull Request resolved: #2366
## Context
pytorch/pytorch#121598 introduces the ability to support dynamic shapes through tensor metadata updates.
The idea is fairly simple. Instead of shaders accepting a UBO with size data for all arguments:
```
layout(set = 0, binding = 2) uniform PRECISION restrict Block {
ivec4 output_sizes;
ivec4 other_sizes;
float alpha;
}
```
Shaders will accept separate UBOs for each piece of tensor metadata:
```
layout(set = 0, binding = 3) uniform PRECISION restrict OutSizes {
ivec4 data;
}
out_sizes;
layout(set = 0, binding = 4) uniform PRECISION restrict InSizes {
ivec4 data;
}
in_sizes;
layout(set = 0, binding = 5) uniform PRECISION restrict OtherSizes {
ivec4 data;
}
other_sizes;
layout(set = 0, binding = 6) uniform PRECISION restrict Alpha {
float data;
}
alpha;
```
Each UBO will be owned and maintained by the corresponding `vTensor` instance. To support a graph input resize, every tensor in the graph only needs to update their metadata UBOs via the `tensor.virtual_resize(new_sizes)` call. Shader dispatches in subsequent command buffer submissions will then see the updated metadata and execute as if the tensor were the updated sizes.
This changeset introduces a new shader library for the Vulkan graph runtime that enables dynamic shapes through this technique in favor of relying on the shader library from PyTorch Vulkan.
## Considerations
Technically, the UBO update technique can be applied to the shaders from PyTorch Vulkan as well. If that's the case, why introduce a new shader library for the graph runtime?
The primary motivation is code quality.
First, having `vTensor` supply UBOs for their own metadata greatly reduces the need to have operator specifc ad-hoc `Params` structs to organize arguments to write into a `api::UniformParamsBuffer`.
Constructing an `ExecuteNode` for binary operators is now
```
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
api::shader_registry().get_shader_info(kernel_name.str()),
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
{t_out.gpu_sizes_ubo(),
t_in1.gpu_sizes_ubo(),
t_in2.gpu_sizes_ubo(),
graph.create_params_buffer(alpha_val)}))
```
instead of
```
ArithmeticParams block{
get_size_as_ivec4(t_out),
get_size_as_ivec4(t_in1),
get_size_as_ivec4(t_in2),
alpha_val,
};
api::UniformParamsBuffer params(graph.context(), block);
graph.execute_nodes().emplace_back(new ExecuteNode(
graph,
shader,
global_size,
local_size,
{{out, api::MemoryAccessType::WRITE},
{{arg1, arg2}, api::MemoryAccessType::READ}},
std::move(params)));
```
Another consideration is that pytorch/pytorch#115948 which was landed fairly recently enables much more expressive shader templates through the use of Python code blocks in the GLSL template. This enables shader templates that can easily express variants for different data types, packing structures, etc. Introducing a new shader library provides the opportunity to rewrite the shaders in PyTorch Vulkan in a more generic and extensible way.
ghstack-source-id: 218421178
@exported-using-ghexport
Differential Revision: [D54754545](https://our.internmc.facebook.com/intern/diff/D54754545/)
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Mar 13, 2024

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This pull request has been merged in 835279e.
junpi3 pushed a commit
that referenced
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Mar 13, 2024
Jorge Pineda
[ET-VK][EZ] Include alpha to fix aten.sub.Tensor
ae0776f
Missed this in #2366
Differential Revision: [D54880024](https://our.internmc.facebook.com/intern/diff/D54880024/)
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Mar 13, 2024
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junpi3 pushed a commit
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Mar 13, 2024
Jorge Pineda
[ET-VK][EZ] Include alpha to fix aten.sub.Tensor
fe1491e
Missed this in #2366
Differential Revision: [D54880024](https://our.internmc.facebook.com/intern/diff/D54880024/)
ghstack-source-id: 218593311
Pull Request resolved: #2418
facebook-github-bot pushed a commit
that referenced
this pull request
Mar 14, 2024
Jorge Pineda@facebook-github-bot
Include alpha to fix aten.sub.Tensor (#2418)
a5faf03
Summary:
Pull Request resolved: #2418
Missed this in #2366
ghstack-source-id: 218593311
exported-using-ghexport
bypass-github-export-checks
Reviewed By: SS-JIA
Differential Revision: D54880024
fbshipit-source-id: c4e19d8fefbb9d2fc4547ec2edc236060638da5e
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