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CUDA graph support for TRT EP #16081

Merged
merged 18 commits into from
Jun 21, 2023
Merged

CUDA graph support for TRT EP #16081

merged 18 commits into from
Jun 21, 2023

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chilo-ms
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CUDA EP already supports CUDA graph, also we observed some models can benefit from using CUDA graph with trtexec. Therefore, this PR enables the CUDA graph support for TRT EP.

The implementation is based on #9978 with the same constraints as below:

  • Models with control-flow ops (i.e. If, Loop and Scan ops) are not supported.
  • Usage of CUDA Graphs is limited to models where-in all the model ops (graph nodes) can be partitioned to the TRT EP.
  • The input/output types of models need to be tensors.
  • Shapes of inputs/outputs cannot change across inference calls.
  • IObinding is required.

@hariharans29
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hariharans29 commented May 24, 2023 •
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Thanks @chilo-ms.
@tlwu had a couple of fixes for CUDA Graphs (open PRs) - I was wondering if you needed fixes similar to that while testing your models ?

@chilo-ms
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chilo-ms commented May 25, 2023 •
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Thanks @chilo-ms. @tlwu had a couple of fixes for CUDA Graphs (open PRs) - I was wondering if you needed fixes similar to that while testing your models ?

yes, I noticed @tianleiwu had a PR (#15005) and I'm checking with him does he plan to get the PR merged? Since he is OOF, do you know why the PR is pending?
I will ask our partner from Nvidia to test the model to see whether they encountered the issue that needs the fix.

@tianleiwu
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Thanks @chilo-ms. @tlwu had a couple of fixes for CUDA Graphs (open PRs) - I was wondering if you needed fixes similar to that while testing your models ?

yes, I noticed @tianleiwu had a PR (#15005) and I'm checking with him does he plan to get the PR merged? Since he is OOF, do you know why the PR is pending? I will ask our partner from Nvidia to test the model to see whether they encountered the issue that needs the fix.

The PR (#15005) is ready for review. Please help review it. You may need resolve the conflicts later after it is merged. Thanks.

@chilo-ms
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Thanks @chilo-ms. @tlwu had a couple of fixes for CUDA Graphs (open PRs) - I was wondering if you needed fixes similar to that while testing your models ?

yes, I noticed @tianleiwu had a PR (#15005) and I'm checking with him does he plan to get the PR merged? Since he is OOF, do you know why the PR is pending? I will ask our partner from Nvidia to test the model to see whether they encountered the issue that needs the fix.

The PR (#15005) is ready for review. Please help review it. You may need resolve the conflicts later after it is merged. Thanks.

The PR looks good to me (not reviewing the multi-stream part). Will wait for your PR merged to main first, and then remove the cuda version macro in my PR as well as test it again.

@tianleiwu
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LGTM. Please resolve conflicts.

@chilo-ms chilo-ms merged commit 4e3cff6 into main Jun 21, 2023
88 of 91 checks passed
@chilo-ms chilo-ms deleted the chi/cuda_graph branch June 21, 2023 16:36
@qjia7 qjia7 mentioned this pull request Jan 11, 2024
Merged
fs-eire pushed a commit that referenced this pull request Jan 31, 2024
@qjia7
[js/webgpu] Support capture and replay for jsep (#18989)
85cef0a 
### Description
This PR expands the graph capture capability to JS EP, which is similar
to #16081. But for JS EP, we don't use the CUDA Graph, instead, we
records all gpu commands and replay them, which removes most of the cpu
overhead to avoid the the situation that gpu waiting for cpu.

mobilenetv2-12 becomes 3.7ms from 6ms on NV 3090 and becomes 3.38ms from
4.58ms on Intel A770.

All limitations are similar with CUDA EP:
1. Models with control-flow ops (i.e. If, Loop and Scan ops) are not
supported.
2. Usage of graph capture is limited to models where-in all ops in the
model can be partitioned to the JS EP or CPU EP and no memory copy
between them.
3. Shapes of inputs/outputs cannot change across inference calls.
4. IObinding is required.

The usage is like below:
Method 1: specify outputs buffers explicitly.
```
    const sessionOptions = {
        executionProviders: [
          {
            name: "webgpu",
          },
        ],
        enableGraphCapture: true,
      };
    const session = await ort.InferenceSession.create('./models/mobilenetv2-12.onnx', sessionOptions);
   
    // prepare the inputBuffer/outputBuffer
    ... ...

   const feeds = {
       'input': ort.Tensor.fromGpuBuffer(inputBuffer, { dataType: 'float32', dims })
   };

   const fetches = {
       'output': ort.Tensor.fromGpuBuffer(outputBuffer, { dataType: 'float32', dims: [1, 1000] })
   };

   let results = await session.run(feeds, fetches);  // The first run will begin to capture the graph.

   // update inputBuffer content
  ... ...
   results = = await session.run(feeds, fetches);  // The 2ed run and after will directly call replay to execute the graph.

  ... ...
   session.release();
```
Method 2: Don't specify outputs buffers explicitly. Internally, when
graph capture is enabled, it will set all outputs location to
'gpu-buffer'.
```
    const sessionOptions = {
        executionProviders: [
          {
            name: "webgpu",
          },
        ],
        enableGraphCapture: true,
      };
    const session = await ort.InferenceSession.create('./models/mobilenetv2-12.onnx', sessionOptions);

    // prepare the inputBuffer
    ... ...

   const feeds = {
       'input': ort.Tensor.fromGpuBuffer(inputBuffer, { dataType: 'float32', dims })
   };

   let results = await session.run(feeds);  // The first run will begin to capture the graph.
   
   // update inputBuffer content
  ... ...
   results = = await session.run(feeds);  // The 2ed run and after will directly call replay to execute the graph.

  ... ...
   session.release();
fs-eire pushed a commit that referenced this pull request Mar 15, 2024
@qjia7 @fs-eire
[js/webgpu] Support capture and replay for jsep (#18989)
73640b6 
This PR expands the graph capture capability to JS EP, which is similar
to #16081. But for JS EP, we don't use the CUDA Graph, instead, we
records all gpu commands and replay them, which removes most of the cpu
overhead to avoid the the situation that gpu waiting for cpu.

mobilenetv2-12 becomes 3.7ms from 6ms on NV 3090 and becomes 3.38ms from
4.58ms on Intel A770.

All limitations are similar with CUDA EP:
1. Models with control-flow ops (i.e. If, Loop and Scan ops) are not
supported.
2. Usage of graph capture is limited to models where-in all ops in the
model can be partitioned to the JS EP or CPU EP and no memory copy
between them.
3. Shapes of inputs/outputs cannot change across inference calls.
4. IObinding is required.

The usage is like below:
Method 1: specify outputs buffers explicitly.
```
    const sessionOptions = {
        executionProviders: [
          {
            name: "webgpu",
          },
        ],
        enableGraphCapture: true,
      };
    const session = await ort.InferenceSession.create('./models/mobilenetv2-12.onnx', sessionOptions);

    // prepare the inputBuffer/outputBuffer
    ... ...

   const feeds = {
       'input': ort.Tensor.fromGpuBuffer(inputBuffer, { dataType: 'float32', dims })
   };

   const fetches = {
       'output': ort.Tensor.fromGpuBuffer(outputBuffer, { dataType: 'float32', dims: [1, 1000] })
   };

   let results = await session.run(feeds, fetches);  // The first run will begin to capture the graph.

   // update inputBuffer content
  ... ...
   results = = await session.run(feeds, fetches);  // The 2ed run and after will directly call replay to execute the graph.

  ... ...
   session.release();
```
Method 2: Don't specify outputs buffers explicitly. Internally, when
graph capture is enabled, it will set all outputs location to
'gpu-buffer'.
```
    const sessionOptions = {
        executionProviders: [
          {
            name: "webgpu",
          },
        ],
        enableGraphCapture: true,
      };
    const session = await ort.InferenceSession.create('./models/mobilenetv2-12.onnx', sessionOptions);

    // prepare the inputBuffer
    ... ...

   const feeds = {
       'input': ort.Tensor.fromGpuBuffer(inputBuffer, { dataType: 'float32', dims })
   };

   let results = await session.run(feeds);  // The first run will begin to capture the graph.

   // update inputBuffer content
  ... ...
   results = = await session.run(feeds);  // The 2ed run and after will directly call replay to execute the graph.

  ... ...
   session.release();
fs-eire pushed a commit that referenced this pull request Mar 15, 2024
@qjia7 @fs-eire
[js/webgpu] Support capture and replay for jsep (#18989)
43b95b0 
This PR expands the graph capture capability to JS EP, which is similar
to #16081. But for JS EP, we don't use the CUDA Graph, instead, we
records all gpu commands and replay them, which removes most of the cpu
overhead to avoid the the situation that gpu waiting for cpu.

mobilenetv2-12 becomes 3.7ms from 6ms on NV 3090 and becomes 3.38ms from
4.58ms on Intel A770.

All limitations are similar with CUDA EP:
1. Models with control-flow ops (i.e. If, Loop and Scan ops) are not
supported.
2. Usage of graph capture is limited to models where-in all ops in the
model can be partitioned to the JS EP or CPU EP and no memory copy
between them.
3. Shapes of inputs/outputs cannot change across inference calls.
4. IObinding is required.

The usage is like below:
Method 1: specify outputs buffers explicitly.
```
    const sessionOptions = {
        executionProviders: [
          {
            name: "webgpu",
          },
        ],
        enableGraphCapture: true,
      };
    const session = await ort.InferenceSession.create('./models/mobilenetv2-12.onnx', sessionOptions);

    // prepare the inputBuffer/outputBuffer
    ... ...

   const feeds = {
       'input': ort.Tensor.fromGpuBuffer(inputBuffer, { dataType: 'float32', dims })
   };

   const fetches = {
       'output': ort.Tensor.fromGpuBuffer(outputBuffer, { dataType: 'float32', dims: [1, 1000] })
   };

   let results = await session.run(feeds, fetches);  // The first run will begin to capture the graph.

   // update inputBuffer content
  ... ...
   results = = await session.run(feeds, fetches);  // The 2ed run and after will directly call replay to execute the graph.

  ... ...
   session.release();
```
Method 2: Don't specify outputs buffers explicitly. Internally, when
graph capture is enabled, it will set all outputs location to
'gpu-buffer'.
```
    const sessionOptions = {
        executionProviders: [
          {
            name: "webgpu",
          },
        ],
        enableGraphCapture: true,
      };
    const session = await ort.InferenceSession.create('./models/mobilenetv2-12.onnx', sessionOptions);

    // prepare the inputBuffer
    ... ...

   const feeds = {
       'input': ort.Tensor.fromGpuBuffer(inputBuffer, { dataType: 'float32', dims })
   };

   let results = await session.run(feeds);  // The first run will begin to capture the graph.

   // update inputBuffer content
  ... ...
   results = = await session.run(feeds);  // The 2ed run and after will directly call replay to execute the graph.

  ... ...
   session.release();
siweic0 pushed a commit to siweic0/onnxruntime-web that referenced this pull request May 9, 2024
@qjia7
[js/webgpu] Support capture and replay for jsep (microsoft#18989)
bc2b24b 
### Description
This PR expands the graph capture capability to JS EP, which is similar
to microsoft#16081. But for JS EP, we don't use the CUDA Graph, instead, we
records all gpu commands and replay them, which removes most of the cpu
overhead to avoid the the situation that gpu waiting for cpu.

mobilenetv2-12 becomes 3.7ms from 6ms on NV 3090 and becomes 3.38ms from
4.58ms on Intel A770.

All limitations are similar with CUDA EP:
1. Models with control-flow ops (i.e. If, Loop and Scan ops) are not
supported.
2. Usage of graph capture is limited to models where-in all ops in the
model can be partitioned to the JS EP or CPU EP and no memory copy
between them.
3. Shapes of inputs/outputs cannot change across inference calls.
4. IObinding is required.

The usage is like below:
Method 1: specify outputs buffers explicitly.
```
    const sessionOptions = {
        executionProviders: [
          {
            name: "webgpu",
          },
        ],
        enableGraphCapture: true,
      };
    const session = await ort.InferenceSession.create('./models/mobilenetv2-12.onnx', sessionOptions);
   
    // prepare the inputBuffer/outputBuffer
    ... ...

   const feeds = {
       'input': ort.Tensor.fromGpuBuffer(inputBuffer, { dataType: 'float32', dims })
   };

   const fetches = {
       'output': ort.Tensor.fromGpuBuffer(outputBuffer, { dataType: 'float32', dims: [1, 1000] })
   };

   let results = await session.run(feeds, fetches);  // The first run will begin to capture the graph.

   // update inputBuffer content
  ... ...
   results = = await session.run(feeds, fetches);  // The 2ed run and after will directly call replay to execute the graph.

  ... ...
   session.release();
```
Method 2: Don't specify outputs buffers explicitly. Internally, when
graph capture is enabled, it will set all outputs location to
'gpu-buffer'.
```
    const sessionOptions = {
        executionProviders: [
          {
            name: "webgpu",
          },
        ],
        enableGraphCapture: true,
      };
    const session = await ort.InferenceSession.create('./models/mobilenetv2-12.onnx', sessionOptions);

    // prepare the inputBuffer
    ... ...

   const feeds = {
       'input': ort.Tensor.fromGpuBuffer(inputBuffer, { dataType: 'float32', dims })
   };

   let results = await session.run(feeds);  // The first run will begin to capture the graph.
   
   // update inputBuffer content
  ... ...
   results = = await session.run(feeds);  // The 2ed run and after will directly call replay to execute the graph.

  ... ...
   session.release();
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@hariharans29 hariharans29 hariharans29 left review comments

@tianleiwu tianleiwu tianleiwu approved these changes

@jywu-msft jywu-msft jywu-msft approved these changes

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@chilo-ms @hariharans29 @tianleiwu @jywu-msft