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Add support for stablehlo.composite with pytorch F.interpolate-`bilinear` lowered directly to TFL_ResizeBilinearOp
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Add support for stablehlo.composite with pytorch F.interpolate-`bilinear` lowered directly to TFL_ResizeBilinearOp
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#tf-data `index_mapper` returns a `StatusOr`.
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Fix op profile root/program node metrics that:
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Fix op profile root/program node metrics that:
- Fix the total time of program node to be closer to the real total time as duty_cycle total time (idle time is not counted still).
- Fix the bandwidth util of program node with adjusted total_time. The util numbers will be slightly smaller now given the increased total time.
- Fix the bandwidth util of root node a bit, the number should be slightly smaller as the divider time used now becomes larger.
- The avgTime of program node is no longer accessible for program node, as we currently cannot get this number correctly.
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Adds KVCache op to GenAI custom op resolver.
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Adds KVCache op to GenAI custom op resolver.
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[xla:gpu] Disable set_xla_gpu_use_memcpy_local_p2p by default
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[xla:gpu] Disable set_xla_gpu_use_memcpy_local_p2p by default
Breaks jax tests
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Set metadata and frontend attributes for instructions added in tpu copy normalizer HLO pass.
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Set metadata and frontend attributes for instructions added in tpu copy normalizer HLO pass.
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Add unbounded dynamism test for AllToAllOp.
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Add unbounded dynamism test for AllToAllOp.
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PR #11053: [XLA:CPU][oneDNN] Variant patterns of GELU tanh approximation
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PR #11053: [XLA:CPU][oneDNN] Variant patterns of GELU tanh approximation
Imported from GitHub PR https://github.com/openxla/xla/pull/11053
This PR supports more patterns from TF->XLA GELU Approximate. It was found that the variations were resulting from associativity of multiply instructions like in the expression a * b * c. An utility function is added in this PR that captures patterns from two consecutive multiplies.
Copybara import of the project:
--
74a287c7784138da3905d618d65a8301c821d7f6 by mdfaijul <[email protected]>:
Patterns from tf.nn.gelu(approximate=True)
--
51b4962709de6cee8ec92c9fc225b5fc3371c276 by mdfaijul <[email protected]>:
Fix typos
Merging this change closes #11053
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11053 from Intel-tensorflow:amin/tf-gelu 51b4962709de6cee8ec92c9fc225b5fc3371c276
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#tf-data-service Explicitly checks if snapshot metadata exists.
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NONE
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#tf-data-service Explicitly checks if snapshot metadata exists.
The `GFile` documentation does not mention what exceptions it raises:
https://github.com/tensorflow/tensorflow/blob/38b17d708344a91234dad879794735e79f9af42a/tensorflow/python/platform/gfile.py#L37.
It would be safer to explicitly check for file existence and return the
appropriate result.
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Porting https://github.com/openxla/stablehlo/pull/2203ato G3
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[tsl:concurrency] Test that async payloads with different alignment works
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NONE
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[tsl:concurrency] Test that async payloads with different alignment works
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PR_kwDOArmXAs5sZrHz
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Make AutoShardingSolverRequest visibility public.
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Make AutoShardingSolverRequest visibility public.
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No public changes
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NONE
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#tf-data-service Write snapshot metadata atomically.
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#tf-data-service Write snapshot metadata atomically.
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PR_kwDOArmXAs5sZS0W
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Automatic rollback of Use PTX version corresponding to CUDA version
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NONE
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Automatic rollback of Use PTX version corresponding to CUDA version
Reverts 99f256965598b505ea0f47d2ae290243b4ac4b81
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[oneDNN] Removed deprecated oneDNN ops
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[
"@penpornk Thank you!"
] | 2024-04-11T18:46:18 | 2024-04-16T03:07:46 | 2024-04-11T20:41:52 |
CONTRIBUTOR
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There are quite a few oneDNN ops which have been deprecated since TF 2.15.
This PR is intended to fully remove them, as part of oneDNN code cleanup effort.
Please note: the implementations of these ops were removed in TF 2.15.
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[
"@sawantkumar what's the status on this? Is there anything else I can provide to help this along",
"Hi @andrew-lyons ,\r\n\r\nI will replicate your issue and will get back to you. Please give me some time while i figure it out. Sorry for the delayed response .",
"Hi @andrew-lyons, can you share your model file -- if you can't share the true model .. can you create a model with just that op that reproduces the issue and share that? Thanks for your help.\r\n\r\nAlso if you can share what type of model it is? A CV model? an NLP model? An Audio Model? a Video Model? ... generally the more information we have the easier it will be for us.",
"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.",
"@pkgoogle \r\n\r\nSure thing, here's an example model: https://drive.google.com/file/d/1L-SfTJz5i4sUBAkozYOG5yaInLkC3Qft/view?usp=drive_link\r\n\r\nI also can share some code used to generate this model:\r\n\r\nconcrete func\r\n```python\r\[email protected]\r\ndef predict(prompt):\r\n preprocessed = gpt2_preprocessor(prompt)\r\n tokenized_prompt_shifted = tf.shape(tf.where(preprocessed[1] != 50256))[0]\r\n input_map = {\r\n 'token_ids': tf.reshape(preprocessed[0]['token_ids'], [1, 255]),\r\n 'padding_mask': tf.reshape(preprocessed[0]['padding_mask'], [1, 255])\r\n }\r\n result = gpt2_lm(input_map)[0]\r\n result = tf.slice(result, [tokenized_prompt_shifted-1, 0], [1, 50256])\r\n topk = tf.math.top_k(tf.nn.softmax(result), k=25)\r\n return topk.indices[0]\r\n\r\nconcrete_func = predict.get_concrete_function(tf.TensorSpec([], tf.string))\r\n```\r\n\r\nconversion\r\n```python\r\ngpt2_lm.jit_compile = False\r\nconverter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func], gpt2_lm)\r\nconverter.target_spec.supported_ops = [\r\n tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.\r\n tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.\r\n]\r\nconverter.allow_custom_ops = True\r\nconverter.target_spec.experimental_select_user_tf_ops = [\"UnsortedSegmentJoin\", \"UpperBound\"]\r\nconverter._experimental_guarantee_all_funcs_one_use = True\r\ngenerate_tflite = converter.convert()\r\nrun_inference(\"I'm enjoying a\", generate_tflite)\r\n```\r\n\r\nbasically just trying to grab the next token, and the top 25 probabilities",
"Hi @andrew-lyons, every time I try to reproduce it, the build fails after adding the TFL Select Ops `'TensorFlowLiteSelectTfOps', '~> 0.0.1-nightly'`\r\n\r\nI'm modifying the Bert-QA example as that seems most similar to your use case: https://github.com/tensorflow/examples/tree/master/lite/examples/bert_qa/ios\r\n\r\nThis is the error I keep getting:\r\n\r\n```\r\nBuilding for 'iOS-simulator', but linking in object file (/Users/xxxxxx/Library/Developer/Xcode/DerivedData/BertQA-eatnorjmqyqinlekyqphgwusovgt/Build/Products/Debug-iphonesimulator/XCFrameworkIntermediates/TensorFlowLiteSelectTfOps/TensorFlowLiteSelectTfOps.framework/TensorFlowLiteSelectTfOps[872](any.o)) built for 'iOS'\r\n```\r\n\r\n@yishuangP can you please take a look? Thanks.",
"@pkgoogle not sure if it matters here but I didn't attempt building on the simulator, only on an iPhone 14.",
"Hi @andrew-lyons, we're wondering if you may be able to resolve your issue by using [AI-Edge-Torch](https://github.com/google-ai-edge/ai-edge-torch), our PyTorch conversion library, you can find more information here: [googleblog](https://developers.googleblog.com/en/ai-edge-torch-high-performance-inference-of-pytorch-models-on-mobile-devices/).\r\n\r\nWe actually have examples of converting and quantizing decoder-only LLMs here: https://github.com/google-ai-edge/ai-edge-torch/tree/main/ai_edge_torch/generative/examples.\r\n\r\nIf the conversion is successful, you can also try visualizing the result in [model-explorer](https://github.com/google-ai-edge/model-explorer) as well.\r\n\r\nPlease try them out and let us know if this resolves your issue. If you still need further help, feel free to open a new issue at the respective repo. Thanks for your help."
] | 2024-04-11T18:43:15 | 2024-06-10T21:39:43 | null |
NONE
| null | null | null |
**System information**
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- Macbook M1 Pro
- XCode version 15.0.1
- TensorFlow installed from (source or binary):
```
pod 'TensorFlowLiteSwift', '~> 0.0.1-nightly', :subspecs => ['Metal']
pod 'TensorFlowLiteSelectTfOps', '~> 0.0.1-nightly'
```
I updated the pods and the nightly versions are as seen in my podfile.lock:
```
PODS:
// ...
- TensorFlowLiteC (0.0.1-nightly.20240411):
- TensorFlowLiteC/Core (= 0.0.1-nightly.20240411)
- TensorFlowLiteC/Core (0.0.1-nightly.20240411)
- TensorFlowLiteC/Metal (0.0.1-nightly.20240411):
- TensorFlowLiteC/Core
- TensorFlowLiteSelectTfOps (0.0.1-nightly.20240411)
- TensorFlowLiteSwift/Core (0.0.1-nightly.20240411):
- TensorFlowLiteC (= 0.0.1-nightly.20240411)
- TensorFlowLiteSwift/Metal (0.0.1-nightly.20240411):
- TensorFlowLiteC/Metal (= 0.0.1-nightly.20240411)
- TensorFlowLiteSwift/Core (= 0.0.1-nightly.20240411)
```
**Standalone code to reproduce the issue**
Provide a reproducible test case that is the bare minimum necessary to generate
the problem. If possible, please share a link to Colab/Jupyter/any notebook.
Also, please include a link to a GraphDef or the model if possible.
**Any other info / logs**
Include any logs or source code that would be helpful to diagnose the problem.
If including tracebacks, please include the full traceback. Large logs and files
should be attached.
---
I'm only attempting to load a model locally, not sure if an example is necessary. For the code anyway I have this:
```swift
@objc func initializeODI(_ call: CAPPluginCall) {
var modelFile = //
guard let modelPath = Bundle(for: type(of: self)).path(forResource: modelFile.name, ofType: modelFile.ext)
else {
fatalError("Failed to load the model file: \(modelFile.description)");
}
var options = Interpreter.Options();
do {
interpreter = try Interpreter(modelPath: modelPath, options: options)
try interpreter?.allocateTensors();
} catch {
print("Error instantiating interpreter...", error);
}
}
```
gives me this error:
```
2024-04-11 14:33:24.676552: E tensorflow/core/framework/node_def_util.cc:676] NodeDef mentions attribute use_inter_op_parallelism which is not in the op definition: Op<name=MutableHashTableV2; signature= -> table_handle:resource; attr=container:string,default=""; attr=shared_name:string,default=""; attr=use_node_name_sharing:bool,default=false; attr=key_dtype:type; attr=value_dtype:type; is_stateful=true> This may be expected if your graph generating binary is newer than this binary. Unknown attributes will be ignored. NodeDef: {{node MutableHashTableV2}}
TensorFlow Lite Error: Op type not registered 'RegexSplitWithOffsets' in binary running on localhost. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib (e.g. `tf.contrib.resampler`), accessing should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.
TensorFlow Lite Error: Op type not registered 'RegexSplitWithOffsets' in binary running on localhost. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib (e.g. `tf.contrib.resampler`), accessing should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.
```
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#tf-data returns bool instead of int64_t for PrefetchAutotuner::HasElementSize
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#tf-data returns bool instead of int64_t for PrefetchAutotuner::HasElementSize
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| 65,473 |
Add missing conversion for CompilationCachingSettings
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Add missing conversion for CompilationCachingSettings
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Add missing conversion for ArmNNSettings
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| 65,471 |
Define MtkNeuronSettings in configuration.proto
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Define MtkNeuronSettings in configuration.proto
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PR_kwDOArmXAs5sYv8t
| 65,470 |
Integrate LLVM at llvm/llvm-project@ffb5bea2be9f
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Integrate LLVM at llvm/llvm-project@ffb5bea2be9f
Updates LLVM usage to match
[ffb5bea2be9f](https://github.com/llvm/llvm-project/commit/ffb5bea2be9f)
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Add an option in TF2XLA to control `always_return_tuple` in XlaCompiler::CompileOptions.
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Add an option in TF2XLA to control `always_return_tuple` in XlaCompiler::CompileOptions.
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I_kwDOArmXAs6FZv-_
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loss growing to negative infinity val accuracy remaining constant, testing accuracy above 100%
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[
"@ripjohnbrown1859 Could you please have a look at this gist and confirm the error reported [here](https://colab.research.google.com/gist/sushreebarsa/2f66623bf38d27b0a0a8b87bc9c56cc4/65468.ipynb#scrollTo=vtO_j7dCzOyO). \r\nThank you!",
"that is because I am running 2 gpus and the notebook does not have access to 2 gpus so it says the list index for gpus is out of range. it should work if you simply cut out all the gpu[1] stuff.",
"this is not a gpu issue, the issue persists whether or not i run it on my gpus. or cpu. \r\n",
"Hi @ripjohnbrown1859 ,\r\n\r\nSince the submitted code snippet has dependencies with your dataset could you please submit a minimal code with some dummy set. There seems to be issue as I can notice accuracy is >1 from the attached logs.",
"heres the link to the dataset. https://physionet.org/content/ecg-arrhythmia/1.0.0/"
] | 2024-04-11T16:29:04 | 2024-04-30T17:44:32 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
wsl ubuntu 22.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
8.6.0
### GPU model and memory
sli titan x maxwell
### Current behavior?
currently my loss is increasing to negative infinity, my test accuracy is sitting around 1.85 and my val accuracy is staying at .9374 no matter how may epochs i run. i am training my model to read ekgs, i have 64 not mutually exclusive outputs that correspond with different diagnoses. each input is a 12 lead ekg with 5000 time series datapoints for each lead.
### Standalone code to reproduce the issue
```shell
tensorflow as tf
import os
import concurrent.futures
import csv
import numpy as np
import scipy.io as sio
from tensorflow.keras.callbacks import (ModelCheckpoint, TensorBoard, ReduceLROnPlateau,
CSVLogger, EarlyStopping)
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.utils import Sequence
from tensorflow.keras.layers import (
Input, Conv1D, MaxPooling1D, Dropout, BatchNormalization, Activation, Add, Flatten, Dense)
from tensorflow.keras.models import Model
import numpy as np
class ResidualUnit(object):
def __init__(self, n_samples_out, n_filters_out, kernel_initializer='he_normal',
dropout_keep_prob=0.8, kernel_size=20, preactivation=True,
postactivation_bn=False, activation_function='relu'):
self.n_samples_out = n_samples_out
self.n_filters_out = n_filters_out
self.kernel_initializer = kernel_initializer
self.dropout_rate = 1 - dropout_keep_prob
self.kernel_size = kernel_size
self.preactivation = preactivation
self.postactivation_bn = postactivation_bn
self.activation_function = activation_function
def _skip_connection(self, y, downsample, n_filters_in):
"""Implement skip connection."""
# Deal with downsampling
if downsample > 1:
y = MaxPooling1D(downsample, strides=downsample, padding='same')(y)
elif downsample == 1:
y = y
else:
raise ValueError("Number of samples should always decrease.")
# Deal with n_filters dimension increase
if n_filters_in != self.n_filters_out:
# This is one of the two alternatives presented in ResNet paper
# Other option is to just fill the matrix with zeros.
y = Conv1D(self.n_filters_out, 1, padding='same',
use_bias=False, kernel_initializer=self.kernel_initializer)(y)
return y
def _batch_norm_plus_activation(self, x):
if self.postactivation_bn:
x = Activation(self.activation_function)(x)
x = BatchNormalization(center=False, scale=False)(x)
else:
x = BatchNormalization()(x)
x = Activation(self.activation_function)(x)
return x
def __call__(self, inputs):
"""Residual unit."""
x, y = inputs
n_samples_in = y.shape[1]
downsample = n_samples_in // self.n_samples_out
n_filters_in = y.shape[2]
y = self._skip_connection(y, downsample, n_filters_in)
# 1st layer
x = Conv1D(self.n_filters_out, self.kernel_size, padding='same',
use_bias=False, kernel_initializer=self.kernel_initializer)(x)
x = self._batch_norm_plus_activation(x)
if self.dropout_rate > 0:
x = Dropout(self.dropout_rate)(x)
# 2nd layer
x = Conv1D(self.n_filters_out, self.kernel_size, strides=downsample,
padding='same', use_bias=False,
kernel_initializer=self.kernel_initializer)(x)
if self.preactivation:
x = Add()([x, y]) # Sum skip connection and main connection
y = x
x = self._batch_norm_plus_activation(x)
if self.dropout_rate > 0:
x = Dropout(self.dropout_rate)(x)
else:
x = BatchNormalization()(x)
x = Add()([x, y]) # Sum skip connection and main connection
x = Activation(self.activation_function)(x)
if self.dropout_rate > 0:
x = Dropout(self.dropout_rate)(x)
y = x
return [x, y]
def get_model(n_classes, last_layer='sigmoid'):
kernel_size = 20
kernel_initializer = 'he_normal'
signal = Input(shape=(5000, 12), dtype=tf.float32, name='signal')
x = signal
x = Conv1D(64, kernel_size, padding='same', use_bias=False,
kernel_initializer=kernel_initializer)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x, y = ResidualUnit(1024, 128, kernel_size=kernel_size,
kernel_initializer=kernel_initializer)([x, x])
x, y = ResidualUnit(256, 196, kernel_size=kernel_size,
kernel_initializer=kernel_initializer)([x, y])
x, y = ResidualUnit(64, 256, kernel_size=kernel_size,
kernel_initializer=kernel_initializer)([x, y])
x, _ = ResidualUnit(16, 320, kernel_size=kernel_size,
kernel_initializer=kernel_initializer)([x, y])
x = Flatten()(x)
diagn = Dense(n_classes, activation=last_layer, kernel_initializer=kernel_initializer)(x)
model = Model(signal, diagn)
return model
gpu = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpu[0], True)
tf.config.experimental.set_memory_growth(gpu[1], True)
#tf.config.experimental.set_virtual_device_configuration(gpu[1], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=11000)])
#tf.config.experimental.set_virtual_device_configuration(gpu[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=11000)])
print(tf.version.GIT_VERSION, tf.version.VERSION)
print(tf.config.list_logical_devices())
num_classes=64
num_samples = 45561
options = tf.data.Options()
options.autotune.ram_budget = 51539607552
#from tensorflow.keras import layers, models
def parse_hea_file(file_path):
with open(file_path, 'r') as file:
lines = file.readlines()
dx_values = None
for line in lines:
if line.startswith('#Dx'):
dx_values = line.strip().split(': ')[1].split(',') if len(line.strip().split(': ')) > 1 else 0
dx_values = [dx[key] for key in dx_values if key in dx]
break
return dx_values
# Initialize an empty dictionary
dx = {}
# Open the CSV file for reading
def create_dx_dict():
with open('a-large-scale-12-lead-electrocardiogram-database-for-arrhythmia-study-1.0.0/ConditionNames_SNOMED-CT.csv', newline='') as csvfile:
# Create a CSV reader object
csv_reader = csv.reader(csvfile)
# Skip the header row
next(csv_reader)
i = 0
# Iterate over each row in the CSV file
for row in csv_reader:
i += 1
# Assuming the CSV file has 3 columns and they are indexed as 0, 1, and 2
# Construct the dictionary with the third column as keys and the second column as values
key = row[2]
value = i
dx[key] = value
return dx
dx=create_dx_dict()
# Specify the path to the parent directory
def normalize_data(data):
for i in range(data.shape[1]):
for j in range(data.shape[0]):
max_abs = 32768
data[j,i] /= max_abs
return data
input_dataset = []
output_dataset = []
# Get a list of directories within the parent directory
def process_subdirectory(subdir_path):
output_data = []
input_data = []
sub_subdirectories = [d for d in os.listdir(subdir_path) if os.path.isdir(os.path.join(subdir_path, d))]
for sub_subdir in sub_subdirectories:
files_path = os.path.join(subdir_path, sub_subdir)
print(files_path)
files = [f for f in os.listdir(files_path) if os.path.isfile(os.path.join(files_path, f))]
for file in files:
if file.endswith('.hea'):
dx_values = parse_hea_file(os.path.join(files_path, file))
output_data.append(dx_values)
matfile = os.path.splitext(file)[0] + '.mat'
data = sio.loadmat(os.path.join(files_path, matfile))
ecg_data = data['val']
input_data.append(np.array(ecg_data))
return input_data, output_data
class ECGDataGenerator(Sequence):
def __init__(self, data_dir ="a-large-scale-12-lead-electrocardiogram-database-for-arrhythmia-study-1.0.0/WFDBRecords", batch_size=512):
self.data_dir = data_dir
self.batch_size = batch_size
self.filenames = self._find_data_files()
def __len__(self):
return int(np.ceil(len(self.filenames) / self.batch_size))
def _find_data_files(self):
filenames = []
subdirectories = [d for d in os.listdir(self.data_dir) if os.path.isdir(os.path.join(self.data_dir, d))]
for subdir in subdirectories:
subsubdirectories = [d for d in os.listdir(os.path.join(self.data_dir, subdir)) if os.path.isdir(os.path.join(self.data_dir, subdir, d))]
for subsubdir in subsubdirectories:
files_path = os.path.join(self.data_dir, subdir, subsubdir)
files = [f for f in os.listdir(files_path) if os.path.isfile(os.path.join(files_path, f))]
for file in files:
if file.endswith('.hea'):
file = os.path.join(subdir, subsubdir, file)
filenames.append(file)
return filenames
def __getitem__(self, idx):
batch_filenames = self.filenames[idx * self.batch_size:(idx + 1) * self.batch_size]
with concurrent.futures.ProcessPoolExecutor() as executor:
batch_data_labels = list(executor.map(self._process_file, batch_filenames))
batch_data, batch_labels = zip(*batch_data_labels)
return np.array(batch_data), np.array(batch_labels)
def _process_file(self, filename):
dx_values = parse_hea_file(os.path.join(self.data_dir, filename))
padded_dx_values = pad_sequences([dx_values], maxlen=64, padding = "post")[0]
if dx_values is not None:
mat_filename = os.path.splitext(filename)[0] + '.mat'
mat_data = sio.loadmat(os.path.join(self.data_dir, mat_filename)).get('val')
mat_data = np.reshape(mat_data, (5000,12))
normalized_data = normalize_data(mat_data)
return normalized_data, padded_dx_values
lr = 0.001
strategy = tf.distribute.MirroredStrategy()
callbacks = [ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=7,
min_lr=lr / 100),
EarlyStopping(patience=9, # Patience should be larger than the one in ReduceLROnPlateau
min_delta=0.00001)]
with tf.device('/CPU:0'):
dataset = tf.data.Dataset.from_generator(generator=ECGDataGenerator, output_types=(tf.float32, tf.float32), output_shapes=([None, 5000, 12], [None, 64]))
dataset = dataset.shuffle(86)
training_dataset = dataset.take(80)
validation_dataset = dataset.skip(80)
with strategy.scope():
training_dataset = training_dataset.cache().repeat().prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
validation_dataset = validation_dataset.cache().repeat().prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
model = get_model(num_classes)
print(model.summary())
model.compile(optimizer=tf.keras.optimizers.Adam(lr), loss=tf.keras.losses.BinaryCrossentropy(), metrics=['binary_accuracy'])
model.fit(training_dataset, validation_data=validation_dataset, epochs=70, steps_per_epoch=80, validation_steps=7, callbacks=callbacks)
model.export("ecg_model", "tf_saved_model")
```
### Relevant log output
```shell
80/80 ━━━━━━━━━━━━━━━━━━━━ 2449s 16s/step - binary_accuracy: 1.8140 - loss: -92.5793 - val_binary_accuracy: 0.9372 - val_loss: -96.8444 - learning_rate: 0.0010
Epoch 2/70
80/80 ━━━━━━━━━━━━━━━━━━━━ 110s 1s/step - binary_accuracy: 1.8469 - loss: -742.3646 - val_binary_accuracy: 0.9374 - val_loss: -279.6367 - learning_rate: 0.0010
Epoch 3/70
80/80 ━━━━━━━━━━━━━━━━━━━━ 111s 1s/step - binary_accuracy: 1.8468 - loss: -1657.0448 - val_binary_accuracy: 0.9374 - val_loss: -712.0684 - learning_rate: 0.0010
Epoch 4/70
80/80 ━━━━━━━━━━━━━━━━━━━━ 111s 1s/step - binary_accuracy: 1.8459 - loss: -2816.5115 - val_binary_accuracy: 0.9374 - val_loss: -1348.7988 - learning_rate: 0.0010
etc...
```
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| 2,238,047,298 |
PR_kwDOArmXAs5sYLxx
| 65,467 |
Integrate StableHLO at openxla/stablehlo@714d9aca
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[] | 2024-04-11T15:45:34 | 2024-04-15T20:10:27 | 2024-04-15T20:10:27 |
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Integrate StableHLO at openxla/stablehlo@714d9aca
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| 2,237,993,982 |
PR_kwDOArmXAs5sX_8-
| 65,466 |
Integrate Triton up to [8e0c7b42](https://github.com/openai/triton/commits/8e0c7b425ac149c43183de966ffa423fd46e4762)
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Integrate Triton up to [8e0c7b42](https://github.com/openai/triton/commits/8e0c7b425ac149c43183de966ffa423fd46e4762)
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PR_kwDOArmXAs5sX0IG
| 65,465 |
Update visibility of TFL Runtime.
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[] | 2024-04-11T14:57:28 | 2024-04-11T15:04:36 | 2024-04-11T15:04:34 |
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Update visibility of TFL Runtime.
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| 2,237,826,345 |
PR_kwDOArmXAs5sXaiR
| 65,464 |
Testing presubmit because is failing in llvm-integration
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Testing presubmit because is failing in llvm-integration
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Unable to run Object Detection API i.e. AttributeError: module 'keras._tf_keras.keras.layers' has no attribute 'experimental'
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"@girraj96,\r\nThe temporary workaround for the issue is Could you please try to limit the versions of **tf-models-official >=2.5.1, <2.16.0** and comment out keras in the **research/object_detection/packages/tf2/setup.py**. By default tensorflow v2.16.1 import keras3.0.\r\n\r\nhttps://github.com/tensorflow/tensorflow/issues/56889\r\n\r\nFor further queries please try to connect with [tensorflow/models](https://github.com/tensorflow/models) repo for the quick resolution.\r\n\r\nThank you!",
"@tilakrayal Thanks it's working 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/65463\">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/65463\">No</a>\n"
] | 2024-04-11T12:57:23 | 2024-04-17T05:51:31 | 2024-04-17T05:50:49 |
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
Apple M2
### 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?
Command : python object_detection/builders/model_builder_tf2_test.py
Output :
I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-11 12:54:15.652670: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Traceback (most recent call last):
File "/content/models/research/object_detection/builders/model_builder_tf2_test.py", line 24, in <module>
from object_detection.builders import model_builder
File "/usr/local/lib/python3.10/dist-packages/object_detection/builders/model_builder.py", line 26, in <module>
from object_detection.builders import hyperparams_builder
File "/usr/local/lib/python3.10/dist-packages/object_detection/builders/hyperparams_builder.py", line 27, in <module>
from object_detection.core import freezable_sync_batch_norm
File "/usr/local/lib/python3.10/dist-packages/object_detection/core/freezable_sync_batch_norm.py", line 20, in <module>
class FreezableSyncBatchNorm(tf.keras.layers.experimental.SyncBatchNormalization
AttributeError: module 'keras._tf_keras.keras.layers' has no attribute 'experimental'
### Standalone code to reproduce the issue
```shell
Step 1 - Open Google collab's new note
Step 2 - command
!git clone https://github.com/tensorflow/models.git
Step 3 - command
%cd models/research
# Compile protos.
!protoc object_detection/protos/*.proto --python_out=.
# Install TensorFlow Object Detection API.
!cp object_detection/packages/tf2/setup.py .
!python -m pip install .
Step 4 - command
!python object_detection/builders/model_builder_tf2_test.py
```
### Relevant log output
_No response_
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When no weight cache is provided to XNNPack, create one to share packed weights between operations.
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When no weight cache is provided to XNNPack, create one to share packed weights between operations.
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Switch on the `BatchMatMul` delegate.
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Switch on the `BatchMatMul` delegate.
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Integrate LLVM at llvm/llvm-project@1fda1776e32b
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Integrate LLVM at llvm/llvm-project@1fda1776e32b
Updates LLVM usage to match
[1fda1776e32b](https://github.com/llvm/llvm-project/commit/1fda1776e32b)
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PR #10525: Fix tan inaccuracies on complex inputs with large and small absolute values
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PR #10525: Fix tan inaccuracies on complex inputs with large and small absolute values
Imported from GitHub PR https://github.com/openxla/xla/pull/10525
As in the title.
Tan evaluation is verified against mpmath tan function. The corresponding unit tests are included in xla/tests/complex_unary_op_test.cc
When https://github.com/google/jax/pull/20688 lands, the follow-up PR will enable Tan tests at complex infinities.
Copybara import of the project:
--
005ff68200fe6a343c10b8e7129448dfc0d9aa57 by Pearu Peterson <[email protected]>:
Fix tan inaccuracies on complex inputs with large and small absolute values.
--
ad3d31f69fb62a9d024cf5f9011ce30fdfd4dfa4 by Pearu Peterson <[email protected]>:
Make generate_complex_unary_op_samples.py output clang-format stable
--
d88445489f525186800fb1c2a095bfca6b1de7bf by Pearu Peterson <[email protected]>:
Add unittests for Tan with complex inputs
Merging this change closes #10525
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10525 from pearu:pearu/tan d88445489f525186800fb1c2a095bfca6b1de7bf
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PR #11394: [XLA GPU] Add command buffer BarrierCmd
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PR #11394: [XLA GPU] Add command buffer BarrierCmd
Imported from GitHub PR https://github.com/openxla/xla/pull/11394
This PR adds barrier command into command buffer. The description for the command:
//===----------------------------------------------------------------------===//
// BarrierCmd
// BarrierCmd insert a barrier from the execution scope created from the
// 'from_stream_id' to the execution scope created from the
// 'execution_stream_id', e.g. Async operator lowered to command buffer requires
// a barrier from the launching stream to the async operator's execution stream.
//===----------------------------------------------------------------------===//
This PR is a split of PR: https://github.com/openxla/xla/pull/11348
Copybara import of the project:
--
56758cb97f5cee224258d722eb42f2f1faf4cd05 by Shawn Wang <[email protected]>:
Add command buffer BarrierCmd
--
c740aea329e9ab5f92442b93d64c88c7f8e8bed7 by Shawn Wang <[email protected]>:
Change NCCL stream id type
--
61cb7c622a0193b404e458e072f282ad0d7a1859 by Shawn Wang <[email protected]>:
fix test failures
Merging this change closes #11394
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11394 from shawnwang18:shawnw/add_command_buffer_barrier_cmd 61cb7c622a0193b404e458e072f282ad0d7a1859
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[HloValueSemanticsAnalysis] Fix handling for async start and done.
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Use PTX version corresponding to CUDA version.
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Use PTX version corresponding to CUDA version.
For CUDA 11/12, the PT major version is offset by 4 (i.e. ptx 7 for cuda 11, ptx 8 for cuda 12), and the minor version is the same.
Use the maximum currently known PTX version for unrecognized CUDA versions (13 and up).
https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#release-notes
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Automated Code Change
Reverts de029c02b59c50be15eee59415d1d8f2eb378acd
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Add support for mutable zero-copy buffers
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[xla] Add minimal alignment requirement for Literals stored inline
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[xla] Add minimal alignment requirement for Literals stored inline
Make sure that literals stored on heap and on stack have the same memory alignment.
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PR_kwDOArmXAs5sUYfm
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Refactor out an util function that load a registered named variable tensor into ifrt array.
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NONE
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PR_kwDOArmXAs5sUXxo
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Adds KVCache op to GenAI custom op resolver.
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NONE
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FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/65476 from Intel-tensorflow:remove_deprecated_ops de617657f3db268fa1db5f85786ada770d27e793
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PR_kwDOArmXAs5sUS07
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[XLA] Allow for partially constrained and automatic layouts within tuple parameters and results.
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opal_wrapper-wrapper-data.txt not found
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[
"@thangckt,\r\nCould you please let us know the specific reason for using tensorflow v2.13 which is pretty old. Could you please try installing tensorflow latest version 2.15 or 2.16.1 and let me know if you are facing the same issue and Could you please confirm the sequence of steps. Tf2.13v supports protobuf >=3.20 versions as per [source](https://github.com/tensorflow/tensorflow/blob/r2.13/tensorflow/tools/pip_package/setup.py#L96) and make sure you are trying the same. Thank you!\r\n",
"I have an old Linux, and the maximum bazel version that can run is 5.2.0 (from conda). With this limitation, i can just try up to tf 2.13.",
"@thangckt,\r\nThe file which you mentioned that was missing **opal_wrapper-wrapper-data.txt**, looks like this is not related to the Tensorflow doc files. I suspect that is related to openmpi. If possible could you try in that repo for the response. https://github.com/open-mpi/ompi/issues\r\n\r\nThank you!",
"I gave up TF since can not solve problem\r\nThank you for your help",
"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/65438\">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/65438\">No</a>\n"
] | 2024-04-11T04:38:09 | 2024-06-03T14:59:24 | 2024-06-03T14:58:36 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.13
### Custom code
No
### OS platform and distribution
Centos 6.9
### Mobile device
_No response_
### Python version
3.11
### Bazel version
5.2.0
### GCC/compiler version
GCC 9.5.0 + OpenMPI 4.1.7
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
compiling error as below:
```
ERROR: /home1/p001cao/app/miniconda3/envs/py11deepmd/share/bazel/c22f5c495debb6ef458b8cca313d2d01/external/com_google_protobuf/BUILD.bazel:75:11: Compiling src/google/protobuf/compiler/importer.cc failed: (Exit 243): opal_wrapper failed: error executing command
(cd /home1/p001cao/app/miniconda3/envs/py11deepmd/share/bazel/c22f5c495debb6ef458b8cca313d2d01/execroot/org_tensorflow && \
exec env - \
LD_LIBRARY_PATH=/home1/p001cao/app/miniconda3/envs/py11deepmd/lib:/home1/p001cao/app/mpi/openmpi4.1.7-clang17-IB/lib:/home2/app/compiler/gcc/9.5.0/lib64:/home1/p001cao/app/mpi/openmpi4.1.7-clang17-IB/lib:/home1/p001cao/app/compiler/llvm-17/libexec:/home1/p001cao/app/compiler/llvm-17/lib:/home2/app/compiler/gcc/9.5.0/libexec/gcc/x86_64-pc-linux-gnu/9.5.0:/home2/app/compiler/gcc/9.5.0/lib64:/home2/app/compiler/gcc/9.5.0/lib/gcc/x86_64-pc-linux-gnu/9.5.0:/home1/p001cao/app/tools/binutils-2.41/lib:/home1/p001cao/app/miniconda3/lib \
PATH=/home2/Modules//bin:/home1/p001cao/app/mpi/openmpi4.1.7-clang17-IB/bin:/home1/p001cao/app/mpi/openmpi4.1.7-clang17-IB/bin:/home1/p001cao/app/compiler/llvm-17/bin:/home2/app/compiler/gcc/9.5.0/bin:/home1/p001cao/app/tools/binutils-2.41/bin:/home1/p001cao/app/miniconda3/envs/py11deepmd/bin:/home1/p001cao/app/miniconda3/condabin:/home1/p001cao/app/miniconda3/bin:/home1/app/sge/bin/linux-x64:/usr/lib64/qt-3.3/bin:/home2/Modules/bin:/usr/local/bin:/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/sbin:/opt/ibutils/bin:/home1/p001cao/bin \
PWD=/proc/self/cwd \
/home1/p001cao/app/mpi/openmpi4.1.7-clang17-IB/bin/opal_wrapper -U_FORTIFY_SOURCE -fstack-protector -Wall -Wthread-safety -Wself-assign -Wunused-but-set-parameter -Wno-free-nonheap-object -fcolor-diagnostics -fno-omit-frame-pointer -g0 -O2 '-D_FORTIFY_SOURCE=1' -DNDEBUG -ffunction-sections -fdata-sections '-std=c++0x' -MD -MF bazel-out/k8-opt-exec-50AE0418/bin/external/com_google_protobuf/_objs/protobuf/importer.d '-frandom-seed=bazel-out/k8-opt-exec-50AE0418/bin/external/com_google_protobuf/_objs/protobuf/importer.o' -iquote external/com_google_protobuf -iquote bazel-out/k8-opt-exec-50AE0418/bin/external/com_google_protobuf -iquote external/zlib -iquote bazel-out/k8-opt-exec-50AE0418/bin/external/zlib -isystem external/com_google_protobuf/src -isystem bazel-out/k8-opt-exec-50AE0418/bin/external/com_google_protobuf/src -isystem external/zlib -isystem bazel-out/k8-opt-exec-50AE0418/bin/external/zlib -g0 -w -g0 '-std=c++17' -DHAVE_ZLIB -Woverloaded-virtual -Wno-sign-compare -fno-canonical-system-headers -Wno-builtin-macro-redefined '-D__DATE__="redacted"' '-D__TIMESTAMP__="redacted"' '-D__TIME__="redacted"' -c external/com_google_protobuf/src/google/protobuf/compiler/importer.cc -o bazel-out/k8-opt-exec-50AE0418/bin/external/com_google_protobuf/_objs/protobuf/importer.o)
# Configuration: 25b75394c5b3297b8c64984caa144939808df936b3058e3be498ec8a5b4cebea
# Execution platform: @local_execution_config_platform//:platform
Cannot open configuration file /home1/p001cao/app/mpi/openmpi4.1.7-clang17-IB/share/openmpi/opal_wrapper-wrapper-data.txt
Error parsing data file opal_wrapper: Not found
```
### Standalone code to reproduce the issue
```shell
export TF_PYTHON_VERSION=3.11
export PYTHON_VERSION=python3.11
PYTHON_BIN_PATH=$(which $PYTHON_VERSION) \
PYTHON_LIB_PATH=$($PYTHON_VERSION -c "import pip; print(pip.__path__[0].rstrip('/pip'))") \
TF_NEED_MPI=1 \
TF_ENABLE_XLA=1 \
TF_NEED_CUDA=0 \
TF_NEED_ROCM=0 \
TF_CUDA_CLANG=0 \
TF_DOWNLOAD_CLANG=0 \
TF_NEED_TENSORRT=0 \
TF_SET_ANDROID_WORKSPACE=0 \
TF_NEED_OPENCL=0 \
TF_NEED_OPENCL_SYCL=0 \
TF_NEED_AWS=0 \
TF_NEED_GCP=0 \
TF_NEED_S3=0 \
TF_NEED_NCCL=0 \
CC_OPT_FLAGS="-march=native -Wno-sign-compare" \
./configure
```sh
bazel build --jobs=16 -c opt --config=noaws --config=nogcp --config=nonccl --verbose_failures //tensorflow/tools/pip_package:build_pip_package
```
```
### Relevant log output
_No response_
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I_kwDOArmXAs6FU9BN
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model.summary() broken for custom models subclassed from tf.keras.Model
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[
"Hi @eschmitt88 ,\r\n\r\nI have replicated the reported problem and attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/4978624270e8883613a278b5de451af7/65436.ipynb) here.\r\n\r\nThis should be addressed in keras repo. Could you please file an issue there ?",
"Hi @SuryanarayanaY,\r\n\r\nThank you for looking into it! I see it is a Keras issue. I've filed an [issue over there](https://github.com/keras-team/keras/issues/19535). Feel free to close with appropriate tag (\"complete\" or \"not planned\") or please advise which (I'm not sure which one is right).\r\n\r\nCheers",
"Hi @eschmitt88 ,\r\n\r\nWill wait until the response of Dev team from Keras. Thanks"
] | 2024-04-11T03:57:52 | 2024-04-29T05:46:41 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
**Custom model classes built from tf.keras.Model do not think they get built properly, and the model.summary() is missing information.** However, the model will run just fine. In tf version 2.15.0, we see it working properly, for example (from "code to reproduce," taken exactly from [tf documentation ](https://www.tensorflow.org/api_docs/python/tf/keras/Model#by_subclassing_the_model_class)), the output is as expected:
```
Model: "my_model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) multiple 352
dense_1 (Dense) multiple 165
=================================================================
Total params: 517 (2.02 KB)
Trainable params: 517 (2.02 KB)
Non-trainable params: 0 (0.00 Byte)
```
In tf 2.16.1 ([colab](https://colab.research.google.com/drive/1pmsOTPtTQbKXdWVYBXapCd8H3WJzmvh5?usp=sharing)) and tf-nightly ([colab](https://colab.research.google.com/drive/1SQgtc-Y08hGBZCWgZEju7L-20OlGOEQz?usp=sharing)), we instead see this:
```
/usr/local/lib/python3.10/dist-packages/keras/src/layers/layer.py:360: UserWarning: `build()` was called
on layer 'my_model', however the layer does not have a `build()` method implemented and it looks like
it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which
may cause failures down the line. Make sure to implement a proper `build()` method.
warnings.warn(
Model: "my_model"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type) ┃ Output Shape ┃ Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense (Dense) │ ? │ 0 (unbuilt) │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_1 (Dense) │ ? │ 0 (unbuilt) │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 0 (0.00 B)
Trainable params: 0 (0.00 B)
Non-trainable params: 0 (0.00 B)
```
While it doesn't break model training and inference, I still think it's an important issue, because I often rely on the model.summary() to check my work as I develop. Thank you to whoever helps out.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
class MyModel(tf.keras.Model):
def __init__(self):
super().__init__()
self.dense1 = tf.keras.layers.Dense(32, activation="relu")
self.dense2 = tf.keras.layers.Dense(5, activation="softmax")
def call(self, inputs):
x = self.dense1(inputs)
return self.dense2(x)
model = MyModel()
model.build(input_shape=(None, 10))
model.summary()
```
### Relevant log output
```shell
/usr/local/lib/python3.10/dist-packages/keras/src/layers/layer.py:360: UserWarning: `build()` was called on layer 'my_model', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method.
warnings.warn(
Model: "my_model"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type) ┃ Output Shape ┃ Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense (Dense) │ ? │ 0 (unbuilt) │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_1 (Dense) │ ? │ 0 (unbuilt) │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 0 (0.00 B)
Trainable params: 0 (0.00 B)
Non-trainable params: 0 (0.00 B)
```
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PR_kwDOArmXAs5sUBk8
| 65,435 |
Add new sets of XlaSparseDenseMatmulWithStaticBufferSizeOps that takes a static buffer size as an attribute.
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Add new sets of XlaSparseDenseMatmulWithStaticBufferSizeOps that takes a static buffer size as an attribute.
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PR_kwDOArmXAs5sThUZ
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Reintroduces the "tiny term trick."
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Reintroduces the "tiny term trick."
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[tsl] Rewrite `TF_ASSERT_OK` and `TF_EXPECT_OK` in terms of `IsOk()` matcher
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[tsl] Rewrite `TF_ASSERT_OK` and `TF_EXPECT_OK` in terms of `IsOk()` matcher
* This will allow using these macros on `absl::StatusOr` objects
* Since `absl::StatusOr` lacks an `operator==` with `absl::Status`, it couldn't be used here before
* Now it uses `tsl::testing::IsOk()` which works for both `absl::Status` and `absl::StatusOr`
* This should also likely result in nicer error messages in case of failure
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Refactor LayoutCanonicalizer.
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Refactor LayoutCanonicalizer.
Moved Layout altering functions into the LayoutCanonicalizer class.
Define a map that holds canonicalized instructions.
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Handle bitcast in LayoutCanonicalization pass.
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Handle convolution in LayoutCanonicalization pass.
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Handle convolution in LayoutCanonicalization pass.
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PR_kwDOArmXAs5sS5-R
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Integrate LLVM at llvm/llvm-project@1fda1776e32b
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Integrate LLVM at llvm/llvm-project@1fda1776e32b
Updates LLVM usage to match
[1fda1776e32b](https://github.com/llvm/llvm-project/commit/1fda1776e32b)
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Integrate LLVM at llvm/llvm-project@1fda1776e32b
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Integrate LLVM at llvm/llvm-project@1fda1776e32b
Updates LLVM usage to match
[1fda1776e32b](https://github.com/llvm/llvm-project/commit/1fda1776e32b)
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Add a new op ConvertToListOfSparseCoreCooTensors for OSS sparse core embedding API.
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Add a new op ConvertToListOfSparseCoreCooTensors for OSS sparse core embedding API.
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[pjrt] Add JoinFutures function to join multiple stateless futures
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[pjrt] Add JoinFutures function to join multiple stateless futures
Reverts de029c02b59c50be15eee59415d1d8f2eb378acd
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PR_kwDOArmXAs5sSsOs
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Remove unnecessary DeviceMemoryBase::Reset call in StreamExecutor::Deallocate.
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Remove unnecessary DeviceMemoryBase::Reset call in StreamExecutor::Deallocate.
Only one test was counting on it, and this makes it easier to collapse StreamExecutor with StreamExecutorInterface.
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PR_kwDOArmXAs5sSsJk
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Remove mock_nccl collateral, as it no longer compiles, and is unused.
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PR_kwDOArmXAs5sSYiM
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[IFRT] Add type checking for `Deserialize`
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[IFRT] Add type checking for `Deserialize`
`Deserialize` now takes an expected type for a deserialized object. It also
checks if a supplied `DeserializeOptions` is expected for the type of the
deserialized object; `Serializable` class can define a `DeserializeOptions`
type within the class to express the expected `DeserializeOptions` type.
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Add author of internal change as assignee to github PR
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Add author of internal change as assignee to github PR
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PR_kwDOArmXAs5sSP7Y
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Support of C++20 for OSS TF for Linux x86_64 Ubuntu 22.04
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Support of C++20 for OSS TF for Linux x86_64 Ubuntu 22.04
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PR_kwDOArmXAs5sSK8h
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Skip adding reshapes for resharding tensors where their dimension sizes are not divisible by the mesh dimensions.
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Skip adding reshapes for resharding tensors where their dimension sizes are not divisible by the mesh dimensions.
Also get rid of a duplicate debug print statement
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I_kwDOArmXAs6FSosX
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ValueError: Only instances of `keras.Layer` can be added to a Sequential model. " Keras version - 3.5.0"
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[
"After searching i found out that potential cause of the issue in tensorflow_hub/keras_layer.py\r\n\r\nthis is issue is occuring only in keras version > 3.X.X\r\n\r\n\r\nsolution :\r\n\r\n# Use Keras 2.\r\nversion_fn = getattr(tf.keras, \"version\", None)\r\nif version_fn and version_fn().startswith(\"3.\"):\r\n import tf_keras as keras\r\nelse:\r\n keras = tf.keras\r\n\r\n👉🏻 https://github.com/tensorflow/tensorflow/issues/63849#issuecomment-2002501172",
"Try to use Keras 2. version 👍🏻\r\n\r\n\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nprint(tf.__version__)\r\n2.16.1\r\n\r\nimport keras\r\nprint(keras.__version__)\r\n3.2.0\r\n\r\nimport tf_keras\r\nprint(tf_keras.__version__)\r\n2.16.0\r\n",
"@Pavun-KumarCH Thank you for your responses here. \r\nYou can continue using Keras 2 or later with a workaround. That is to wrap the TensorFlow Hub layer returned by tf.keras.layers.experimental.load in a keras.Layer object before adding it to your model.\r\n\r\nPlease let us know if we can move this issue to closed status as it is resolved from your side?\r\n\r\nThank 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/65419\">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/65419\">No</a>\n"
] | 2024-04-10T19:13:06 | 2024-04-17T16:34:27 | 2024-04-17T16:34:25 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
TensorFlow tf version: 2.16.1
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
3.11.8
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Adding TensorFlow Hub KerasLayer to Sequential Model Raises ValueError
I'm not able to run this model with "keras.Layer` to a Sequential model"
as it works in well in google colab.
### Standalone code to reproduce the issue
```shell
CLASSIFIER_URL ="https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/2"
IMAGE_RES = 224
model = tf.keras.models.Sequential([
hub.KerasLayer(CLASSIFIER_URL, input_shape=(IMAGE_RES, IMAGE_RES, 3))
])
```
### Relevant log output
```shell
ValueError: Only instances of `keras.Layer` can be added to a Sequential model. Received: <tensorflow_hub.keras_layer.KerasLayer object at 0x28ac6d4d0> (of type <class 'tensorflow_hub.keras_layer.KerasLayer'>)
```
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PR_kwDOArmXAs5sR5zh
| 65,418 |
#tf-data Index flat map dataset does not require random access.
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#tf-data Index flat map dataset does not require random access.
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PR_kwDOArmXAs5sRx3N
| 65,417 |
Reintroduces the "tiny term trick."
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Reintroduces the "tiny term trick."
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PR_kwDOArmXAs5sRt-8
| 65,416 |
Add DISABLED_ON_TPU for ConstantsHloTest.BitcastOfConstant
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Add DISABLED_ON_TPU for ConstantsHloTest.BitcastOfConstant
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PR_kwDOArmXAs5sRmy9
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Fix RocmExecutor copy/paste errors in refactoring FFT and BLAS refactoring CLs.
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Fix RocmExecutor copy/paste errors in refactoring FFT and BLAS refactoring CLs.
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PR_kwDOArmXAs5sRltH
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remove non-standard strnlen.
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remove non-standard strnlen.
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KV Cache: optimize data motion.
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[xla:gpu] Check fusion root opcode before declaring it multi-output fusion
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Updates the lazy memory implementation to automatically include low-cardinality memory constraints.
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Segmentation fault when using tflite_model_maker searcher.TextDataLoader.create(EmbeddingModel, l2_normalize=True)
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[
"Hi **@ankur2210** ,\r\n\r\nSorry for the delay, I reproduced the code shared but facing different error .Could you please share the colab gist with all the dependencies to analyze more of it. And could you please execute your code using Latest Version (2.16.1) and let us know if the issue still persists?\r\n\r\nThank you!",
"> Hi **@ankur2210** ,\r\n> \r\n> Sorry for the delay, I reproduced the code shared but facing different error .Could you please share the colab gist with all the dependencies to analyze more of it. And could you please execute your code using Latest Version (2.16.1) and let us know if the issue still persists?\r\n> \r\n> Thank you!\r\n\r\nHi @Venkat6871 \r\nThanks for the reply.\r\n\r\nI am using 'tflite_model_maker' library to create the searcher model.\r\nfrom tflite_model_maker import searcher\r\n\r\non installing 'tflite_model_maker' using `pip install tflite-model-maker`\r\n\r\nIt install tensorflow 2.8.4 and uninstalls latest tensorflow.\r\n\r\n\r\n",
"@Venkat6871 Did you check the Segmentation fault issue ?",
"Hi @ankur2210 ,\r\n\r\nI will replicate the issue and will get back to you. ",
"Hi @ankur2210 ,\r\n\r\nI am having issues installing tensorflow_text using pip . Did you face any such issue ? Either way i will get back to you once i am able to reproduce the issue ."
] | 2024-04-10T13:27:25 | 2024-06-11T14:34:53 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.8.4
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04
### Mobile device
Ubuntu 22.04
### Python version
3.9.19
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I am trying to make TextSearcher object using tflite_model_maker python library.
`data_loader = searcher.TextDataLoader.create(EmbeddingModel, l2_normalize=True)`
At this step, getting segmentation fault error.
Embedding model is tflite model converted from https://www.kaggle.com/models/google/universal-sentence-encoder/tensorFlow2/multilingual
using below code -
`import tensorflow as tf
# These imports are required to load operators' definition.
import tensorflow_text as tf_text
import sentencepiece as spm
path = '/path/to/embedding/model'
converter = tf.lite.TFLiteConverter.from_saved_model(path)
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS
]
model_data = converter.convert()
with open('model.tflite', 'wb') as f:
f.write(model_data)`
On analysing tflite model using -
`import tensorflow as tf
tf.lite.experimental.Analyzer.analyze('model.tflite')`
getting following output -
---------------------------------------------------------------
Your TFLite model has ‘1’ signature_def(s).
Signature#0 key: 'serving_default'
- Subgraph: Subgraph#0
- Inputs:
'inputs' : T#0
- Outputs:
'outputs' : T#399
---------------------------------------------------------------
Model size: 278328956 bytes
Non-data buffer size: 2616702 bytes (00.94 %)
Total data buffer size: 275712254 bytes (99.06 %)
(Zero value buffers): 3085 bytes (00.00 %)
### Standalone code to reproduce the issue
```shell
Mentioned above
```
### Relevant log output
```shell
Backtrace of segmentation fault-
Thread 1 "python" received signal SIGSEGV, Segmentation fault.
0x00007fff518df70d in tflite::Subgraph::ReplaceNodeSubsetsWithDelegateKernels(TfLiteRegistration, TfLiteIntArray const*, TfLiteDelegate*) () from python3.9/site-packages/tensorflow_lite_support/python/task/text/pybinds/_pywrap_text_embedder.so
(gdb) stacktrace
Undefined command: "stacktrace". Try "help".
(gdb) backtrace
#0 0x00007fff518df70d in tflite::Subgraph::ReplaceNodeSubsetsWithDelegateKernels(TfLiteRegistration, TfLiteIntArray const*, TfLiteDelegate*) ()
from python3.9/site-packages/tensorflow_lite_support/python/task/text/pybinds/_pywrap_text_embedder.so
#1 0x00007fff64064dc1 in tflite::(anonymous namespace)::DelegatePrepare(TfLiteContext*, TfLiteDelegate*) () from python3.9/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so
#2 0x00007fff518e05ee in tflite::Subgraph::ModifyGraphWithDelegateImpl(TfLiteDelegate*) ()
from python3.9/site-packages/tensorflow_lite_support/python/task/text/pybinds/_pywrap_text_embedder.so
#3 0x00007fff518e097f in tflite::Subgraph::ModifyGraphWithDelegate(TfLiteDelegate*) () from python3.9/site-packages/tensorflow_lite_support/python/task/text/pybinds/_pywrap_text_embedder.so
#4 0x00007fff518d17b0 in tflite::impl::Interpreter::ModifyGraphWithDelegateImpl(TfLiteDelegate*) ()
from python3.9/site-packages/tensorflow_lite_support/python/task/text/pybinds/_pywrap_text_embedder.so
#5 0x00007fff518d66f1 in tflite::impl::InterpreterBuilder::ApplyDelegates(tflite::impl::Interpreter*) ()
from python3.9/site-packages/tensorflow_lite_support/python/task/text/pybinds/_pywrap_text_embedder.so
#6 0x00007fff518d81a9 in tflite::impl::InterpreterBuilder::operator()(std::unique_ptr<tflite::impl::Interpreter, std::default_delete<tflite::impl::Interpreter> >*) ()
from python3.9/site-packages/tensorflow_lite_support/python/task/text/pybinds/_pywrap_text_embedder.so
#7 0x00007fff514ee774 in tflite::task::core::TfLiteEngine::InitInterpreter(tflite::proto::ComputeSettings const&)::{lambda(tflite::support::InterpreterCreationResources const&, std::unique_ptr<tflite::impl::Interpreter, std::default_delete<tflite::impl::Interpreter> >*)#1}::operator()(tflite::support::InterpreterCreationResources const&, std::unique_ptr<tflite::impl::Interpreter, std::default_delete<tflite::impl::Interpreter> >*) const [clone .isra.0] ()
from python3.9/site-packages/tensorflow_lite_support/python/task/text/pybinds/_pywrap_text_embedder.so
#8 0x00007fff514ee8e5 in std::_Function_handler<absl::lts_20210324::Status (tflite::support::InterpreterCreationResources const&, std::unique_ptr<tflite::impl::Interpreter, std::default_delete<tflite::impl::Interpreter> >*), tflite::task::core::TfLiteEngine::InitInterpreter(tflite::proto::ComputeSettings const&)::{lambda(tflite::support::InterpreterCreationResources const&, std::unique_ptr<tflite::impl::Interpreter, std::default_delete<tflite::impl::Interpreter>
>*)#1}>::_M_invoke(std::_Any_data const&, tflite::support::InterpreterCreationResources const&, std::unique_ptr<tflite::impl::Interpreter, std::default_delete<tflite::impl::Interpreter> >*&&) ()
from python3.9/site-packages/tensorflow_lite_support/python/task/text/pybinds/_pywrap_text_embedder.so
#9 0x00007fff514fbda2 in tflite::support::TfLiteInterpreterWrapper::InitializeWithFallbackAndResize(std::function<absl::lts_20210324::Status (tflite::impl::Interpreter*)>) ()
from python3.9/site-packages/tensorflow_lite_support/python/task/text/pybinds/_pywrap_text_embedder.so
#10 0x00007fff514fd1c1 in tflite::support::TfLiteInterpreterWrapper::InitializeWithFallback(std::function<absl::lts_20210324::Status (tflite::support::InterpreterCreationResources const&, std::unique_ptr<tflite::impl::Interpreter, std::default_delete<tflite::impl::Interpreter> >*)>, tflite::proto::ComputeSettings const&) ()
from python3.9/site-packages/tensorflow_lite_support/python/task/text/pybinds/_pywrap_text_embedder.so
#11 0x00007fff514eef21 in tflite::task::core::TfLiteEngine::InitInterpreter(tflite::proto::ComputeSettings const&) ()
from python3.9/site-packages/tensorflow_lite_support/python/task/text/pybinds/_pywrap_text_embedder.so
#12 0x00007fff5148350e in absl::lts_20210324::StatusOr<std::unique_ptr<tflite::task::text::TextEmbedder, std::default_delete<tflite::task::text::TextEmbedder> > > tflite::task::core::TaskAPIFactory::CreateFromBaseOptions<tflite::task::text::TextEmbedder, (void*)0>(tflite::task::core::BaseOptions const*, std::unique_ptr<tflite::OpResolver, std::default_delete<tflite::OpResolver> >) ()
from python3.9/site-packages/tensorflow_lite_support/python/task/text/pybinds/_pywrap_text_embedder.so
#13 0x00007fff5148488a in tflite::task::text::TextEmbedder::CreateFromOptions(tflite::task::text::TextEmbedderOptions const&, std::unique_ptr<tflite::OpResolver, std::default_delete<tflite::OpResolver> >) ()
from python3.9/site-packages/tensorflow_lite_support/python/task/text/pybinds/_pywrap_text_embedder.so
#14 0x00007fff51480c07 in pybind11::cpp_function::initialize<tflite::task::text::pybind11_init__pywrap_text_embedder(pybind11::module_&)::{lambda(tflite::python::task::core::BaseOptions const&, tflite::task::processor::EmbeddingOptions const&)#1}, std::unique_ptr<tflite::task::text::TextEmbedder, std::default_delete<tflite::task::text::TextEmbedder> >, tflite::python::task::core::BaseOptions const&, tflite::task::processor::EmbeddingOptions const&, pybind11::name, pybind11::scope, pybind11::sibling>(tflite::task::text::pybind11_init__pywrap_text_embedder(pybind11::module_&)::{lambda(tflite::python::task::core::BaseOptions const&, tflite::task::processor::EmbeddingOptions const&)#1}&&, std::unique_ptr<tflite::task::text::TextEmbedder, std::default_delete<tflite::task::text::TextEmbedder> > (*)(tflite::python::task::core::BaseOptions const&, tflite::task::processor::EmbeddingOptions const&), pybind11::name const&, pybind11::scope const&, pybind11::sibling const&)::{lambda(pybind11::detail::function_call&)#3}::_FUN(pybind11::detail::function_call&) ()
from python3.9/site-packages/tensorflow_lite_support/python/task/text/pybinds/_pywrap_text_embedder.so
#15 0x00007fff5147b016 in pybind11::cpp_function::dispatcher(_object*, _object*, _object*) ()
from python3.9/site-packages/tensorflow_lite_support/python/task/text/pybinds/_pywrap_text_embedder.so
#16 0x00005555556a26b6 in cfunction_call (func=0x7ffff348aa40, args=<optimized out>, kwargs=<optimized out>) at /usr/local/src/conda/python-3.9.19/Objects/methodobject.c:543
#17 0x000055555568b7ac in _PyObject_MakeTpCall (tstate=0x5555559378f0, callable=0x7ffff348aa40, args=<optimized out>, nargs=<optimized out>, keywords=0x0) at /usr/local/src/conda/python-3.9.19/Objects/call.c:191
#18 0x0000555555687f10 in _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=<optimized out>, callable=0x7ffff348aa40, tstate=<optimized out>) at /usr/local/src/conda/python-3.9.19/Include/cpython/abstract.h:116
#19 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=0x7fffbc66c1d0, callable=0x7ffff348aa40, tstate=<optimized out>) at /usr/local/src/conda/python-3.9.19/Include/cpython/abstract.h:103
#20 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fffbc66c1d0, callable=0x7ffff348aa40) at /usr/local/src/conda/python-3.9.19/Include/cpython/abstract.h:127
#21 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x5555559378f0) at /usr/local/src/conda/python-3.9.19/Python/ceval.c:5077
#22 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=0x7fffbc66c040, throwflag=<optimized out>) at /usr/local/src/conda/python-3.9.19/Python/ceval.c:3489
#23 0x0000555555693802 in _PyEval_EvalFrame (throwflag=0, f=0x7fffbc66c040, tstate=0x5555559378f0) at /usr/local/src/conda/python-3.9.19/Include/internal/pycore_ceval.h:40
--Type <RET> for more, q to quit, c to continue without paging--
#24 function_code_fastcall (tstate=0x5555559378f0, co=0x7ffff348e240, args=<optimized out>, nargs=2, globals=0x7ffff3486b40) at /usr/local/src/conda/python-3.9.19/Objects/call.c:330
#25 0x00005555556a094e in _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, stack=0x55555b824af8, func=0x7ffff3484dc0) at /usr/local/src/conda/python-3.9.19/Objects/call.c:367
#26 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=0x55555b824af8, callable=0x7ffff3484dc0, tstate=0x5555559378f0) at /usr/local/src/conda/python-3.9.19/Include/cpython/abstract.h:118
#27 method_vectorcall (method=<optimized out>, args=0x55555b824b00, nargsf=<optimized out>, kwnames=0x0) at /usr/local/src/conda/python-3.9.19/Objects/classobject.c:53
#28 0x0000555555687a2e in _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=0x55555b824b00, callable=0x7fffbc318c40, tstate=0x5555559378f0) at /usr/local/src/conda/python-3.9.19/Include/cpython/abstract.h:118
#29 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x55555b824b00, callable=0x7fffbc318c40) at /usr/local/src/conda/python-3.9.19/Include/cpython/abstract.h:127
#30 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=0x5555559378f0) at /usr/local/src/conda/python-3.9.19/Python/ceval.c:5077
#31 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=0x55555b824950, throwflag=<optimized out>) at /usr/local/src/conda/python-3.9.19/Python/ceval.c:3489
#32 0x0000555555682184 in _PyEval_EvalFrame (throwflag=0, f=0x55555b824950, tstate=0x5555559378f0) at /usr/local/src/conda/python-3.9.19/Include/internal/pycore_ceval.h:40
#33 _PyEval_EvalCode (tstate=0x5555559378f0, _co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x7ffff7e40508, kwargs=0x55555591d428, kwcount=1, kwstep=1,
defs=0x7fffbc39cdd8, defcount=<optimized out>, kwdefs=0x0, closure=0x0, name=0x7ffff748c3b0, qualname=0x7fffbc3b0030) at /usr/local/src/conda/python-3.9.19/Python/ceval.c:4329
#34 0x00005555556a09fb in _PyFunction_Vectorcall (kwnames=<optimized out>, nargsf=<optimized out>, stack=0x55555591d418, func=0x7fffbc3a2e50) at /usr/local/src/conda/python-3.9.19/Objects/call.c:396
#35 _PyObject_VectorcallTstate (kwnames=<optimized out>, nargsf=<optimized out>, args=0x55555591d418, callable=0x7fffbc3a2e50, tstate=0x5555559378f0) at /usr/local/src/conda/python-3.9.19/Include/cpython/abstract.h:118
#36 method_vectorcall (method=<optimized out>, args=0x55555591d420, nargsf=<optimized out>, kwnames=<optimized out>) at /usr/local/src/conda/python-3.9.19/Objects/classobject.c:53
#37 0x0000555555684162 in _PyObject_VectorcallTstate (kwnames=0x7ffff7e404f0, nargsf=<optimized out>, args=<optimized out>, callable=0x7fffbc318800, tstate=0x5555559378f0)
at /usr/local/src/conda/python-3.9.19/Include/cpython/abstract.h:118
#38 PyObject_Vectorcall (kwnames=0x7ffff7e404f0, nargsf=<optimized out>, args=<optimized out>, callable=0x7fffbc318800) at /usr/local/src/conda/python-3.9.19/Include/cpython/abstract.h:127
#39 call_function (kwnames=0x7ffff7e404f0, oparg=<optimized out>, pp_stack=<synthetic pointer>, tstate=<optimized out>) at /usr/local/src/conda/python-3.9.19/Python/ceval.c:5077
#40 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=0x55555591d2b0, throwflag=<optimized out>) at /usr/local/src/conda/python-3.9.19/Python/ceval.c:3537
#41 0x0000555555682184 in _PyEval_EvalFrame (throwflag=0, f=0x55555591d2b0, tstate=0x5555559378f0) at /usr/local/src/conda/python-3.9.19/Include/internal/pycore_ceval.h:40
#42 _PyEval_EvalCode (tstate=0x5555559378f0, _co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=0x0, kwargs=0x0, kwcount=0, kwstep=2, defs=0x0,
defcount=<optimized out>, kwdefs=0x0, closure=0x0, name=0x0, qualname=0x0) at /usr/local/src/conda/python-3.9.19/Python/ceval.c:4329
#43 0x0000555555681e58 in _PyEval_EvalCodeWithName (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kwnames=<optimized out>, kwargs=0x0, kwcount=0, kwstep=2,
defs=0x0, defcount=0, kwdefs=0x0, closure=0x0, name=0x0, qualname=0x0) at /usr/local/src/conda/python-3.9.19/Python/ceval.c:4361
#44 0x0000555555681e09 in PyEval_EvalCodeEx (_co=<optimized out>, globals=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=<optimized out>, kws=<optimized out>, kwcount=0, defs=0x0, defcount=0, kwdefs=0x0,
closure=0x0) at /usr/local/src/conda/python-3.9.19/Python/ceval.c:4377
#45 0x000055555572f4ab in PyEval_EvalCode (co=co@entry=0x7ffff75c7920, globals=globals@entry=0x7ffff7bb9fc0, locals=locals@entry=0x7ffff7bb9fc0) at /usr/local/src/conda/python-3.9.19/Python/ceval.c:828
#46 0x000055555575c87a in run_eval_code_obj (tstate=0x5555559378f0, co=0x7ffff75c7920, globals=0x7ffff7bb9fc0, locals=0x7ffff7bb9fc0) at /usr/local/src/conda/python-3.9.19/Python/pythonrun.c:1221
#47 0x0000555555758c03 in run_mod (mod=<optimized out>, filename=<optimized out>, globals=0x7ffff7bb9fc0, locals=0x7ffff7bb9fc0, flags=<optimized out>, arena=<optimized out>)
at /usr/local/src/conda/python-3.9.19/Python/pythonrun.c:1242
#48 0x00005555555ec175 in pyrun_file (fp=0x55555591dc90, filename=0x7ffff7e59930, start=<optimized out>, globals=0x7ffff7bb9fc0, locals=0x7ffff7bb9fc0, closeit=1, flags=0x7fffffffaef8)
at /usr/local/src/conda/python-3.9.19/Python/pythonrun.c:1140
#49 0x0000555555752841 in pyrun_simple_file (flags=0x7fffffffaef8, closeit=1, filename=0x7ffff7e59930, fp=0x55555591dc90) at /usr/local/src/conda/python-3.9.19/Python/pythonrun.c:450
#50 PyRun_SimpleFileExFlags (fp=0x55555591dc90, filename=<optimized out>, closeit=1, flags=0x7fffffffaef8) at /usr/local/src/conda/python-3.9.19/Python/pythonrun.c:483
#51 0x000055555574fc6d in pymain_run_file (cf=0x7fffffffaef8, config=0x55555590ea90) at /usr/local/src/conda/python-3.9.19/Modules/main.c:377
#52 pymain_run_python (exitcode=0x7fffffffaef0) at /usr/local/src/conda/python-3.9.19/Modules/main.c:602
#53 Py_RunMain () at /usr/local/src/conda/python-3.9.19/Modules/main.c:681
#54 0x00005555557232e7 in Py_BytesMain (argc=<optimized out>, argv=<optimized out>) at /usr/local/src/conda/python-3.9.19/Modules/main.c:1101
#55 0x00007ffff7c29d90 in __libc_start_call_main (main=main@entry=0x5555557232a0 <main>, argc=argc@entry=2, argv=argv@entry=0x7fffffffb128) at ../sysdeps/nptl/libc_start_call_main.h:58
#56 0x00007ffff7c29e40 in __libc_start_main_impl (main=0x5555557232a0 <main>, argc=2, argv=0x7fffffffb128, init=<optimized out>, fini=<optimized out>, rtld_fini=<optimized out>, stack_end=0x7fffffffb118) at ../csu/libc-start.c:392
#57 0x00005555557231e1 in _start ()
```
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| 2,235,557,427 |
PR_kwDOArmXAs5sPnyf
| 65,408 |
Remove jaxlib compatibiliy hack
|
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Remove jaxlib compatibiliy hack
jaxlib updates it's upstream XLA version, so this ~hack~ compatiblity layer is not needed anymore.
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PR_kwDOArmXAs5sPUuT
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PR #11331: [ROCm] Fix compilation issues related to aa08925e2dd1351c68e7838e074fe35908e3
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PR #11331: [ROCm] Fix compilation issues related to aa08925e2dd1351c68e7838e074fe35908e3
Imported from GitHub PR https://github.com/openxla/xla/pull/11331
Copybara import of the project:
--
a4f80a3f32a10230bfdaa1dadc12f1f385883d8a by Zoran Jovanovic <[email protected]>:
Fix compilatio issues related to aa08925e2dd1351c68e7838e074fe35908e3ea07
(Triton in XLA for ROCm - ir_emitter_triton related changes)
Merging this change closes #11331
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11331 from ROCm:rocm_triton_fix_1 a4f80a3f32a10230bfdaa1dadc12f1f385883d8a
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PR_kwDOArmXAs5sPSiD
| 65,406 |
Enforces cumulative solver timeouts during lazy memory instantiation.
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NONE
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Enforces cumulative solver timeouts during lazy memory instantiation.
|
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I_kwDOArmXAs6FPkrY
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Tensorflow doesn't work on my data
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[
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/65405\">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/65405\">No</a>\n",
"open as a joke with my friend pls ignore"
] | 2024-04-10T12:12:38 | 2024-04-10T12:13:45 | 2024-04-10T12:13:05 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
22
### 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?
Tensorflow doesn't work on my data
### Standalone code to reproduce the issue
```shell
Tensorflow doesn't work on my data
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5sPPgJ
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Re-enable quantized reduce mean.
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Re-enable quantized reduce mean.
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PR_kwDOArmXAs5sPAvJ
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PR #11398: [ROCm] Build fix
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PR #11398: [ROCm] Build fix
Imported from GitHub PR https://github.com/openxla/xla/pull/11398
This contains two fixes:
* A compilation fix for stream_executor/rocm/rocm_executor.cc after it got broken by the commit https://github.com/openxla/xla/commit/3c5cf0ac2adbc0a651d3a2ec822e47927218b153
* A workaround that allows static_assert(false) to be compiled with compilers not implementing https://cplusplus.github.io/CWG/issues/2518.html
Copybara import of the project:
--
5cdd25c3190140fec619073d25a20952fed2fd8b by Eugene Kuznetsov <[email protected]>:
Build fix
--
b86d12ff02a2a6247a7f991f77dfdafb08a79703 by Eugene Kuznetsov <[email protected]>:
Workaround for static_assert needed for some compilers
(https://cplusplus.github.io/CWG/issues/2518.html)
Merging this change closes #11398
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11398 from ROCm:ci_rocm_fix_240410 b86d12ff02a2a6247a7f991f77dfdafb08a79703
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PR_kwDOArmXAs5sPAmq
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Temporarily disable newer cuDNN fMHA features due to miscompile
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NONE
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Temporarily disable newer cuDNN fMHA features due to miscompile
I'm trying to make XLA compatible with cuDNN 9 and stumbled upon
a miscompile in the fMHA rewriter.
It seems to be related with the cause mask pattern matcher, at least
forcing the causal mask flag to true, makes the broken test path.
I extract a reproducer and added an (integration) test which
can be removed or converted into a proper unit test once this is fixed.
IN the meantime I disable all fMHA features that require a version
of cuDNN 8.9.6+.
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PR_kwDOArmXAs5sOwF6
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Use stable_sort to make horizontal loop fusion pass deterministic.
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Use stable_sort to make horizontal loop fusion pass deterministic.
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PR_kwDOArmXAs5sOrMV
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Automated Code Change
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Documentation for RaggedTensors does not match Input layer documentation
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[
"Hi @damienwojtowicz ,\r\n\r\nStarting from TF2.16v tensorflow bundles with Keras3. Since Kera3 is supporting multi backend now ragged tensors are not supported on other frameworks. Keras team also has plans to support Ragged Tensors with tensorflow backend in nearby future.\r\n\r\nSince then if you want to use ragged tensors I recommend to install tf_keras package and set environment varaibale TF_USE_LEGACY_KERAS=1 . \r\n",
"Please refer [this](https://github.com/keras-team/keras/issues/18467) for more details.",
"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/65399\">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/65399\">No</a>\n"
] | 2024-04-10T10:22:57 | 2024-05-01T01:49:35 | 2024-05-01T01:49:31 |
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?
Documentation of RaggedTensors in section [TensorFlow APIs > Keras](https://www.tensorflow.org/guide/ragged_tensor#keras) claim the following, and provides an example where argument `ragged=True` is passed to the constructor of a `tf.keras.Input` object:
> Ragged tensors may be passed as inputs to a Keras model by setting ragged=True on [tf.keras.Input](https://www.tensorflow.org/api_docs/python/tf/keras/Input) or [tf.keras.layers.InputLayer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/InputLayer).
However, `ragged` is neither an argument of [`tf.keras.Input`](https://www.tensorflow.org/api_docs/python/tf/keras/Input), [`tf.keras.layers.InputLayer`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/InputLayer), nor any of their parent classes. Their respective documentation does not mention it. Trying to execute the example provided with either TF v. 2.16.1 ir nightly (see the MWE) raises a `TypeError` stating that `ragged` is an unexpected keyword for `Input`.
It is puzzling as several sources on this internet as well as issues of this very repository mention it. As a consequence, it seems that ragged tensors cannot be used within TF at the moment.
### Standalone code to reproduce the issue
```shell
# This is the example provided by the RaggedTensor documentation
import tensorflow as tf
print(tf.version.GIT_VERSION, tf.version.VERSION)
# Task: predict whether each sentence is a question or not.
sentences = tf.constant(
['What makes you think she is a witch?',
'She turned me into a newt.',
'A newt?',
'Well, I got better.'])
is_question = tf.constant([True, False, True, False])
# Preprocess the input strings.
hash_buckets = 1000
words = tf.strings.split(sentences, ' ')
hashed_words = tf.strings.to_hash_bucket_fast(words, hash_buckets)
# Build the Keras model.
keras_model = tf.keras.Sequential([
tf.keras.layers.Input(shape=[None], dtype=tf.int64, ragged=True),
tf.keras.layers.Embedding(hash_buckets, 16),
tf.keras.layers.LSTM(32, use_bias=False),
tf.keras.layers.Dense(32),
tf.keras.layers.Activation(tf.nn.relu),
tf.keras.layers.Dense(1)
])
keras_model.compile(loss='binary_crossentropy', optimizer='rmsprop')
keras_model.fit(hashed_words, is_question, epochs=5)
print(keras_model.predict(hashed_words))
```
### Relevant log output
```shell
2024-04-10 12:22:30.701897: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-04-10 12:22:30.733806: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-10 12:22:31.321292: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
v1.12.1-108775-gb453565cae0 2.17.0-dev20240409
Traceback (most recent call last):
File "/home/dwojtowicz/PycharmProjects/japetus_ml/issue.py", line 19, in <module>
tf.keras.layers.Input(shape=[None], dtype=tf.int64, ragged=True),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: Input() got an unexpected keyword argument 'ragged'
```
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As per documentation of the Round Op, it supports bfloat16, half, float32, float64, int8, int16, int32, int64, complex64, complex128. But It errors out with complex dtypes and int8, int16 also. Probably this might be registered only for float,int32 and int64 as per the source here.
https://github.com/tensorflow/tensorflow/blob/a787405cf0adab1793b8f01c32d222ec469c8818/tensorflow/core/kernels/cwise_op_round.cc#L20-L21
Hence changed the registration accordingly.
Might address #65317
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[
"@Dobiasd Could you try to use model.save('model.h5', save_format='tf') to save the model in TensorFlow SavedModel format as the default model.save might not capture all model components. Please have a look at this gist and let us know if it helps?\r\nThank you!",
"I tried:\r\n\r\n```python3\r\nfrom tensorflow.keras.layers import Input, LSTM, Bidirectional\r\nfrom tensorflow.keras.models import Model, load_model\r\n\r\ninputs = [Input(shape=(17, 4))]\r\nlayer = Bidirectional(LSTM(units=4), merge_mode='concat')(inputs[0])\r\noutputs = [layer]\r\nmodel = Model(inputs=inputs, outputs=outputs)\r\n\r\nmodel.save('model.h5', save_format='tf')\r\nload_model('model.h5')\r\n```\r\n\r\n`model.save('model.h5', save_format='tf')` raises a `ValueError`:\r\n\r\n```\r\n File \"h5py/h5d.pyx\", line 137, in h5py.h5d.create\r\nValueError: Unable to synchronously create dataset (name already exists)\r\n```\r\n\r\nHowever, the `model.h5` is written to the disk nonetheless.\r\n\r\nBut loading it (`load_model('model.h5')`) fails:\r\n\r\n```\r\n File \"h5py/h5o.pyx\", line 189, in h5py.h5o.open\r\nKeyError: \"Unable to synchronously open object (object 'input_layer' doesn't exist)\"\r\n```",
"@Dobiasd Thank you for the response here.\r\n@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/3f15df3f6a633a1764539d312f9461ff/65395.ipynb#scrollTo=wBmnOmhoga-D).\r\nThank you!",
"I am having the same issue. This and other issues have resulted in frustrating experiences when moving from previous versions of tf into tf 2.15 and TF 2.16, including 2.16.1. ",
"Having same issue with tensorflow==2.16.1 and keras==3.2.1\r\nIs downgrading really the only fix right now?",
"Hi @Dobiasd ,\r\n\r\nThere is an open issue in Keras repo [# 18913](https://github.com/keras-team/keras/issues/18913) where Keras Team looking into same and expect a fix ASAP. \r\n\r\nThanks!",
"Hi @Dobiasd ,\r\n\r\nPlease note that Keras3 supports saving the model in .keras format only as It is now supporting multiple backends like Torch and Jax also.With .keras format this has been working fine in nightly and attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/1c5a886ebaaaa3d73772472619e46131/65395_r2.ipynb) for reference.\r\n\r\nIf you want to save the model in tf format you might need to use model.export(\"saved_model\") instead of model.save().\r\n\r\nPlease refer the migration guide [here](https://keras.io/guides/migrating_to_keras_3/#saving-a-model-in-the-tf-savedmodel-format).",
"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/65395\">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/65395\">No</a>\n"
] | 2024-04-10T09:34:16 | 2024-05-15T01:49:57 | 2024-05-15T01:49:54 |
NONE
| null | null | null |
With TensorFlow `2.15.1`, the following code works fine:
```python3
from tensorflow.keras.layers import Input, LSTM, Bidirectional
from tensorflow.keras.models import Model, load_model
inputs = [Input(shape=(17, 4))]
layer = Bidirectional(LSTM(units=4), merge_mode='concat')(inputs[0])
outputs = [layer]
model = Model(inputs=inputs, outputs=outputs)
model.save('model.keras')
load_model('model.keras')
```
But with `2.16.1`, it no longer does:
```
ValueError: A total of 1 objects could not be loaded. Example error message for object <LSTMCell name=lstm_cell, built=True>:
Layer 'lstm_cell' expected 3 variables, but received 0 variables during loading. Expected: ['kernel', 'recurrent_kernel', 'bias']
```
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TimeDistributed layer with nested model no longer working in TensorFlow 2.16.1
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[
"Hi **@Dobiasd** ,\r\n\r\nSorry for the delay, I tried to run your code on colab using TF v2.15, 2.16 and nightly faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/c789131d6e3f056091fc9ac82107d613/65393_2-15-2-16-1-nightly.ipynb) here for reference.\r\n\r\nThank you!",
"Any news on this? Is this going to be resolved, or should we avoid using tf.keras.Model inside TimeDistributed layers from now on?",
"Hi **@Dobiasd** ,\r\n- Please post this issue on [keras-team/keras repo.](https://github.com/keras-team/keras/issues)\r\nTo know more see;\r\n[https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999](https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999).\r\n\r\nThank you!",
"Ok, done: https://github.com/keras-team/keras/issues/19792",
"@Dobiasd This is fixed in [upstream](https://github.com/keras-team/keras/commit/bf607fffbac61ab3bf509094c9b7a6a17853458c), so you can close this 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/65393\">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/65393\">No</a>\n"
] | 2024-04-10T09:06:03 | 2024-06-05T11:03:55 | 2024-06-05T11:03:52 |
NONE
| null | null | null |
With TensorFlow `2.15.1`, the following code works fine:
```python3
import numpy as np
from tensorflow.keras.layers import Input, TimeDistributed, Flatten
from tensorflow.keras.models import Model, Sequential
inputs = [Input((17, 4))]
nested_model = Sequential()
nested_model.add(Flatten(input_shape=(4,)))
nested_model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
outputs = [TimeDistributed(nested_model)(inputs[0])]
model = Model(inputs=inputs, outputs=outputs)
model.compile(loss='mse', optimizer='adam')
x = np.random.random((1, 17, 4))
model.predict(x)
```
But with `2.16.1`, it no longer does:
```
NotImplementedError: Exception encountered when calling TimeDistributed.call().
Layer Sequential should implement `def compute_output_shape(self, input_shape)`.
```
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