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Add missing dependency to cuda_headers.
stream_executor_util_kernel needs to depend on cuda_headers.
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Add missing dependency to cuda_headers.
stream_executor_util_kernel needs to depend on cuda_headers.
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[XLA:GPU][IndexAnalysis] Add a method to remove unused dims.
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[XLA:GPU][IndexAnalysis] Add a method to remove unused dims.
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Integrate LLVM at llvm/llvm-project@8ba880b58707
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NONE
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Integrate LLVM at llvm/llvm-project@8ba880b58707
Updates LLVM usage to match
[8ba880b58707](https://github.com/llvm/llvm-project/commit/8ba880b58707)
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Automated Code Change
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Use tsl::Env instead of tensorflow::Env to fix the build.
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Use tsl::Env instead of tensorflow::Env to fix the build.
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Add missing dependency to cuda_headers.
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Add missing dependency to cuda_headers.
stream_executor_util_kernel needs to depend on cuda_headers.
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Handle fusion parameters correctly when removing instructions.
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Handle fusion parameters correctly when removing instructions.
When a fusion parameter is removed, we need to renumber all fusion parameters
with a higher number, and also remove the corresponding operand from the fusion
instruction.
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TFLiteGPUDelegate : FirstNLargestPartitions : It would not be very good solution
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[
"Hi @easyhardhoon ,\r\n\r\nThere is a option to select the start and end nodes which you want to be delegated in the tflite benchmarking tool as you can see below. \r\n\r\n_first_delegate_node_index: int (default=0)\r\nThe index of the first node that could be delegated. Debug only. Add '--define=tflite_debug_delegate=true' in your build command line to use it.\r\nCurrently only supported by CoreML delegate.\r\nlast_delegate_node_index: int (default=INT_MAX)\r\nThe index of the last node that could be delegated. Debug only. Add '--define=tflite_debug_delegate=true' in your build command line to use it._\r\n\r\n\r\nThis is intended for debugging purposes only. However you raise some excellent points regarding the current delegation policy and partition selection strategy. I will get back to you if i find something related to this.\r\n",
"This seems like a reasonable request, @grantjensen, can you please take a look? Thanks.",
"This seems good to me. This is pretty low priority because there is a work-around as listed above if need be. And moreover, in general we will delegate the entire graph to GPU because context switching is so slow. However, if you were to implement a clean solution, I would approve it.",
"Thank you for your reply @grantjensen @pkgoogle @sawantkumar . It would be reasonable to delegate the entire graph to the GPU, but in reality, I think there could be enough layers that are incompatible with the GPU. For models with such conditions, I think there is room for a little more improvement on the current policy of delegating to the GPU. I'll think about a more efficient policy in terms of inference latency and user convenience. I'll get back to you. Thank you"
] | 2024-04-30T07:47:48 | 2024-05-16T16:40:35 | null |
NONE
| null | null | null |
### Issue type
Performance
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.4.1
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 18.04
### Mobile device
Jetson-Xavier nx
### Python version
3.6.9
### Bazel version
3.1.0
### GCC/compiler version
7.5.0
### CUDA/cuDNN version
x
### GPU model and memory
_No response_
### Current behavior?
Let's suppose that the input tflite model is accelerated using GPUs. If there are incompatible layers for GPU backend (called Fallback layer in tflite) in the middle of the input model, "partition_helper class" divides the input model into delegatable & non-delegatable partition using graph_info class. Then, Apply the FirstNLargestPartitions functions to select the part to be delegated. In FirstNLargestPartitions logic, partitions with many layers are chosen first. Here, the variable N can be set directly by the user, and the default is 1. In this tflite's gpu delegation policy, there are two major problems, I think.
First, It may not be appropriate to preferentially select a partition with many layers containing it. In most cases, such partitions will have the highest computational amount, but there may be partitions with a small number of layers but a very large amount of computation. For example, a partition with two convolutional layers may have more computational power than a partition with 10 simple computational layers, such as add mul. In such cases, FirstNLargest logic may not see the benefits of acceleration well.
Second, It is inconvenient to have the user set the N value, which is a variable for how many partitions to select and delegate. The user must tune and test one by one to see when the N value can most accelerate the input model. Since there is data exchange overhead between CPU and GPU, in most cases the inference time is fastest when the N value is 1, but depending on the structure of the model, there may be cases where it is not.
As a result, I think the existing delegation policy has room for more efficient change by improving the aforementioned problems. **However, it seems that the most recent version of the tflite also uses the policy as it is.**
Currently, I conducted an inference performance test with yolov4-tiny by delegating all possible partitions to all combinations. As a result, the more computational partitions were delegated, the better the inference performance.
However, the above trend was not always correct because the larger the N value, the greater the CPU GPU data exchange overhead.
Given this overall tendency, it would be better to choose partitions with a large amount of computation rather than to select partitions with a large number of layers contained, in same "N" value condition.
In addition, the N value, which is the variable of how many partitions to select and delegate, may vary depending on the performance of the target hardware. I have not yet developed a logic that prevents users from tuning these N values and automatically selects and delegates partitions internally. It seems difficult to find the most appropriate N value by analyzing the performance of the target hardware without going through tasks such as the profiling process.
On the other hand, the method of obtaining an approximate amount of computation for delegatable partitions and delegating large partitions preferentially will be very simple and effective compared to the previous method.
I wonder why tflite still uses FirstNLargestPartitions logic and whether the aforementioned logic is appropriate from an overall perspective.
### Standalone code to reproduce the issue
```shell
if(strcmp(GetOpName(reg), "CONV_2D") == 0){
double mac = tensor->dims->data[1] * tensor->dims->data[2] * tensor->dims->data[3] * i_tensor->dims->data[3] * filter * filter;
flops = 2*mac/1000000;
tot += flops;
printf("\033[0;31mFLOPs : %.1f\033[0m\n", flops);
}
Above code shows roughly calculate amount of computation per layers.
If the contents mentioned in the above current behavior are appropriate, a new delegation policy can be developed based on the above code.
A program to analyze the inference results by delegating delegatable partitions to all possible combinations was conducted at the link below.
[tflite APP code] https://github.com/easyhardhoon/FBF-TF-hoon/tree/hoon/APP_DOT
[tflite source code] https://github.com/easyhardhoon/FBF-TF
```
### Relevant log output
```shell
testing yolov4-tiny.tflite
there are seven delegatable partitions by partition helper class.
below log is the result of delegation test in jetson-xavier nx, using my custom tflite application & tflite source.
below log shows that the default gpu delegation policy in tflite is not appropriate.
=== Fallback node number info === :
8 20 32 55 57 60 63 67 69 70 73 75 76 81 86 91 95 97 98 103 105 108 111 117 122 127 132 134 135 138 140 141 144 146 147
=== Delegated_partitions info === :
[0] : 0 1 2 3 4 5 6 7
[1] : 9 10 11 12 13 14 15 16 17 18 19
[2] : 21 22 23 24 25 26 27 28 29 30 31
[3] : 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 102
[4] : 56 59 62 66 72 78 79 80 83 84 85 88 89 90 94 104 107 110 114 115 116 119 120 121 124 125 126 131 137 143
[5] : 58 61 64 65 68 74 82 87 92 93 96 106 109 112 113 118 123 128 129 130 133 139 145
[6] : 71 77 99 100 101 136 142 148 149 150 151
=== Fallback reason info === :
ERROR: Following operations are not supported by GPU delegate:
ADD: tensor type error
MUL: tensor type error
SPLIT: Operation is not supported.
SPLIT_V: Operation is not supported.
N = 1
[0 ]case's latency is 669ms
[1 ]case's latency is 677ms
[2 ]case's latency is 645ms
[3 ]case's latency is 402.00ms -> Best case
[4 ]case's latency is 632ms ---> chosen at default delegation.
[5 ]case's latency is 630ms
[6 ]case's latency is 618ms
[END]...Choose_1's average latency is 610.43ms
N = 2
[0 1 ]case's latency is 393ms
[0 2 ]case's latency is 486ms
[0 3 ]case's latency is 293ms
[0 4 ]case's latency is 534ms
[0 5 ]case's latency is 511ms
[0 6 ]case's latency is 510ms
[1 2 ]case's latency is 408ms
[1 3 ]case's latency is 289.00ms
[1 4 ]case's latency is 519ms
[1 5 ]case's latency is 520ms
[1 6 ]case's latency is 519ms
[2 3 ]case's latency is 296ms --> best case
[2 4 ]case's latency is 536ms
[2 5 ]case's latency is 550ms
[2 6 ]case's latency is 534ms
[3 4 ]case's latency is 403ms
[3 5 ]case's latency is 404ms
[3 6 ]case's latency is 408ms
[4 5 ]case's latency is 638ms --> chosen at default delegation.
[4 6 ]case's latency is 628ms
[5 6 ]case's latency is 635ms
[END]...Choose_2's average latency is 476.86ms
N = 3
[0 1 2 ]case's latency is 280ms
[0 1 3 ]case's latency is 171.00ms
[0 1 4 ]case's latency is 389ms
[0 1 5 ]case's latency is 385ms
[0 1 6 ]case's latency is 391ms
[0 2 3 ]case's latency is 216ms
[0 2 4 ]case's latency is 470ms
[0 2 5 ]case's latency is 481ms
[0 2 6 ]case's latency is 454ms
[0 3 4 ]case's latency is 285ms
[0 3 5 ]case's latency is 287ms
[0 3 6 ]case's latency is 277ms
[0 4 5 ]case's latency is 526ms
[0 4 6 ]case's latency is 546ms
[0 5 6 ]case's latency is 517ms
[1 2 3 ]case's latency is 187ms
[1 2 4 ]case's latency is 407ms
[1 2 5 ]case's latency is 414ms
[1 2 6 ]case's latency is 408ms
[1 3 4 ]case's latency is 292ms
[1 3 5 ]case's latency is 293ms
[1 3 6 ]case's latency is 292ms
[1 4 5 ]case's latency is 529ms
[1 4 6 ]case's latency is 522ms
[1 5 6 ]case's latency is 524ms
[2 3 4 ]case's latency is 298ms
[2 3 5 ]case's latency is 301ms
[2 3 6 ]case's latency is 306ms
[2 4 5 ]case's latency is 535ms
[2 4 6 ]case's latency is 547ms
[2 5 6 ]case's latency is 549ms
[3 4 5 ]case's latency is 406ms
[3 4 6 ]case's latency is 402ms
[3 5 6 ]case's latency is 408ms
[4 5 6 ]case's latency is 640ms
[END]...Choose_3's average latency is 398.14ms
N = 4
[0 1 2 3 ]case's latency is 65.00ms
[0 1 2 4 ]case's latency is 287ms
[0 1 2 5 ]case's latency is 281ms
[0 1 2 6 ]case's latency is 277ms
[0 1 3 4 ]case's latency is 168ms
[0 1 3 5 ]case's latency is 169ms
[0 1 3 6 ]case's latency is 168ms
[0 1 4 5 ]case's latency is 390ms
[0 1 4 6 ]case's latency is 387ms
[0 1 5 6 ]case's latency is 389ms
[0 2 3 4 ]case's latency is 216ms
[0 2 3 5 ]case's latency is 232ms
[0 2 3 6 ]case's latency is 230ms
[0 2 4 5 ]case's latency is 456ms
[0 2 4 6 ]case's latency is 436ms
[0 2 5 6 ]case's latency is 480ms
[0 3 4 5 ]case's latency is 283ms
[0 3 4 6 ]case's latency is 277ms
[0 3 5 6 ]case's latency is 281ms
[0 4 5 6 ]case's latency is 534ms
[1 2 3 4 ]case's latency is 189ms
[1 2 3 5 ]case's latency is 188ms
[1 2 3 6 ]case's latency is 187ms
[1 2 4 5 ]case's latency is 410ms
[1 2 4 6 ]case's latency is 410ms
[1 2 5 6 ]case's latency is 405ms
[1 3 4 5 ]case's latency is 300ms
[1 3 4 6 ]case's latency is 292ms
[1 3 5 6 ]case's latency is 312ms
[1 4 5 6 ]case's latency is 525ms
[2 3 4 5 ]case's latency is 301ms
[2 3 4 6 ]case's latency is 313ms
[2 3 5 6 ]case's latency is 298ms
[2 4 5 6 ]case's latency is 543ms
[3 4 5 6 ]case's latency is 411ms
[END]...Choose_4's average latency is 316.86ms
N = 5
[0 1 2 3 4 ]case's latency is 66ms
[0 1 2 3 5 ]case's latency is 67ms
[0 1 2 3 6 ]case's latency is 64.00ms
[0 1 2 4 5 ]case's latency is 299ms
[0 1 2 4 6 ]case's latency is 285ms
[0 1 2 5 6 ]case's latency is 277ms
[0 1 3 4 5 ]case's latency is 178ms
[0 1 3 4 6 ]case's latency is 175ms
[0 1 3 5 6 ]case's latency is 177ms
[0 1 4 5 6 ]case's latency is 389ms
[0 2 3 4 5 ]case's latency is 193ms
[0 2 3 4 6 ]case's latency is 202ms
[0 2 3 5 6 ]case's latency is 177ms
[0 2 4 5 6 ]case's latency is 462ms
[0 3 4 5 6 ]case's latency is 286ms
[1 2 3 4 5 ]case's latency is 189ms
[1 2 3 4 6 ]case's latency is 190ms
[1 2 3 5 6 ]case's latency is 188ms
[1 2 4 5 6 ]case's latency is 412ms
[1 3 4 5 6 ]case's latency is 299ms
[2 3 4 5 6 ]case's latency is 332ms
[END]...Choose_5's average latency is 233.67ms
N = 6
[0 1 2 3 4 5 ]case's latency is 69ms
[0 1 2 3 4 6 ]case's latency is 67.00ms
[0 1 2 3 5 6 ]case's latency is 68ms
[0 1 2 4 5 6 ]case's latency is 292ms
[0 1 3 4 5 6 ]case's latency is 174ms
[0 2 3 4 5 6 ]case's latency is 225ms
[1 2 3 4 5 6 ]case's latency is 193ms
[END]...Choose_6's average latency is 155.43ms
N = 7
[0 1 2 3 4 5 6 ]case's latency is 70.00ms
[END]...Choose_7's average latency is 70.00ms
Minimum CAES's value : 64.00ms
Minimum CASE's N : 5
Minimum CASE's th : 2
Minimum CASE's combination : [0 1 2 3 6 ]
```
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Fix input/output indexing for side broadcast outputs.
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Fix input/output indexing for side broadcast outputs.
The current logic is only correct for elementwise ops, but it
is possible to have broadcasts in reduction epilogues.
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Integrate LLVM at llvm/llvm-project@d566a5cd22b4
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Integrate LLVM at llvm/llvm-project@d566a5cd22b4
Updates LLVM usage to match
[d566a5cd22b4](https://github.com/llvm/llvm-project/commit/d566a5cd22b4)
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Reduce the frequency of connection termination log
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Reduce the frequency of connection termination log
This can become really verbose when the server shuts down and the client doesn't terminate immediately.
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Explicitly close the host callback queues inside `IfrtBackend` destruction
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Explicitly close the host callback queues inside `IfrtBackend` destruction
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[pjrt] NFC: Merge implementation of stateful and stateless futures
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[pjrt] NFC: Merge implementation of stateful and stateless futures
Move shared code into the PjRtFutureBase and keep only meaningfully different functions in PjRtFuture<T> and PjRtFuture<>.
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Optimize`SparseTensor::Split()` by not calling `Tensor::matrix()` for each element.
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Optimize`SparseTensor::Split()` by not calling `Tensor::matrix()` for each element.
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PR #11908: [GPU][NFC] Annotate cuDNN graphs with operation names from HLO.
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PR #11908: [GPU][NFC] Annotate cuDNN graphs with operation names from HLO.
Imported from GitHub PR https://github.com/openxla/xla/pull/11908
Copybara import of the project:
--
2722433efae66dfce1444ecfbe5d8bb2915a1000 by Ilia Sergachev <[email protected]>:
[GPU][NFC] Annotate cuDNN graphs with operation names from HLO.
Merging this change closes #11908
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11908 from openxla:cudnn_annotation 2722433efae66dfce1444ecfbe5d8bb2915a1000
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PR #11881: Improve NVTX ranges emitted during auto-tuning
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PR #11881: Improve NVTX ranges emitted during auto-tuning
Imported from GitHub PR https://github.com/openxla/xla/pull/11881
New ranges:
- XlaAutotunerCompilation
- XlaAutotunerMeasurement
Allow easier analysis of which parts of autotuning are related to compilation.
Copybara import of the project:
--
921c3c39c3cc919f360ef8c6bb9828d96fb0cf49 by Olli Lupton <[email protected]>:
Improve NVTX ranges emitted during auto-tuning
New ranges:
- XlaAutotunerCompilation
- XlaAutotunerMeasurement
Merging this change closes #11881
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11881 from olupton:autotuner-annotations 921c3c39c3cc919f360ef8c6bb9828d96fb0cf49
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Fix TFLite converter regression for softmax due to Jax upstream change in https://github.com/google/jax/pull/15677.
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Fix TFLite converter regression for softmax due to Jax upstream change in https://github.com/google/jax/pull/15677.
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PR_kwDOArmXAs5uFRAX
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Remove `IfrtSessionHandler`
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Remove `IfrtSessionHandler`
We never end up creating more than one backend and we don't have chunked requests/responses anymore, so `IfrtSessionHandler` adds more complexity with no value. This CL changes the server implementation to directly instantiate a backend without having to go through `IfrtSessionHandler`.
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PR_kwDOArmXAs5uFJij
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[XLA:CPU] Enable constant host offloading
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[XLA:CPU] Enable constant host offloading
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TEST Enable cuda11 TAP build for xprof CI.
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TEST Enable cuda11 TAP build for xprof CI.
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[XLA:TPU] HostOffloader: Refactor HandleInputStreaming to occur before tracing the graph for custom calls.
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[XLA:TPU] HostOffloader: Refactor HandleInputStreaming to occur before tracing the graph for custom calls.
Currently, input streaming gets handled in arbitrary order, after we have already possibly searched the graph for the corresponding streamed custom calls. This refactors the input streaming to occur before any graph searching, which makes more sense and prepares for output streaming.
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Create DeviceMemoryHandle which fulfills a similar function to ScopedDeviceMemory, with no ties to DeviceMemoryAllocator.
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Create DeviceMemoryHandle which fulfills a similar function to ScopedDeviceMemory, with no ties to DeviceMemoryAllocator.
This is a step in breaking circular dependencies between DeviceMemoryAllocator and StreamExecutor.
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Extends the TFLite Benchmarker to support multiple signatures.
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Extends the TFLite Benchmarker to support multiple signatures.
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Adding parameter support for disabling per channel quantization while running quantizer.
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Adding parameter support for disabling per channel quantization while running quantizer.
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#tf-data Use multi-threading to run flat map func.
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#tf-data Use multi-threading to run flat map func.
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Define GetDefaultLayoutForDevice for CompileOnlyIfRtClient.
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Define GetDefaultLayoutForDevice for CompileOnlyIfRtClient.
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PR_kwDOArmXAs5uEa8m
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Implement a Freeze() api to release host memory for device tensors.
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Implement a Freeze() api to release host memory for device tensors.
Whether a restored tensor can be released is based on whether it is
ever used by the CPU/Host ops and this information comes
from compiler.
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Remove unnecessary tags on gpu tests
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NONE
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Remove unnecessary tags on gpu tests
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PR_kwDOArmXAs5uEWOS
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Prevent generating ReadVariableOp which has a corresponding read op top of tf_device.replate op
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Do not include CUDA deps for LIBTPU build.
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Do not include CUDA deps for LIBTPU build.
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Allows real_output_multipliers to be > 1 for quantized sub operations
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Allows real_output_multipliers to be > 1 for quantized sub operations
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Don't allocate while uses on retry_number > 0
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Don't allocate while uses on retry_number > 0
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Optimize KVCache memory.
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Some refactoring to WhileLoopUnroller pass.
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[xla:gpu] Added the missing C* switch cases to xla::ffi::ToDataType
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[xla:gpu] Added the missing C* switch cases to xla::ffi::ToDataType
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Reverts changelist 473051719
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Reverts changelist 473051719
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PR #11551: Dimensional Ordering of Layer Norm Operands
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PR #11551: Dimensional Ordering of Layer Norm Operands
Imported from GitHub PR https://github.com/openxla/xla/pull/11551
Orders the dimensions of the operands of layer norm Custom Calls as required by cuDNN 9.1 and newer.
Copybara import of the project:
--
d9369e905968697139b6c3d7ecf12698abcc1e04 by Philipp Hack <[email protected]>:
Aligns the ordering of the dimensions of layer norm operands.
Merging this change closes #11551
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PR #11597: Compile F32, BF16 and S32 constants in cuDNN graph.
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PR #11597: Compile F32, BF16 and S32 constants in cuDNN graph.
Imported from GitHub PR https://github.com/openxla/xla/pull/11597
Support compiling F32, BF16 and S32 constants in cuDNN graph.
This PR is blocked until cuDNN frontend dependency is upgraded to v1.3.
Copybara import of the project:
--
e8c6a3f4186242d812a00e8849350febd204f867 by Elfie Guo <[email protected]>:
Compile F32, BF16 and S32 constants in cuDNN graph.
Merging this change closes #11597
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11597 from elfiegg:cudnn_scalar_constants_2 e8c6a3f4186242d812a00e8849350febd204f867
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Fix remove unused variables
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Fix remove unused variables
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Reverts changelist 629123951
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Use `:gtest` instead of `:gtest_main` in PJRT API test libraries
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Use `:gtest` instead of `:gtest_main` in PJRT API test libraries
- This is to allow actual test definitions which depend on the test library to override the heap checker behavior.
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PR_kwDOArmXAs5uCxMu
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PR #11880: InitializeBuffer: use maximum 2 h2d copies
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PR #11880: InitializeBuffer: use maximum 2 h2d copies
Imported from GitHub PR https://github.com/openxla/xla/pull/11880
This provides a significant speedup when autotuning, where buffers are repeatedly initialized.
Instead of repeatedly copying the same data to different locations on the device, it is copied once (using up to two host to device copies) and then replicated on the device using a custom kernel.
Using the paxml container from JAX-Toolbox and the 5B configuration on H100, the total runtime of the `gemm-algorithm-picker` pass for the main JITed function decreased from around 4.8s to 0.16s. With the same model and `--xla_gpu_triton_gemm_any=true` to make sure the Triton autotuner does a lot of work, that pass speeds up from 17.2s to 14.2s with this change.
Copybara import of the project:
--
bc9de3720516b4450d3a9791eae8a6979a3a08a9 by Olli Lupton <[email protected]>:
InitializeBuffer: use maximum 2 h2d copies
This provides a significant speedup when autotuning, where buffers are
repeatedly re-initialized. Instead of repeatedly copying the same data
to different locations on the device, it is copied once (using up to two
host to device copies) and then replicated on the device using a custom
kernel.
--
ee92fc52d06604ab615bca9eb278faa40e22caf0 by Olli Lupton <[email protected]>:
Do not refer to repeat_buffer_kernel::kernel() in CPU builds.
Merging this change closes #11880
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11880 from olupton:random-init-with-3-operations ee92fc52d06604ab615bca9eb278faa40e22caf0
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[ROCm] Add OUTPUT_PATH repo env to set destination for artifacts
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"@penpornk and @gbaned please review this.",
"Hi @hsharsha Can you please review this PR? Thank you!",
"> Hi @hsharsha Can you please review this PR? Thank you!\r\n\r\n@gbaned I am the author for this PR, Awaiting review from @penpornk ",
"> > Hi @hsharsha Can you please review this PR? Thank you!\r\n> \r\n> @gbaned I am the author for this PR, Awaiting review from @penpornk\r\n\r\nHi @hsharsha My apologies for the typo. \r\nHi @penpornk Can you please review this PR? Thank you!",
"Hi @penpornk Can you please review this PR? Thank you!"
] | 2024-04-29T17:28:32 | 2024-06-07T17:19:20 | null |
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TFLite: Stable Delegate: how to use stable_delegate with python API
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"Hi @sushreebarsa \r\nany updates on this?\r\nthanks",
"@suyash-narain \r\ntflite.load_delegate directly searches for the shared library (.so file) and doesn't handle settings files like stable_delegate_settings.json. There's a workaround to achieve your goal using the Python API.\r\nCould you try to Parse the `stable_delegate_settings.json` file;\r\nFirstly Import the json library:\r\n```\r\nimport json\r\n\r\n```\r\nthen Open the stable_delegate_settings.json file:\r\n\r\n```\r\nwith open('stable_delegate_settings.json', 'r') as f:\r\n settings = json.load(f)\r\n\r\n```\r\n\r\nThank you!\r\n",
"Hi @sushreebarsa \r\nthank you for your reply. \r\n\r\nSo do I need to specify this 'settings' somewhere in my python code, or will the delegate automatically get loaded and execute the tflite model? \r\n\r\nsince interpreter api loads external shared delegate library .so files using tfltie experimental load_delegate function... \r\nso does it need to be provided this settings variable with loaded json contents?\r\n\r\ni mean, in the below format, how will i add this settings file after its parsed?\r\n\r\n```\r\ntf.lite.Interpreter(\r\n model_path=None,\r\n model_content=None,\r\n experimental_delegates=None,\r\n num_threads=None,\r\n experimental_op_resolver_type=[tf.lite.experimental.OpResolverType.AUTO]\r\n)\r\n```\r\n\r\nsecondly, stable delegate does not make use of tflite_plugin_create_delegate or tflite_plugin_destroy_delegate since it has its own stable delegate settings... but if i pass it through python interpreter api, it forces it to search for tflite_plugin_create_delegate or tflite_plugin_destroy_delegate.\r\n\r\ndo you know of a workaround to make stable delegate work with python?\r\n\r\nthanks",
"@suyash-narain If you're using a custom delegate library with its own settings, follow the TFLite delegate loading [documentation](https://www.tensorflow.org/lite/performance/delegates) to incorporate it. There isn't a well-established workaround to directly use the stable delegate with the Python API for TFLite.\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66632\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66632\">No</a>\n"
] | 2024-04-29T17:26:37 | 2024-05-25T01:48:41 | 2024-05-25T01:48:38 |
NONE
| null | null | null |
I am using tflite 2.16.1
My target is an aarch64 linux system
I have a tflite stable_delegate .so shared library, and a stable_delegate settings json file called 'stable_delegate_settings.json which I can execute using TFLite benchmark model using the commands
./benchmark_model --graph=model.tflite --stable_delegate_settings_file=stable_delegate_settings.json
this executes the model using stable delegate through the json stable_delegate settings file.
I want to know how can I use this stable_delegate_settings.json file to execute the model via stable delegate using python API.
I am not sure whats the format to execute stable delegate using tflite.load_delegate, since it searches for the .so shared library, not the json settings file.
thanks
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[tsl:concurrency] Workaround for MSVC compiler bug for std::enable_if_t specialization
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[tsl:concurrency] Workaround for MSVC compiler bug for std::enable_if_t specialization
https://godbolt.org/z/oj9o6eraa - fixed in msvc 19.32
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xla_compile_lib: Fix reads of uninitialized memory.
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xla_compile_lib: Fix reads of uninitialized memory.
C++ still doesn't zero-initialize primitives, so we need to do it ourselves.
Discovered with MSAN.
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[XLA:GPU][MLIR-Based emitters] Make lower_func pass a FunctionPass.
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[XLA:GPU][MLIR-Based emitters] Make lower_func pass a FunctionPass.
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Move TPU Sharding Identification Pass to internal tf2xla directory
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Move TPU Sharding Identification Pass to internal tf2xla directory
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Fix linker error in `autotuner_compile_util_test_gpu` by always linking against `main` even when gpu is not configured
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Fix linker error in `autotuner_compile_util_test_gpu` by always linking against `main` even when gpu is not configured
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Introduce utility function GetPrevAllowedBatchSize.
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Introduce utility function GetPrevAllowedBatchSize.
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PR_kwDOArmXAs5uBpIw
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Introduce the MaybeBatchDown helper method, only supporting kBatchDown at this time.
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Introduce the MaybeBatchDown helper method, only supporting kBatchDown at this time.
This method is not used anywhere yet; It will be called from the SharedBatchScheduler in a follow up CL.
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PR_kwDOArmXAs5uBl6K
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Switch tfl dialect to use properties.
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Switch tfl dialect to use properties.
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PR_kwDOArmXAs5uBXrs
| 66,621 |
Fix transpose fusions with side outputs.
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Fix transpose fusions with side outputs.
- Evaluate all transposes in one loop.
- Remove side outputs from epilogues.
- Produce side output values in first loop (otherwise, there's no chance of sharing data, making the whole side output thing pointless).
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Introduce the --tensorflow_batch_padding_policy flag.
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Introduce the --tensorflow_batch_padding_policy flag.
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[] | 2024-04-29T13:54:42 | 2024-04-29T21:59:25 | 2024-04-29T21:59:24 |
NONE
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[xla:gpu] FFI handlers can now return a variable number of results
The API mirrors that of .RemainingArgs(), so a handler with variadic inputs/inputs
now looks like this
Ffi::Bind().RemainingArgs().RemainingRets().To(...)
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[XLA:Array] Allow array indexing to be optionally out of bounds.
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[XLA:Array] Allow array indexing to be optionally out of bounds.
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make error in Tensorflow Lite Micro
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[
"Hi @abcd123abcd ,\r\n\r\nI tried replicating your error but i kept running into library issues. Can you please update your python version and then try installing tflite micro and let me know if the issue still persists for you .",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66616\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66616\">No</a>\n"
] | 2024-04-29T12:18:03 | 2024-06-04T01:49:53 | 2024-06-04T01:49:50 |
NONE
| null | null | null |
System information
Linux Ubuntu 18.04
Python version: 2.7.17
The problem
I'm trying to generate the example projects from the TensorFlow Lite for Microcontrollers C++ library with Make and I run in this error:
Traceback (most recent call last):
File "tensorflow/lite/micro/tools/make/generate_keil_project.py", line 122, in <module>
parse_args()
File "tensorflow/lite/micro/tools/make/generate_keil_project.py", line 118, in parse_args
main(unparsed, flags)
File "tensorflow/lite/micro/tools/make/generate_keil_project.py", line 40, in main
six.ensure_str(flags.executable),
AttributeError: 'module' object has no attribute 'ensure_str'
tensorflow/lite/micro/examples/network_tester/Makefile.inc:41: recipe for target 'tensorflow/lite/micro/tools/make/gen/linux_x86_64_default/prj/network_tester_test/keil/keil_project.uvprojx' failed
make: *** [tensorflow/lite/micro/tools/make/gen/linux_x86_64_default/prj/network_tester_test/keil/keil_project.uvprojx] Error 1
Sequence of commands
git clone https://github.com/tensorflow/tflite-micro
cd tflite-micro
make -f tensorflow/lite/micro/tools/make/Makefile generate_projects
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[XLA:GPU][MLIR-Based emitters] Lower xla_gpu.indexing_op -> arith.
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[XLA:GPU][MLIR-Based emitters] Lower xla_gpu.indexing_op -> arith.
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Fix compile error in collective_ops_test_e2e.
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Fix compile error in collective_ops_test_e2e.
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Remove restriction on tuple shapes in hlo_to_elemental_mlir.
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Remove restriction on tuple shapes in hlo_to_elemental_mlir.
- fix input slices fusion (now it actually does what the legacy
version did). I'm not sure this fusion type is particularly
useful though (unless the input is the same for all slices).
- fix loop fusion (support root tuples with bitcasts)
- disable side outputs for reduction and transpose fusions: it
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[XLA:GPU] Fix the output shape in the autotune unified redzone buffers.
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[XLA:GPU] Fix the output shape in the autotune unified redzone buffers.
After this CL, the output shape will not be a tuple if it contains only a single element. I broke this by accident in cl/626122610.
I also added tests that ensure the correct behavior.
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Allow HloDCE to remove unused tuple elements of MOF
Multi-Output fusions (MOF) should not have tuple outputs that are unused. This
change adds logic to clean up such fusions.
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PR #11422: [NVIDIA GPU] Remove control knobs for each individual async collective and use the global xla_gpu_disable_async_collectives
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PR #11422: [NVIDIA GPU] Remove control knobs for each individual async collective and use the global xla_gpu_disable_async_collectives
Imported from GitHub PR https://github.com/openxla/xla/pull/11422
Currently we have 1 global flag(xla_gpu_enable_async_collectives) to control whether we want to asynchronize collectives or not. This flag overrides all other control knobs for each collective. The usage of it is confusing, instead we introduce a new flag xla_gpu_disable_async_collectives which will consolidate all the async flags we have now. We remove xla_gpu_enable_async_collectives and all other individual control knobs.
Sample usage:
xla_gpu_disable_async_collectives=allreduce,reducescatter
disables async allreduce and reducescatter
By default it's empty which indicates enabling async for all collectives.
Copybara import of the project:
--
afff139cb742662801c49052b70a8f234bb280e4 by TJ Xu <[email protected]>:
Remove control knobs for each individual async collective and use the
global xla_gpu_enable_async_collectives
--
e1385c5b1f82aee1d3ea98e5d453c3e60fc5fa8a by TJ Xu <[email protected]>:
Consolidate all flags into one
--
72020bef1368f514760cbfd6630c22d2348121c9 by TJ Xu <[email protected]>:
Change description of xla_gpu_disable_async_collectives
Merging this change closes #11422
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11422 from Tixxx:tixxx/flag_clean_up 72020bef1368f514760cbfd6630c22d2348121c9
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Some confusion about Tensorflow Distributed Training（The amount of synchronized data is much larger than the theoretical value）
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"@certainly-cyber,\r\n**tf.contrib.distribute.CollectiveAllReduceStrategy** is not available and it was the deprecated API. The CollectiveAllReduceStrategy is [exported as](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/distribute/collective_all_reduce_strategy.py#L56) **MultiWorkerMirroredStrategy**. \r\n\r\nhttps://www.tensorflow.org/api_docs/python/tf/distribute/MultiWorkerMirroredStrategy\r\n\r\nAlso I suspect you are using the code which was related tensorflow 1.x version which is not actively supported now. Kindly convert the code for the latest version and use the tensorflow v2.15 or v2.16. Thank you!",
"I understand what you mean, but this API used to be available...right？\r\nDue to some reasons, I may not be able to migrate and use the relevant version of TF2. In fact, the above code can run successfully and obtain good training results using TF1.14 and CollectiveAllReduceStrategy API. The only point I am confused about is why there is a much larger communication volume between workers than the theoretical value.\r\nLooking forward to your reply, and have a nice day~",
"@certainly-cyber,\r\nThe API **tf.contrib.distribute.CollectiveAllReduceStrategy** is not available and as it is part of the deprecated **tf.contrib**\r\nCollectiveAllReduceStrategy is MultiWorkerMirrorStrategy. CollectiveAllReduceStrategy is a name we used in the implementation. Please refer to documentations of MultiWorkerMirrorStrategy.\r\n\r\n\r\n\r\n\r\nAlso CollectiveAllReduceStrategy reduces computation overhead by distributing it across workers, there's additional overhead associated with transferring data between workers. This overhead can include serialization, deserialization, and network latency, all of which can contribute to a larger communication volume than the theoretical minimum. Thank you!",
"Okay, I got it. Thank you again for your answer~\r\nBy the way, if I have adopted **MultiWorkerMirroredStrategy**, can we accurately calculate (or roughly estimate) the amount of data that needs to be synchronized? I have already know how to use model parameters for estimation, but I think it's not accurate enough:) Such like what the format of the synchronized message will be like, or what is the proportion of overhead information? Thank you!",
"@certainly-cyber,\r\nIn MultiWorkerMirroredStrategy, the synchronized messages primarily consist of gradients associated with the model's weights and biases. These gradients are numerical tensors representing the direction and magnitude for updating the model parameters during training.\r\nhttps://www.tensorflow.org/api_docs/python/tf/distribute/MultiWorkerMirroredStrategy\r\n\r\nAlso this is not the bug/feature request, Kindly file the issue on [TensorFlow Forum](https://discuss.tensorflow.org/) . There is also a larger community that reads questions there. Thank you!",
"ok, I got it~ \r\nThanks again for your answer, I'll keep exploring, thank you and have a nice day！",
"@certainly-cyber,\r\nGlad the issue was resolved. Could you please feel free to move this issue to closed status. Thank you!",
"Sure, my pleasure.\r\nThanks again~",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66601\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66601\">No</a>\n"
] | 2024-04-29T07:24:24 | 2024-05-21T13:51:06 | 2024-05-21T13:51:02 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
TF1.14
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
2
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
As far as I know, TensorFlow uses ring allreduce, which seems to mean that the number of synchronized data should be [2 * (N -1)/N ]* number of parameters * 4bytes(Reduce Scatter + All Gather), N represents the number of workers. If we do some simplification, this should be like:
(2 * number of parameters * 4bytes)
The distributed approach I adopt is：
`train_distribute=tf.contrib.distribute.CollectiveAllReduceStrategy(num_gpus_per_worker=0)`
On the other hand, I conducted a simple experiment，my model parameters are like this:
```
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 16) 176
_________________________________________________________________
dense_1 (Dense) (None, 1) 17
=================================================================
Total params: 193
Trainable params: 193
Non-trainable params: 0
```
This should result about:2 * 193 * 4 = 1544bytes, but in fact, I caught around 2500 bytes of sending packages when I use tcpdump to capture package about tensorflow at one worker node.
Why there have such a big gap? Is this related to the tensorflow environment I am using(Kubeflow+Tensorflowtraining operator in K8s)? May I ask if there exists a more accurate formula?
### Standalone code to reproduce the issue
```shell
This is the code I am using：
https://github.com/kubeflow/training-operator/blob/master/examples/tensorflow/distribution_strategy/estimator-API/keras_model_to_estimator.py
```
### Relevant log output
_No response_
|
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Fix flakiness in TFLite DRQ TransposeConv op test
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Fix flakiness in TFLite DRQ TransposeConv op test
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CUDNN_STATUS_BAD_PARAM in external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc(4175): 'op' CUDNN_BACKEND_OPERATION: cudnnFinalize Failed
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[
"Hi @sylviahamidah ,\r\n\r\nCould you please try with model.fit() instead of model.fit_generator() as it is deprecated. Also request you to share minimal code snippet if above change won't work for you. Thanks.",
"Hi @SuryanarayanaY \r\nI've tried model.fit() but the error is still the same.\r\n\r\nHere I attached the minimal reproducible example, let me know if you need further infomation [https://colab.research.google.com/drive/1FnwvnzkmeLYoaW42wkCKWw0j5xVPQ1Id?usp=sharing](url)",
"Hi @sylviahamidah ,\r\n\r\nCould you please confirm the cuDNN version in your system. Please cross check the compatible versions from [here](https://www.tensorflow.org/install/source#gpu).\r\n\r\nAlso, please use tensorflow[and-cuda] package which will install compatible cuda/cudnn libraries.",
"Sorry for my late response, Sir @SuryanarayanaY \r\n\r\nMy system version is written below. \r\nTensorflow: 2.15.0\r\nPython: 3.11.7\r\nCUDA version: 12.2\r\nCUDNN version: 8\r\n\r\nAlso I've tried to install tensorflow[and-cuda] with code below but the error is still the same.\r\n`!pip install --extra-index-url https://pypi.nvidia.com tensorrt-bindings==8.6.1 tensorrt-libs==8.6.1`\r\n`!pip install -U tensorflow[and-cuda]==2.15.0` \r\n ",
"Update: After I installed `pip install tensorflow[and-cuda]`, the error disappeared. Thank you for your assistance",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66598\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66598\">No</a>\n"
] | 2024-04-29T03:57:49 | 2024-05-13T06:35:08 | 2024-05-13T06:35:05 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.15.0
### Custom code
Yes
### OS platform and distribution
Google Colaboratory
### Mobile device
Google Colaboratory
### Python version
3.10.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.2
### GPU model and memory
_No response_
### Current behavior?
When I try "model.fit()" server stops with error message below
CUDNN_STATUS_BAD_PARAM
in external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc(4175): 'op' CUDNN_BACKEND_OPERATION: cudnnFinalize Failed
[[{{node AlbuNet/conv1/Conv2D}}]] [Op:__inference_train_function_49877]
How can I solve the issue?
Thanks!
### Standalone code to reproduce the issue
```shell
you can see my work here
https://colab.research.google.com/drive/1n97wV5cfgdQfWmngK5EBJjsYSjJzfi_d?usp=sharing
```
### Relevant log output
_No response_
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[
"@harrylal Could you try for disabling specific GPU delegate optimizations related to Batch Matrix Multiplication, if applicable?\r\nThank you!",
"@sushreebarsa Thank you for the response. \r\n\r\nExporting the model on a different enviornement ([ultralytics docker](https://github.com/ultralytics/ultralytics)) resolved this issue.\r\n\r\n\r\n\r\n",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66597\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66597\">No</a>\n"
] | 2024-04-29T03:53:38 | 2024-05-11T01:44:57 | 2024-05-11T01:44:54 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16
### Custom code
Yes
### Mobile device
Android 10
### GPU model and memory
Mali - G72*3
### Tflite Model
[tflite model](https://drive.google.com/file/d/1rga1drfZMuMB2BFuU2_4lMqhJxbR5tVA/view?usp=sharing)
### Current behavior?
Expected Behavior: The model should initialize and run on the GPU as it does on the CPU.
Actual Behavior: When attempting to run the model on the GPU using the TensorFlow Lite GPU delegate, the initialization fails.
### Standalone code to reproduce the issue
```shell
adb install -r -d -g android_arm_benchmark_model.apk
adb push TwinLiteNetLane.tflite /data/local/tmp/
adb shell am start -S -n org.tensorflow.lite.benchmark/.BenchmarkModelActivity --es args '"--graph=/data/local/tmp/TwinLiteNetLane.tflite --use_gpu=true"'
```
### Relevant log output
```shell
04-29 08:55:31.116 502 502 I ADB_SERVICES: service_to_fd shell,v2,TERM=xterm-256color,raw:am start -S -n org.tensorflow.lite.benchmark/.BenchmarkModelActivity --es args "--graph=/data/local/tmp/TwinLiteNetLane.tflite --use_gpu=true"
04-29 08:55:32.058 2766 2766 I tflite_BenchmarkModelActivity: Running TensorFlow Lite benchmark with args: --graph=/data/local/tmp/TwinLiteNetLane.tflite --use_gpu=true
04-29 08:55:32.062 2766 2766 I tflite : Log parameter values verbosely: [0]
04-29 08:55:32.062 2766 2766 I tflite : Graph: [/data/local/tmp/TwinLiteNetLane.tflite]
04-29 08:55:32.063 2766 2766 I tflite : Use gpu: [1]
04-29 08:55:32.064 2766 2766 I tflite : Loaded model /data/local/tmp/TwinLiteNetLane.tflite
04-29 08:55:32.064 2766 2766 I tflite : Initialized TensorFlow Lite runtime.
04-29 08:55:32.065 2766 2766 I tflite : Created TensorFlow Lite delegate for GPU.
04-29 08:55:32.065 2766 2766 I tflite : GPU delegate created.
04-29 08:55:32.072 2766 2766 I tflite : Replacing 395 out of 395 node(s) with delegate (TfLiteGpuDelegateV2) node, yielding 1 partitions for the whole graph.
04-29 08:55:32.077 2766 2766 E tflite : TfLiteGpuDelegate Init: BATCH_MATMUL: Node 333 is already a consumer of the value 334
04-29 08:55:32.077 2766 2766 I tflite : Created 0 GPU delegate kernels.
04-29 08:55:32.077 2766 2766 E tflite : TfLiteGpuDelegate Prepare: delegate is not initialized
04-29 08:55:32.077 2766 2766 E tflite : Node number 395 (TfLiteGpuDelegateV2) failed to prepare.
04-29 08:55:32.078 2766 2766 E tflite : Restored original execution plan after delegate application failure.
04-29 08:55:32.079 2766 2766 E tflite : Failed to apply GPU delegate.
```
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[XLA:GPU] Use parent fusion adaptor to correctly find operands and users.
After this change, HloInstruction adaptor wouldn't look inside kFusion instruction that not part of the relevant fusion adaptor.
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IllegalArgumentException: Internal error: Failed to run on the given Interpreter
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[
"hi @Coder-HuangBH ,\r\n\r\nCan you please briefly describe the error, i can see two inference results from the logs, but i don't understand if they are from the same interpreter or different interpreter , also can you share your inference code from the android .",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66594\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66594\">No</a>\n"
] | 2024-04-29T02:27:23 | 2024-06-09T01:54:14 | 2024-06-09T01:54:10 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
org.tensorflow:tensorflow-lite:2.11.0
### Custom code
Yes
### OS platform and distribution
win10
### Mobile device
Android12
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I want to output the text normally, but it's going to be an exception
### Standalone code to reproduce the issue
```shell
The only difference between success and failure is the tflite file，This is their parameter print :
success Input Tensor Dump ===>
2024-04-29 09:50:13.724 24920-25027 WhisperEngineJava com.whispertflite D shape.length: 3
2024-04-29 09:50:13.725 24920-25027 WhisperEngineJava com.whispertflite D shape[0]: 1
2024-04-29 09:50:13.725 24920-25027 WhisperEngineJava com.whispertflite D shape[1]: 80
2024-04-29 09:50:13.725 24920-25027 WhisperEngineJava com.whispertflite D shape[2]: 3000
2024-04-29 09:50:13.725 24920-25027 WhisperEngineJava com.whispertflite D dataType: FLOAT32
2024-04-29 09:50:13.726 24920-25027 WhisperEngineJava com.whispertflite D name: serving_default_input_ids:0
2024-04-29 09:50:13.726 24920-25027 WhisperEngineJava com.whispertflite D numBytes: 960000
2024-04-29 09:50:13.726 24920-25027 WhisperEngineJava com.whispertflite D index: 0
2024-04-29 09:50:13.726 24920-25027 WhisperEngineJava com.whispertflite D numDimensions: 3
2024-04-29 09:50:13.727 24920-25027 WhisperEngineJava com.whispertflite D numElements: 240000
2024-04-29 09:50:13.727 24920-25027 WhisperEngineJava com.whispertflite D shapeSignature.length: 3
2024-04-29 09:50:13.727 24920-25027 WhisperEngineJava com.whispertflite D quantizationParams.getScale: 0.0
2024-04-29 09:50:13.727 24920-25027 WhisperEngineJava com.whispertflite D quantizationParams.getZeroPoint: 0
2024-04-29 09:50:13.727 24920-25027 WhisperEngineJava com.whispertflite D ==================================================================
2024-04-29 09:50:13.728 24920-25027 WhisperEngineJava com.whispertflite D Output Tensor Dump ===>
2024-04-29 09:50:13.728 24920-25027 WhisperEngineJava com.whispertflite D shape.length: 2
2024-04-29 09:50:13.728 24920-25027 WhisperEngineJava com.whispertflite D shape[0]: 1
2024-04-29 09:50:13.728 24920-25027 WhisperEngineJava com.whispertflite D shape[1]: 448
2024-04-29 09:50:13.729 24920-25027 WhisperEngineJava com.whispertflite D dataType: INT32
2024-04-29 09:50:13.729 24920-25027 WhisperEngineJava com.whispertflite D name: StatefulPartitionedCall:0
2024-04-29 09:50:13.729 24920-25027 WhisperEngineJava com.whispertflite D numBytes: 1792
2024-04-29 09:50:13.729 24920-25027 WhisperEngineJava com.whispertflite D index: 1047
2024-04-29 09:50:13.729 24920-25027 WhisperEngineJava com.whispertflite D numDimensions: 2
2024-04-29 09:50:13.729 24920-25027 WhisperEngineJava com.whispertflite D numElements: 448
2024-04-29 09:50:13.730 24920-25027 WhisperEngineJava com.whispertflite D shapeSignature.length: 2
2024-04-29 09:50:13.730 24920-25027 WhisperEngineJava com.whispertflite D quantizationParams.getScale: 0.0
2024-04-29 09:50:13.730 24920-25027 WhisperEngineJava com.whispertflite D quantizationParams.getZeroPoint: 0
2024-04-29 09:50:13.730 24920-25027 WhisperEngineJava com.whispertflite D ==================================================================
failed Input Tensor Dump ===>
2024-04-29 09:48:45.685 24207-24753 WhisperEngineJava com.whispertflite D shape.length: 3
2024-04-29 09:48:45.685 24207-24753 WhisperEngineJava com.whispertflite D shape[0]: 1
2024-04-29 09:48:45.685 24207-24753 WhisperEngineJava com.whispertflite D shape[1]: 80
2024-04-29 09:48:45.685 24207-24753 WhisperEngineJava com.whispertflite D shape[2]: 3000
2024-04-29 09:48:45.685 24207-24753 WhisperEngineJava com.whispertflite D dataType: FLOAT32
2024-04-29 09:48:45.685 24207-24753 WhisperEngineJava com.whispertflite D name: serving_default_input_ids:0
2024-04-29 09:48:45.685 24207-24753 WhisperEngineJava com.whispertflite D numBytes: 960000
2024-04-29 09:48:45.685 24207-24753 WhisperEngineJava com.whispertflite D index: 0
2024-04-29 09:48:45.685 24207-24753 WhisperEngineJava com.whispertflite D numDimensions: 3
2024-04-29 09:48:45.685 24207-24753 WhisperEngineJava com.whispertflite D numElements: 240000
2024-04-29 09:48:45.685 24207-24753 WhisperEngineJava com.whispertflite D shapeSignature.length: 3
2024-04-29 09:48:45.685 24207-24753 WhisperEngineJava com.whispertflite D quantizationParams.getScale: 0.0
2024-04-29 09:48:45.685 24207-24753 WhisperEngineJava com.whispertflite D quantizationParams.getZeroPoint: 0
2024-04-29 09:48:45.685 24207-24753 WhisperEngineJava com.whispertflite D ==================================================================
2024-04-29 09:48:45.685 24207-24753 WhisperEngineJava com.whispertflite D Output Tensor Dump ===>
2024-04-29 09:48:45.685 24207-24753 WhisperEngineJava com.whispertflite D shape.length: 2
2024-04-29 09:48:45.685 24207-24753 WhisperEngineJava com.whispertflite D shape[0]: 1
2024-04-29 09:48:45.685 24207-24753 WhisperEngineJava com.whispertflite D shape[1]: 451
2024-04-29 09:48:45.685 24207-24753 WhisperEngineJava com.whispertflite D dataType: INT32
2024-04-29 09:48:45.686 24207-24753 WhisperEngineJava com.whispertflite D name: StatefulPartitionedCall:0
2024-04-29 09:48:45.686 24207-24753 WhisperEngineJava com.whispertflite D numBytes: 1804
2024-04-29 09:48:45.686 24207-24753 WhisperEngineJava com.whispertflite D index: 559
2024-04-29 09:48:45.686 24207-24753 WhisperEngineJava com.whispertflite D numDimensions: 2
2024-04-29 09:48:45.686 24207-24753 WhisperEngineJava com.whispertflite D numElements: 451
2024-04-29 09:48:45.686 24207-24753 WhisperEngineJava com.whispertflite D shapeSignature.length: 2
2024-04-29 09:48:45.686 24207-24753 WhisperEngineJava com.whispertflite D quantizationParams.getScale: 0.0
2024-04-29 09:48:45.686 24207-24753 WhisperEngineJava com.whispertflite D quantizationParams.getZeroPoint: 0
2024-04-29 09:48:45.686 24207-24753 WhisperEngineJava com.whispertflite D ==================================================================
The script for generating the failed tflite file is as follows:
import tensorflow as tf
from datasets import load_dataset
from transformers import WhisperProcessor, WhisperFeatureExtractor, TFWhisperForConditionalGeneration, WhisperTokenizer
whisperPath = "openai/whisper-tiny.en"
saved_model_dir = 'path/to/tf_whisper_saved'
tflite_model_path = 'path/to/whisper111.tflite'
feature_extractor = WhisperFeatureExtractor.from_pretrained(whisperPath)
tokenizer = WhisperTokenizer.from_pretrained(whisperPath, predict_timestamps=True)
processor = WhisperProcessor(feature_extractor, tokenizer)
model = TFWhisperForConditionalGeneration.from_pretrained(whisperPath, from_pt=True)
# Loading dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code = True)
inputs = feature_extractor(
ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="tf"
)
input_features = inputs.input_features
# Generating Transcription
generated_ids = model.generate(input_features=input_features)
print(generated_ids)
transcription = processor.tokenizer.decode(generated_ids[0])
print(transcription)
model.save(saved_model_dir)
# Convert the model
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.
tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.
]
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()
# Save the model
with open(tflite_model_path, 'wb') as f:
f.write(tflite_model)
class GenerateModel(tf.Module):
def __init__(self, model):
super(GenerateModel, self).__init__()
self.model = model
@tf.function(
# shouldn't need static batch size, but throws exception without it (needs to be fixed)
input_signature=[
tf.TensorSpec((1, 80, 3000), tf.float32, name="input_ids"),
],
)
def serving(self, input_features):
outputs = self.model.generate(
input_features,
max_new_tokens=450, #change as needed
return_dict_in_generate=True,
)
return {"sequences": outputs["sequences"]}
# saved_model_dir = '/content/tf_whisper_saved'
# tflite_model_path = 'whisper-tiny.en.tflite'
# tflite_model_path = 'path/to/whisper222.tflite'
tflite_model_path = 'path/to/whisper_vi222.tflite'
generate_model = GenerateModel(model=model)
tf.saved_model.save(generate_model, saved_model_dir, signatures={"serving_default": generate_model.serving})
# Convert the model
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.
tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.
]
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()
# Save the model
with open(tflite_model_path, 'wb') as f:
f.write(tflite_model)
```
### Relevant log output
```shell
2024-04-29 09:48:50.970 24207-24753 Whisper com.whispertflite E Error...
java.lang.IllegalArgumentException: Internal error: Failed to run on the given Interpreter: tensorflow/lite/kernels/reduce.cc:390 std::apply(optimized_ops::Mean<T, U>, args) was not true.
tensorflow/lite/kernels/reduce.cc:390 std::apply(optimized_ops::Mean<T, U>, args) was not true.
tensorflow/lite/kernels/reduce.cc:390 std::apply(optimized_ops::Mean<T, U>, args) was not true.
tensorflow/lite/kernels/reduce.cc:390 std::apply(optimized_ops::Mean<T, U>, args) was not true.
tensorflow/lite/kernels/reduce.cc:390 std::apply(optimized_ops::Mean<T,
at org.tensorflow.lite.NativeInterpreterWrapper.run(Native Method)
at org.tensorflow.lite.NativeInterpreterWrapper.run(NativeInterpreterWrapper.java:247)
at org.tensorflow.lite.InterpreterImpl.runForMultipleInputsOutputs(InterpreterImpl.java:107)
at org.tensorflow.lite.Interpreter.runForMultipleInputsOutputs(Interpreter.java:80)
at org.tensorflow.lite.InterpreterImpl.run(InterpreterImpl.java:100)
at org.tensorflow.lite.Interpreter.run(Interpreter.java:80)
at com.whispertflite.engine.WhisperEngine.runInference(WhisperEngine.java:147)
at com.whispertflite.engine.WhisperEngine.transcribeFile(WhisperEngine.java:74)
at com.whispertflite.asr.Whisper.threadFunction(Whisper.java:129)
at com.whispertflite.asr.Whisper.lambda$start$0$com-whispertflite-asr-Whisper(Whisper.java:76)
at com.whispertflite.asr.Whisper$$ExternalSyntheticLambda0.run(Unknown Source:2)
at java.lang.Thread.run(Thread.java:930)
```
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PR_kwDOArmXAs5t9SOS
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Integrate StableHLO at openxla/stablehlo@b6406a43
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[] | 2024-04-29T02:15:49 | 2024-05-01T22:03:47 | 2024-05-01T22:03:46 |
NONE
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Integrate StableHLO at openxla/stablehlo@b6406a43
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PR_kwDOArmXAs5t9FgT
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Automated Code Change
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[] | 2024-04-29T01:14:29 | 2024-04-29T02:34:51 | 2024-04-29T02:34:51 |
NONE
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