[llava][21/N] Add llava runner test binary and build script #4667
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As titled. This PR moves the token generation loop in llama2 runner into a new class so it can be reused. [ghstack-poisoned]
…o a class" As titled. This PR moves the token generation loop in llama2 runner into a new class so it can be reused. Differential Revision: [D61047601](https://our.internmc.facebook.com/intern/diff/D61047601) [ghstack-poisoned]
As titled. This PR moves the token generation loop in llama2 runner into a new class so it can be reused. Differential Revision: [D61047601](https://our.internmc.facebook.com/intern/diff/D61047601) [ghstack-poisoned]
[ghstack-poisoned]
[ghstack-poisoned]
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/4667
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 5b687ce with merge base 84100d1 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
This was referenced Aug 12, 2024
facebook-github-bot
added
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approved these changes
Aug 13, 2024
… and build script"
Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.
Run `build.sh` to build the runner.
To serialize the image into a `.pt` file, run the following script:
```python
import torch
from torch import nn
copy = torch.tensor(resized)
m = nn.Module()
par = nn.Parameter(copy, requires_grad=False)
m.register_parameter("0",par)
tensors = torch.jit.script(m)
tensors.save("image.pt")
```
To run the runner, use the following command:
```
cmake-out/examples/models/llava/llava_main \
--tokenizer_path tokenizer.bin \
--model_path llava_kv_768.pte \
--prompt "\nWhat are the things I should be cautious about when I visit here?" \
--image_path image.pt \
--temperature 0
```
[ghstack-poisoned]
Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.
Run `build.sh` to build the runner.
To serialize the image into a `.pt` file, run the following script:
```python
import torch
from torch import nn
copy = torch.tensor(resized)
m = nn.Module()
par = nn.Parameter(copy, requires_grad=False)
m.register_parameter("0",par)
tensors = torch.jit.script(m)
tensors.save("image.pt")
```
To run the runner, use the following command:
```
cmake-out/examples/models/llava/llava_main \
--tokenizer_path tokenizer.bin \
--model_path llava_kv_768.pte \
--prompt "\nWhat are the things I should be cautious about when I visit here?" \
--image_path image.pt \
--temperature 0
```
[ghstack-poisoned]
… and build script"
Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.
Run `build.sh` to build the runner.
To serialize the image into a `.pt` file, run the following script:
```python
import torch
from torch import nn
copy = torch.tensor(resized)
m = nn.Module()
par = nn.Parameter(copy, requires_grad=False)
m.register_parameter("0",par)
tensors = torch.jit.script(m)
tensors.save("image.pt")
```
To run the runner, use the following command:
```
cmake-out/examples/models/llava/llava_main \
--tokenizer_path tokenizer.bin \
--model_path llava_kv_768.pte \
--prompt "\nWhat are the things I should be cautious about when I visit here?" \
--image_path image.pt \
--temperature 0
```
[ghstack-poisoned]
Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.
Run `build.sh` to build the runner.
To serialize the image into a `.pt` file, run the following script:
```python
import torch
from torch import nn
copy = torch.tensor(resized)
m = nn.Module()
par = nn.Parameter(copy, requires_grad=False)
m.register_parameter("0",par)
tensors = torch.jit.script(m)
tensors.save("image.pt")
```
To run the runner, use the following command:
```
cmake-out/examples/models/llava/llava_main \
--tokenizer_path tokenizer.bin \
--model_path llava_kv_768.pte \
--prompt "\nWhat are the things I should be cautious about when I visit here?" \
--image_path image.pt \
--temperature 0
```
[ghstack-poisoned]
|
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@larryliu0820 has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
… and build script"
Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.
Run `build.sh` to build the runner.
To serialize the image into a `.pt` file, run the following script:
```python
import torch
from torch import nn
copy = torch.tensor(resized)
m = nn.Module()
par = nn.Parameter(copy, requires_grad=False)
m.register_parameter("0",par)
tensors = torch.jit.script(m)
tensors.save("image.pt")
```
To run the runner, use the following command:
```
cmake-out/examples/models/llava/llava_main \
--tokenizer_path tokenizer.bin \
--model_path llava_kv_768.pte \
--prompt "\nWhat are the things I should be cautious about when I visit here?" \
--image_path image.pt \
--temperature 0
```
Differential Revision: [D61146432](https://www.internalfb.com/diff/D61146432)
[ghstack-poisoned]
Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.
Run `build.sh` to build the runner.
To serialize the image into a `.pt` file, run the following script:
```python
import torch
from torch import nn
copy = torch.tensor(resized)
m = nn.Module()
par = nn.Parameter(copy, requires_grad=False)
m.register_parameter("0",par)
tensors = torch.jit.script(m)
tensors.save("image.pt")
```
To run the runner, use the following command:
```
cmake-out/examples/models/llava/llava_main \
--tokenizer_path tokenizer.bin \
--model_path llava_kv_768.pte \
--prompt "\nWhat are the things I should be cautious about when I visit here?" \
--image_path image.pt \
--temperature 0
```
Differential Revision: [D61146432](https://www.internalfb.com/diff/D61146432)
[ghstack-poisoned]
|
@larryliu0820 has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
… and build script"
Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.
Run `build.sh` to build the runner.
To serialize the image into a `.pt` file, run the following script:
```python
import torch
from torch import nn
copy = torch.tensor(resized)
m = nn.Module()
par = nn.Parameter(copy, requires_grad=False)
m.register_parameter("0",par)
tensors = torch.jit.script(m)
tensors.save("image.pt")
```
To run the runner, use the following command:
```
cmake-out/examples/models/llava/llava_main \
--tokenizer_path tokenizer.bin \
--model_path llava_kv_768.pte \
--prompt "\nWhat are the things I should be cautious about when I visit here?" \
--image_path image.pt \
--temperature 0
```
Differential Revision: [D61146432](https://www.internalfb.com/diff/D61146432)
[ghstack-poisoned]
Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.
Run `build.sh` to build the runner.
To serialize the image into a `.pt` file, run the following script:
```python
import torch
from torch import nn
copy = torch.tensor(resized)
m = nn.Module()
par = nn.Parameter(copy, requires_grad=False)
m.register_parameter("0",par)
tensors = torch.jit.script(m)
tensors.save("image.pt")
```
To run the runner, use the following command:
```
cmake-out/examples/models/llava/llava_main \
--tokenizer_path tokenizer.bin \
--model_path llava_kv_768.pte \
--prompt "\nWhat are the things I should be cautious about when I visit here?" \
--image_path image.pt \
--temperature 0
```
Differential Revision: [D61146432](https://www.internalfb.com/diff/D61146432)
[ghstack-poisoned]
|
@larryliu0820 has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
facebook-github-bot
pushed a commit
that referenced
this pull request
Aug 16, 2024
Summary:
Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.
Run `build.sh` to build the runner.
To serialize the image into a `.pt` file, run the following script:
```python
import torch
from torch import nn
copy = torch.tensor(resized)
m = nn.Module()
par = nn.Parameter(copy, requires_grad=False)
m.register_parameter("0",par)
tensors = torch.jit.script(m)
tensors.save("image.pt")
```
To run the runner, use the following command:
```
cmake-out/examples/models/llava/llava_main \
--tokenizer_path tokenizer.bin \
--model_path llava_kv_768.pte \
--prompt "\nWhat are the things I should be cautious about when I visit here?" \
--image_path image.pt \
--temperature 0
```
imported-using-ghimport
Test Plan: Imported from OSS
Reviewed By: kirklandsign
Differential Revision: D61146432
Pulled By: larryliu0820
… and build script"
Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.
Run `build.sh` to build the runner.
To serialize the image into a `.pt` file, run the following script:
```python
import torch
from torch import nn
copy = torch.tensor(resized)
m = nn.Module()
par = nn.Parameter(copy, requires_grad=False)
m.register_parameter("0",par)
tensors = torch.jit.script(m)
tensors.save("image.pt")
```
To run the runner, use the following command:
```
cmake-out/examples/models/llava/llava_main \
--tokenizer_path tokenizer.bin \
--model_path llava_kv_768.pte \
--prompt "\nWhat are the things I should be cautious about when I visit here?" \
--image_path image.pt \
--temperature 0
```
Differential Revision: [D61146432](https://www.internalfb.com/diff/D61146432)
[ghstack-poisoned]
Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.
Run `build.sh` to build the runner.
To serialize the image into a `.pt` file, run the following script:
```python
import torch
from torch import nn
copy = torch.tensor(resized)
m = nn.Module()
par = nn.Parameter(copy, requires_grad=False)
m.register_parameter("0",par)
tensors = torch.jit.script(m)
tensors.save("image.pt")
```
To run the runner, use the following command:
```
cmake-out/examples/models/llava/llava_main \
--tokenizer_path tokenizer.bin \
--model_path llava_kv_768.pte \
--prompt "\nWhat are the things I should be cautious about when I visit here?" \
--image_path image.pt \
--temperature 0
```
Differential Revision: [D61146432](https://www.internalfb.com/diff/D61146432)
[ghstack-poisoned]
|
@larryliu0820 has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
larryliu0820
added a commit
that referenced
this pull request
Aug 16, 2024
Summary: Pull Request resolved: #4750 Pull Request resolved: #4667 Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner. Run `build.sh` to build the runner. To serialize the image into a `.pt` file, run the following script: ```python import torch from torch import nn copy = torch.tensor(resized) m = nn.Module() par = nn.Parameter(copy, requires_grad=False) m.register_parameter("0",par) tensors = torch.jit.script(m) tensors.save("image.pt") ``` To run the runner, use the following command: ``` cmake-out/examples/models/llava/llava_main \ --tokenizer_path tokenizer.bin \ --model_path llava_kv_768.pte \ --prompt "\nWhat are the things I should be cautious about when I visit here?" \ --image_path image.pt \ --temperature 0 ``` imported-using-ghimport Test Plan: Imported from OSS Reviewed By: kirklandsign Differential Revision: D61146432 Pulled By: larryliu0820
… and build script"
Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.
Run `build.sh` to build the runner.
To serialize the image into a `.pt` file, run the following script:
```python
import torch
from torch import nn
copy = torch.tensor(resized)
m = nn.Module()
par = nn.Parameter(copy, requires_grad=False)
m.register_parameter("0",par)
tensors = torch.jit.script(m)
tensors.save("image.pt")
```
To run the runner, use the following command:
```
cmake-out/examples/models/llava/llava_main \
--tokenizer_path tokenizer.bin \
--model_path llava_kv_768.pte \
--prompt "\nWhat are the things I should be cautious about when I visit here?" \
--image_path image.pt \
--temperature 0
```
Differential Revision: [D61146432](https://www.internalfb.com/diff/D61146432)
[ghstack-poisoned]
Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.
Run `build.sh` to build the runner.
To serialize the image into a `.pt` file, run the following script:
```python
import torch
from torch import nn
copy = torch.tensor(resized)
m = nn.Module()
par = nn.Parameter(copy, requires_grad=False)
m.register_parameter("0",par)
tensors = torch.jit.script(m)
tensors.save("image.pt")
```
To run the runner, use the following command:
```
cmake-out/examples/models/llava/llava_main \
--tokenizer_path tokenizer.bin \
--model_path llava_kv_768.pte \
--prompt "\nWhat are the things I should be cautious about when I visit here?" \
--image_path image.pt \
--temperature 0
```
Differential Revision: [D61146432](https://www.internalfb.com/diff/D61146432)
[ghstack-poisoned]
|
@larryliu0820 has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
larryliu0820
added a commit
that referenced
this pull request
Aug 16, 2024
Summary: Pull Request resolved: #4750 Pull Request resolved: #4667 Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner. Run `build.sh` to build the runner. To serialize the image into a `.pt` file, run the following script: ```python import torch from torch import nn copy = torch.tensor(resized) m = nn.Module() par = nn.Parameter(copy, requires_grad=False) m.register_parameter("0",par) tensors = torch.jit.script(m) tensors.save("image.pt") ``` To run the runner, use the following command: ``` cmake-out/examples/models/llava/llava_main \ --tokenizer_path tokenizer.bin \ --model_path llava_kv_768.pte \ --prompt "\nWhat are the things I should be cautious about when I visit here?" \ --image_path image.pt \ --temperature 0 ``` imported-using-ghimport Test Plan: Imported from OSS Reviewed By: kirklandsign Differential Revision: D61146432 Pulled By: larryliu0820
… and build script"
Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.
Run `build.sh` to build the runner.
To serialize the image into a `.pt` file, run the following script:
```python
import torch
from torch import nn
copy = torch.tensor(resized)
m = nn.Module()
par = nn.Parameter(copy, requires_grad=False)
m.register_parameter("0",par)
tensors = torch.jit.script(m)
tensors.save("image.pt")
```
To run the runner, use the following command:
```
cmake-out/examples/models/llava/llava_main \
--tokenizer_path tokenizer.bin \
--model_path llava_kv_768.pte \
--prompt "\nWhat are the things I should be cautious about when I visit here?" \
--image_path image.pt \
--temperature 0
```
Differential Revision: [D61146432](https://www.internalfb.com/diff/D61146432)
[ghstack-poisoned]
Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.
Run `build.sh` to build the runner.
To serialize the image into a `.pt` file, run the following script:
```python
import torch
from torch import nn
copy = torch.tensor(resized)
m = nn.Module()
par = nn.Parameter(copy, requires_grad=False)
m.register_parameter("0",par)
tensors = torch.jit.script(m)
tensors.save("image.pt")
```
To run the runner, use the following command:
```
cmake-out/examples/models/llava/llava_main \
--tokenizer_path tokenizer.bin \
--model_path llava_kv_768.pte \
--prompt "\nWhat are the things I should be cautious about when I visit here?" \
--image_path image.pt \
--temperature 0
```
Differential Revision: [D61146432](https://www.internalfb.com/diff/D61146432)
[ghstack-poisoned]
kirklandsign
pushed a commit
to kirklandsign/executorch
that referenced
this pull request
Aug 16, 2024
Summary: Pull Request resolved: pytorch#4750 Pull Request resolved: pytorch#4667 Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner. Run `build.sh` to build the runner. To serialize the image into a `.pt` file, run the following script: ```python import torch from torch import nn copy = torch.tensor(resized) m = nn.Module() par = nn.Parameter(copy, requires_grad=False) m.register_parameter("0",par) tensors = torch.jit.script(m) tensors.save("image.pt") ``` To run the runner, use the following command: ``` cmake-out/examples/models/llava/llava_main \ --tokenizer_path tokenizer.bin \ --model_path llava_kv_768.pte \ --prompt "\nWhat are the things I should be cautious about when I visit here?" \ --image_path image.pt \ --temperature 0 ``` imported-using-ghimport Test Plan: Imported from OSS Reviewed By: kirklandsign Differential Revision: D61146432 Pulled By: larryliu0820
|
@larryliu0820 has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
larryliu0820
added a commit
that referenced
this pull request
Aug 16, 2024
Summary: Pull Request resolved: #4750 Pull Request resolved: #4667 Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner. Run `build.sh` to build the runner. To serialize the image into a `.pt` file, run the following script: ```python import torch from torch import nn copy = torch.tensor(resized) m = nn.Module() par = nn.Parameter(copy, requires_grad=False) m.register_parameter("0",par) tensors = torch.jit.script(m) tensors.save("image.pt") ``` To run the runner, use the following command: ``` cmake-out/examples/models/llava/llava_main \ --tokenizer_path tokenizer.bin \ --model_path llava_kv_768.pte \ --prompt "\nWhat are the things I should be cautious about when I visit here?" \ --image_path image.pt \ --temperature 0 ``` imported-using-ghimport Test Plan: Imported from OSS Reviewed By: kirklandsign Differential Revision: D61146432 Pulled By: larryliu0820
… and build script"
Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.
Run `build.sh` to build the runner.
To serialize the image into a `.pt` file, run the following script:
```python
import torch
from torch import nn
copy = torch.tensor(resized)
m = nn.Module()
par = nn.Parameter(copy, requires_grad=False)
m.register_parameter("0",par)
tensors = torch.jit.script(m)
tensors.save("image.pt")
```
To run the runner, use the following command:
```
cmake-out/examples/models/llava/llava_main \
--tokenizer_path tokenizer.bin \
--model_path llava_kv_768.pte \
--prompt "\nWhat are the things I should be cautious about when I visit here?" \
--image_path image.pt \
--temperature 0
```
Differential Revision: [D61146432](https://www.internalfb.com/diff/D61146432)
[ghstack-poisoned]
Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.
Run `build.sh` to build the runner.
To serialize the image into a `.pt` file, run the following script:
```python
import torch
from torch import nn
copy = torch.tensor(resized)
m = nn.Module()
par = nn.Parameter(copy, requires_grad=False)
m.register_parameter("0",par)
tensors = torch.jit.script(m)
tensors.save("image.pt")
```
To run the runner, use the following command:
```
cmake-out/examples/models/llava/llava_main \
--tokenizer_path tokenizer.bin \
--model_path llava_kv_768.pte \
--prompt "\nWhat are the things I should be cautious about when I visit here?" \
--image_path image.pt \
--temperature 0
```
Differential Revision: [D61146432](https://www.internalfb.com/diff/D61146432)
[ghstack-poisoned]
|
@larryliu0820 has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
larryliu0820
added a commit
that referenced
this pull request
Aug 16, 2024
Summary: Pull Request resolved: #4750 Pull Request resolved: #4667 Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner. Run `build.sh` to build the runner. To serialize the image into a `.pt` file, run the following script: ```python import torch from torch import nn copy = torch.tensor(resized) m = nn.Module() par = nn.Parameter(copy, requires_grad=False) m.register_parameter("0",par) tensors = torch.jit.script(m) tensors.save("image.pt") ``` To run the runner, use the following command: ``` cmake-out/examples/models/llava/llava_main \ --tokenizer_path tokenizer.bin \ --model_path llava_kv_768.pte \ --prompt "\nWhat are the things I should be cautious about when I visit here?" \ --image_path image.pt \ --temperature 0 ``` imported-using-ghimport Test Plan: Imported from OSS Reviewed By: kirklandsign Differential Revision: D61146432 Pulled By: larryliu0820
… and build script"
Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.
Run `build.sh` to build the runner.
To serialize the image into a `.pt` file, run the following script:
```python
import torch
from torch import nn
copy = torch.tensor(resized)
m = nn.Module()
par = nn.Parameter(copy, requires_grad=False)
m.register_parameter("0",par)
tensors = torch.jit.script(m)
tensors.save("image.pt")
```
To run the runner, use the following command:
```
cmake-out/examples/models/llava/llava_main \
--tokenizer_path tokenizer.bin \
--model_path llava_kv_768.pte \
--prompt "\nWhat are the things I should be cautious about when I visit here?" \
--image_path image.pt \
--temperature 0
```
Differential Revision: [D61146432](https://www.internalfb.com/diff/D61146432)
[ghstack-poisoned]
Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.
Run `build.sh` to build the runner.
To serialize the image into a `.pt` file, run the following script:
```python
import torch
from torch import nn
copy = torch.tensor(resized)
m = nn.Module()
par = nn.Parameter(copy, requires_grad=False)
m.register_parameter("0",par)
tensors = torch.jit.script(m)
tensors.save("image.pt")
```
To run the runner, use the following command:
```
cmake-out/examples/models/llava/llava_main \
--tokenizer_path tokenizer.bin \
--model_path llava_kv_768.pte \
--prompt "\nWhat are the things I should be cautious about when I visit here?" \
--image_path image.pt \
--temperature 0
```
Differential Revision: [D61146432](https://www.internalfb.com/diff/D61146432)
[ghstack-poisoned]
|
@larryliu0820 has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
kirklandsign
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that referenced
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Aug 16, 2024
Summary: Pull Request resolved: pytorch#4750 Pull Request resolved: pytorch#4667 Add a `main.cpp` and CMakeLists.txt for llava runner. This runner takes in an image in the format of `.pt` (a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner. Run `build.sh` to build the runner. To serialize the image into a `.pt` file, run the following script: ```python import torch from torch import nn copy = torch.tensor(resized) m = nn.Module() par = nn.Parameter(copy, requires_grad=False) m.register_parameter("0",par) tensors = torch.jit.script(m) tensors.save("image.pt") ``` To run the runner, use the following command: ``` cmake-out/examples/models/llava/llava_main \ --tokenizer_path tokenizer.bin \ --model_path llava_kv_768.pte \ --prompt "\nWhat are the things I should be cautious about when I visit here?" \ --image_path image.pt \ --temperature 0 ``` imported-using-ghimport Test Plan: Imported from OSS Reviewed By: kirklandsign Differential Revision: D61146432 Pulled By: larryliu0820
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Stack from ghstack (oldest at bottom):
Add a
main.cppand CMakeLists.txt for llava runner. This runner takes in an image in the format of.pt(a serialized pytorch model) along with text prompt. It will generate text tokens in a way similar to llama runner.Run
build.shto build the runner.To serialize the image into a
.ptfile, run the following script:To run the runner, use the following command:
Differential Revision: D61146432