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Reverts f47b0b66880ba3d947d55ca88a75e5d4c2bcd3ba
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} | Running this tutorial https://www.tensorflow.org/tutorials/images/classification, I am observing a run-time error:
```E tensorflow/core/grappler/optimizers/meta_optimizer.cc:961] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape inStatefulPartitionedCall/sequential_2_1/dropout_1/stateless_dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer```
The error occurs because grappler's layout optimizer encounters this code
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/nn_ops.py#L5809
```
zero_tensor = constant_op.constant(0, dtype=x_dtype)
ret = array_ops.where_v2(keep_mask, ret, zero_tensor)
```
where keep_mask and ret are both rank 4, but zero_tensor is rank 0. Since this is a legal input for array_ops.where_v2, the fault lies in the layout optimizer. Supplied fix preventsit from flagging an error when it needs to propagate a transpose to a scalar input. | {
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"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/68478/checks?check_run_id=25304225149) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request."
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- Upgrade CPU custom calls to FFI.
- Fix the array shape bug in the Tridiagonal Reduction kernel (insufficient allocation, shape off by one).
- Correct and extend the `_lapack` module stub to not produce warnings.
- Make Schur Decomposition return a fixed number of values.
- (WiP) Update the implementation of Schur form to consider the `select` parameter.
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Without this change, we were silently upgrading to the local worker.
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This helps eliminate some circular dependencies.
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"Hi @filip-halt ,\r\n\r\nCan you please provide the tflite model file so that i can replicate the issue?",
"> Hi @filip-halt ,\r\n> \r\n> Can you please provide the tflite model file so that i can replicate the issue?\r\n\r\nYou can find a copy here: https://github.com/filip-halt/tflite_bug It was too large to upload directly into this chat.",
"Turns out that this is most likely due to a conv2dtranspose layer in the model. I was under the impression that [conv2dtranpose](https://github.com/tensorflow/tensorflow/blob/43ce5b4267d89b9e7f96821495ce4f6bdc7aa1e6/tensorflow/lite/delegates/gpu/gl/kernels/transpose_conv.cc#L39) was supported but I could be wrong. \r\n\r\n\r\nAnother interesting thing that happens is that when you convert the model with:\r\n\r\n```python\r\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\r\nconverter.target_spec.supported_types = [tf.float16]\r\n```\r\n\r\nthe resulting binary is 2x as large as float32 and 20% slower on mobile. When I inspected the graph with Netron, it looks like nothing was converted to float16, not even the conv2d's",
"Hi @filip-halt ,\r\n\r\nI ran your model using GPU delegate on dimensity 9000 and it ran fine without giving any issues. Can you please try it out with a different device and let me know if it worked there. However the list of supported operators for tflite is [here](https://www.tensorflow.org/lite/performance/gpu) and TRANSPOSE_CONV is listed there.",
"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.",
"I believe that this is a grouped TRANSPOSE_CONV conversion problem. Tensorflow seems to barely support this and is what is breaking when converting onnx to tf. The default conversion creates a large amount of layers that ultimately cause an OOM on the phone when loading. \r\n\r\n<img width=\"1403\" alt=\"image\" src=\"https://github.com/tensorflow/tensorflow/assets/81822489/5c93b64a-b188-4fa3-9ea8-8c01abdf073a\">\r\n"
] | "2024-05-22T21:10:26" | "2024-06-06T19:21:22" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
org.tensorflow:tensorflow-lite:2.16.1
### Custom code
Yes
### OS platform and distribution
Android
### Mobile device
Samsung S23
### 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?
Currently trying to get a larger model to load on an S23 but I am running into OOM errors. When initializing an Interpreter using a GPUDelegate with factory options grabbed from CompatibilityList.getBestOptionsForThisDevice(), the Interpreter crashes with `Failed to apply delegate: Failed to build program executable - Out of host memoryError: Program not built!`. This seems to pop up from an OpenCL error that is parsed with: https://github.com/tensorflow/tensorflow/blob/dd5c42638ac3c19c7facffb4c3cdadd2524cc6a5/tensorflow/lite/delegates/gpu/cl/util.cc#L42
My best guess is that this is due to hitting the Dalvik-heap memory limit of 512mb found on my device with Runtime.maxMemory(). I profiled the memory usage and it seems to crash around the 450mb mark. Does Tflite on android not use native memory to get around this? I seem to recall people getting 1gb+ models running on their devices. I guess this could possibly be a build step that is going over the limits, but once built it would be offloaded to native?
Note: I am using pyjnius to do this which might be causing problems, but I feel like that isn't the cause.
### Standalone code to reproduce the issue
```shell
Not sure how useful.
```
### Relevant log output
```shell
05-22 16:31:08.869 23806 23859 I python : jnius.jnius.JavaException: JVM exception occurred: Internal error: Failed to apply delegate: Failed to build program executable - Out of host memoryError: Program not built!
05-22 16:31:08.869 23806 23859 I python : Falling back to OpenGL
05-22 16:31:08.869 23806 23859 I python : TfLiteGpuDelegate Init: No shader implementation for transpose
05-22 16:31:08.869 23806 23859 I python : TfLiteGpuDelegate Prepare: delegate is not initialized
05-22 16:31:08.869 23806 23859 I python : Node number 2612 (TfLiteGpuDelegateV2) failed to prepare.
```
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"Hi @gcelano,\r\n\r\nIn Keras3, In order to save weights using model.save_weights method you need to add `.weights.h5` extension to the path.\r\n\r\n`model.save_weights('dir/my_model.weights.h5')`\r\n\r\nPlease check the [doc](https://keras.io/api/models/model_saving_apis/weights_saving_and_loading/#load_weights-method) source for same.",
"These issues might share common themes or underlying causes related to saving and loading model weights in TensorFlow. Exploring them further could provide insights into resolving the \"Model weights cannot be saved\" issue. #65279 , #62483 , #61859 , #61279 , #60064 , #59287",
"@SuryanarayanaY , in my code at link above I use \".weights.h5\", and this has no impact on the problem. @testhowtest, is there a provisional workaround? Without saving, there is not much use in using TF "
] | "2024-05-22T20:31:50" | "2024-05-23T08:36:11" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16.1 and inferior
### Custom code
Yes
### OS platform and distribution
Debian 12 Bookworm
### Mobile device
_No response_
### 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?
After `model.save_weights` is used, if a new process is started and `model.load_weights` is used, the results produced by the model are poor (very different from the ones at training time). The code can be found at https://git.informatik.uni-leipzig.de/celano/dithrax_test (with instructions).
(Since it is not clear if it is a keras or tensorflow problem, an issue has also been opened at https://github.com/keras-team/keras/issues/19693#issuecomment-2125675600)
### Standalone code to reproduce the issue
```shell
https://git.informatik.uni-leipzig.de/celano/dithrax_test
```
### Relevant log output
_No response_ | {
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Fixes segfault in JAX if an invalid cuda version is installed (https://github.com/google/jax/issues/21349).
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The size of input_mapping depends on metadata.num_cores_per_replica(). And metadata is a protobuffer assigned by the poc.mlir. Missing validation for the index of input_mappings will lead to an out-of-bound vulnerability.
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} | [xla:gpu] Fix a bug in pipelined-p2p-rewriter.
Previously, we call module schedule update each time when we transform a
while-body as well as at the end of the pass. It turns out that updating the
schedule for each while-body is not necessary and can also bring in changes
that aren't track and cause problem in keeping track of the instructions in
instruction_sequence. This CL removes the updating for each while-body.
Add a test.
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"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/68447/checks?check_run_id=25294636701) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request."
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} | Fix incorrect call to `getInt` when type is unsigned
Produces a runtime error. Use `getUInt` instead.
Fixes #12915
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} | Add TensorFlow version to `TPU_ML_PLATFORM_VERSION` environment variables.
This will allow us to track the version of TensorFlow that is being used to run on Cloud TPU.
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} | [Multi-host GPU]Integrate GPU topology into PjRtClient for multi-host GPU support
--Integrate GpuTopology into PjRtTopologyDescription to represent multi-host GPU topologies
--Utilize GlobalTopology to build GpuTopology during PjRt client creation
--Update relevant tests
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12942 from openxla:fix_test_cudnn c438b08ea7240c23ae98bc8dcf4ef45fa6d2e89c
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} | PR #12633: Fix handling of unknown flags in the multihost HLO runner.
Imported from GitHub PR https://github.com/openxla/xla/pull/12633
Fixes https://github.com/openxla/xla/issues/7923
Copybara import of the project:
--
71083c7d6dfdcaec1a5458b3f1cd04b11e13f8bd by Ilia Sergachev <isergachev@nvidia.com>:
Fix handling of unknown flags in the multihost HLO runner.
Merging this change closes #12633
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12633 from openxla:fix_multihost_runner_flag_parsing 71083c7d6dfdcaec1a5458b3f1cd04b11e13f8bd
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} | PR #12951: Update PJRT C API readme to link to header
Imported from GitHub PR https://github.com/openxla/xla/pull/12951
Copybara import of the project:
--
edab0ad80b2c2cdc5d7debcb5dc1511de76a3908 by Skye Wanderman-Milne <skyewm@google.com>:
Update PJRT C API readme to link to header
Merging this change closes #12951
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} | PR #66023: [oneDNN] Add accidentally removed ops back to MklList for auto_mixed_precision
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/66023
[PR-62817](https://github.com/tensorflow/tensorflow/pull/62817) accidentally removed these ops, so adding them back.
Copybara import of the project:
--
b9077f91f4c1462b37b4c03bfb5aa8e1d8908933 by Gauri1 Deshpande <gauri1.deshpande@intel.com>:
add ops back to MklList for auto_mixed_precision; accidentally removed in another PR
Merging this change closes #66023
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/66023 from Intel-tensorflow:gaurides/add_back_bf16_sum_mean b9077f91f4c1462b37b4c03bfb5aa8e1d8908933
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Add a knob to force unroll if needed.
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} | PR #12942: [GPU] Fix cuDNN GEMM test tolerances.
Imported from GitHub PR https://github.com/openxla/xla/pull/12942
Use the maximum absolute difference observed on 20 runs of these tests with different seed values.
Copybara import of the project:
--
c438b08ea7240c23ae98bc8dcf4ef45fa6d2e89c by Ilia Sergachev <isergachev@nvidia.com>:
[GPU] Fix cuDNN GEMM test tolerances.
Use the maximum absolute difference observed on 20 runs of these tests with different seed values.
Merging this change closes #12942
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12942 from openxla:fix_test_cudnn c438b08ea7240c23ae98bc8dcf4ef45fa6d2e89c
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The same logic to print roughly the same parameters in duplicated between to places.
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} | [XLA:GPU] Simplify launch dimensions estimate for non-kernel fusions.
That estimate should not be used in production code, because fusions that don't implement KernelFusionInterface don't generate CUDA kernels and there is nothing to fuse. We keep this estimate as a fallback mechanism. We don't have any additional information about the kernel, so we can simplify the heuristic and assume we launch one thread per output element and 128 threads per block.
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- Move & and * next to the type, where they belong, as
everyone knows.
- fix `reserve` calls in FoldApplyIndexingOperands
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} | MLIR emitters: Vectorize column reductions.
Special thanks to github user lingzhi98 who experimented with this in
https://github.com/openxla/xla/pull/11018.
I tried to make the logic as similar for vectorized and non-vectorized
reductions as I could. The vectorized logic looks like this:
- produce N reduced elements per thread, store the intermediate results in
a vector V
- loop over the N elements of V, writing each one to shmem
- loop over N elements, reading them from shmem and writing the result to
global memory
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"@abichoi The significant difference in inference time between your two models is expected. Kindly follow the process below to reduce the invoke() time for the SSD Mobilenet v2 FPNLite 640x640 model on your Raspberry Pi 4B:\r\n\r\nPlease enable NNAPI acceleration by setting the target option in the Interpreter creation:\r\n```\r\n\r\ninterpreter = tf.lite.Interpreter(model_path=\"ssd_mobilenet_v2_fpnlite_640x640.tflite\", experimental_options=tf.lite.InterpreterOptions(target=tf.lite.REFERRED_TARGETS[0]))\r\ninterpreter.allocate_tensors()\r\n\r\n```\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/68424\">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/68424\">No</a>\n"
] | "2024-05-22T12:35:35" | "2024-06-12T01:50:57" | "2024-06-12T01:50:53" | NONE | null | null | null | ### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
arm64 Debian GNU/Linux 11
### Mobile device
_No response_
### Python version
3.9.2
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I have converted ssd mobilenet v2 320 x320 and ssd mobilenet v2 fpnlite 640 x 640 into tflite models with iou = 0.3 for both of them. On a raspberry pi 4B 4GB, I found out that invoke() takes 0.22 seconds to run for ssd mobilenet v2 320 x 320 and 2.5 seconds to run for ssd mobilenet v2 fpnlite 640 x 640.
Is there a way to reduce the time needed for invoke() to run?
### Standalone code to reproduce the issue
```shell
import numpy as np
import tensorflow as tf
import cv2
import time
model_name = "ssd_mobilenet_v2_fpnlite_640x640_iou03" #conf 0.3
model_path = "MODEL_PATH"
interpreter = tf.lite.Interpreter(model_path=model_path)
# Get model details
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
height = input_details[0]['shape'][1]
width = input_details[0]['shape'][2]
floating_model = (input_details[0]['dtype'] == np.float32)
input_mean = 127.5
input_std = 127.5
interpreter.allocate_tensors()
min_conf_threshold = 0.3
image_path = "IMAGE_PATH"
frame1=cv2.imread(image_path)
imH, imW, channels = frame1.shape
frame=frame1.copy()
frame_rgb = frame
frame_resized = cv2.resize(frame_rgb, (width, height))
input_data = np.expand_dims(frame_resized, axis=0)
floating_model = (input_details[0]['dtype'] == np.float32)
if floating_model:
input_data = (np.float32(input_data) - input_mean) / input_std
# Perform the actual detection by running the model with the image as input
interpreter.set_tensor(input_details[0]['index'],input_data)
invoke_start = time.time()
interpreter.invoke()
print(time.time()-invoke_start)
# Retrieve detection results
boxes = interpreter.get_tensor(output_details[1]['index'])[0] # Bounding box coordinates of detected objects
classes = interpreter.get_tensor(output_details[3]['index'])[0] # Class index of detected objects
scores = interpreter.get_tensor(output_details[0]['index'])[0] # Confidence of detected objects
for i in range(len(scores)):
if ((scores[i] > min_conf_threshold) and (scores[i] <= 1.0)):
ymin = int(max(1,((boxes[i][0]* imH))))
xmin = int(max(1,(boxes[i][1] * imW)))
ymax = int(min(imH,((boxes[i][2]* imH))))
xmax = int(min(imW,(boxes[i][3] * imW)))
cv2.rectangle(frame, (xmin,ymin), (xmax,ymax), (10, 255, 0), 2)
cv2.imshow(frame)
```
### Relevant log output
_No response_ | {
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} | Normalize layouts for Gather.
Unfortunately this requires to temporarily use unsorted offset_dims attribute.
GatherSimplifier can turn this into valid HLO again.
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"Hi @ShabbirMarfatiya ,\r\n\r\nThanks for filing an issue, Could you please file a bug in the [TensorFlow](https://github.com/tensorflow/tensorflow/issues) where you can get the issue resolution faster.It is not related to the Model Garden models.\r\n\r\nThanks.",
"Hi @laxmareddyp,\r\n\r\nThanks for the reply. I have filed a bug at the TensorFlow repo but haven't received a resolution yet.",
"Hi @ShabbirMarfatiya ,\r\n\r\nThere is already an issue with the same bug, so I am closing this one since its a duplicate. Please track [this](https://github.com/tensorflow/tensorflow/issues/67789) for reference .",
"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/68474\">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/68474\">No</a>\n"
] | "2024-05-22T11:53:06" | "2024-05-30T06:18:01" | "2024-05-30T06:17:58" | NONE | null | null | null | I'm facing an issue while trying to run inference with a TensorFlow Lite model quantized to INT8 precision. The model was trained using CenterNet MobileNet for hand keypoint detection, and I'm getting a `RuntimeError` when invoking the interpreter, with the following error message:
```
Cell In[6], line 34
31 # Note that CenterNet doesn't require any pre-processing except resizing to the
32 # input size that the TensorFlow Lite Interpreter was generated with.
33 input_tensor = tf.image.resize(input_tensor, (224, 224))
---> 34 (boxes, classes, scores, num_detections, kpts, kpts_scores) = detect(interpreter, input_tensor,include_keypoint=True)
35 print("kpts:",scores[0])
36 print("kpts_scores:",kpts[0][0]*image_numpy.shape[1])
Cell In[5], line 40
38 print(input_tensor.dtype)
39 interpreter.set_tensor(input_details[0]['index'], input_tensor)
---> 40 interpreter.invoke()
42 scores = interpreter.get_tensor(output_details[3]['index'])
43 boxes = interpreter.get_tensor(output_details[2]['index'])
File ~/.local/lib/python3.8/site-packages/tensorflow/lite/python/interpreter.py:923, in Interpreter.invoke(self)
911 """Invoke the interpreter.
912
913 Be sure to set the input sizes, allocate tensors and fill values before
(...)
920 ValueError: When the underlying interpreter fails raise ValueError.
921 """
922 self._ensure_safe()
--> 923 self._interpreter.Invoke()
RuntimeError: Type 'INT8' is not supported by tile.Node number 198 (TILE) failed to invoke.
```
Environment:
- TensorFlow version: 2.7.0
- TensorFlow GPU version: 2.7.0
- Python version: 3.8.10
- Operating System: Ubuntu 20.04
Steps to Reproduce:
1. Train a CenterNet MobileNet model for hand keypoint detection
2. Convert the trained model to TensorFlow Lite INT8 precision using the following code:
```
import tensorflow as tf
def parse_tfrecord_fn(example):
feature_description = {
'image/encoded': tf.io.FixedLenFeature([], tf.string),
# Add other features here if necessary
}
example = tf.io.parse_single_example(example, feature_description)
image = tf.io.decode_jpeg(example['image/encoded'], channels=3)
image = tf.image.resize(image, [224, 224]) # Adjust size as necessary
image = tf.cast(image, tf.float32) / 255.0 # Normalize to [0,1] if required
return image
def load_tfrecord_dataset(tfrecord_path, batch_size=1):
raw_dataset = tf.data.TFRecordDataset(tfrecord_path)
dataset = raw_dataset.map(parse_tfrecord_fn)
dataset = dataset.batch(batch_size)
return dataset
def representative_dataset(tfrecord_path, num_samples):
dataset = load_tfrecord_dataset(tfrecord_path)
for data in dataset.take(num_samples):
yield [data]
# Load the TensorFlow SavedModel
saved_model_dir = 'model_weights/tflite/saved_model'
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
# Set optimization to default for INT8 conversion
converter.optimizations = [tf.lite.Optimize.DEFAULT]
# Set the representative dataset
tfrecord_path = '/home/ai_server/Shabbir/Hand_Keypoint_Detection/data/coco_testdev.record-00001-of-00050'
num_samples = 100 # Adjust the number of samples as needed
converter.representative_dataset = lambda: representative_dataset(tfrecord_path, num_samples)
# Ensure that input and output tensors are quantized
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.allow_custom_ops = True
converter.inference_input_type = tf.uint8 # or tf.int8
converter.inference_output_type = tf.uint8 # or tf.int8
# Convert the model
tflite_quant_model = converter.convert()
# Save the quantized model
with open('centernet_int8_03.tflite', 'wb') as f:
f.write(tflite_quant_model)
```
3. Run inference with the converted INT8 model using the following code:
```
import tensorflow as tf
from object_detection.utils import label_map_util
from object_detection.utils import config_util
from object_detection.utils import visualization_utils as viz_utils
from object_detection.utils import config_util
from object_detection.builders import model_builder
%matplotlib inline
# Print the image we are going to test on as a sanity check.
def load_image_into_numpy_array(path):
"""Load an image from file into a numpy array.
Puts image into numpy array to feed into tensorflow graph.
Note that by convention we put it into a numpy array with shape
(height, width, channels), where channels=3 for RGB.
Args:
path: a file path.
Returns:
uint8 numpy array with shape (img_height, img_width, 3)
"""
img_data = tf.io.gfile.GFile(path, 'rb').read()
image = Image.open(BytesIO(img_data))
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
from tensorflow.python.ops.numpy_ops import np_config
np_config.enable_numpy_behavior()
def detect(interpreter, input_tensor, include_keypoint=False):
"""Run detection on an input image.
Args:
interpreter: tf.lite.Interpreter
input_tensor: A [1, height, width, 3] Tensor of type tf.float32.
Note that height and width can be anything since the image will be
immediately resized according to the needs of the model within this
function.
include_keypoint: True if model supports keypoints output. See
https://cocodataset.org/#keypoints-2020
Returns:
A sequence containing the following output tensors:
boxes: a numpy array of shape [N, 4]
classes: a numpy array of shape [N]. Note that class indices are
1-based, and match the keys in the label map.
scores: a numpy array of shape [N] or None. If scores=None, then
this function assumes that the boxes to be plotted are groundtruth
boxes and plot all boxes as black with no classes or scores.
category_index: a dict containing category dictionaries (each holding
category index `id` and category name `name`) keyed by category
indices.
If include_keypoints is True, the following are also returned:
keypoints: (optional) a numpy array of shape [N, 17, 2] representing
the yx-coordinates of the detection 17 COCO human keypoints
(https://cocodataset.org/#keypoints-2020) in normalized image frame
(i.e. [0.0, 1.0]).
keypoint_scores: (optional) a numpy array of shape [N, 17] representing the
keypoint prediction confidence scores.
"""
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
input_tensor = (input_tensor*255).astype(np.uint8)
print(input_tensor.dtype)
interpreter.set_tensor(input_details[0]['index'], input_tensor)
interpreter.invoke()
scores = interpreter.get_tensor(output_details[3]['index'])
boxes = interpreter.get_tensor(output_details[2]['index'])
num_detections = interpreter.get_tensor(output_details[5]['index'])
classes = interpreter.get_tensor(output_details[0]['index'])
if include_keypoint:
kpts_scores = interpreter.get_tensor(output_details[4]['index'])
kpts = interpreter.get_tensor(output_details[1]['index'])
return boxes, classes, scores, num_detections, kpts, kpts_scores
else:
return boxes, classes, scores, num_detections
# Utility for visualizing results
def plot_detections(image_np,
boxes,
classes,
scores,
category_index,
keypoints=None,
keypoint_scores=None,
figsize=(12, 16),
image_name=None):
"""Wrapper function to visualize detections.
Args:
image_np: uint8 numpy array with shape (img_height, img_width, 3)
boxes: a numpy array of shape [N, 4]
classes: a numpy array of shape [N]. Note that class indices are 1-based,
and match the keys in the label map.
scores: a numpy array of shape [N] or None. If scores=None, then
this function assumes that the boxes to be plotted are groundtruth
boxes and plot all boxes as black with no classes or scores.
category_index: a dict containing category dictionaries (each holding
category index `id` and category name `name`) keyed by category indices.
keypoints: (optional) a numpy array of shape [N, 17, 2] representing the
yx-coordinates of the detection 17 COCO human keypoints
(https://cocodataset.org/#keypoints-2020) in normalized image frame
(i.e. [0.0, 1.0]).
keypoint_scores: (optional) anumpy array of shape [N, 17] representing the
keypoint prediction confidence scores.
figsize: size for the figure.
image_name: a name for the image file.
"""
keypoint_edges = [(0, 1), (1, 2), (0, 3), (3, 4), (0, 5), (5, 6), (0, 7), (7, 8), (0, 9), (9, 10)]
image_np_with_annotations = image_np.copy()
# Only visualize objects that get a score > 0.3.
viz_utils.visualize_boxes_and_labels_on_image_array(
image_np_with_annotations,
boxes,
classes,
scores,
category_index,
keypoints=keypoints,
keypoint_scores=keypoint_scores,
keypoint_edges=keypoint_edges,
use_normalized_coordinates=True,
min_score_thresh=0.2)
if image_name:
plt.imsave(image_name, image_np_with_annotations)
else:
return image_np_with_annotations
```
```
# Load the TFLite model and allocate tensors.
# model_path = "workspace/tflite/model_6_May.tflite"
model_path = "/home/ai_server/Shabbir/DISIGN/model_weights/centernet_int8_03.tflite"
label_map_path = '/home/ai_server/Shabbir/DISIGN/model_weights/label_map.pbtxt'
image_path = '/home/ai_server/Shabbir/Hand_Keypoint_Detection/test/806.jpg'
# image_path = '/home/shabbirmarfatiya/Shabbir/Project/ML_Tasks/Hand_Gesture_Recognition/Hand_Keypoint_Detection/FreiHAND_Dataset/test/'+str(dir_list[15])
# Initialize TensorFlow Lite Interpreter.object_detection.utils import label_map_util
interpreter = tf.lite.Interpreter(model_path=model_path)
interpreter.allocate_tensors()
# Label map can be used to figure out what class ID maps to what
# label. `label_map.txt` is human-readable.
# category_index = {1: {'id': 1, 'name': 'person'}}
category_index = label_map_util.create_category_index_from_labelmap(
label_map_path)
# print(category_index)
label_id_offset = 1
image = tf.io.read_file(image_path)
image = tf.compat.v1.image.decode_jpeg(image)
image = tf.expand_dims(image, axis=0)
image_numpy = image.numpy()
print(image_numpy.shape)
input_tensor = tf.convert_to_tensor(image_numpy, dtype=tf.uint8)
# Note that CenterNet doesn't require any pre-processing except resizing to the
# input size that the TensorFlow Lite Interpreter was generated with.
input_tensor = tf.image.resize(input_tensor, (224, 224))
(boxes, classes, scores, num_detections, kpts, kpts_scores) = detect(interpreter, input_tensor,include_keypoint=True)
print("kpts:",scores[0])
print("kpts_scores:",kpts[0][0]*image_numpy.shape[1])
print("Boxes:", boxes)
print("classes:", classes)
print("num_detections:", num_detections)
# print("kpts_scores:",kpts[0][0])
vis_image = plot_detections(
image_numpy[0],
boxes[0],
classes[0].astype(np.uint32) + label_id_offset,
scores[0],
category_index,
keypoints=kpts[0],
keypoint_scores=kpts_scores[0])
plt.figure(figsize = (15, 10))
plt.imshow(vis_image)
```
Expected Behavior:
The TensorFlow Lite INT8 model should run inference successfully without any errors.
Actual Behavior:
The RuntimeError is raised when invoking the interpreter, indicating that the tile operation is not supported in INT8 precision.
Additional Information:
I've followed the recommended steps for INT8 quantization, including setting the optimization flags, providing a representative dataset, and setting the target specs for INT8 operations. However, the issue persists.
@srjoglekar246 @PINTO0309 @NobuoTsukamoto @aeozyalcin @mpa74, Could you please assist me in resolving this issue? I would greatly appreciate any guidance or suggestions. | {
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} | [XLA:GPU] Remove StatusOr from GetFusionEmitter.
GetFusionEmitter never returns an error.
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https://api.github.com/repos/tensorflow/tensorflow/issues/68421 | https://api.github.com/repos/tensorflow/tensorflow | https://api.github.com/repos/tensorflow/tensorflow/issues/68421/labels{/name} | https://api.github.com/repos/tensorflow/tensorflow/issues/68421/comments | https://api.github.com/repos/tensorflow/tensorflow/issues/68421/events | https://github.com/tensorflow/tensorflow/pull/68421 | 2,310,128,774 | PR_kwDOArmXAs5wLnmX | 68,421 | [XLA:GPU] Move buffer assignment logic to MemcpyFusion::Emit. | {
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} | [XLA:GPU] Move buffer assignment logic to MemcpyFusion::Emit.
GetFusionEmitter should be responsible only for matching the correct emitter. If buffer assignment fails, there is no way to fix that and we propagate status to the caller anyway, so it's better to do it in MemcpyFusion::Emit.
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"Hi **@gcelano** ,\r\n- This issue belongs to NVIDIA, We are not working on NVIDIA repo. Please post this issue on NVIDIA repo.\r\n\r\nThank you!",
"@Venkat6871 , in the Tensorflow instructions [1], one requirement is installing NVIDIA Docker support [2], but this is wrong, because that software has been deprecated in January 2024 and superseded by NVIDIA Container Toolkit [3]: therefore, no reference should appear in current Tensorflow instructions.\r\n\r\nI have been able to run Docker images recently just installing NVIDIA container Toolkit, but probably you guys should run a few tests.\r\n\r\n---\r\n[1] https://www.tensorflow.org/install/docker\r\n[2] https://github.com/NVIDIA/nvidia-docker?tab=readme-ov-file\r\n[3] https://github.com/NVIDIA/nvidia-container-toolkit"
] | "2024-05-22T09:52:28" | "2024-05-28T09:55:49" | null | NONE | null | null | null | ### Issue type
Documentation Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16
### Custom code
No
### OS platform and distribution
Debian 12 Bookworm
### Mobile device
_No response_
### 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?
Installation instructions for Tensorflow Docker [1] refer to Invidia Docker, which however has been archived on January 22 2024. The page needs to be updated.
---
[1] https://www.tensorflow.org/install/docker
### Standalone code to reproduce the issue
```shell
No code required
```
### Relevant log output
_No response_ | {
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The temporary folder is located at `/data/local/tmp/` for Android.
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} | PR #12438: [XLA:GPU] add force inline option to get better llvm splits
Imported from GitHub PR https://github.com/openxla/xla/pull/12438
Add option to XLA to enforce inlining before llvm splitModule or set preserveLocals=False to get more balanced splits in parallel compilation case.
Some data of GPT3 5B model with different setting:
```
Compilation: TSL:XlaCompile:#module=pjit__wrapped_step_fn,program_id=24#: 3.754429084s (parallel + inline) runtime: 1.0s
Compilation: TSL:XlaCompile:#module=pjit__wrapped_step_fn,program_id=24#: 4.676450341s (parallel + no inline) runtime: 1.0s
Compilation: TSL:XlaCompile:#module=pjit__wrapped_step_fn,program_id=24#: 3.051018704s (parallel + perserve_local=False) runtime: 1.4s
Compilation: TSL:XlaCompile:#module=pjit__wrapped_step_fn,program_id=24#: 4.862938161s (serial) runtime: 1.0s
```
However, the runtime per step of perserve_locals=False vs other three setup is 1.4s vs 1.0s.
Copybara import of the project:
--
5f61718691d265c8609a89dc2349b1362045f7c5 by Cjkkkk <ske@nvidia.com>:
add force inline and no preserve local option to get better llvm splits
--
f32419eb792206de332be481bc59650c9d37a564 by Cjkkkk <ske@nvidia.com>:
remove preserveLocals option
Merging this change closes #12438
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We did not correctly check whether a fusion root is accessed in two different
iteration orders. If there is another root that has a transpose hero, the
iteration orders will be different.
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Reverts 02195873785c2a582bc4a41363433b9361d701da
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} | Integrate LLVM at llvm/llvm-project@446f66d685c2
Updates LLVM usage to match
[446f66d685c2](https://github.com/llvm/llvm-project/commit/446f66d685c2)
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} | Also allow epilogues for kCopy ops.
Normally we replace kCopy with transpose in LayoutNormalization. But if some
other pass reintroduces copies, we should still handle them in the same way as
logical transposes.
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} | Use rocm_library in rocm_embedded_test_modules
This is refactoring the `rocm_embedded_test_modules` so that it is using the `rocm_library` macro instead of invoking the compiler directly.
This has the advantage that hipcc is not needed anymore and a standard clang with the amdgpu backend enabled can be used.
Reverts df40f8d088774e2e938896b7f7cf2e2051d00a3a
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13088 from ROCm:ci_reduce_atomic_min f894f1954513019f0ca6890a27e09e0fee9d462e
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GetFusionEmitter should be responsible only for matching the correct emitter. If buffer assignment fails, there is no way to fix that and we propagate status to the caller anyway, so it's better to do it in MemcpyFusion::Emit.
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FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12915 from openxla:aportnoy/ffi-attribute-map-handle-unsigned-integers 7a26d6fa5ca122ad3ad56ba9e3668eab8c9b0235
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} | First cl to implement a pass to unstack loop operands.
This pass implements unstacking for loop operands. Generally speaking, unstacking is the act of breaking a rank n tensor into n smaller n-1 rank tensors without changing the semantics of the program. There are different patterns that can benefit from unstacking. This pass aims to implement such patterns. The patterns implemented are not exhaustive by any means. There are more patterns to be added.
The pass is not added to the compiler yet.
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} | Fix configure.py issue with --clang_path.
If --clang_path is passed a symlink, an error would occur when building C++ files that had includes from the standard library. E.g., a possible error was:
ERROR: /root/.cache/bazel/_bazel_root/e4ab50d61a21943a819d1e092972a817/external/zlib/BUILD.bazel:5:11: Compiling zutil.c [for tool] failed: undeclared inclusion(s) in rule '@zlib//:zlib':
this rule is missing dependency declarations for the following files included by 'zutil.c':
'/usr/lib/clang/17/include/stddef.h'
'/usr/lib/clang/17/include/__stddef_max_align_t.h'
'/usr/lib/clang/17/include/limits.h'
'/usr/lib/clang/17/include/stdarg.h'
The clang on the PATH, such as /usr/bin/clang-17, is usually symlinked to a file such as /usr/lib/llvm-17/bin/clang, so this would typically cause issues when building with --clang_path.
The issue is that bazel gives such errors when compiled with a symlinked clang. Without --clang_path, we resolved symlinks, but not with --clang_path. This change also resolves symlinks with --clang_path.
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} | PR #12915: Handle unsigned MLIR integers during FFI attribute conversion
Imported from GitHub PR https://github.com/openxla/xla/pull/12915
Copybara import of the project:
--
7a26d6fa5ca122ad3ad56ba9e3668eab8c9b0235 by Andrey Portnoy <aportnoy@nvidia.com>:
Handle unsigned MLIR integers during FFI attribute conversion
Merging this change closes #12915
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12915 from openxla:aportnoy/ffi-attribute-map-handle-unsigned-integers 7a26d6fa5ca122ad3ad56ba9e3668eab8c9b0235
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"@gcelano,\r\nThank you for the issue. We observed the same, please allow some time to have a concern on this issue and will provide the update on the same. Thank you!",
"@gcelano,\r\nWe are trying to resolve the above mentioned issue by raising the request for the changes and those will be reflected once the request has been resolved. So that the mentioned changes will be available in the upcoming releases. Thank 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."
] | "2024-05-21T22:50:15" | "2024-06-06T01:49:31" | null | NONE | null | null | null | ### Issue type
Documentation Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16
### Custom code
No
### OS platform and distribution
Debian 12 Bookworm
### Mobile device
_No response_
### 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?
What version of CUDA Toolkit is required by Tensorflow 2.16? The instructions here [1] speak of 11.8, but those here [2] seem to suggest 12.3. This is very confusing; please, update the documentation.
---
[1] https://www.tensorflow.org/install/pip
[2] https://www.tensorflow.org/install/source
### Standalone code to reproduce the issue
```shell
No link required
```
### Relevant log output
_No response_ | {
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