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} | Revert usage of always_inline attribute.
This is not necessary, we already inline functions with just one caller. The
problem that we didn't inline in some cases was just that there were dead
functions which called them. After removing dead functions, inlining works.
Rename PreInlinerPass to EraseDeadFunctions pass.
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This is needed to optimize the `tf.math.top_k` function with floating-point inputs.
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} | PR #11832: [XLA:CPU][oneDNN] Enable oneDNN MatMul + BiasAdd + Sigmoid fusion.
Imported from GitHub PR https://github.com/openxla/xla/pull/11832
This PR enables oneDNN MatMul+BiasAdd+Sigmoid fusion for FP32, BF16 and F16.
Copybara import of the project:
--
0a3429d4ae71db667878e7a04c9f3bd021a5aad5 by nhatle <nhat.le@intel.com>:
Enable OneDNN MatMul + BiasAdd + Sigmoid fusion
--
21cf1253d5a284849a1dc0adc0e4d3d2253305d7 by nhatle <nhat.le@intel.com>:
Small fix
--
e6fff8b7bead347f3c725deb99922ebcbdb7d090 by nhatle <nhat.le@intel.com>:
Update HandleDivide to use new version of ElementwiseSafeIntermediates function
Merging this change closes #11832
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11832 from Intel-tensorflow:nhatle/mm-bias-sigmoid-fusion a96013ce31dcb32c82982c61ba2a7baafab97ed4
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} | PR #13065: [NVIDIA] Disable the bias reuse for fp8 dot
Imported from GitHub PR https://github.com/openxla/xla/pull/13065
For some operations, we might want to do the `output to operand aliasing` to reuse the memory space. For example, in the dot operation, we can alias the output to the bias operand, when they have the same shape.
However, this aliasing is not always safe especially when the dot outputs fp8 outputs. In this case the bias is 2x larger than expected output and the HLO verifier will complain.
This PR fixes this issue by disabling the `output to operand aliasing` for fp8 dot.
cc. @philipphack @hx89 @nluehr
Copybara import of the project:
--
711e3097a80d9ff6e48b80924a1e81ff940ac240 by kaixih <kaixih@nvidia.com>:
Disable bias reuse for fp8 dot
--
7d86b99396385f85e6e04a28dc8d824fd53e91f3 by kaixih <kaixih@nvidia.com>:
Update to a general solution
--
15f774667013045c7d8d9e38ac80a45645313b1e by kaixih <kaixih@nvidia.com>:
Address comments
Merging this change closes #13065
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13065 from kaixih:disable_bias_reuse_in_fp8 15f774667013045c7d8d9e38ac80a45645313b1e
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} | [xla:cpu] NFC: Give human readable names to LLVM IR instructions in host kernel prototype
Example:
```
%tdim_x_gep = getelementptr inbounds %SE_HOST_KernelThreadDim, ptr %2, i32 0
%tdim_y_gep = getelementptr inbounds %SE_HOST_KernelThreadDim, ptr %2, i32 1
%tdim_z_gep = getelementptr inbounds %SE_HOST_KernelThreadDim, ptr %2, i32 2
%tdim_x = load i64, ptr %tdim_x_gep, align 4
%tdim_y = load i64, ptr %tdim_y_gep, align 4
%tdim_z = load i64, ptr %tdim_z_gep, align 4
%3 = getelementptr inbounds %SE_HOST_KernelCallFrame, ptr %0, i32 0, i32 1
%tid_x_gep = getelementptr inbounds %SE_HOST_KernelThread, ptr %3, i32 0, i32 0
%tid_y_gep = getelementptr inbounds %SE_HOST_KernelThread, ptr %3, i32 0, i32 1
%tid_z_gep = getelementptr inbounds %SE_HOST_KernelThread, ptr %3, i32 0, i32 2
%tid_x = load i64, ptr %tid_x_gep, align 4
%tid_y = load i64, ptr %tid_y_gep, align 4
%tid_z = load i64, ptr %tid_z_gep, align 4
%args_gep = getelementptr inbounds %SE_HOST_KernelCallFrame, ptr %0
%args = load ptr, ptr %args_gep, align 8
%arg0_gep = getelementptr %SE_HOST_KernelArg, ptr %args, i32 0, i32 0
%arg0 = load ptr, ptr %arg0_gep, align 8
%args_gep1 = getelementptr inbounds %SE_HOST_KernelCallFrame, ptr %0
%args2 = load ptr, ptr %args_gep1, align 8
%arg1_gep = getelementptr %SE_HOST_KernelArg, ptr %args2, i32 1, i32 0
%arg1 = load ptr, ptr %arg1_gep, align 8
```
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} | [xla:cpu] Add support for emitting host kernels for fusions
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13018 from ROCm:ci_amdtags_20240523 0bf9ab80d06284883df19f17f421eb6a691c0dd8
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} | Pattern-match FP8 calls with input converts without scales.
This change pattern matches the following into an FP8 cublasLT custom call
dot(x.astype(jnp.float16), y.astype(jnp.float16))
We previously matched a version of this that also included scaling into a custom call.
dot(x.astype(jnp.float16) * x_scale, y.astype(jnp.float16) * y_scale)
Now the scales are optional even when the inputs are cast to FP16. There is no particular reason to cast inputs to FP16 if there are no input scales, but at least one internal model does this.
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} | creat jax config api to allow custom pjrt client create option settings. this allows a device platform's pjrt client be aware of the calling (customer) ml framework
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The bug is that the function does not handle the case where the indices are a copy of a concatenate. Thus this we add code to search up a tree of copies to see if the first non-copy op is a concatenate, in which case we can use that to test if it is a concatenate of an iota, to then find parallel dims.
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Replace repetitive code with calls to the helper.
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"I believe it's ok to adjust tolerances to 1e-4 unconditionally.",
"I see okay, but we need a minimum relative tolerance of 3e-4 for some of the F64 outputs to match the corresponding outputs of oneDNN's F32 implementation of batch reduce convolution."
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} | In this test, the F32 output is compared to the F64 output and is expected to match with a tolerance of 1e-5. However, due to significant accumulations caused by large accumulation chains for some inputs, oneDNN algorithms loose precision in some cases. Hence, we need to adjust the tolerance levels accordingly. | {
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} | [XLA:GPU] Remove structured matchers for `SymbolicTile` tests and use string matcher instead.
Also filter out the printing of the string "rt_vars" when the symbolic tile
does not contain any runtime variable.
This is in preparation for adding support for constraints, which would have
required yet another matcher without this change.
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Imported from GitHub PR https://github.com/openxla/xla/pull/13018
Add `requires-gpu-amd` for AMD gpus and unit tests that require AMD backend. Use `requires-gpu-nvidia` for any nvidia gpu backend.
Copybara import of the project:
--
8c3c52760c37b6c9fc17910174df6bfa396b5b5e by Harsha HS <Harsha.HavanurShamsundara@amd.com>:
[ROCm] Distinguish between AMD and NVIDIA GPUs with relevant tags
--
0bf9ab80d06284883df19f17f421eb6a691c0dd8 by Harsha HS <Harsha.HavanurShamsundara@amd.com>:
Change ALL_GPU_BACKENDS to GPU_BACKENDS
Merging this change closes #13018
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13018 from ROCm:ci_amdtags_20240523 0bf9ab80d06284883df19f17f421eb6a691c0dd8
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Updates LLVM usage to match
[9b79acedd689](https://github.com/llvm/llvm-project/commit/9b79acedd689)
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} | Do not assign `ExecutionStreamAssignments` to `HloComputations` that are only reachable through embedded calls.
`ExecutionStreamAssignment` relies on the the input `HloModule` being flat. In other words, each computation must be called by a single instruction. This is generally achieved by processing the `HloModule` with `FlattenCallGraph`. However, this pass only flattens sequential calls.
The good news is that we don't need to assign `ExecutionStreamIds` to instructions invoked via embedded calls because they end up in the same kernel as the parent instruction. So this change ignores all instructions reachable through embedded calls (e.g. fusions, sorts, etc.).
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} | PR #13047: [XLA:GPU] Lowering cublasLt thunk to command buffer
Imported from GitHub PR https://github.com/openxla/xla/pull/13047
Copybara import of the project:
--
ee872b54d6301f2dfb1168b57dcd5b6e8a0c4561 by Shawn Wang <shawnw@nvidia.com>:
Lowering cublasLt thunk to command buffer
Merging this change closes #13047
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13047 from shawnwang18:shawnw/command_buffer_add_cublas_lt ee872b54d6301f2dfb1168b57dcd5b6e8a0c4561
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} | [xla:gpu] NFC: Simplify AddressComputationThunk by replacing llvm::zip with struct
Remove accidental copies of various temporaries by packing all arguments into struct and iterating over a single vector.
Also fix a bug with accessing offsets buffers stored on host, original [arg_idx + offset] offset computation is simply incorrect.
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} | [TEST] Remove xla_cc_test
For now it just forwards to cc_test. If that works we can entirely remove it.
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} | [XLA:GPU] Add a util to reset programs count and deflake `gpu_compiler_test`.
Previously, running `gpu_compiler_test` in different orders would yield
different results for `GpuCompilerTest.CompiledProgramsCount`.
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} | Integrate LLVM at llvm/llvm-project@9b79acedd689
Updates LLVM usage to match
[9b79acedd689](https://github.com/llvm/llvm-project/commit/9b79acedd689)
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} | Fix shuffle_reduce with unsigned arguments.
arith.bitcast can't convert from ui64 to i64.
Reverts df40f8d088774e2e938896b7f7cf2e2051d00a3a
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13088 from ROCm:ci_reduce_atomic_min f894f1954513019f0ca6890a27e09e0fee9d462e
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This change both extracts the logic and generalizes it so that it works with more than one dimension.
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This is terrible. I would like to order one progressive
lowering please.
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Reverts df40f8d088774e2e938896b7f7cf2e2051d00a3a
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13088 from ROCm:ci_reduce_atomic_min f894f1954513019f0ca6890a27e09e0fee9d462e
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} | [XLA:GPU] Add TritonSupportTest for dynamic-slice
Also support indices of type S8, S16. (S32 was already supported.)
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} | Remove experimental_adaptive_avx_optimization flag from XNNPACK delegate options
It's always on now.
Reverts df40f8d088774e2e938896b7f7cf2e2051d00a3a
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13088 from ROCm:ci_reduce_atomic_min f894f1954513019f0ca6890a27e09e0fee9d462e
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} | Add missing newline in the output of run_hlo_module
This is very annoying if one uses the tool frequently
Reverts df40f8d088774e2e938896b7f7cf2e2051d00a3a
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13088 from ROCm:ci_reduce_atomic_min f894f1954513019f0ca6890a27e09e0fee9d462e
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"Please find the code below:\r\n`// Import necessary libraries\r\nimport '@mediapipe/face_detection';\r\nimport '@tensorflow/tfjs-core';\r\n// Register WebGL backend.\r\nimport '@tensorflow/tfjs-backend-webgl';\r\nimport * as faceDetection from '@tensorflow-models/face-detection';\r\nimport * as cocoSsd from '@tensorflow-models/coco-ssd';\r\n\r\nexport const createFaceDetector = async () => {\r\n // Define the model from SupportedModels\r\n const model = await faceDetection.SupportedModels.MediaPipeFaceDetector;\r\n\r\n // Define the detector configuration\r\n const detectorConfig = {\r\n runtime: 'mediapipe', // Set runtime to 'mediapipe'\r\n maxFaces: 2, // Set to detect up to 2 faces\r\n modelType: 'full', // Use the full-range model\r\n solutionPath: 'https://cdn.jsdelivr.net/npm/@mediapipe/face_detection', // Path to the solution\r\n };\r\n\r\n try {\r\n console.log('Initializing face detector...');\r\n // Create the detector\r\n const detector = await faceDetection.createDetector(model, detectorConfig);\r\n console.log('Face detector initialized');\r\n if (!detector) throw new Error('Face detector not created');\r\n return detector;\r\n } catch (error) {\r\n console.error('Face detector loading error:', error);\r\n throw error;\r\n }\r\n};`",
"Hi @mirzamukkarambaig ,\r\n\r\nFor TensorFlow JS we have separate repo. Please report JS issues there. [Link](https://github.com/tensorflow/tfjs/issues) attached for reference.\r\n\r\nThanks!",
"Great Thank you",
"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/68717\">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/68717\">No</a>\n"
] | 2024-05-28T13:26:02 | 2024-05-29T08:27:54 | 2024-05-29T08:27:51 | NONE | null | null | null | I am creating a React JS-based front end that employs the Tensorflow JS Simple Face Detection model. The application runs fine when being run on the dev environment using `npm run dev`. However when I run `npm run build` and `npm run preview` the model throws the following error:
`index-BEYgabte.js:15635 Uncaught (in promise) TypeError: Zse.FaceDetection is not a constructor
at new t (index-BEYgabte.js:15635:1827)
at index-BEYgabte.js:15635:3968
at index-BEYgabte.js:15635:1457
at Object.next (index-BEYgabte.js:15635:1562)
at index-BEYgabte.js:15635:499
at new Promise (<anonymous>)
at l0 (index-BEYgabte.js:15635:250)
at SEe (index-BEYgabte.js:15635:3683)
at index-BEYgabte.js:15635:15604
at index-BEYgabte.js:15635:1457`
This is the picture of the preview
This is the picture of the dev
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} | Enable batched radix sort using CUB library
CUB library provides `DeviceSegmentedRadixSort::SortKeys` and `DeviceSegmentedRadixSort::SortPairs` functions that allow sorting multiple segments at the same time.
`GpuSortRewriter` pass now supports any sorts where the sort dimension is minor.
If the `batch_size` passed to the custom kernel is greater than one, scratch size is increased to hold the segment offsets at the end of the buffer.
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"Hm, afaik I should not be the reviewer on this -- it should be someone at Nvidia affiliated with TensorRT.",
"@beckerhe who can review this PR?"
] | 2024-05-28T11:11:23 | 2024-05-29T03:27:57 | null | CONTRIBUTOR | null | true | {
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} | TensorRT 10 makes a number of breaking changes to its API, requiring fixes in many places in the TF-TRT code. Most of the changes are straightforward though.
Opening as a draft because there is one set of tests that are still known to fail.
cc @nluehr @cliffwoolley | {
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"Hi @terryheo Can you please review this PR ? Thank you!"
] | 2024-05-28T09:11:22 | 2024-06-07T17:32:58 | null | NONE | null | false | {
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} | The headers delegate.h and delegate_options.h were missing when tensorflow lite was installed with the GPU delegates using CMake.
This PR just add these 2 files into TFLITE_DELEGATES_GPU_SRCS variable and it fixes the issue. It works because TFLITE_DELEGATES_GPU_SRCS is used to create _ALL_TFLITE_SRCS which is used to have _ALL_TFLITE_HDRS and then all headers from this variables are installed.
Previous PR discarded because of CLA : https://github.com/tensorflow/tensorflow/pull/63016 | {
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"@383156098 Could you please try to upgrade to the latset TF version as you are using an older version which is not actively supported. If your application allows it, kindly consider changing the data_format parameter in your Conv1D layer to channels_last. This is the more common format for TFLite and might avoid the allocation issue.\r\n\r\nThank you!",
"import tensorflow as tf\r\n\r\n# Define the Conv1D layer with 'channels_last'\r\nconv1d = tf.keras.layers.Conv1D(data_format=\"channels_last\",\r\n filters=128,\r\n kernel_size=3,\r\n strides=1,\r\n padding=\"same\",\r\n groups=128,\r\n dilation_rate=2)\r\n# Build the model\r\nmodel = tf.keras.Sequential([conv1d])\r\nmodel.build(input_shape=[1, 2000, 128]) # Update input shape accordingly\r\nmodel.summary()\r\n\r\n# Convert to TFLite model\r\nconverter = tf.lite.TFLiteConverter.from_keras_model(model)\r\nconverter.experimental_new_converter = True\r\nconverter.target_spec.supported_ops = [\r\n tf.lite.OpsSet.TFLITE_BUILTINS,\r\n tf.lite.OpsSet.SELECT_TF_OPS\r\n]\r\ntflite_model = converter.convert()\r\n\r\n# Save the TFLite model\r\nwith open('conv_tas_net.tflite', 'wb') as wb:\r\n wb.write(tflite_model)\r\n\r\n# Load and allocate tensors with TFLite interpreter\r\ninterpreter = tf.lite.Interpreter(model_content=tflite_model)\r\ninterpreter.allocate_tensors()\r\ninput_details = interpreter.get_input_details()\r\noutput_details = interpreter.get_output_details()\r\n\r\nprint(\"Model loaded and tensors allocated successfully.\")\r\n",
"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."
] | 2024-05-28T08:57:36 | 2024-06-11T01:51:08 | null | NONE | null | null | null | ### 1. System information
- Windows 10 and Centos 7
- Tensorflow 2.10.0 ~ 2.10.1
### 2. Code
conv1d = tf.keras.layers.Conv1D(data_format="channels_first",
filters=128,
kernel_size=3,
strides=1,
padding="same",
groups=128,
dilation_rate=2)
model = tf.keras.Sequential(conv1d)
model.build(input_shape=[1, 128, 2000])
model.summary()
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.
tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.
]
tflite_model = converter.convert()
with open('../tflite_checkpoint/conv_tas_net.tflite', 'wb') as wb:
wb.write(tflite_model)
interpreter = tf.lite.Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
### 5. error logs
interpreter.allocate_tensors()
File "G:\anaconda3\envs\bragi\lib\site-packages\tensorflow\lite\python\interpreter.py", line 513, in allocate_tensors
return self._interpreter.AllocateTensors()
RuntimeError: tensorflow/lite/kernels/space_to_batch_nd.cc:67 op_context->block_shape->dims->data[0] != spatial_dims_num (2 != 1)Node number 0 (SPACE_TO_BATCH_ND) failed to prepare.Failed to apply the default TensorFlow Lite delegate indexed at 0.
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Reverts 33bfff8ce3d639c64f8582c8013ac583a7faf543
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12940 from ROCm:ci_dot_bf16 c3bb3a7349266a51ff22a2e18dab0afb6e81bad4
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"I have the same issue: #68710",
"Please make sure that you have installed the Nvidia container toolkit before. Your docker container is not able to find the relevant CUDA, hence the error. Here's the documention: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html",
"I have tried that already and it did not make a difference. AFAIK it's only necessary when running Docker on a native Linux host. In my configuration with WSL2 it should already be covered by Docker Desktop.\nI have found this YouTube tutorial where it works without the container Toolkit too: https://youtu.be/YozfiLI1ogY.",
"**I'm getting the same issue, within the docker image tensorflow/tensorflow:latest-gpu-jupyter (2.16.1) I am getting...\r\n\r\n# ./__nvcc_device_query\r\n./__nvcc_device_query failed to call cudaLoader::cuInit(0) with error 0x1f4 (CUDA_ERROR_NOT_FOUND)\r\n# ./nvcc --version \r\nnvcc: NVIDIA (R) Cuda compiler driver\r\nCopyright (c) 2005-2023 NVIDIA Corporation\r\nBuilt on Wed_Nov_22_10:17:15_PST_2023\r\nCuda compilation tools, release 12.3, V12.3.107\r\nBuild cuda_12.3.r12.3/compiler.33567101_0\r\n# nvidia-smi\r\nTue May 28 10:03:54 2024 \r\n+-----------------------------------------------------------------------------------------+\r\n| NVIDIA-SMI 555.42.03 Driver Version: 555.85 CUDA Version: 12.5 |\r\n|-----------------------------------------+------------------------+----------------------+\r\n| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|=========================================+========================+======================|\r\n| 0 NVIDIA GeForce RTX 3070 On | 00000000:02:00.0 On | N/A |\r\n| 0% 48C P8 21W / 220W | 637MiB / 8192MiB | 0% Default |\r\n| | | N/A |\r\n+-----------------------------------------+------------------------+----------------------+\r\n \r\n+-----------------------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=========================================================================================|\r\n| No running processes found |\r\n+-----------------------------------------------------------------------------------------+**",
"I'm getting the same issue with tensorflow/tensorflow:2.13.0-gpu-jupyter ....\r\nE tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_NOT_FOUND: named symbol not found",
"I can install and run CUDA samples in WSL....(Ubuntu 22.04) by installing the CUDA tool kit 12.5\r\n\r\nDevice 0: \"NVIDIA GeForce RTX 3070\" with Compute 8.6 capability\r\nprintf() is called. Output:\r\n\r\n[2, 0]: Value is:10\r\n[2, 1]: Value is:10\r\n[2, 2]: Value is:10\r\n[2, 3]: Value is:10\r\n[2, 4]: Value is:10\r\n[2, 5]: Value is:10\r\n[2, 6]: Value is:10\r\n[2, 7]: Value is:10\r\n[3, 0]: Value is:10",
"I have found a solution and shared it here: #68710 ",
"The workaround is working. Since this issue is pretty much a duplicate of the other one I will close it now. The other one should be left open until the latest drivers is working 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/68711\">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/68711\">No</a>\n",
"\r\n\r\nNvidia 555.85 not working. Cuda 12.5 buggy\r\n\r\nUse 551.86 game ready driver and Cuda 12.4 with docker tensorflow/tensorflow:latest-gpu\r\n"
] | 2024-05-27T18:13:29 | 2024-06-01T18:40:19 | 2024-05-28T15:57:05 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.16.1-0-g5bc9d26649c
### Custom code
No
### OS platform and distribution
Windows 11 Pro + WSL2 Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.11.0rc1
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
RTX 4080
### Current behavior?
I am using Docker Desktop with WSL2 on Windows 11 Pro to run the `tensorflow-gpu:latest` Docker container. After starting it no GPU is found and the error `failed call to cuInit: CUDA_ERROR_NOT_FOUND: named symbol not found` occurs. CUDA, cuDNN and Python versions are all dictated by the Docker image so I cannot change them.
Things I have tried without success:
- using a different container version (2.16.1, 2.15.0, 2.14.0, 2.13.0)
- reinstalling WSL2/Ubuntu
- reinstalling Docker Desktop
- reinstalling GPU drivers
Reinstalling the drivers is the only thing that had any effect at all. It changed the error from CUDA_ERROR_NO_DEVICE to CUDA_ERROR_NOT_FOUND.
I have found https://github.com/google/jax/issues/13570 with a similar error but it's fix doesn't apply here. RTX 4000 cards require CUDA >=11.8 which I am using.
### Standalone code to reproduce the issue
```shell
docker run --rm --gpus all -it tensorflow/tensorflow:latest-gpu bash
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
```
### Relevant log output
```shell
2024-05-27 18:05:30.149964: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-05-27 18:05:31.089452: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_NOT_FOUND: named symbol not found
[]
```
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"Based on the log output and the steps you’ve taken, it appears that TensorFlow within your Docker container is unable to initialize CUDA, leading to the error `CUDA_ERROR_NOT_FOUND: named symbol not found`. This usually indicates an issue with CUDA installation or GPU driver compatibility within the Docker environment.\r\n\r\nHere's a standalone code snippet and Docker run command to reproduce the issue, as well as additional steps to troubleshoot and resolve it.\r\n\r\n### Standalone Code to Reproduce the Issue\r\n\r\n```sh\r\ndocker run --rm --gpus all -it tensorflow/tensorflow:latest-gpu bash -c \"python3 -c 'import tensorflow as tf; print(tf.config.list_physical_devices(\\\"GPU\\\"))'\"\r\n```\r\n\r\n### Relevant Log Output\r\n\r\n```plaintext\r\n2024-05-27 18:05:30.149964: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-05-27 18:05:31.089452: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_NOT_FOUND: named symbol not found\r\n[]\r\n```\r\n\r\n### Troubleshooting Steps\r\n\r\n#### 1. Verify GPU Driver and CUDA Toolkit in WSL2\r\n\r\nEnsure that the GPU driver and CUDA toolkit are correctly installed in your WSL2 environment.\r\n\r\n1. **Install NVIDIA Drivers**:\r\n\r\n Follow the [NVIDIA guide for WSL2](https://developer.nvidia.com/cuda/wsl) to install the latest GPU driver for WSL2.\r\n\r\n2. **Install CUDA Toolkit in WSL2**:\r\n\r\n Install the CUDA toolkit by following the official [NVIDIA CUDA on WSL user guide](https://docs.nvidia.com/cuda/wsl-user-guide/index.html).\r\n\r\n ```sh\r\n sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/3bf863cc.pub\r\n sudo sh -c 'echo \"deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64 /\" > /etc/apt/sources.list.d/cuda.list'\r\n sudo apt-get update\r\n sudo apt-get -y install cuda\r\n ```\r\n\r\n#### 2. Check Docker Configuration\r\n\r\nEnsure Docker is configured to use the NVIDIA runtime.\r\n\r\n1. **Install NVIDIA Container Toolkit**:\r\n\r\n ```sh\r\n sudo apt-get install -y nvidia-docker2\r\n sudo systemctl restart docker\r\n ```\r\n\r\n2. **Set Default Runtime**:\r\n\r\n Add the following to `/etc/docker/daemon.json`:\r\n\r\n ```json\r\n {\r\n \"runtimes\": {\r\n \"nvidia\": {\r\n \"path\": \"nvidia-container-runtime\",\r\n \"runtimeArgs\": []\r\n }\r\n }\r\n }\r\n ```\r\n\r\n Restart Docker:\r\n\r\n ```sh\r\n sudo systemctl restart docker\r\n ```\r\n\r\n#### 3. Verify CUDA in Docker Container\r\n\r\nEnsure that the Docker container can access CUDA.\r\n\r\n1. **Run CUDA Container**:\r\n\r\n ```sh\r\n docker run --rm --gpus all nvidia/cuda:11.8-base nvidia-smi\r\n ```\r\n\r\n2. **Test CUDA Program**:\r\n\r\n Run a simple CUDA program inside the TensorFlow Docker container.\r\n\r\n ```sh\r\n docker run --rm --gpus all -it tensorflow/tensorflow:latest-gpu bash -c \"apt-get update && apt-get install -y cuda-samples-11-8 && cd /usr/local/cuda-11.8/samples/1_Utilities/deviceQuery && make && ./deviceQuery\"\r\n ```\r\n\r\n#### 4. Set Environment Variables in Docker Container\r\n\r\nEnsure the correct CUDA environment variables are set within the container.\r\n\r\n1. **Run TensorFlow with CUDA Environment Variables**:\r\n\r\n ```sh\r\n docker run --rm --gpus all -it tensorflow/tensorflow:latest-gpu bash -c \"export LD_LIBRARY_PATH=/usr/local/cuda/lib64 && export CUDA_HOME=/usr/local/cuda && python3 -c 'import tensorflow as tf; print(tf.config.list_physical_devices(\\\"GPU\\\"))'\"\r\n ```\r\n\r\n#### 5. Reinstall GPU Drivers on Host\r\n\r\nReinstall the NVIDIA GPU drivers on your host machine to ensure compatibility with WSL2 and Docker.\r\n\r\n1. **Clean Installation of NVIDIA Drivers**:\r\n\r\n - Use the [Display Driver Uninstaller (DDU)](https://www.guru3d.com/files-details/display-driver-uninstaller-download.html) to uninstall existing drivers.\r\n - Download and install the latest drivers from the [NVIDIA website](https://www.nvidia.com/Download/index.aspx).\r\n\r\n#### 6. Update Docker and WSL2\r\n\r\nEnsure Docker Desktop and WSL2 are up to date.\r\n\r\n1. **Update Docker Desktop**:\r\n\r\n Download and install the latest version of Docker Desktop from the [Docker website](https://www.docker.com/products/docker-desktop).\r\n\r\n2. **Update WSL2 Kernel**:\r\n\r\n Update the WSL2 kernel:\r\n\r\n ```sh\r\n wsl --update\r\n ```\r\n",
"You do not need the Nvidia Container Toolkit in WSL. It's only necessary on a native Ubuntu host. I have tried with and without and it doesn't make a difference.",
"@koryphaee I have solved the issue by installing an older NVIDIA driver (552.44). I have installed it with the \"Perform a clean install\" option from the Custom (Advanced) installation menu. You can find older versions here: [https://www.nvidia.com/Download/Find.aspx#](https://www.nvidia.com/Download/Find.aspx#)\r\n\r\n\r\n\r\nThe only difference is downgrading the driver version.",
"@nikolayDemirev,\r\nI was successfully able to use tensorflow-gpu with docker with Tensorflow 2.16.1. Could you please check if the requirements are installed properly or not.\r\n\r\nhttps://stackoverflow.com/questions/78418499/using-tensorflow-with-gpu-on-docker-on-ubuntu\r\n\r\n\r\n\r\n",
"> @nikolayDemirev, I was successfully able to use tensorflow-gpu with docker with Tensorflow 2.16.1. Could you please check if the requirements are installed properly or not.\r\n> \r\n> https://stackoverflow.com/questions/78418499/using-tensorflow-with-gpu-on-docker-on-ubuntu\r\n> \r\n>  \r\n\r\nI have only gotten it working by downgrading the Nvidia driver to version 552.44. Nothing else helped. It seems the latest GPU driver, 555.85, has some issues.",
"I can confirm the workaround suggested by @nikolayDemirev is working. After downgrading from driver version 555.85 to 552.44 TensorFlow successfully detects the GPU with CUDA.\r\nSince this appears to be a regression in the NVidia driver it would probably be best to lets them know. Is there somebody at TensorFlow who can do that? I could contact customer support but I'm not sure I would reach the right people.",
"The solution works for me...\r\n\r\nNum GPUs Available: 1",
"> I can confirm the workaround suggested by @nikolayDemirev is working. After downgrading from driver version 555.85 to 552.44 TensorFlow successfully detects the GPU with CUDA. Since this appears to be a regression in the NVidia driver it would probably be best to lets them know. Is there somebody at TensorFlow who can do that? I could contact customer support but I'm not sure I would reach the right people.\r\n\r\nI had the same problem too, 555 detects GPU with nvidia-smi but does not pass it to TensorFlow. I have downgraded to 552 and now TensorFlow detects the GPU and uses it.\r\nThank you :)",
"@nikolayDemirev , @koryphaee ,\r\nLooks like the issue is from the Nvidia GPU driver, where it is working with the driver version **552.44 or below**. And on Nvidia driver and the tool kit, we don't have control to make the changes on the same. Could you please try to raise the concern on the Nvidia forum for the quick resolution. Thank you!",
"The relevant forums seems to be https://forums.developer.nvidia.com/c/developer-tools/cuda-developer-tools/285. Sadly I am not allowed to post there (I don't know why). I have submitted a support ticket instead and linked this issue. I will let you know if they respond.",
"Their support redirected me back to a more specific forum and I made a post there: https://forums.developer.nvidia.com/t/driver-555-85-is-unable-to-detect-gpu/294495",
"NVIDIA is working on this. If you user Docker CE on the Linux side of WSL, then update your nvidia-container-toolkit to 1.14.4 or later. If you are using Docker Desktop, then for now the best approach is to use a driver 552.xx or earlier. I will post back here as soon as the remaining incompatibility is resolved.\r\n\r\nCliff Woolley\r\nSr. Director, Deep Learning Software\r\nNVIDIA",
"We will track this in https://github.com/NVIDIA/nvidia-container-toolkit/issues/520 going forward, thanks.",
"[Docker Desktop 4.31](https://docs.docker.com/desktop/release-notes/#4310) was released yesterday and includes NVIDIA Container Toolkit 1.15.0, which resolves this issue.",
"I can confirm it works now using\r\n* Docker Desktop 4.31\r\n* GPU Driver 555.99\r\n\r\nRunning\r\n```\r\ndocker run --rm --gpus all -it tensorflow/tensorflow:latest-gpu python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\n```\r\nyields\r\n```\r\n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n```\r\n\r\nThank you for the quick fix and letting us know <3",
"@nikolayDemirev @koryphaee\r\nHope this issue has been fixed from the Nvidia side with the mentioned issue https://github.com/NVIDIA/nvidia-container-toolkit/issues/520\r\n\r\n\r\nCould you please feel free to move this issue to the closed status. Thank you!"
] | 2024-05-27T18:03:12 | 2024-06-10T05:30:03 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.16.1-gpu-jupyter
### Custom code
No
### OS platform and distribution
Windows 11 and WSL2
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
release 10.1, V10.1.243
### GPU model and memory
_No response_
### Current behavior?
I installed the latest NVIDIA driver on Windows 11 and configured the Docker Desktop to run with WSL2. On Ubuntu, I have installed the Nvidia Container Toolkit, following the instructions not to install drivers on Linux.
nvidia-smi is showing me the following output in WSL:
Running a docker container with the following command:
```
sudo docker run -it --rm -p 8888:8888 --gpus all tensorflow/tensorflow:latest-gpu-jupyter
```
And running:
```
import tensorflow as tf
tf.config.list_physical_devices()
```
results in the following error "external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_NOT_FOUND: named symbol not found":
Running nvidia-smi in the same container results in the same result as running in on WLS.
### Standalone code to reproduce the issue
```shell
You only need to run
sudo docker run -it --rm -p 8888:8888 --gpus all tensorflow/tensorflow:latest-gpu-jupyter
```
in WSL2 and then run:
```
import tensorflow as tf
tf.config.list_physical_devices()
```
```
### Relevant log output
```shell
2024-05-27 18:02:30.907995: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-05-27 18:02:31.117531: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-05-27 18:02:32.496598: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_NOT_FOUND: named symbol not found
```
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} | [XLA] Simplify multihost_hlo_runner interface
Remove redundant --hlo_file flag, and allow multiple modules.
Also SPMD and num hosts/replicas is now inferred from the module.
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} | [NFC] More informative error message on error from MLIR emitters
1. Dump MLIR module to dumping folder for inspection
2. Don't attempt to attach MLIR module contents to error: it's too large to
fit, inconsistent with other errors, and will lose the stack trace.
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} | Implement "bad_indcies_policy" for ScatterNd.
For testing, we also introduced "ScatterNdTest" that verifies the existing "default" behavior for comparison.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13360 from Intel-tensorflow:akhil/vnni2_fp16 d138f97cb5004b199e626d4f705e50269b79e160
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} | `ScatterNdFunctor` to update operands of all valid indices and continue on bad indices.
This is to support the new attribute "bad_indices_policy". Passing downs the behavior also works, but it makes `ScatterNdFunctor` unnecessarily complicated while the only gain is the performance with out-of-bound error.
For testing, the existing "Error_OutOfBound" test is modified so it can also ensure that the bad index in the middle still correctly raise error and the caller still handles it.
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} | Remove experimental_adaptive_avx_optimization flag from XNNPACK delegate options
It's always on now.
Reverts changelist 637594837
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13089 from ROCm:ci_reduce_large_row_to_scalar ae97058c01ca57107a2566a6f190d51f5ad4ca0e
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"Sure. So just update the website with the correct instructions I guess.",
"> Sure. So just update the website with the correct instructions I guess.\r\n\r\nlol"
] | 2024-05-27T14:13:27 | 2024-05-30T09:20:10 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
r2.16
### Custom code
No
### OS platform and distribution
Arch Linux
### Mobile device
_No response_
### Python version
3.10
### Bazel version
6.5.0
### GCC/compiler version
13.3.0
### CUDA/cuDNN version
9.1.1
### GPU model and memory
NVIDIA 4090
### Current behavior?
You say to
```
bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow
```
But there isn't even a target called "wheel"
### Standalone code to reproduce the issue
```shell
bazel query "//tensorflow/tools/pip_package:*"
//tensorflow/tools/pip_package:BUILD
//tensorflow/tools/pip_package:MANIFEST.in
//tensorflow/tools/pip_package:README
//tensorflow/tools/pip_package:THIRD_PARTY_NOTICES.txt
//tensorflow/tools/pip_package:build_pip_package
//tensorflow/tools/pip_package:build_pip_package.sh
//tensorflow/tools/pip_package:included_headers
//tensorflow/tools/pip_package:included_headers_gather
//tensorflow/tools/pip_package:licenses
//tensorflow/tools/pip_package:setup.py
//tensorflow/tools/pip_package:simple_console
//tensorflow/tools/pip_package:simple_console.py
//tensorflow/tools/pip_package:xla_build/CMakeLists.txt
//tensorflow/tools/pip_package:xla_cmake
//tensorflow/tools/pip_package:xla_compiled_cpu_runtime_srcs.txt
//tensorflow/tools/pip_package:xla_compiled_cpu_runtime_srcs.txt_file
Loading: 1 packages loaded
```
### Relevant log output
```shell
ERROR: Skipping '//tensorflow/tools/pip_package:wheel': no such target '//tensorflow/tools/pip_package:wheel': target 'wheel' not declared in package 'tensorflow/tools/pip_package' defined by /fourth/github.com/tensorflow/tensorflow/tools/pip_package/BUILD (Tip: use `query "//tensorflow/tools/pip_package:*"` to see all the targets in that package)
WARNING: Target pattern parsing failed.
ERROR: no such target '//tensorflow/tools/pip_package:wheel': target 'wheel' not declared in package 'tensorflow/tools/pip_package' defined by /fourth/github.com/tensorflow/tensorflow/tools/pip_package/BUILD (Tip: use `query "//tensorflow/tools/pip_package:*"` to see all the targets in that package)
```
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} | [XLA:GPU] Pass CUDA and cuDNN versions (or ROCm counterparts) explicitly into CudnnFusedConvRewriter.
Reverts dbf3cd351cdac416d635a9725746f4bcd96ffd83
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} | [XLA:GPU] Handle 0-D `MakeBlockPtr` in `EmitParameterLoad` instead of `EmitTiledSoftmax`.
This enables cleaner code in followup changes.
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} | PR #12940: [ROCm] Fix dot_bf16.hlo.test on ROCm
Imported from GitHub PR https://github.com/openxla/xla/pull/12940
Added additional params for `hlo_lit_tests` as a workaround, so `mi200.txtpb` would be used in `dot_bf16.hlo.test` for rocm.
Copybara import of the project:
--
c3bb3a7349266a51ff22a2e18dab0afb6e81bad4 by mmakevic <Milica.Makevic@amd.com>:
Have dot_bf16.hlo.test use mi200.txtpb for rocm
Merging this change closes #12940
Reverts changelist 637594837
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12940 from ROCm:ci_dot_bf16 c3bb3a7349266a51ff22a2e18dab0afb6e81bad4
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} | [XLA:GPU] Use HloFusionAdaptor::GetParameters() instead of FindFusionArguments.
We now have two functions that produce the same results. GetParameters() is more convenient.
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"@KushanManahara If you're using TensorFlow v2.16.1, kindly ensure your CUDA and cuDNN versions are compatible. \r\nPlease refer to TensorFlow [documentation](https://www.tensorflow.org/install/pip) for supported versions for TensorFlow v2.16.1 and let us know?\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."
] | 2024-05-27T12:15:16 | 2024-06-08T01:49:16 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
Linux
### Mobile device
Linux
### Python version
3.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.4/V11.8.89
### GPU model and memory
_No response_
### Current behavior?
I attempted to run **TensorFlow version 2.16.1** on my local machine, which is equipped with an NVIDIA GeForce 940MX GPU. However, I encountered a kernel crash error while executing the following code in a Jupyter Notebook cell,
This code snippet worked perfectly fine when I used **TensorFlow v2.4.1**, which I installed using Conda (`conda install anaconda::tensorflow-gpu==2.4.1`). However, after upgrading to TensorFlow v2.16.1 via Conda (`conda install anaconda::tensorflow-gpu==2.16.11`), the kernel crashed with the error message:
_**"The Kernel crashed while executing code in the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure."**_
**System Configuration:**
- CUDA Version: 12.4
- cuDNN Version: Not specified
- GPU: NVIDIA GeForce 940MX
- Driver Version: 550.54.14
**Issue is,**
- Verified that the code runs **successfully with TensorFlow v2.4.1**.
- Encountered a **kernel crash error** when running the code **with TensorFlow v2.16.1**.
Seeking guidance on how to resolve the kernel crash issue and successfully run TensorFlow v2.16.1 with my NVIDIA GeForce 940MX GPU. Any insights, troubleshooting steps, or workaround suggestions would be greatly appreciated.
Thank you for your assistance!
### Standalone code to reproduce the issue
```shell
model = keras.Sequential(
[
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(100, activation="relu"),
keras.layers.Dense(10, activation="sigmoid"),
]
)
```
### Relevant log output
```shell
16:43:31.562 [info] Generated code for 5 = <ipython-input-5-4e58535241f8> with 6 lines
16:43:32.012 [error] Disposing session as kernel process died ExitCode: undefined, Reason: 2024-05-27 16:43:14.979186: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-05-27 16:43:31.611060: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-05-27 16:43:31.678484: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-05-27 16:43:31.678870: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-05-27 16:43:31.679090: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2343] TensorFlow was not built with CUDA kernel binaries compatible with compute capability 5.0. CUDA kernels will be jit-compiled from PTX, which could take 30 minutes or longer.
2024-05-27 16:43:31.680484: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-05-27 16:43:31.680861: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-05-27 16:43:31.681155: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-05-27 16:43:31.681340: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2343] TensorFlow was not built with CUDA kernel binaries compatible with compute capability 5.0. CUDA kernels will be jit-compiled from PTX, which could take 30 minutes or longer.
2024-05-27 16:43:31.741853: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-05-27 16:43:31.742222: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-05-27 16:43:31.742465: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-05-27 16:43:31.742647: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1169 MB memory: -> device: 0, name: NVIDIA GeForce 940MX, pci bus id: 0000:01:00.0, compute capability: 5.0
16:43:32.071 [info] Cell 4 completed in -1716808411.592s (start: 1716808411592, end: undefined)
16:43:38.622 [error] Failed to write data to the kernel channel shell [
<Buffer 3c 49 44 53 7c 4d 53 47 3e>,
<Buffer 64 34 30 65 66 35 36 34 32 62 65 33 61 61 63 36 30 32 62 32 61 65 33 32 30 36 62 30 36 36 34 36 61 33 39 38 32 61 62 32 31 32 39 39 64 34 62 33 38 37 ... 14 more bytes>,
<Buffer 7b 22 64 61 74 65 22 3a 22 32 30 32 34 2d 30 35 2d 32 37 54 31 31 3a 31 33 3a 33 38 2e 36 32 31 5a 22 2c 22 6d 73 67 5f 69 64 22 3a 22 34 62 61 36 35 ... 177 more bytes>,
<Buffer 7b 7d>,
<Buffer 7b 7d>,
<Buffer 7b 22 73 69 6c 65 6e 74 22 3a 66 61 6c 73 65 2c 22 73 74 6f 72 65 5f 68 69 73 74 6f 72 79 22 3a 66 61 6c 73 65 2c 22 75 73 65 72 5f 65 78 70 72 65 73 ... 12329 more bytes>
] Error: Socket is closed
at a.postToSocket (/home/~/.vscode/extensions/ms-toolsai.jupyter-2024.4.0-linux-x64/dist/extension.node.js:318:10349)
at /home/~/.vscode/extensions/ms-toolsai.jupyter-2024.4.0-linux-x64/dist/extension.node.js:318:10099 {
errno: 9,
code: 'EBADF'
}
```
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"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/68695\">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/68695\">No</a>\n"
] | 2024-05-27T12:03:53 | 2024-06-02T08:04:58 | 2024-06-02T08:04:55 | NONE | null | null | null | ### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
No
### OS platform and distribution
android
### Mobile device
android
### 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'm trying to use tflite with opengl ssbo, just like the following code does
https://github.com/google-ai-edge/mediapipe/blob/master/mediapipe/calculators/tensor/inference_calculator_gl_advanced.cc.
https://github.com/google-ai-edge/mediapipe/blob/master/mediapipe/util/tflite/tflite_gpu_runner.cc
I would like to know how to change input shape size when I'm using `InferenceRunner` and `InferenceBuilder`, just like `ResizeInputTensor` when using `tflite::Interpreter`?
### Standalone code to reproduce the issue
```shell
https://github.com/google-ai-edge/mediapipe/blob/master/mediapipe/calculators/tensor/inference_calculator_gl_advanced.cc.
```
### Relevant log output
_No response_ | {
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