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Update TFRT dependency to use revision
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Update TFRT dependency to use revision
http://github.com/tensorflow/runtime/commit/7b008a767fce1d12c85f25772323ae0ee0bdc31c.
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[XLA:GPU] Fix possible bug in libdevice path logic.
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[XLA:GPU] Fix possible bug in libdevice path logic.
* When copying a C string, we need to include the null terminator byte. It seems likely this code reads off the end of the array since it only copies non-null bytes. But if we use a std::string it will handle this for us.
* dirname() may not be thread-safe. On Linux it likely is, but better to avoid it and just use the TSL utility for this.
This may be the cause of a flaky test failure in JAX CI.
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[XLA] Add missing HTML stage from flag description.
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[XLA] Add missing HTML stage from flag description.
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[Take 2] Expose .layout on jax.Array. Also add checks in the AOT path to make sure that the input Array's layout matches the layout given to jax.jit.
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[Take 2] Expose .layout on jax.Array. Also add checks in the AOT path to make sure that the input Array's layout matches the layout given to jax.jit.
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[XLA:GPU] [NFC] Switch DynCast to Cast
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[XLA:GPU] [NFC] Switch DynCast to Cast
The pointer is dereferenced later anyway, DynCast is just hiding bugs.
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#shlo_ref Add `subtract` op.
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#shlo_ref Add `subtract` op.
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#shlo_ref Add `divide` op.
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#shlo_ref Add `divide` op.
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[XLA:GPU] Add option to return FDO profile as textproto.
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[XLA:GPU] Add option to return FDO profile as textproto.
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Allocating all memory, CUDA OOM
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[
"@dennisushi I wasn't able to replicate the issue on colab using TF v2.15, please find the [gist](https://colab.research.google.com/gist/sushreebarsa/75c81cfc0a7a5664336835db4a062cb2/64284.ipynb) here. \r\nKindly use TF latest Version as TF v1.x is no longer actively supported. 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.",
"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/64284\">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/64284\">No</a>\n"
] | 2024-03-22T14:09:19 | 2024-04-11T01:47:54 | 2024-04-11T01:47:51 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
1.13, 1.10
### Custom code
Yes
### OS platform and distribution
Linux, Ubuntu 20
### Mobile device
_No response_
### Python version
3.8
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.7
### GPU model and memory
V100, 34GB
### Current behavior?
TF tries to allocate ALL memory despite not calling any functions that should put any data on the GPU.
### Standalone code to reproduce the issue
```shell
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import tensorflow as tf
from keras import backend as K
tf.config.experimental_run_functions_eagerly(not True)
message = "No GPU found. To actually train on CPU remove this assert."
assert tf.config.experimental.list_physical_devices("GPU"), message
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
print("Found GPUs: ", gpus)
# Restrict TensorFlow to only use the first GPU
try:
# Currently, memory growth needs to be the same across GPUs
for gpu in gpus:
print("Setting memory growth for ", gpu)
tf.config.experimental.set_memory_growth(gpu, True)
print("Setting visible devices to ", gpus[0])
tf.config.experimental.set_visible_devices(gpus[0], 'GPU')
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPU")
except RuntimeError as e:
# Visible devices must be set before GPUs have been initialized
print(e)
```
### Relevant log output
```shell
WARNING:tensorflow:From mvmwm/_tf_error_test.py:6: experimental_run_functions_eagerly (from tensorflow.python.eager.polymorphic_function.quarantine) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.config.run_functions_eagerly` instead of the experimental version.
Found GPUs: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
Setting memory growth for PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')
Setting visible devices to PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')
2024-03-22 14:06:37.455972: F tensorflow/tsl/platform/statusor.cc:33] Attempting to fetch value instead of handling error INTERNAL: failed initializing StreamExecutor for CUDA device ordinal 0: INTERNAL: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_OUT_OF_MEMORY: out of memory; total memory reported: 34087305216
Aborted (core dumped)
```
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[xla:gpu] Factor out AddressComputationFusionRewriter::Run to handle dynamic slices
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[xla:gpu] Factor out AddressComputationFusionRewriter::Run to handle dynamic slices
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I_kwDOArmXAs6DSD8p
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TensorflowLite for Windows on Arm
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[
"Hi @terryheo, I believe you would know this best, can you please take a look? Thanks.",
"Hi @terryheo, sorry to bother but just wanted to check if you had an opinion on this. "
] | 2024-03-22T13:52:22 | 2024-04-11T13:23:49 | null |
CONTRIBUTOR
| null | null | null |
I have been working on making Tensorflow Lite available for the Windows on Arm platform. My current patch can be found here https://github.com/everton1984/tensorflow.git on the branch `woa_enablement2`. I have written a wiki on how to compile that can be accessed here https://linaro.atlassian.net/wiki/spaces/WOAR/pages/29206609925/Tensorflow+Lite. All that is left is getting the XNNPACK patch into mainstream and I am currently discussing it with the maintaners.
I am wondering if there is the possibility and interest in supporting a new platform, like Windows on Arm and if so if that patch is enough or if there is something else that needs to be tackled.
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[XLA:Python] Fix build failures on Windows.
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[XLA:Python] Fix build failures on Windows.
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Implement EmitReduceWindow in the new MLIR emitters
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Implement EmitReduceWindow in the new MLIR emitters
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[XLA] Tiny fixes for indexing.md
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Integrate LLVM at llvm/llvm-project@de7a50fb88fa
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Integrate LLVM at llvm/llvm-project@de7a50fb88fa
Updates LLVM usage to match
[de7a50fb88fa](https://github.com/llvm/llvm-project/commit/de7a50fb88fa)
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[XLA:Python] Include missing nanobind std::set header.
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[XLA:Python] Include missing nanobind std::set header.
Fixes a failure reported after the nanobind switch.
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[XLA:GPU] Add missing jaxlib:gpu_kernels dep in third party hlo_runner_main.
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[XLA:GPU] Add missing jaxlib:gpu_kernels dep in third party hlo_runner_main.
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PR #10316: [NVIDIA GPU] Add a mechanism in GpuAsyncTracker to force delaying scheduling of an instruction.
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PR #10316: [NVIDIA GPU] Add a mechanism in GpuAsyncTracker to force delaying scheduling of an instruction.
Imported from GitHub PR https://github.com/openxla/xla/pull/10316
latency hiding scheduler has a property for each HloScheduleNode called SetForceDelay, setting this to true for a node will result in forcing LHS to ignore all cost info and schedule the instruction to the very beginning of the instruction sequence.
This is useful in manually enforcing an instruction to run before anything else in the scheduled graph.
Passes can set `should_force_delay` attribute in the GpuBackendConfig to instruct how this instruction will be scheduled. It's set to false by default.
An example usage:
```
original sequence:
allreduce-start
allreduce-done
add0, backend_config={should_force_delay=true}
add1
schedule after LHS:
add0, backend_config={should_force_delay=true}
allreduce-start
add1
allreduce-done
```
Copybara import of the project:
--
74267e60142c5161310580db5b760a3fe6d4f625 by TJ <[email protected]>:
Add a mechanism in GpuAsyncTracker to force delaying scheduling
of an instruction.
--
0aac4cf8c42f2ca210450581f05239378d8a33c2 by TJ <[email protected]>:
Fix failing backend config test
--
4deab8393ecb01ca010e0cf4ea30930ac448b815 by TJ <[email protected]>:
Use force delay for windowed einsum loops
--
a1321e88fd336f83a4fcfcc89037b8d4a3d1903f by TJ <[email protected]>:
Revert changes in gpu_windowed_einsum_handler, moving them to a separate
pr
--
86ffb97db8ce167438562f997316033b61316003 by TJ Xu <[email protected]>:
Fix internal linter error
Merging this change closes #10316
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10316 from Tixxx:tixxx/force_delay_lhs 86ffb97db8ce167438562f997316033b61316003
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Automated Code Change
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tflite converter crashes with version 2.16.1
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[
"@omontf,\r\nBy default Tensorflow v2.16 uses keras v3.0 which might be the reason for the error you are facing. As the work-around, Could you please try to install keras 2 where the code was executed without any issue/error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/115ecf8fb4b1661329d44d1dc3140d58/untitled1775.ipynb).\r\n\r\n```python\r\n!pip install -U tf_keras # Keras 2\r\nimport os\r\nos.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\"\r\n```\r\nThank you!",
"Hi @tilakrayal \r\n\r\nI tried your suggestion.\r\nI confirm that it works well with Keras v2.\r\n\r\nThank you !\r\n\r\nOlivier\r\n",
"@omontf,\r\nGlad the issue is resolved for you, Could you please feel free to move this issue to closed status. 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/64273\">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/64273\">No</a>\n",
"Same issue here. macOS M2.\r\nAbove solution is not working for me.\r\n\r\n\r\n```\r\n2024-04-03 18:55:04.158262: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M2\r\n2024-04-03 18:55:04.158289: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 16.00 GB\r\n2024-04-03 18:55:04.158295: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 5.33 GB\r\n2024-04-03 18:55:04.158337: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\r\n2024-04-03 18:55:04.158350: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\r\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\r\nW0000 00:00:1712166904.332563 84530 tf_tfl_flatbuffer_helpers.cc:390] Ignored output_format.\r\nW0000 00:00:1712166904.332624 84530 tf_tfl_flatbuffer_helpers.cc:393] Ignored drop_control_dependency.\r\n2024-04-03 18:55:04.333233: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: mypath\r\n2024-04-03 18:55:04.334679: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve }\r\n2024-04-03 18:55:04.334692: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: mypath\r\n2024-04-03 18:55:04.352055: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled\r\n2024-04-03 18:55:04.354424: I tensorflow/cc/saved_model/loader.cc:234] Restoring SavedModel bundle.\r\n2024-04-03 18:55:04.420357: I tensorflow/cc/saved_model/loader.cc:218] Running initialization op on SavedModel bundle at path: mypath\r\n2024-04-03 18:55:04.440781: I tensorflow/cc/saved_model/loader.cc:317] SavedModel load for tags { serve }; Status: success: OK. Took 107549 microseconds.\r\n2024-04-03 18:55:04.459571: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\r\nloc(fused[\"ReadVariableOp:\", \"sequential_4_1/conv1d_24_1/Reshape/ReadVariableOp@__inference_serving_default_142497\"]): error: missing attribute 'value'\r\nLLVM ERROR: Failed to infer result type(s).\r\nzsh: abort python3 convert_model.py\r\n\r\n```",
"This issue should be reopened till the problem is really fixed. \r\nThe converter should be updated to work with latest Keras as well. \r\nDowngrading Keras version(and so losing all the improvements introduced with Keras 3) to prevent crash of Tflite model converter is just a temporary workaround not a proper solution. "
] | 2024-03-22T10:58:44 | 2024-06-02T13:48:13 | 2024-03-22T18:44:14 |
NONE
| null | null | null |
Hello ,
I've got a crash when trying to quantize mnist model with tf 2.16.1 (tested with python 3.10 & 3.11)
Same example works with tf 2.15.0 (python 3.10)
### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 20.04.5 LTS
- TensorFlow installation (pip package or built from source): pip package
- TensorFlow library (version, if pip package or github SHA, if built from source): 2.16.1 - python 3.11
### 2. Code
Provide code to help us reproduce your issues using one of the following options:
```
import tensorflow as tf
import numpy as np
print("TensorFlow version: ", tf.__version__)
# Load MNIST dataset
mnist = tf.keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
# Normalize the input image so that each pixel value is between 0 to 1.
train_images = train_images.astype(np.float32) / 255.0
test_images = test_images.astype(np.float32) / 255.0
# Define the model architecture
model = tf.keras.Sequential([
tf.keras.layers.InputLayer(input_shape=(28, 28)),
tf.keras.layers.Reshape(target_shape=(28, 28, 1)),
tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(10)
])
# Train the digit classification model
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True),
metrics=['accuracy'])
model.fit(
train_images,
train_labels,
epochs=1,
validation_data=(test_images, test_labels)
)
def representative_data_gen():
for input_value in tf.data.Dataset.from_tensor_slices(train_images).batch(1).take(10):
yield [input_value]
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_data_gen
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
tflite_model_quant = converter.convert()
```
### 3. Error Log
warnings.warn(
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
W0000 00:00:1711105049.664360 1507500 tf_tfl_flatbuffer_helpers.cc:390] Ignored output_format.
W0000 00:00:1711105049.664404 1507500 tf_tfl_flatbuffer_helpers.cc:393] Ignored drop_control_dependency.
2024-03-22 11:57:29.664698: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: /tmp/tmpm0lgi7rh
2024-03-22 11:57:29.665327: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve }
2024-03-22 11:57:29.665351: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: /tmp/tmpm0lgi7rh
2024-03-22 11:57:29.671257: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled
2024-03-22 11:57:29.672141: I tensorflow/cc/saved_model/loader.cc:234] Restoring SavedModel bundle.
2024-03-22 11:57:29.694969: I tensorflow/cc/saved_model/loader.cc:218] Running initialization op on SavedModel bundle at path: /tmp/tmpm0lgi7rh
2024-03-22 11:57:29.702319: I tensorflow/cc/saved_model/loader.cc:317] SavedModel load for tags { serve }; Status: success: OK. Took 37624 microseconds.
2024-03-22 11:57:29.712683: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
loc(fused["ReadVariableOp:", callsite("sequential_1/conv2d_1/Reshape/ReadVariableOp@__inference_serving_default_7052"("/net/minos/minos0/olm_dir/workdir/git/raptor_sdk/tests/model_tests/test/minimal_tf_quant.py":50:1) at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/tensorflow/lite/python/lite.py":1175:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/tensorflow/lite/python/lite.py":1129:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/tensorflow/lite/python/lite.py":1636:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/tensorflow/lite/python/lite.py":1614:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/tensorflow/lite/python/convert_phase.py":205:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/tensorflow/lite/python/lite.py":1537:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/backend/tensorflow/layer.py":58:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/backend/tensorflow/layer.py":120:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py":117:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/layers/layer.py":814:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py":117:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/ops/operation.py":48:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py":156:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/models/sequential.py":202:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/models/functional.py":194:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/ops/function.py":151:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/models/functional.py":578:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py":117:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/layers/layer.py":814:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py":117:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/ops/operation.py":48:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py":156:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/layers/convolutional/base_conv.py":233:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/ops/numpy.py":4507:1 at callsite("/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/backend/tensorflow/numpy.py":1545:1 at "/scorpion/home/olm/.conda/envs/tensorflow_2.16.1/lib/python3.10/site-packages/keras/src/backend/tensorflow/core.py":64:1))))))))))))))))))))))))))]): error: missing attribute 'value'
LLVM ERROR: Failed to infer result type(s).
Aborted (core dumped)
|
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PR_kwDOArmXAs5qeXuW
| 64,272 |
PR #10519: [GPU] Upgrade cuDNN frontend to v1.2.0.
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PR #10519: [GPU] Upgrade cuDNN frontend to v1.2.0.
Imported from GitHub PR https://github.com/openxla/xla/pull/10519
This will for instance allow fusion of scalar constants.
Copybara import of the project:
--
4695023e035516db05b707d79409a60eba9f47f7 by Ilia Sergachev <[email protected]>:
[GPU] Upgrade cuDNN frontend to v1.2.0.
Merging this change closes #10519
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10519 from openxla:cudnn_frontend_1_2_0 4695023e035516db05b707d79409a60eba9f47f7
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how to solve the exception with "UnboundLocalError: local variable 'batch_index' referenced before assignment"
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"Hey! For issues related to general coding questions, stackoverflow is a great resource, this issue tracker is primarily for bugs that directly impact the project\r\n\r\nMaybe this post on stackoverflow helps: https://stackoverflow.com/questions/63364588/unboundlocalerror-local-variable-batch-outputs-referenced-before-assignment\r\nIf it doesn't solve your problem, feel free to reply back and I will try my best to help you",
"@Roych13,\r\nWe see that you are using tf version 1.14, 1.x is not actively supported, please update to 2.x and let us know if you are facing the same issue. Could you please provide the complete code to reproduce the issue.\r\n\r\nAlso the batch size is the number of samples in each batch. Each batch process is referred to as one step and An epoch is completed when all the data in the training set is used for training. The smaller the batch_size is, the larger number of batches is processed per one epoch.\r\n\r\nThe ideal combination of epochs and batch size depends on your specific dataset, model architecture, and computational resources. Also better way is to start from smaller batches to find wide and plain minima, which are better from the generalization point of view, and then increase batch size for steadier convergence. 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.",
"Hi @Roych13 ,\r\n\r\n`UnboundLocalError: local variable 'batch_index' referenced before assignment\r\n`\r\n\r\nIn general the above error arises when you are using a variable (here eg. \"batch_index\") passed as argument or assigning to some other variable before actually assigning a value to it. Search if your code have **batch_index** anywhere and check whetehr it is instantiated before using somewhere else.\r\n\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.",
"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/64268\">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/64268\">No</a>\n"
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tensorflow: 1.14.0
keras: 2.2.5
when I run :
model.fit(x, y, epochs=epochs, batch_size=batch_size, verbose=2, shuffle=False)
and then catch the exception as below:
UnboundLocalError: local variable 'batch_index' referenced before assignment
I guess it maybe concern with epochs or batch_size, or shape of my dataset? Can someone tell me what's the relation between epochs and batch_size?Is it the epoches must be divided by batch_size?
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Expose `.layout` on jax.Array. Also add checks in the AOT path to make sure that the input `Array`'s layout matches the layout given to `jax.jit`.
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Expose `.layout` on jax.Array. Also add checks in the AOT path to make sure that the input `Array`'s layout matches the layout given to `jax.jit`.
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Experimental: Allow users to enable all features of default delegates in python
XNNPack is the only default delegate and this will allow users to benefit from all flag protected features. Other delegates can use this in the future.
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Check that zero points of FullyConnected filter is 0 for int4 quantization
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Check that zero points of FullyConnected filter is 0 for int4 quantization
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Refactor patterns in `quantize_patterns.cc` depending on compute-heaviness.
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Refactor patterns in `quantize_patterns.cc` depending on compute-heaviness.
* Implemented and added tests for `GetElementType`, and had minor cleanups.
* Refactor singular op patterns into `QuantizeSingularOpPattern`, excluding unnecessary requantize for singular ops with qi8 output.
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[xla:gpu][NFC] Documenting address_computation_fusion_rewriter
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[xla:gpu][NFC] Documenting address_computation_fusion_rewriter
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[xla:gpu][NFC] Simplify AddressComputationFusionRewriter::Run
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[xla:gpu][NFC] Simplify AddressComputationFusionRewriter::Run
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[xla:gpu] Simplify the search for operand slices in DynamicAddressComputationFusion
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Enable weight-only quantization of stablehlo.convolution
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Enable weight-only quantization of stablehlo.convolution
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PR_kwDOArmXAs5qc-AZ
| 64,258 |
Rollback of GpuTimer change
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Rollback of GpuTimer change
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PR_kwDOArmXAs5qcxKp
| 64,257 |
PR #62807: Validate argument minvalue of tf.random.uniform
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PR #62807: Validate argument minvalue of tf.random.uniform
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/62807
At present the API, `tf.random.uniform` raises `InvalidArgumentError` if `minval` >= `maxval` for `int` dtype only. This check happens in C++ backend for `int` data type only.
However same is not true for `float` datatype. It will not raise an error and It is ignoring min and max values in this case considering the range only.
The behaviour should be same for `int` and `float` dtypes. Hence for uniformity, I am adding validation for same at python level itself.
Attaching gist for same.
This is effecting `keras.initializers.RandomUniform` also which was discussed in tf-kears repo at #[449](https://github.com/keras-team/tf-keras/issues/449)
Copybara import of the project:
--
a4687946257fe104f393dc65a9b024a82b9955ee by Surya <[email protected]>:
Validate argument minvalue of tf.random.uniform
At present the API, tf.random.uniform raises InvalidArgumentError if minval>=maxval for int dtype only. This check happens in C++ backend for int data type only.
However same is not true for float datatype. It will not raise an error and , It will consider a range ignoring min and max values in this case.
The behaviour should be same for int and float dtypes.Hence I am adding validation for same at python level itself.
--
6ba45e1b2a794905ca59562467b82a09a2f1c4c5 by Surya <[email protected]>:
Validated random.uniform args with check_ops
Validated random.uniform args with check_ops.
Merging this change closes #62807
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/62807 from tensorflow:SuryanarayanaY-patch-5 6ba45e1b2a794905ca59562467b82a09a2f1c4c5
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I_kwDOArmXAs6DO6CV
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tf.raw_ops.ApproximateEqual/Erfinv fail support a few data types, inconsistent with the documentation
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"@SuryanarayanaY I was able to replicate the issue reported here in TF [v2.15](https://colab.research.google.com/gist/sushreebarsa/e47de13ce3d7ab964df87812d20dbbf6/64256-2-15.ipynb) and [nightly](https://colab.research.google.com/gist/sushreebarsa/fc9c337ff30691104e65d46be9b7434c/64256-tf-nightly.ipynb). Thank you!",
"Hi @drewshark ,\r\n\r\nI can see the Op `ApproximateEqual` is registered for number types but kernel not built for same. Will propose a probable fix for same.\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/dadeddbce13b7d422c73ed872e386c56dd41a478/tensorflow/core/ops/math_ops.cc#L786\r\n\r\n",
"The Op `Erfinv` is registered for `bfloat16, half, float32, float64` but fails with `half` and `bfloat16`. Other int and complex types not supported hence it will fail. \r\n\r\nFor `Erfinv` Op `half` and `bfloat16` needs to be added of not working. Thanks!",
"I think the fix for `Erfinv` should be done at Eigen repo."
] | 2024-03-22T05:21:16 | 2024-04-03T07:07:38 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
_No response_
### 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?
Hi, I want to report two issues: both tf.raw_ops.ApproximateEqual and tf.raw_ops.Erfinv can only support float32, float64, and double and fail on other datatype with the error message `NotFoundError: Could not find device for node: {{node xxx}} = xxx[T=DT_XXX]`
These behaviors are inconsistent with related documentations of these two APIs:
https://www.tensorflow.org/api_docs/python/tf/raw_ops/ApproximateEqual
https://www.tensorflow.org/api_docs/python/tf/raw_ops/Erfinv
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
import warnings
warnings.filterwarnings("ignore")
dtype_list = [
'bfloat16', 'bool', 'complex128', 'complex64',
'double', 'float16', 'float32',
'float64', 'half', 'int16', 'int32', 'int64', 'int8',
'uint16', 'uint32', 'uint64', 'uint8'
]
for dtype in dtype_list:
x = tf.constant(np.random.randint(-50, 50, ()), dtype=dtype)
y = tf.constant(np.random.randint(-50, 50, ()), dtype=dtype)
try:
out = tf.raw_ops.ApproximateEqual(x=x,y=y)
print(f"ApproximateEqual Success on dtype: {dtype}")
except:
print(f"ApproximateEqual Fail on dtype: {dtype}")
import tensorflow as tf
import numpy as np
import warnings
warnings.filterwarnings("ignore")
dtype_list = [
'bfloat16', 'bool', 'complex128', 'complex64',
'double', 'float16', 'float32',
'float64', 'half', 'int16', 'int32', 'int64', 'int8',
'uint16', 'uint32', 'uint64', 'uint8'
]
for dtype in dtype_list:
x = tf.constant(np.random.randint(-50, 50, ()), dtype=dtype)
try:
out = tf.raw_ops.Erfinv(x=x)
print(f"Erfinv Success on dtype: {dtype}")
except:
print(f"Erfinv Fail on dtype: {dtype}")
```
### Relevant log output
```shell
ApproximateEqual Fail on dtype: bfloat16
ApproximateEqual Fail on dtype: bool
ApproximateEqual Fail on dtype: complex128
ApproximateEqual Fail on dtype: complex64
ApproximateEqual Success on dtype: double
ApproximateEqual Fail on dtype: float16
ApproximateEqual Success on dtype: float32
ApproximateEqual Success on dtype: float64
ApproximateEqual Fail on dtype: half
ApproximateEqual Fail on dtype: int16
ApproximateEqual Fail on dtype: int32
ApproximateEqual Fail on dtype: int64
ApproximateEqual Fail on dtype: int8
ApproximateEqual Fail on dtype: uint16
ApproximateEqual Fail on dtype: uint32
ApproximateEqual Fail on dtype: uint64
ApproximateEqual Fail on dtype: uint8
Erfinv Fail on dtype: bfloat16
Erfinv Fail on dtype: bool
Erfinv Fail on dtype: complex128
Erfinv Fail on dtype: complex64
Erfinv Success on dtype: double
Erfinv Fail on dtype: float16
Erfinv Success on dtype: float32
Erfinv Success on dtype: float64
Erfinv Fail on dtype: half
Erfinv Fail on dtype: int16
Erfinv Fail on dtype: int32
Erfinv Fail on dtype: int64
Erfinv Fail on dtype: int8
Erfinv Fail on dtype: uint16
Erfinv Fail on dtype: uint32
Erfinv Fail on dtype: uint64
Erfinv Fail on dtype: uint8
```
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PR_kwDOArmXAs5qclSJ
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Support for DT_INT64 in Reciprocal and ReciprocalGrad Operations
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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/64255/checks?check_run_id=22965236711) 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.",
"Hi @hvaria Can you please sign CLA. Thank you!",
"I signed, Maybe it takes time to update\r\n\r\n",
"Hi @hvaria Can you please check @cantonios's [comments](https://github.com/tensorflow/tensorflow/pull/64255#pullrequestreview-2025512767) and keep us posted ? Thank you!",
"Sorry For delay, directory is deleted",
"Reciprocal also doesn't make sense for any integer types- the result will always be zero (except if the input is 1). What is the use-case?",
"I added int64 support to the Reciprocal operation to make TensorFlow more consistent in how it handles different data types. This change helps prevent errors and simplifies processing when working with integers and floating-point numbers together.",
"> I added int64 support to the Reciprocal operation to make TensorFlow more consistent in how it handles different data types. This change helps prevent errors and simplifies processing when working with integers and floating-point numbers together.\r\n\r\nExcept it doesn't really help at all, since the reciprocal op only makes sense for floating-point types. There's never a case where you would instead replace a float with an int in this case.",
"Thanks for your input. You're absolutely right that the reciprocal operation makes the most sense for floating-point types due to their non-integer results. The idea behind adding int64 support isn’t to replace floating-point operations with integers but rather to ensure TensorFlow can handle any data type consistently. This helps prevent errors and keeps the door open for any future functionalities that might need this capability, even if it’s a rare case.",
"> This helps prevent errors and keeps the door open for any future functionalities that might need this capability, even if it’s a rare case.\r\n\r\nNo, you can't just swap types likes this and expect it to work. The ops have meanings, and some ops will only work with some times. Closing this without a valid use-case.\r\n\r\n"
] | 2024-03-22T05:11:53 | 2024-04-29T20:03:34 | 2024-04-29T20:03:31 |
NONE
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This pull request introduces support for the DT_INT64 data type in the Reciprocal and ReciprocalGrad operations within TensorFlow. Previously, these operations did not support DT_INT64, which could lead to NotFoundError exceptions when attempting to use these operations with tensors of type int64. This limitation could affect a range of computational graphs where int64 tensors are used, especially in scenarios requiring reciprocal transformations of integer-based tensors.
Problem: #64224
The Reciprocal operation was found to be unsupported for DT_INT64 tensors, resulting in a NotFoundError when attempting to execute this operation on an int64 tensor.
Solution:
The proposed solution involves modifying the kernel registration in cwise_op_reciprocal.cc to include DT_INT64 for both the Reciprocal and ReciprocalGrad operations. By adding support for DT_INT64, TensorFlow can handle reciprocal operations on int64 tensors without throwing a NotFoundError, thus extending the operation's applicability and enhancing usability for a broader range of applications. The proposed solution involves modifying the kernel registration in cwise_op_reciprocal.cc to include DT_INT64 for both the Reciprocal and ReciprocalGrad operations. By adding support for DT_INT64, TensorFlow can handle reciprocal operations on int64 tensors without throwing a NotFoundError, thus extending the operation's applicability and enhancing usability for a broader range of applications. Changes Made Updated the registration of Reciprocal and ReciprocalGrad operations to include DT_INT64 for both CPU and GPU implementations.
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Implement deferring `stablehlo.transpose` ops for the input of `stablehlo.reduce_window` op with `maximum` reduce function.
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Implement deferring `stablehlo.transpose` ops for the input of `stablehlo.reduce_window` op with `maximum` reduce function.
Implements a pattern `RewriteMaxPoolReduceWindowOpWithActivationTranspose`. This pattern defers `stablehlo.transpose` op from the input to the result of `stablehlo.reduce_window` op. The `reduce_window`'s reduction function should be semantically equivalent to a `stablehlo.maximum`, which represents a max pool operation.
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[
"Ok I tried converting the input_length to input_shape of(10,) formulating what it was trying to say in the documentation. (On searching I found this). But shouldn't that be updated in documentation as well?",
"ValueError: Unrecognized keyword arguments passed to Embedding: {'input_length': 10}\r\nhow to solve this error",
"Instead of input length:10 I used input_shape=(10,) to reproduce the same effect. It worked for me. But when u read the documentation u won't find anything like input_shape you would only find input_length. ",
"Hi **@theraghavjuneja** ,\r\nSorry for the delay, I tried to run your code on Colab using TF v2.16.1, nightly and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/99ce9eb697012021b325ca1c1e7f5b00/64252_2-16-1-nightly-v.ipynb) here for reference.\r\n\r\nThank you!",
"Hi **@theraghavjuneja** ,\r\nThe error occurs because the Embedding layer in TensorFlow 2.x does not have an input_length argument. This argument was removed in version 2.0.0 of TensorFlow.\r\n\r\nTo fix this error, you need to remove the input_length argument from the Embedding layer. The input_length argument is used to specify the maximum length of the input sequences. However, in TensorFlow 2.x, the Embedding layer automatically infers the input length from the input data. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/fcbd5a333c8a32a7ef57c883ed513c96/64252_2-16-1.ipynb) here for reference.",
"> Instead of input length:10 I used input_shape=(10,) to reproduce the same effect. It worked for me. But when u read the documentation u won't find anything like input_shape you would only find input_length.\r\n\r\nI created an LSTM-based sequential model for text generation. I updated my keras to 3.1.1 and had the same issue. Using `input_shape=(25,)` worked for my model. Thanks. ",
"Hi @TomBerton : I am hitting this same issue with TF==2.16 version for my CNN Word2Vector Embeddings\r\n\r\n# Define the CNN model\r\nmodel = Sequential()\r\nmodel.add(Embedding(vocab_size, 100, weights=[embedding_matrix],input_length=max_length, trainable=False))\r\nmodel.add(Conv1D(128, 5, activation='relu'))\r\n\r\n(DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 % \r\n(DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 % pip list | grep Tensroflow\r\n(DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 % \r\n(DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 % pip list | grep Tensorflow \r\n]% (DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 % ]\r\nzsh: command not found: ]\r\n(DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 % pip list | grep tensorflow\r\ntensorflow 2.16.1\r\ntensorflow-datasets 4.9.4\r\ntensorflow-estimator 2.12.0\r\ntensorflow-io-gcs-filesystem 0.36.0\r\ntensorflow-metadata 1.14.0\r\n(DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 % \r\n(DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 % pip list | grep keras \r\nkeras 3.1.1\r\ntf_keras 2.16.0\r\n(DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 % ",
"> Hi @TomBerton : I am hitting this same issue with TF==2.16 version for my CNN Word2Vector Embeddings\r\n> \r\n> # Define the CNN model\r\n> model = Sequential() model.add(Embedding(vocab_size, 100, weights=[embedding_matrix],input_length=max_length, trainable=False)) model.add(Conv1D(128, 5, activation='relu'))\r\n> \r\n> (DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 % (DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 % pip list | grep Tensroflow (DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 % (DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 % pip list | grep Tensorflow ]% (DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 % ] zsh: command not found: ] (DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 % pip list | grep tensorflow tensorflow 2.16.1 tensorflow-datasets 4.9.4 tensorflow-estimator 2.12.0 tensorflow-io-gcs-filesystem 0.36.0 tensorflow-metadata 1.14.0 (DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 % (DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 % pip list | grep keras keras 3.1.1 tf_keras 2.16.0 (DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 %\r\n\r\n(DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 % python CNN_WordVec.py\r\nTraceback (most recent call last):\r\n File \"/Users/akram_personal/AKRAM_CODE_FOLDER/DS_EXERCISES/DS_2024/CNN_WordVec.py\", line 114, in <module>\r\n model.add(Embedding(vocab_size, 100, weights=[embedding_matrix], trainable=False))\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/anaconda3/envs/DS/lib/python3.11/site-packages/keras/src/layers/core/embedding.py\", line 81, in __init__\r\n super().__init__(**kwargs)\r\n File \"/opt/anaconda3/envs/DS/lib/python3.11/site-packages/keras/src/layers/layer.py\", line 264, in __init__\r\n raise ValueError(\r\nValueError: Unrecognized keyword arguments passed to Embedding: {'weights': [array([[0., 0., 0., ..., 0., 0., 0.],\r\n [0., 0., 0., ..., 0., 0., 0.],\r\n [0., 0., 0., ..., 0., 0., 0.],\r\n ...,\r\n [0., 0., 0., ..., 0., 0., 0.],\r\n [0., 0., 0., ..., 0., 0., 0.],\r\n [0., 0., 0., ..., 0., 0., 0.]])]}\r\n(DS) akram_personal@AKRAMs-MacBook-Pro DS_2024 %",
"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/64252\">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/64252\">No</a>\n"
] | 2024-03-22T04:42:27 | 2024-04-14T06:58:06 | 2024-04-14T06:58:03 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
v2.16.0-rc0-18-g5bc9d26649c 2.16.1
### Custom code
Yes
### OS platform and distribution
Windows 11
### Mobile device
_No response_
### Python version
3.11.5
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Ok so if i head over to the tensorflow documentation of Embeddings layer, as a sample code they show me something like this
Example:
>>> model = keras.Sequential()
>>> model.add(keras.layers.Embedding(1000, 64, input_length=10))
But when I try to reproduce the same code (asitis ) or in my another Sequential model it would always show Unrecognized keyword arguments passed to Embedding: {'input_length': 10}
Is the documentation not updated? or this thing has been removed or it is a bug since I have tried replacing it with some other keyword argument I found over stackoverflow but nothing worked for me
### Standalone code to reproduce the issue
```shell
model=Sequential()
model.add(keras.layers.Embedding(vocab_size,embedded_vector_size,input_length=max_length))
model.add(Flatten())
model.add(Dense(1,activation='sigmoid'))
```
### Relevant log output
```shell
----> 8 model.add(keras.layers.Embedding(vocab_size,embedded_vector_size,input_length=max_length))
9 model.add(Flatten())
10 model.add(Dense(1,activation='sigmoid'))
File c:\Users\Charanjeet Juneja\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\layers\core\embedding.py:81, in Embedding.__init__(self, input_dim, output_dim, embeddings_initializer, embeddings_regularizer, embeddings_constraint, mask_zero, lora_rank, **kwargs)
70 def __init__(
71 self,
72 input_dim,
(...)
79 **kwargs,
80 ):
---> 81 super().__init__(**kwargs)
82 self.input_dim = input_dim
83 self.output_dim = output_dim
File c:\Users\Charanjeet Juneja\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\layers\layer.py:265, in Layer.__init__(self, activity_regularizer, trainable, dtype, autocast, name, **kwargs)
263 self._input_shape_arg = input_shape_arg
264 if kwargs:
...
268 )
270 self.built = False
271 self.dtype_policy = dtype_policies.get(dtype)
ValueError: Unrecognized keyword arguments passed to Embedding: {'input_length': 10}
```
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Fix the comment for an Env. It is currently full of lies.
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Fix the comment for an Env. It is currently full of lies.
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[
"@hilmyas,\r\nBy default Tensorflow v2.16 uses keras v3.0 which might be the reason for the error you are facing. As the work-around, Could you please try to install keras 2 where the code was executed without any issue/error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/4cd97fa3a806aa1315ba193773225755/transfer_learning_with_movinet.ipynb).\r\n```python\r\n!pip install -U tf_keras # Keras 2\r\nimport os\r\nos.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\"\r\n```\r\n\r\nThank you!\r\n\r\n",
"It works. Thanks for your help.",
"@hilmyas,\r\nGlad the issue is resolved for you, Could you please feel free to move this issue to closed status. 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/64250\">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/64250\">No</a>\n"
] | 2024-03-22T03:34:09 | 2024-03-23T02:20:56 | 2024-03-23T02:20:53 |
NONE
| null | null | null |
### Issue type
Documentation Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
_No response_
### 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?
Some days ago, I tried to run codes by using Google Colab from a tutorial of [transfer learning with movinet](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/video/transfer_learning_with_movinet.ipynb) without any error. Today, I tried to run codes by using Google Colab from the same tutorial and it produced error when I tried to compile the model.
`num_epochs = 2
loss_obj = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam(learning_rate = 0.001)
model.compile(loss=loss_obj, optimizer=optimizer, metrics=['accuracy'])`
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/video/transfer_learning_with_movinet.ipynb#scrollTo=dVqBLrn1tBsd
```
### Relevant log output
```shell
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-12-0c295092a661> in <cell line: 7>()
5 optimizer = tf.keras.optimizers.Adam(learning_rate = 0.001)
6
----> 7 model.compile(loss=loss_obj, optimizer=optimizer, metrics=['accuracy'])
/usr/local/lib/python3.10/dist-packages/tf_keras/src/utils/traceback_utils.py in error_handler(*args, **kwargs)
68 # To get the full stack trace, call:
69 # `tf.debugging.disable_traceback_filtering()`
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
/usr/local/lib/python3.10/dist-packages/tf_keras/src/optimizers/__init__.py in get(identifier, **kwargs)
333 )
334 else:
--> 335 raise ValueError(
336 f"Could not interpret optimizer identifier: {identifier}"
337 )
ValueError: Could not interpret optimizer identifier: <keras.src.optimizers.adam.Adam object at 0x7af7a01536d0>
```
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PR_kwDOArmXAs5qcQfG
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Add quantization/legalization for `stablehlo.add` and respective pipeline changes.
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Add quantization/legalization for `stablehlo.add` and respective pipeline changes.
* Added `enable_full_int_quantization` in `StaticRangePtqPreset` to determine full int quantization. This value will be `false` by default, meaning only compute-heavy ops will be quantized unless specified.
* Added tests for the above config change.
* Follow up tests will include e2e python tests.
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PR_kwDOArmXAs5qb0sL
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#tf-data Re-use `GlobalShuffleIterator` for the Bag dataset.
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NONE
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#tf-data Re-use `GlobalShuffleIterator` for the Bag dataset.
Added an `AnyContext` class for use cases that need to pass
in either an `OpKernelContext` or `IteratorContext`.
For the `BagDataset::Get`, it needs to access `ctx.allocator`
from either `OpKernelContext` or `IteratorContext`.
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Relocate the graph executor mode metric.
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Relocate the graph executor mode metric.
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PR_kwDOArmXAs5qbrBW
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Add unbounded dynamism test for XorOp.
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Add unbounded dynamism test for XorOp.
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Do not fuse gelu into FP8 cublasLT matmul on CUDA versions less than 12.4.
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Do not fuse gelu into FP8 cublasLT matmul on CUDA versions less than 12.4.
This causes cublas LT to give an error in certain cases. Specially, it always causes an error on Ada and causes an error when fast accumulation is disabled on Hopper, at least in the cases I've tried. I am just disabling it in general for older CUDA versions in case there are other scenarios where it fails.
The issue actually only occurs on CUDA versions less than 12.3.2, but there is no good way of checking the patch version of CUDA, so I'm disabling it on CUDA versions less than 12.4. There is a CUBLAS_VER_PATCH with the patch version of cublas but this is undocumented so I'd rather not use it.
Thank you @philipphack and @artem-b for helping me debug this.
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Add unbounded dynamism test for OrOp.
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Add unbounded dynamism test for OrOp.
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`DCHECK_EQ(layout, layout_i)` will compare addresses of two pointers, we should compare values.
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`DCHECK_EQ(layout, layout_i)` will compare addresses of two pointers, we should compare values.
Also fixed some headers.
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PR_kwDOArmXAs5qbjZg
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#tf-data To help debugging, output the autotuned `max_outstanding_requests` in xprof.
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#tf-data To help debugging, output the autotuned `max_outstanding_requests` in xprof.
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Allow scheduling from the low priority tasks only which is padded to the allowed batch sizes in the low priority batch params
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Allow scheduling from the low priority tasks only which is padded to the allowed batch sizes in the low priority batch params
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Update TFRT dependency to use revision
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Update TFRT dependency to use revision
http://github.com/tensorflow/runtime/commit/71863e46b69913a87dc676428355d00fa717de8e.
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Removes the deprecated build_pip_package.sh script.
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[XLA:GPU] Add MLIR-based DUS emitter.
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[XLA:GPU] Add MLIR-based DUS emitter.
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Add missing includes and fix typo.
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Add missing includes and fix typo.
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PR_kwDOArmXAs5qbaE0
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PR #10811: Restore ReshapeDecomposer And LayoutNormalization Passes in GPU Compiler
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PR #10811: Restore ReshapeDecomposer And LayoutNormalization Passes in GPU Compiler
Imported from GitHub PR https://github.com/openxla/xla/pull/10811
Restores individual ReshapeDecomposer and LayoutNormalization passes in the GPU compiler previously removed in #9852 and fixes failures in cudnn_norm_rewriter_test.cc.
Copybara import of the project:
--
52065712a6bd4f6bb874575d82b4d99b8f2d16dd by Philipp Hack <[email protected]>:
Restore ReshapeDecomposer and LayoutNormalization passes.
Merging this change closes #10811
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10811 from philipphack:u_layer_reshape_decomposer_xla 52065712a6bd4f6bb874575d82b4d99b8f2d16dd
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#tf-data Support global shuffle for the from_list dataset.
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#tf-data Support global shuffle for the from_list dataset.
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PR_kwDOArmXAs5qbV9g
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[XLA:GPU][NFC] Remove unnecessary check in MlirConcatenateFusion emitter
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[XLA:GPU][NFC] Remove unnecessary check in MlirConcatenateFusion emitter
This is already checked at creation of the emitter.
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PR_kwDOArmXAs5qbRqG
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[PJRT C API] Add a PJRT extension to register custom partitioner.
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NONE
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[PJRT C API] Add a PJRT extension to register custom partitioner.
- This extension has one C API which registers a custom partitioner with callbacks from the input.
- Update xla_client.register_custom_call_partitioner to take an optional PJRT_Api* input.
- Add xla_bridge.register_plugin_initialization_callbacks to register callbacks to be called with PJRT_Api* after plugins are discovered.
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Add new overload of `GraphToFunctionDef()` that can consume the graph.
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Add new overload of `GraphToFunctionDef()` that can consume the graph.
Previously, we copied all of the node data from the input subgraph to the `FunctionDef`. This change reduces the memory footprint of multi-device function instantiation by (where possible) `std::move`-ing the bulk of the node data from the input graph to newly created `NodeDef` members of the resulting `FunctionDef` protocol buffer.
This change should reduce (but not completely eliminate) the need for setting `TF_PFLR_PARALLEL_INSTANTIATE_THRESHOLD` in jobs with large multi-device function graphs.
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[xla][gpu] Change related to the handling of pipelined Send/Recv.
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[xla][gpu] Change related to the handling of pipelined Send/Recv.
Previously, we relied on certain code patterns to find the Send/Recv for
Send-done/Recv-done for a pipelined loop. Now that we realize copy-insertion
can complicate such code patterns and make such pattern matching fragile.
We modify the collective-permute-decomposer to annotate the pipeline decision
on Send-done/Recv-done, in additional to Send/Recv. We also change the HLO
scheduler and verifier to use such annotation instead of relying on code
pattern matching.
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Fix typo in compile_mlir_util deprecation description.
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Fix typo in compile_mlir_util deprecation description.
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Add unbounded dynamism test for NotOp.
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Add unbounded dynamism test for NotOp.
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Enable more warnings for XLA
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Enable more warnings for XLA
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Add public visibility for tensorflow protobuf BUILD targets "for_core_protos".
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Add public visibility for tensorflow protobuf BUILD targets "for_core_protos".
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/62817 from Intel-tensorflow:gaurides/amp_fp16 9780d75ad68275bf30b81c7cf02688fb87e5f1e2
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PR_kwDOArmXAs5qbCjW
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PR #9896: Opaque types for NVTX domain and string handles
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PR #9896: Opaque types for NVTX domain and string handles
Imported from GitHub PR https://github.com/openxla/xla/pull/9896
This avoids having preprocessor checks on `GOOGLE_CUDA` inside some headers, which led to ODR violations.
Copybara import of the project:
--
22e65458993aea1a9b441e6c043f7fe315ff4cc3 by Olli Lupton <[email protected]>:
Opaque types for NVTX domain and string handles
This avoids having preprocessor checks on GOOGLE_CUDA inside some
headers, which led to ODR violations.
Merging this change closes #9896
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/9896 from olupton:opaque-nvtx 22e65458993aea1a9b441e6c043f7fe315ff4cc3
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I_kwDOArmXAs6DNBDm
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using tf.linalg.det in custom loss function yields matrix inversion error
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[
"''' import tensorflow as tf\r\n from keras.losses import Loss\r\n class DeterminantZeroLoss(Loss):\r\n def __init__(self, name=\"determinant_zero_loss\"):\r\n super().__init__(name=name)\r\n\r\n def call(self, y_true, y_pred):\r\n try:\r\n # Calculate the determinant of the predicted matrix\r\n det = tf.linalg.det(y_pred)\r\n except tf.errors.InvalidArgumentError as e:\r\n # Handle the case where the matrix inversion fails\r\n # You can print the error message or take any other appropriate action\r\n print(\"Matrix inversion failed:\", e)\r\n # Return a large loss value to indicate the failure\r\n return 1e6\r\n\r\n # Penalize when the determinant is close to zero\r\n loss = tf.abs(det)\r\n\r\n return loss\r\n\r\n''' \r\nhope it helps ",
"> except tf.errors.InvalidArgumentError as e:\r\n\r\nSadly that didn't catch the error, but I found out the error seems to only occur if I use\r\n`kernel_initializer=tf.keras.initializers.Zeros()`\r\nin one of the layers as well as the determinant=0 loss.\r\n\r\nPS:\r\n\r\n> return 1e6\r\n> # Penalize when the determinant is close to zero\r\n> loss = tf.abs(det)\r\n\r\nYou seem to have missunderstood that, when the matrix is not invertible the determinant should be zero, therefore the loss should be low, if the determinant is close to zero that is good and not penalized ;)",
"Hi @Sur3 ,\r\n\r\nCould you able to submit a reproducible code snippet to debug this? Thanks!",
"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.",
"Sorry I didn't find time to produce a full code example yet, maybe next week."
] | 2024-03-21T21:30:58 | 2024-04-10T21:00:37 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Calculating the determinant should not require a matrix inversion..
The code worked fine in tensorflow 2.15.0 but tensorflow 2.16.1 yields a matrix inversion error when the determinant is included in the loss function:
```
Epoch 1/2000
2024-03-21 22:17:44.390622: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: INVALID_ARGUMENT: Input is not invertible.
[[{{function_node __inference_one_step_on_data_1325}}{{node gradient_tape/compile_loss/Loss_PH_Ne_id_BSE_det0/MatrixInverse}}]]
Traceback (most recent call last):
File "./tf_train_mat_trans_pop_m_HnP.py", line 1427, in <module>
history = model.fit(x_train, y_train, epochs=epochs, batch_size=32, verbose=1, validation_data=(x_test,y_test), callbacks=callback_list)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".local/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py", line 122, in error_handler
raise e.with_traceback(filtered_tb) from None
File ".local/lib/python3.11/site-packages/tensorflow/python/eager/execute.py", line 53, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:
Detected at node gradient_tape/compile_loss/Loss_PH_Ne_id_BSE_det0/MatrixInverse defined at (most recent call last):
File "./tf_train_mat_trans_pop_m_HnP.py", line 1427, in <module>
File ".local/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py", line 117, in error_handler
File ".local/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py", line 325, in fit
File ".local/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py", line 118, in one_step_on_iterator
File ".local/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py", line 106, in one_step_on_data
File ".local/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py", line 70, in train_step
Input is not invertible.
[[{{node gradient_tape/compile_loss/Loss_PH_Ne_id_BSE_det0/MatrixInverse}}]] [Op:__inference_one_step_on_iterator_1360]
```
### Standalone code to reproduce the issue
```shell
class DeterminantZeroLoss(Loss):
def __init__(self, name="determinant_zero_loss"):
super().__init__(name=name)
def call(self, y_true, y_pred):
# Calculate the determinant of the predicted matrix
det = tf.linalg.det(y_pred)
# Penalize when the determinant is close to zero
loss = tf.abs(det)
return loss
```
together with
`kernel_initializer=tf.keras.initializers.Zeros()`
in a Dense layer triggers the bug.
### Relevant log output
_No response_
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I_kwDOArmXAs6DM61_
| 64,224 |
tf.math.reciprocal lacks support of int input on CPU, inconsistent with the documentation
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[
"@drewshark Before applying tf.math.reciprocal, could you try to explicitly cast your integer input tensor to a supported floating-point data type using tf.cast as follows;\r\n```\r\nimport os\r\nos.environ['CUDA_VISIBLE_DEVICES'] = ''\r\nimport numpy as np\r\nimport tensorflow as tf\r\nx = tf.constant(np.random.randint(-50, 50, ()), dtype='int64')\r\nx = tf.cast(x, dtype=tf.float64)\r\ntf.math.reciprocal(x)\r\n```\r\nIt might resolve the issue. Please find the gist [here](https://colab.research.google.com/gist/sushreebarsa/b1f4eab9862172e1e27994f91429ebea/64224.ipynb) for reference.\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.",
"@sushreebarsa Sure, thanks for your effort in fixing it.",
"@drewshark Glad it helped you!\r\nThank you for your response here.\r\nClosing the issue for now.",
"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/64224\">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/64224\">No</a>\n"
] | 2024-03-21T21:15:31 | 2024-04-04T04:13:47 | 2024-04-04T04:13:44 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
_No response_
### 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?
tf.math.reciprocal fails to support integer input on CPU, which is inconsistent with its documentation https://www.tensorflow.org/api_docs/python/tf/math/reciprocal?hl=en.
Instead, it can support integer input on GPU.
### Standalone code to reproduce the issue
```shell
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import numpy as np
import tensorflow as tf
x = tf.constant(np.random.randint(-50, 50, ()), dtype='int64')
tf.math.reciprocal(x)
```
```
### Relevant log output
```shell
tensorflow.python.framework.errors_impl.NotFoundError: Could not find device for node: {{node Reciprocal}} = Reciprocal[T=DT_INT64]
All kernels registered for op Reciprocal:
device='XLA_CPU_JIT'; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT16, DT_INT8, DT_COMPLEX64, DT_INT64, DT_BFLOAT16, DT_COMPLEX128, DT_HALF]
device='XLA_GPU_JIT'; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT16, DT_INT8, DT_COMPLEX64, DT_INT64, DT_BFLOAT16, DT_COMPLEX128, DT_HALF]
device='GPU'; T in [DT_COMPLEX128]
device='GPU'; T in [DT_COMPLEX64]
device='GPU'; T in [DT_INT64]
device='GPU'; T in [DT_DOUBLE]
device='GPU'; T in [DT_FLOAT]
device='GPU'; T in [DT_HALF]
device='GPU'; T in [DT_BFLOAT16]
device='CPU'; T in [DT_COMPLEX128]
device='CPU'; T in [DT_COMPLEX64]
device='CPU'; T in [DT_DOUBLE]
device='CPU'; T in [DT_BFLOAT16]
device='CPU'; T in [DT_HALF]
device='CPU'; T in [DT_FLOAT]
[Op:Reciprocal] name:
```
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[
"Hi @MohamedGehad12 \r\n\r\nWhich type of quantization have chosen? For TFLite micro, you should go for full integer quantization. The default optimization leaves part of the operations as floating point (hence hybrid) and mixing isn't supported in micro. See here:\r\nhttps://www.tensorflow.org/lite/performance/post_training_quantization\r\n\r\nThank You\r\n\r\n\r\n\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.",
"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/64223\">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/64223\">No</a>\n"
] | 2024-03-21T21:13:40 | 2024-04-11T01:47:57 | 2024-04-11T01:47:54 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15
### Custom code
Yes
### OS platform and distribution
_No response_
### 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?
in Ardiono this message show up and i Don't know why . Hybrid models are not supported on TFLite Micro.
Node FULLY_CONNECTED (number 12f) failed to prepare with status 1
### Standalone code to reproduce the issue
```shell
#include <LSM6DS3.h>
#include <Wire.h>
#include <TensorFlowLite.h>
#include <tensorflow/lite/micro/all_ops_resolver.h>
#include <tensorflow/lite/micro/micro_error_reporter.h>
#include <tensorflow/lite/micro/micro_interpreter.h>/
#include <tensorflow/lite/schema/schema_generated.h>
//#include <tensorflow/lite/version.h>
#include "your_model_tflite.h"
#define NUM_READINGS 141
int readingIndex = 0;
float reshaped_readings[1][NUM_READINGS][1];
// global variables used for TensorFlow Lite (Micro)
tflite::MicroErrorReporter tflErrorReporter;
// pull in all the TFLM ops, you can remove this line and
// only pull in the TFLM ops you need, if would like to reduce
// the compiled size of the sketch.
tflite::AllOpsResolver tflOpsResolver;
const tflite::Model* tflModel = nullptr;
tflite::MicroInterpreter* tflInterpreter = nullptr;
TfLiteTensor* tflInputTensor = nullptr;
TfLiteTensor* tflOutputTensor = nullptr;
// Create a static memory buffer for TFLM, the size may need to
// be adjusted based on the model you are using
constexpr int tensorArenaSize = 90 * 1024;
byte tensorArena[tensorArenaSize] __attribute__((aligned(16)));
// array to map gesture index to a name
const char* GESTURES[] = {
"Class 1",
"Class 2",
"Class 3",
"Class 4",
"Class 5"
};
#define NUM_GESTURES (sizeof(GESTURES) / sizeof(GESTURES[0]))
void setup() {
// put your setup code here, to run once:
Serial.begin(115200);
pinMode(2, INPUT); // Setup for leads off detection LO +
pinMode(3, INPUT); // Setup for leads off detection LO -
while(!Serial);
Serial.println("awl haga fe setupp");
// get the TFL representation of the model byte array
tflModel = tflite::GetModel(your_model_tflite);
if (tflModel->version() != TFLITE_SCHEMA_VERSION) {
Serial.println("Model schema mismatch!");
while (1);
}
Serial.println("after while 1 ");
tflInterpreter = new tflite::MicroInterpreter(tflModel, tflOpsResolver, tensorArena, tensorArenaSize, &tflErrorReporter);
Serial.println("after tflInterpreter = new ");
// Allocate memory for the model's input and output tensors
TfLiteStatus allocare_status = tflInterpreter->AllocateTensors();
if(allocare_status !=kTfLiteOk){
Serial.println("arena size failed!");
Serial.println(allocare_status);
Serial.println(kTfLiteOk);
}
Serial.println(" after tflInterpreter->AllocateTensors() ");
}
void loop() {
// put your main code here, to run repeatedly:
Serial.println("awl haga f eloop");
while (readingIndex < NUM_READINGS) {
if((digitalRead(2) == 1)||(digitalRead(3) == 1)){
Serial.println('!');
}
else{
// send the value of analog input 0:
Serial.println(analogRead(A0));
int number = analogRead(A0);
number = number - 512;
float normalized = number / 512.0; // Convert to float for accurate division
reshaped_readings[0][readingIndex][0] = normalized;
}
readingIndex++;
}
Serial.println("redings :");
for(int i=0;i<NUM_READINGS;i++){
Serial.print(reshaped_readings[0][i][0]);
}
float* input_data = tflInputTensor->data.f;
for (int i = 0; i < NUM_READINGS; i++) {
Serial.println(i);
*input_data++ = reshaped_readings[0][i][0];
}
Serial.println("after the inputing the data ");
TfLiteStatus invokeStatus = tflInterpreter->Invoke();
if (invokeStatus != kTfLiteOk) {
Serial.println("Invoke failed!");
while (1);
return;
}
Serial.println("after the invoking ");
delay(100);
Serial.println();
}
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5qaxb4
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[XLA:GPU] Enable GemmFusion to fuse dot with trivial non-contracting dimension
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[XLA:GPU] Enable GemmFusion to fuse dot with trivial non-contracting dimension
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Tensorflow 2.16.1 fails to compile with -march=native on Xeon 4410Y CPU
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[
"I just tested building without cuda. I ran configure again, provided \"-march=native\" as the \"opt\" compiler flag, and then built with --config=opt, without modifying anything other than setting TF_PYTHON_VERSION=3.10. That failed with the same static_cast error in MathFunctions.h. I changed the issue subject to remove the mention of CUDA since it happens without it too.",
"Can you try disabling avx512_fp16? For `gcc` use `--copt=-mno-avx512fp16`.",
"@QuesarVII,\r\nIncorrectly mark vectorized casting as available with **EIGEN_VECTORIZE_AVX512FP16**. Since it doesn't exist, it's triggering all the other errors. Could you please try disabling the avx512_fp16 and try to compile. Thank you!\r\n\r\n",
"Yes, avx512_fp16 triggers the issue. Using -mno-avx512fp16 results in a successful build. Thanks.",
"@QuesarVII \r\nGlad the issue is resolved for you, Could you please feel free to move this issue to closed status. Thank you!",
"Shouldn't the code base be fixed to resolve this issue before closing it? Disabling compiler flags is more of a workaround than a real fix. Thanks!",
"@QuesarVII,\r\nThis issue has been taken to the developer notice and apparently it might be disabled in the upcoming version releases. Also it is tracking internally. So that it will be resolved in the next versions. 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.",
"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/64221\">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/64221\">No</a>\n"
] | 2024-03-21T20:42:20 | 2024-04-08T01:47:39 | 2024-04-08T01:47:36 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
x86_64 Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
6.5.0
### GCC/compiler version
clang 17.0.6 (ubuntu package)
### CUDA/cuDNN version
12.4 / 8.9.7
### GPU model and memory
(2) RTX A4500 20GB
### Current behavior?
Tensorflow 2.16.1 fails to compile with -march=native when building with CUDA support on Xeon 4410Y CPUs with clang 17.0.6. I run configure saying yes to cuda support, then edit .tf_configure.bazelrc and add these lines:
build:cuda --copt=-march=native
build:cuda --host_copt=-march=native
build:cuda --copt=-Wno-error=unused-command-line-argument (see issue 62459 regarding "unused"
and set TF_PYTHON_VERSION=3.10, the bazel build errors out on tensorflow/core/kernels/matmul_op_real.cc in external/eigen_archive/Eigen/src/Core/MathFunctions.h. The error output is in the log field.
This appears to be the same problem as issue 62047.
### Standalone code to reproduce the issue
```shell
N/A
```
### Relevant log output
```shell
...
In file included from tensorflow/core/kernels/matmul_op_real.cc:16:
In file included from ./tensorflow/core/kernels/matmul_op_impl.h:30:
In file included from external/eigen_archive/Eigen/Core:182:
external/eigen_archive/Eigen/src/Core/MathFunctions.h:430:12: error: no matching conversion for static_cast from 'const Eigen::internal::eigen_packet_wrapper<__attribute__((__vector_size__(4 * sizeof(long long)))) long long, 1>' to '__attribute__((__vector_size__(16 * sizeof(float)))) float' (vector of 16 'float' values)
430 | return static_cast<NewType>(x);
| ^~~~~~~~~~~~~~~~~~~~~~~
external/eigen_archive/Eigen/src/Core/GenericPacketMath.h:269:45: note: in instantiation of member function 'Eigen::internal::cast_impl<Eigen::internal::eigen_packet_wrapper<__attribute__((__vector_size__(4 * sizeof(long long)))) long long, 1>, __attribute__((__vector_size__(16 * sizeof(float)))) float>::run' requested here
269 | return cast_impl<SrcPacket, TgtPacket>::run(a);
| ^
external/eigen_archive/Eigen/src/Core/GenericPacketMath.h:290:47: note: in instantiation of member function 'Eigen::internal::pcast_generic<Eigen::internal::eigen_packet_wrapper<__attribute__((__vector_size__(4 * sizeof(long long)))) long long, 1>, __attribute__((__vector_size__(16 * sizeof(float)))) float>::run' requested here
290 | return pcast_generic<SrcPacket, TgtPacket>::run(a);
| ^
external/eigen_archive/Eigen/src/Core/CoreEvaluators.h:790:12: note: in instantiation of function template specialization 'Eigen::internal::pcast<Eigen::internal::eigen_packet_wrapper<__attribute__((__vector_size__(4 * sizeof(long long)))) long long, 1>, __attribute__((__vector_size__(16 * sizeof(float)))) float>' requested here
790 | return pcast<SizedSrcPacketType, DstPacketType>(
| ^
external/eigen_archive/Eigen/src/Core/AssignEvaluator.h:707:87: note: in instantiation of function template specialization 'Eigen::internal::unary_evaluator<Eigen::CwiseUnaryOp<Eigen::internal::core_cast_op<Eigen::half, float>, const Eigen::Map<const Eigen::Array<Eigen::half, -1, 1>>>>::packet<0, __attribute__((__vector_size__(16 * sizeof(float)))) float, true>' requested here
707 | m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(index), m_src.template packet<LoadMode,Packet>(index));
| ^
external/eigen_archive/Eigen/src/Core/AssignEvaluator.h:463:23: note: in instantiation of function template specialization 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Array<float, -1, 1>>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::core_cast_op<Eigen::half, float>, const Eigen::Map<const Eigen::Array<Eigen::half, -1, 1>>>>, Eigen::internal::assign_op<float, float>>::assignPacket<64, 0, __attribute__((__vector_size__(16 * sizeof(float)))) float>' requested here
463 | kernel.template assignPacket<dstAlignment, srcAlignment, PacketType>(index);
| ^
external/eigen_archive/Eigen/src/Core/AssignEvaluator.h:916:46: note: (skipping 3 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)
916 | Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);
| ^
external/eigen_archive/Eigen/src/Core/Map.h:162:5: note: in instantiation of function template specialization 'Eigen::DenseBase<Eigen::Map<Eigen::Array<float, -1, 1>>>::operator=<Eigen::CwiseUnaryOp<Eigen::internal::core_cast_op<Eigen::half, float>, const Eigen::Map<const Eigen::Array<Eigen::half, -1, 1>>>>' requested here
162 | EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Map)
| ^
external/eigen_archive/Eigen/src/Core/util/Macros.h:1124:5: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'
1124 | EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
| ^
external/eigen_archive/Eigen/src/Core/util/Macros.h:1097:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'
1097 | EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }
| ^
./tensorflow/core/kernels/matmul_op_impl.h:867:13: note: in instantiation of function template specialization 'Eigen::Map<Eigen::Array<float, -1, 1>>::operator=<Eigen::CwiseUnaryOp<Eigen::internal::core_cast_op<Eigen::half, float>, const Eigen::Map<const Eigen::Array<Eigen::half, -1, 1>>>>' requested here
867 | dst_eigen = src_eigen.template cast<float>();
| ^
./tensorflow/core/kernels/matmul_op_impl.h:994:7: note: in instantiation of function template specialization 'tensorflow::FastConvertToFloat<Eigen::half>' requested here
994 | FastConvertToFloat(in0_reshaped.flat<Ta>().data(),
| ^
./tensorflow/core/kernels/matmul_op_impl.h:1049:12: note: in instantiation of member function 'tensorflow::BaseBatchMatMulOp<Eigen::ThreadPoolDevice, Eigen::half, Eigen::half, Eigen::half>::Compute' requested here
1049 | explicit BatchMatMulOp(OpKernelConstruction* context)
| ^
tensorflow/core/kernels/matmul_op_real.cc:24:21: note: in instantiation of member function 'tensorflow::BatchMatMulOp<Eigen::ThreadPoolDevice, Eigen::half, Eigen::half, Eigen::half>::BatchMatMulOp' requested here
24 | TF_CALL_FLOAT_TYPES(REGISTER_BATCH_MATMUL_CPU);
| ^
external/eigen_archive/Eigen/src/Core/GenericPacketMath.h:227:23: note: candidate function not viable: 'this' argument has type 'const Eigen::internal::eigen_packet_wrapper<__attribute__((__vector_size__(4 * sizeof(long long)))) long long, 1>', but method is not marked const
227 | EIGEN_ALWAYS_INLINE operator T&() { return m_val; }
| ^
external/eigen_archive/Eigen/src/Core/GenericPacketMath.h:228:23: note: candidate function
228 | EIGEN_ALWAYS_INLINE operator const T&() const { return m_val; }
| ^
In file included from tensorflow/core/kernels/matmul_op_real.cc:16:
In file included from ./tensorflow/core/kernels/matmul_op_impl.h:30:
In file included from external/eigen_archive/Eigen/Core:351:
external/eigen_archive/Eigen/src/Core/products/GeneralBlockPanelKernel.h:2501:123: warning: remainder by zero is undefined [-Wdivision-by-zero]
2501 | constexpr bool kCanLoadSRhsQuad = (unpacket_traits<SLhsPacket>::size < 4) || (unpacket_traits<SRhsPacket>::size % (unpacket_traits<SLhsPacket>::size / 4)) == 0;
| ^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h:261:5: note: in instantiation of member function 'Eigen::internal::gebp_kernel<short, short, long, Eigen::internal::blas_data_mapper<short, long, 0>, 2, 4>::operator()' requested here
261 | GebpKernel()(output_mapper, lhsBlock, rhsBlock, rows, depth, cols, alpha,
| ^
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h:900:18: note: in instantiation of member function 'Eigen::internal::TensorContractionKernel<short, short, short, long, Eigen::internal::blas_data_mapper<short, long, 0>, Eigen::internal::TensorContractionInputMapper<short, long, 1, Eigen::TensorEvaluator<const Eigen::TensorMap<Eigen::Tensor<const short, 2, 1>, 16>, Eigen::ThreadPoolDevice>, Eigen::array<long, 1>, Eigen::array<long, 1>, 1, true, false, 0>, Eigen::internal::TensorContractionInputMapper<short, long, 0, Eigen::TensorEvaluator<const Eigen::TensorMap<Eigen::Tensor<const short, 2, 1>, 16>, Eigen::ThreadPoolDevice>, Eigen::array<long, 1>, Eigen::array<long, 1>, 1, true, true, 0>>::invoke' requested here
900 | kernel.invoke(output_mapper, blockA, blockB, actual_mc, actual_kc,
| ^
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h:794:5: note: in instantiation of function template specialization 'Eigen::TensorContractionEvaluatorBase<Eigen::TensorEvaluator<const Eigen::TensorContractionOp<const Eigen::array<Eigen::IndexPair<long>, 1>, const Eigen::TensorMap<Eigen::Tensor<const short, 2, 1>, 16>, const Eigen::TensorMap<Eigen::Tensor<const short, 2, 1>, 16>>, Eigen::ThreadPoolDevice>>::evalGemmPartial<true, true, true, 0, false>' requested here
794 | evalGemmPartial<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous,
| ^
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h:1240:31: note: in instantiation of function template specialization 'Eigen::TensorContractionEvaluatorBase<Eigen::TensorEvaluator<const Eigen::TensorContractionOp<const Eigen::array<Eigen::IndexPair<long>, 1>, const Eigen::TensorMap<Eigen::Tensor<const short, 2, 1>, 16>, const Eigen::TensorMap<Eigen::Tensor<const short, 2, 1>, 16>>, Eigen::ThreadPoolDevice>>::evalGemmPartialWithoutOutputKernel<true, true, true, 0>' requested here
1240 | evaluator->template evalGemmPartialWithoutOutputKernel, Alignment,
| ^
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h:1384:7: note: in instantiation of function template specialization 'Eigen::TensorEvaluator<const Eigen::TensorContractionOp<const Eigen::array<Eigen::IndexPair<long>, 1>, const Eigen::TensorMap<Eigen::Tensor<const short, 2, 1>, 16>, const Eigen::TensorMap<Eigen::Tensor<const short, 2, 1>, 16>>, Eigen::ThreadPoolDevice>::EvalShardedByInnerDimContext<Eigen::TensorEvaluator<const Eigen::TensorContractionOp<const Eigen::array<Eigen::IndexPair<long>, 1>, const Eigen::TensorMap<Eigen::Tensor<const short, 2, 1>, 16>, const Eigen::TensorMap<Eigen::Tensor<const short, 2, 1>, 16>>, Eigen::ThreadPoolDevice>::NoCallback>::processBlock<0>' requested here
1384 | processBlock<Alignment>(block_idx, block_start, block_end);
| ^
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h:1163:7: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)
1163 | eval<Alignment>(barrier, 0, num_blocks);
| ^
./tensorflow/core/kernels/matmul_op_impl.h:157:20: note: in instantiation of function template specialization 'Eigen::TensorDevice<Eigen::TensorMap<Eigen::Tensor<short, 2, 1>, 16>, Eigen::ThreadPoolDevice>::operator=<Eigen::TensorContractionOp<const Eigen::array<Eigen::IndexPair<long>, 1>, const Eigen::TensorMap<Eigen::Tensor<const short, 2, 1>, 16>, const Eigen::TensorMap<Eigen::Tensor<const short, 2, 1>, 16>>>' requested here
157 | Tz.device(d) = Tx.contract(Ty, contract_pairs);
| ^
./tensorflow/core/kernels/matmul_op_impl.h:440:29: note: in instantiation of member function 'tensorflow::(anonymous namespace)::ParallelMatMulKernel<short, false>::Run' requested here
440 | ParallelMatMulKernel::Run(context, in_x, in_y, adj_x, adj_y, trans_x,
| ^
./tensorflow/core/kernels/matmul_op_impl.h:1016:40: note: in instantiation of member function 'tensorflow::LaunchBatchMatMul<Eigen::ThreadPoolDevice, short>::Launch' requested here
1016 | LaunchBatchMatMul<Device, Tout>::Launch(
| ^
./tensorflow/core/kernels/matmul_op_impl.h:1049:12: note: in instantiation of member function 'tensorflow::BaseBatchMatMulOp<Eigen::ThreadPoolDevice, short, short, short>::Compute' requested here
1049 | explicit BatchMatMulOp(OpKernelConstruction* context)
| ^
tensorflow/core/kernels/matmul_op_real.cc:25:15: note: in instantiation of member function 'tensorflow::BatchMatMulOp<Eigen::ThreadPoolDevice, short, short, short>::BatchMatMulOp' requested here
25 | TF_CALL_int16(REGISTER_BATCH_MATMUL_CPU);
| ^
In file included from tensorflow/core/kernels/matmul_op_real.cc:16:
In file included from ./tensorflow/core/kernels/matmul_op_impl.h:30:
In file included from external/eigen_archive/Eigen/Core:351:
external/eigen_archive/Eigen/src/Core/products/GeneralBlockPanelKernel.h:2501:123: warning: remainder by zero is undefined [-Wdivision-by-zero]
2501 | constexpr bool kCanLoadSRhsQuad = (unpacket_traits<SLhsPacket>::size < 4) || (unpacket_traits<SRhsPacket>::size % (unpacket_traits<SLhsPacket>::size / 4)) == 0;
| ^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h:261:5: note: in instantiation of member function 'Eigen::internal::gebp_kernel<long, long, long, Eigen::internal::blas_data_mapper<long, long, 0>, 2, 4>::operator()' requested here
261 | GebpKernel()(output_mapper, lhsBlock, rhsBlock, rows, depth, cols, alpha,
| ^
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h:900:18: note: in instantiation of member function 'Eigen::internal::TensorContractionKernel<long, long, long, long, Eigen::internal::blas_data_mapper<long, long, 0>, Eigen::internal::TensorContractionInputMapper<long, long, 1, Eigen::TensorEvaluator<const Eigen::TensorMap<Eigen::Tensor<const long, 2, 1>, 16>, Eigen::ThreadPoolDevice>, Eigen::array<long, 1>, Eigen::array<long, 1>, 1, true, false, 0>, Eigen::internal::TensorContractionInputMapper<long, long, 0, Eigen::TensorEvaluator<const Eigen::TensorMap<Eigen::Tensor<const long, 2, 1>, 16>, Eigen::ThreadPoolDevice>, Eigen::array<long, 1>, Eigen::array<long, 1>, 1, true, true, 0>>::invoke' requested here
900 | kernel.invoke(output_mapper, blockA, blockB, actual_mc, actual_kc,
| ^
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h:794:5: note: in instantiation of function template specialization 'Eigen::TensorContractionEvaluatorBase<Eigen::TensorEvaluator<const Eigen::TensorContractionOp<const Eigen::array<Eigen::IndexPair<long>, 1>, const Eigen::TensorMap<Eigen::Tensor<const long, 2, 1>, 16>, const Eigen::TensorMap<Eigen::Tensor<const long, 2, 1>, 16>>, Eigen::ThreadPoolDevice>>::evalGemmPartial<true, true, true, 0, false>' requested here
794 | evalGemmPartial<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous,
| ^
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h:1240:31: note: in instantiation of function template specialization 'Eigen::TensorContractionEvaluatorBase<Eigen::TensorEvaluator<const Eigen::TensorContractionOp<const Eigen::array<Eigen::IndexPair<long>, 1>, const Eigen::TensorMap<Eigen::Tensor<const long, 2, 1>, 16>, const Eigen::TensorMap<Eigen::Tensor<const long, 2, 1>, 16>>, Eigen::ThreadPoolDevice>>::evalGemmPartialWithoutOutputKernel<true, true, true, 0>' requested here
1240 | evaluator->template evalGemmPartialWithoutOutputKernel, Alignment,
| ^
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h:1384:7: note: in instantiation of function template specialization 'Eigen::TensorEvaluator<const Eigen::TensorContractionOp<const Eigen::array<Eigen::IndexPair<long>, 1>, const Eigen::TensorMap<Eigen::Tensor<const long, 2, 1>, 16>, const Eigen::TensorMap<Eigen::Tensor<const long, 2, 1>, 16>>, Eigen::ThreadPoolDevice>::EvalShardedByInnerDimContext<Eigen::TensorEvaluator<const Eigen::TensorContractionOp<const Eigen::array<Eigen::IndexPair<long>, 1>, const Eigen::TensorMap<Eigen::Tensor<const long, 2, 1>, 16>, const Eigen::TensorMap<Eigen::Tensor<const long, 2, 1>, 16>>, Eigen::ThreadPoolDevice>::NoCallback>::processBlock<0>' requested here
1384 | processBlock<Alignment>(block_idx, block_start, block_end);
| ^
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h:1163:7: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)
1163 | eval<Alignment>(barrier, 0, num_blocks);
| ^
./tensorflow/core/kernels/matmul_op_impl.h:157:20: note: in instantiation of function template specialization 'Eigen::TensorDevice<Eigen::TensorMap<Eigen::Tensor<long, 2, 1>, 16>, Eigen::ThreadPoolDevice>::operator=<Eigen::TensorContractionOp<const Eigen::array<Eigen::IndexPair<long>, 1>, const Eigen::TensorMap<Eigen::Tensor<const long, 2, 1>, 16>, const Eigen::TensorMap<Eigen::Tensor<const long, 2, 1>, 16>>>' requested here
157 | Tz.device(d) = Tx.contract(Ty, contract_pairs);
| ^
./tensorflow/core/kernels/matmul_op_impl.h:440:29: note: in instantiation of member function 'tensorflow::(anonymous namespace)::ParallelMatMulKernel<long, false>::Run' requested here
440 | ParallelMatMulKernel::Run(context, in_x, in_y, adj_x, adj_y, trans_x,
| ^
./tensorflow/core/kernels/matmul_op_impl.h:1016:40: note: in instantiation of member function 'tensorflow::LaunchBatchMatMul<Eigen::ThreadPoolDevice, long>::Launch' requested here
1016 | LaunchBatchMatMul<Device, Tout>::Launch(
| ^
./tensorflow/core/kernels/matmul_op_impl.h:1049:12: note: in instantiation of member function 'tensorflow::BaseBatchMatMulOp<Eigen::ThreadPoolDevice, long, long, long>::Compute' requested here
1049 | explicit BatchMatMulOp(OpKernelConstruction* context)
| ^
tensorflow/core/kernels/matmul_op_real.cc:27:15: note: in instantiation of member function 'tensorflow::BatchMatMulOp<Eigen::ThreadPoolDevice, long, long, long>::BatchMatMulOp' requested here
27 | TF_CALL_int64(REGISTER_BATCH_MATMUL_CPU);
| ^
23 warnings and 1 error generated.
Target //tensorflow/tools/pip_package/v2:wheel failed to build
INFO: Elapsed time: 5321.938s, Critical Path: 1114.63s
INFO: 34048 processes: 9211 internal, 24837 local.
FAILED: Build did NOT complete successfully
```
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Add unbounded dynamism test for for Atan2Op.
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PR_kwDOArmXAs5qamHT
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Move `tsl/python` to `xla/tsl/python`
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PR_kwDOArmXAs5qalEx
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PR #10724: [NVIDIA GPU] Fix crash in thunk lowering
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PR #10724: [NVIDIA GPU] Fix crash in thunk lowering
Imported from GitHub PR https://github.com/openxla/xla/pull/10724
send and recv will have tuple as output shape, the tuple contains a token field, we only want to use the memory space of the first element in the tuple which is the actual data, not the token element.
Copybara import of the project:
--
89981b6576a22134831ddfc528fad900129d5058 by TJ <[email protected]>:
fix crash in thunk lowering
Merging this change closes #10724
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10724 from Tixxx:tixxx/shape_crash 89981b6576a22134831ddfc528fad900129d5058
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Use const qualified variables where possible.
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Use const qualified variables where possible.
Modified files:
* third_party/tensorflow/compiler/xla/service/shape_inference_test.cc
* third_party/tensorflow/compiler/xla/client/xla_builder_test.cc
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Add unbounded dynamism test for ComplexOp.
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Add unbounded dynamism test for ComplexOp.
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PR_kwDOArmXAs5qaafP
| 64,215 |
Temporarily comment @fused_batchnorm_no_training() as it breaks the msan check.
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Temporarily comment @fused_batchnorm_no_training() as it breaks the msan check.
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PR_kwDOArmXAs5qaZOT
| 64,214 |
Change pybind11_abseil `type_caster` `const_name` `Span` to `Sequence`.
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Change pybind11_abseil `type_caster` `const_name` `Span` to `Sequence`.
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PR_kwDOArmXAs5qaZE1
| 64,213 |
[XLA:Python] Add a C++ implementation of flatten_one_level.
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[XLA:Python] Add a C++ implementation of flatten_one_level.
Also add a copy of the default registry that doesn't have None registered as a leaf, which is slightly faster than using an is_leaf function.
This is mostly just doing an old TODO.
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PR_kwDOArmXAs5qaUUd
| 64,212 |
Do not pack int4 values on the interpreter in PJRT.
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Do not pack int4 values on the interpreter in PJRT.
The interpreter does not expect int4 values to be packed, so this fixes incorrect results when the interpreter is used and PJRT transfers int4 values to the device.
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PR_kwDOArmXAs5qaSik
| 64,211 |
Enable test for CALIBRATION_METHOD_HISTOGRAM_PERCENTILE.
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Enable test for CALIBRATION_METHOD_HISTOGRAM_PERCENTILE.
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PR_kwDOArmXAs5qaNak
| 64,210 |
Fix the field used to calculate struct size.
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Fix the field used to calculate struct size.
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PR_kwDOArmXAs5qaJx4
| 64,209 |
Call unbounded broadcast only when necessary.
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Call unbounded broadcast only when necessary.
The scale of improvement is more apparent with shapes that have multiple unbounded sizes. Here's a before & after HLO for `add(f32[1,?,2,?,<=2,?,?], f32[?,1,?,2,?,<=2,?])` where implicit broadcasting is not necessary:
Before:
```
HloModule UnboundedBinaryOpTest_0.35, entry_computation_layout={(f32[1,?,2,?,<=2,?,?]{6,5,4,3,2,1,0}, f32[?,1,?,2,?,<=2,?]{6,5,4,3,2,1,0})->f32[?,?,2,2,<=2,<=2,?]{6,5,4,3,2,1,0}}
ENTRY %UnboundedBinaryOpTest_0.35 (lhs.1: f32[1,?,2,?,<=2,?,?], rhs.2: f32[?,1,?,2,?,<=2,?]) -> f32[?,?,2,2,<=2,<=2,?] {
%constant.3 = s32[1]{0} constant({1})
%constant.16 = s32[1]{0} constant({1})
%lhs.1 = f32[1,?,2,?,<=2,?,?]{6,5,4,3,2,1,0} parameter(0)
%constant.4 = s32[1]{0} constant({1})
%get-dimension-size.5 = s32[] get-dimension-size(f32[1,?,2,?,<=2,?,?]{6,5,4,3,2,1,0} %lhs.1), dimensions={1}
%reshape.6 = s32[1]{0} reshape(s32[] %get-dimension-size.5)
%constant.7 = s32[1]{0} constant({2})
%get-dimension-size.8 = s32[] get-dimension-size(f32[1,?,2,?,<=2,?,?]{6,5,4,3,2,1,0} %lhs.1), dimensions={3}
%reshape.9 = s32[1]{0} reshape(s32[] %get-dimension-size.8)
%get-dimension-size.10 = s32[] get-dimension-size(f32[1,?,2,?,<=2,?,?]{6,5,4,3,2,1,0} %lhs.1), dimensions={4}
%reshape.11 = s32[1]{0} reshape(s32[] %get-dimension-size.10)
%get-dimension-size.12 = s32[] get-dimension-size(f32[1,?,2,?,<=2,?,?]{6,5,4,3,2,1,0} %lhs.1), dimensions={5}
%reshape.13 = s32[1]{0} reshape(s32[] %get-dimension-size.12)
%get-dimension-size.14 = s32[] get-dimension-size(f32[1,?,2,?,<=2,?,?]{6,5,4,3,2,1,0} %lhs.1), dimensions={6}
%reshape.15 = s32[1]{0} reshape(s32[] %get-dimension-size.14)
%concatenate.29 = s32[7]{0} concatenate(s32[1]{0} %constant.4, s32[1]{0} %reshape.6, s32[1]{0} %constant.7, s32[1]{0} %reshape.9, s32[1]{0} %reshape.11, /*index=5*/s32[1]{0} %reshape.13, s32[1]{0} %reshape.15), dimensions={0}
%rhs.2 = f32[?,1,?,2,?,<=2,?]{6,5,4,3,2,1,0} parameter(1)
%get-dimension-size.17 = s32[] get-dimension-size(f32[?,1,?,2,?,<=2,?]{6,5,4,3,2,1,0} %rhs.2), dimensions={0}
%reshape.18 = s32[1]{0} reshape(s32[] %get-dimension-size.17)
%constant.19 = s32[1]{0} constant({1})
%get-dimension-size.20 = s32[] get-dimension-size(f32[?,1,?,2,?,<=2,?]{6,5,4,3,2,1,0} %rhs.2), dimensions={2}
%reshape.21 = s32[1]{0} reshape(s32[] %get-dimension-size.20)
%constant.22 = s32[1]{0} constant({2})
%get-dimension-size.23 = s32[] get-dimension-size(f32[?,1,?,2,?,<=2,?]{6,5,4,3,2,1,0} %rhs.2), dimensions={4}
%reshape.24 = s32[1]{0} reshape(s32[] %get-dimension-size.23)
%get-dimension-size.25 = s32[] get-dimension-size(f32[?,1,?,2,?,<=2,?]{6,5,4,3,2,1,0} %rhs.2), dimensions={5}
%reshape.26 = s32[1]{0} reshape(s32[] %get-dimension-size.25)
%get-dimension-size.27 = s32[] get-dimension-size(f32[?,1,?,2,?,<=2,?]{6,5,4,3,2,1,0} %rhs.2), dimensions={6}
%reshape.28 = s32[1]{0} reshape(s32[] %get-dimension-size.27)
%concatenate.30 = s32[7]{0} concatenate(s32[1]{0} %reshape.18, s32[1]{0} %constant.19, s32[1]{0} %reshape.21, s32[1]{0} %constant.22, s32[1]{0} %reshape.24, /*index=5*/s32[1]{0} %reshape.26, s32[1]{0} %reshape.28), dimensions={0}
%maximum.31 = s32[7]{0} maximum(s32[7]{0} %concatenate.29, s32[7]{0} %concatenate.30)
%custom-call.32 = f32[?,?,2,2,<=2,<=2,?]{6,5,4,3,2,1,0} custom-call(f32[1,?,2,?,<=2,?,?]{6,5,4,3,2,1,0} %lhs.1, s32[7]{0} %maximum.31), custom_call_target="mhlo.dynamic_broadcast_in_dim", backend_config={broadcast_dimensions=[0,1,2,3,4,5,6]}
%custom-call.33 = f32[?,?,2,2,<=2,<=2,?]{6,5,4,3,2,1,0} custom-call(f32[?,1,?,2,?,<=2,?]{6,5,4,3,2,1,0} %rhs.2, s32[7]{0} %maximum.31), custom_call_target="mhlo.dynamic_broadcast_in_dim", backend_config={broadcast_dimensions=[0,1,2,3,4,5,6]}
ROOT %add.34 = f32[?,?,2,2,<=2,<=2,?]{6,5,4,3,2,1,0} add(f32[?,?,2,2,<=2,<=2,?]{6,5,4,3,2,1,0} %custom-call.32, f32[?,?,2,2,<=2,<=2,?]{6,5,4,3,2,1,0} %custom-call.33)
}
```
After:
```
HloModule UnboundedBinaryOpTest_0.4, entry_computation_layout={(f32[1,?,2,?,<=2,?,?]{6,5,4,3,2,1,0}, f32[?,1,?,2,?,<=2,?]{6,5,4,3,2,1,0})->f32[?,?,2,2,<=2,<=2,?]{6,5,4,3,2,1,0}}
ENTRY %UnboundedBinaryOpTest_0.4 (lhs.1: f32[1,?,2,?,<=2,?,?], rhs.2: f32[?,1,?,2,?,<=2,?]) -> f32[?,?,2,2,<=2,<=2,?] {
%lhs.1 = f32[1,?,2,?,<=2,?,?]{6,5,4,3,2,1,0} parameter(0)
%rhs.2 = f32[?,1,?,2,?,<=2,?]{6,5,4,3,2,1,0} parameter(1)
ROOT %add.3 = f32[?,?,2,2,<=2,<=2,?]{6,5,4,3,2,1,0} add(f32[1,?,2,?,<=2,?,?]{6,5,4,3,2,1,0} %lhs.1, f32[?,1,?,2,?,<=2,?]{6,5,4,3,2,1,0} %rhs.2)
}
```
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PR_kwDOArmXAs5qaJOA
| 64,208 |
Add unbounded dynamism test for BitcastConvertOp.
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Add unbounded dynamism test for BitcastConvertOp.
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PR_kwDOArmXAs5qaAaH
| 64,207 |
[tflite] Add the skeleton for tflite composite lowering pass.
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[tflite] Add the skeleton for tflite composite lowering pass.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/64539 from rascani:add_ios_and_contributing_links d58be30612134ae62b457bc12f91c7e1d3c7ba23
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PR_kwDOArmXAs5qZ9De
| 64,206 |
Fix a bug in which an absl hashtable is used with inconsistent hash/eq functors.
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Fix a bug in which an absl hashtable is used with inconsistent hash/eq functors.
`eq(k1, k2) -> hash(k1) == hash(k2)` must be true for hashtable usage to be valid.
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PR_kwDOArmXAs5qZ8bC
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PR #10316: [NVIDIA GPU] Add a mechanism in GpuAsyncTracker to force delaying scheduling of an instruction.
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PR #10316: [NVIDIA GPU] Add a mechanism in GpuAsyncTracker to force delaying scheduling of an instruction.
Imported from GitHub PR https://github.com/openxla/xla/pull/10316
latency hiding scheduler has a property for each HloScheduleNode called SetForceDelay, setting this to true for a node will result in forcing LHS to ignore all cost info and schedule the instruction to the very beginning of the instruction sequence.
This is useful in manually enforcing an instruction to run before anything else in the scheduled graph.
Passes can set `should_force_delay` attribute in the GpuBackendConfig to instruct how this instruction will be scheduled. It's set to false by default.
An example usage:
```
original sequence:
allreduce-start
allreduce-done
add0, backend_config={should_force_delay=true}
add1
schedule after LHS:
add0, backend_config={should_force_delay=true}
allreduce-start
add1
allreduce-done
```
Copybara import of the project:
--
74267e60142c5161310580db5b760a3fe6d4f625 by TJ <[email protected]>:
Add a mechanism in GpuAsyncTracker to force delaying scheduling
of an instruction.
--
0aac4cf8c42f2ca210450581f05239378d8a33c2 by TJ <[email protected]>:
Fix failing backend config test
--
4deab8393ecb01ca010e0cf4ea30930ac448b815 by TJ <[email protected]>:
Use force delay for windowed einsum loops
--
a1321e88fd336f83a4fcfcc89037b8d4a3d1903f by TJ <[email protected]>:
Revert changes in gpu_windowed_einsum_handler, moving them to a separate
pr
--
86ffb97db8ce167438562f997316033b61316003 by TJ Xu <[email protected]>:
Fix internal linter error
Merging this change closes #10316
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10316 from Tixxx:tixxx/force_delay_lhs 86ffb97db8ce167438562f997316033b61316003
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PR_kwDOArmXAs5qZ2P6
| 64,204 |
Update implicit broadcast output shape element type to match operand.
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Update implicit broadcast output shape element type to match operand.
Some binary ops return types whose element type differs from the operand element types (e.g. Ne). While broadcasting, it should use the element type of the operand instead of the inferred type.
Before:
```
HloModule UnboundedBinaryOpTest_8.15, entry_computation_layout={(f32[?,10]{1,0}, f32[1]{0})->pred[?,10]{1,0}}
ENTRY %UnboundedBinaryOpTest_8.15 (lhs.1: f32[?,10], rhs.2: f32[1]) -> pred[?,10] {
%constant.3 = s32[1]{0} constant({1})
%lhs.1 = f32[?,10]{1,0} parameter(0)
%get-dimension-size.4 = s32[] get-dimension-size(f32[?,10]{1,0} %lhs.1), dimensions={0}
%reshape.5 = s32[1]{0} reshape(s32[] %get-dimension-size.4)
%constant.6 = s32[1]{0} constant({10})
%concatenate.9 = s32[2]{0} concatenate(s32[1]{0} %reshape.5, s32[1]{0} %constant.6), dimensions={0}
%constant.7 = s32[1]{0} constant({1})
%constant.8 = s32[1]{0} constant({1})
%concatenate.10 = s32[2]{0} concatenate(s32[1]{0} %constant.7, s32[1]{0} %constant.8), dimensions={0}
%maximum.11 = s32[2]{0} maximum(s32[2]{0} %concatenate.9, s32[2]{0} %concatenate.10)
%custom-call.12 = pred[?,10]{1,0} custom-call(f32[?,10]{1,0} %lhs.1, s32[2]{0} %maximum.11), custom_call_target="mhlo.dynamic_broadcast_in_dim", backend_config={broadcast_dimensions=[0,1]}
%rhs.2 = f32[1]{0} parameter(1)
%custom-call.13 = pred[?,10]{1,0} custom-call(f32[1]{0} %rhs.2, s32[2]{0} %maximum.11), custom_call_target="mhlo.dynamic_broadcast_in_dim", backend_config={broadcast_dimensions=[1]}
ROOT %compare.14 = pred[?,10]{1,0} compare(pred[?,10]{1,0} %custom-call.12, pred[?,10]{1,0} %custom-call.13), direction=NE
}
```
After:
```
HloModule UnboundedBinaryOpTest_8.15, entry_computation_layout={(f32[?,10]{1,0}, f32[1]{0})->pred[?,10]{1,0}}
ENTRY %UnboundedBinaryOpTest_8.15 (lhs.1: f32[?,10], rhs.2: f32[1]) -> pred[?,10] {
%constant.3 = s32[1]{0} constant({1})
%lhs.1 = f32[?,10]{1,0} parameter(0)
%get-dimension-size.4 = s32[] get-dimension-size(f32[?,10]{1,0} %lhs.1), dimensions={0}
%reshape.5 = s32[1]{0} reshape(s32[] %get-dimension-size.4)
%constant.6 = s32[1]{0} constant({10})
%concatenate.9 = s32[2]{0} concatenate(s32[1]{0} %reshape.5, s32[1]{0} %constant.6), dimensions={0}
%constant.7 = s32[1]{0} constant({1})
%constant.8 = s32[1]{0} constant({1})
%concatenate.10 = s32[2]{0} concatenate(s32[1]{0} %constant.7, s32[1]{0} %constant.8), dimensions={0}
%maximum.11 = s32[2]{0} maximum(s32[2]{0} %concatenate.9, s32[2]{0} %concatenate.10)
%custom-call.12 = f32[?,10]{1,0} custom-call(f32[?,10]{1,0} %lhs.1, s32[2]{0} %maximum.11), custom_call_target="mhlo.dynamic_broadcast_in_dim", backend_config={broadcast_dimensions=[0,1]}
%rhs.2 = f32[1]{0} parameter(1)
%custom-call.13 = f32[?,10]{1,0} custom-call(f32[1]{0} %rhs.2, s32[2]{0} %maximum.11), custom_call_target="mhlo.dynamic_broadcast_in_dim", backend_config={broadcast_dimensions=[1]}
ROOT %compare.14 = pred[?,10]{1,0} compare(f32[?,10]{1,0} %custom-call.12, f32[?,10]{1,0} %custom-call.13), direction=NE
}
```
Notice the difference in `mhlo.dynamic_broadcast_in_dim` custom call return type `pred[?,10]` vs `f32[?,10]`. The HLO after the change correctly returns unchanged element type from the operand.
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| 64,203 |
Update `Shape::Equals()` to have proper default comparison for unbounded shapes.
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Update `Shape::Equals()` to have proper default comparison for unbounded shapes.
Previously, the default `Shape::Equals()` used `ShapeUtil::SameDimensions(lhs, rhs)` to check whether the two shapes are equal. However, the implementation of `ShapeUtil::SameDimensions()` was updated such that when one the dimension of one shape is unbounded, it compares equal for each dimension.
The default behavior should be that unbounded size compared to any size other than unbounded size should compare not equal.
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[XLA:GPU] Fix layout for kSend/kRecv on GPU to always be default
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[XLA:GPU] Fix layout for kSend/kRecv on GPU to always be default
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[xla:ffi] Refactor custom call tests to reduce code duplication
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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/64201/checks?check_run_id=22948276446) 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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[xla:ffi] Refactor custom call tests to reduce code duplication
Move builder and module setup to test fixture constructor.
Extract 'module build & execute' code shared by most tests as fixture function.
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Integrate StableHLO at openxla/stablehlo@b27ef13c
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Integrate StableHLO at openxla/stablehlo@b27ef13c
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[PJRT C API] Plumb plugin attributes from plugin to JAX python.
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[PJRT C API] Plumb plugin attributes from plugin to JAX python.
Also add a method for the plugin to return an xla_version plugin attribute.
Currently jaxlib pins a TPU/GPU backend, and uses `xla_extension_version` for backend version. As we want to stop pinning TPU/GPU backend and allow pip install different backend separately, we need this `xla_version` for features that are not capture by PJRT C API version. `xla_extension_version` will still be used for API changes such as xla_client.py, or any XLA changes in jaxlib that are not part of plugins.
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No public change.
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NONE
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Use a compact format for const ops in MLRT
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Use a compact format for const ops in MLRT
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PR_kwDOArmXAs5qZj2t
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graphcycles: drop Vec<>; use std::vector instead
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graphcycles: drop Vec<>; use std::vector instead
name old time/op new time/op delta
BM_StressTest/2048 487ns ± 1% 482ns ± 3% ~ (p=0.310 n=5+5)
BM_StressTest/4096 498ns ± 2% 491ns ± 0% ~ (p=0.111 n=5+4)
BM_StressTest/32768 523ns ± 1% 512ns ± 2% ~ (p=0.056 n=5+5)
BM_StressTest/262144 613ns ± 5% 588ns ±11% ~ (p=0.548 n=5+5)
BM_StressTest/1048576 758ns ± 3% 775ns ±26% ~ (p=1.000 n=5+5)
BM_ContractEdge/1000 127ns ± 3% 121ns ± 5% ~ (p=0.056 n=5+5)
BM_ContractEdge/10000 149ns ± 2% 146ns ± 1% -1.47% (p=0.032 n=5+5)
BM_IsReachableNonConst/10 12.1ns ± 9% 10.1ns ±11% -16.69% (p=0.016 n=5+5)
BM_IsReachableNonConst/50 20.6ns ±17% 14.7ns ±13% -29.00% (p=0.008 n=5+5)
BM_IsReachableNonConst/100 25.4ns ±20% 18.3ns ±13% -27.98% (p=0.008 n=5+5)
BM_IsReachableNonConst/200 30.6ns ±27% 20.1ns ± 6% -34.35% (p=0.008 n=5+5)
BM_IsReachableNonConst/1000 40.1ns ±27% 25.8ns ± 5% -35.71% (p=0.008 n=5+5)
BM_IsReachableNonConst/30000 67.1ns ± 2% 49.2ns ± 4% -26.77% (p=0.016 n=4+5)
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10519 from openxla:cudnn_frontend_1_2_0 4695023e035516db05b707d79409a60eba9f47f7
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[xla:gpu] dot_merger: reorder merge checks to call IsReachableNonConst last
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[xla:gpu] dot_merger: reorder merge checks to call IsReachableNonConst last
Only perform the reachability check if necessary, since it can be expensive.
This brings a 1.5x compilation time speedup for a large, dense model.
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Expose `.layout` on jax.Array. Also add checks in the AOT path to make sure that the input `Array`'s layout matches the layout given to `jax.jit`.
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Expose `.layout` on jax.Array. Also add checks in the AOT path to make sure that the input `Array`'s layout matches the layout given to `jax.jit`.
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I_kwDOArmXAs6DLeHb
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INT4 and other low-precision conversion support status
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[
"Hi @AIWintermuteAI,\r\n\r\nPlease share your .tflite file to reproduce the issue.\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.",
"Hi @abattery, can you please take a look or comment on this? Thanks."
] | 2024-03-21T17:52:43 | 2024-04-08T18:11:16 | null |
NONE
| null | null | null |
What is the current status of model conversion (PTQ specifically) with INT4 precision?
The question was raised before here https://github.com/tensorflow/tensorflow/issues/60125.
Also it looks like INT4 support is being added to various parts of tensorflow, as evidenced by https://github.com/tensorflow/tensorflow/pull/63870 and https://github.com/tensorflow/tensorflow/blob/6738c28cf4eed334d75d02b6e97fe16c34069ef1/tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.proto#L76.
However at the moment there seems to be no way to quantize model to INT4 (specifically the weights):
- the tflite converter MLIR based conversion (the "experimental" conversion) seems abandoned. It is not possible to specify the type of weights quantization
- what seems to be a new API for quantization (is it the one from blog article https://blog.tensorflow.org/2023/05/google-io-2023-whats-new-in-tensorflow-and-keras.html ?) lives here https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/mlir/quantization/tensorflow (???). It is possible to use it with INT8, but not INT4 - but the resulting SavedModel cannot be converted to .tflite (SegFault).
Can anyone who actively works on this in TF team shine the light on what is the current direction and where one needs to dig to add INT4 PTQ quantization?
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