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Add checks for output file size ratio in the integration tests
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Add checks for output file size ratio in the integration tests
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Make BackendConfig in HloInstruction thread-compatible
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Make BackendConfig in HloInstruction thread-compatible
`backend_config_` is currently a mutable member variable that was not
protected by a mutex.
This is adding the missing mutex as a short term solution. Long term
we want to avoid the lazy caching logic altogether, but this requires
some restructuring first.
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PR_kwDOArmXAs5puN2-
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Integrate LLVM at llvm/llvm-project@3b5e7c83a6e2
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Integrate LLVM at llvm/llvm-project@3b5e7c83a6e2
Updates LLVM usage to match
[3b5e7c83a6e2](https://github.com/llvm/llvm-project/commit/3b5e7c83a6e2)
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TypeError: Exception encountered when calling layer "custom_crop_layer_2" " f"(type CustomCropLayer).
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[
"@pzy2000,\r\nThe error message \"unsupported operand type(s) for -: 'NoneType' and 'int'\" suggests that a computation is attempted between NoneType (which indicates a missing value) and an integer. Also I tried to execute the mentioned code and it was executed with a different error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/054c94df5403bc8335ceabc6836247db/untitled1766.ipynb) and share the required dependencies. Thank you!",
"> @pzy2000, The error message \"unsupported operand type(s) for -: 'NoneType' and 'int'\" suggests that a computation is attempted between NoneType (which indicates a missing value) and an integer. Also I tried to execute the mentioned code and it was executed with a different error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/054c94df5403bc8335ceabc6836247db/untitled1766.ipynb) and share the required dependencies. Thank you!\r\n\r\nsorry, I forgot to share the model file, it is here:\r\nhttps://drive.google.com/file/d/14Lph3YKjXJVy3huIFCYoQJ4DrqgKs5tf/view?usp=sharing",
"@pzy2000,\r\nI tried to execute the mentioned code using the .h5 file provided and execute with the different error. AFAIK I tried to execute the model with tensorflow v2.16, and tried to run the inference for the .h5 model which was on tensorflow older versions and it was failing due to this extra layer which was not present in before versions. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/5b7be7acfb8b8adfc48c43fa2b8fbebc/untitled1788.ipynb). \r\n\r\nAnd also this issue is related to Keras, Could you please reach out to keras-team/keras repo for the quick response. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63788\">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/63788\">No</a>\n"
] | 2024-03-15T09:19:47
| 2024-04-17T01:48:01
| 2024-04-17T01:47:49
|
NONE
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### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.10.0
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04.4
### Mobile device
_No response_
### Python version
3.7.6
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.2
### GPU model and memory
_No response_
### Current behavior?
Expect:
successfully saved to tf format ckpt.
Now:
Traceback (most recent call last):
File "issue_tf2onnx_001.py", line 207, in <module>
model.save(f'tmp/lenet5_sb', save_format='tf')
File "/opt/conda/envs/tf2onnx/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/opt/conda/envs/tf2onnx/lib/python3.7/contextlib.py", line 119, in __exit__
next(self.gen)
File "issue_tf2onnx_001.py", line 77, in call
indices.append(slice(0 + crop[0], shape - crop[1]))
TypeError: Exception encountered when calling layer "custom_crop_layer_2" " f"(type CustomCropLayer).
unsupported operand type(s) for -: 'NoneType' and 'int'
Call arguments received by layer "custom_crop_layer_2" " f"(type CustomCropLayer):
• args=('tf.Tensor(shape=(None, None, None, 32), dtype=float32)',)
• kwargs={'training': 'False'}
### Standalone code to reproduce the issue
```shell
import keras
import tensorflow as tf
class CustomCastLayer(keras.layers.Layer):
def __init__(self, target_dtype, **kwargs):
self.target_dtype = target_dtype
super().__init__(**kwargs)
def build(self, input_shape):
"""
Do Nothing
"""
super().build(input_shape)
def call(self, inputs, **kwargs):
output = tf.cast(inputs, self.target_dtype)
return output
def get_config(self):
config = super().get_config()
config.update({"target_dtype": self.target_dtype})
return config
def compute_output_shape(self, input_shape):
"""
Do Nothing
"""
return input_shape
class CustomPadLayer(keras.layers.Layer):
def __init__(self, padding=[[0, 0]], constant_values=0, **kwargs):
self.padding = padding # add [0,0] to padding so we will not change the shape of batch dimension
self.constant_values = constant_values
super().__init__(**kwargs)
def build(self, input_shape):
super().build(input_shape)
def call(self, inputs, **kwargs):
output = tf.pad(inputs, paddings=tf.constant([[0, 0]] + self.padding), mode="CONSTANT",
constant_values=self.constant_values)
return output
def get_config(self):
config = super().get_config()
config.update({"padding": self.padding, "constant_values": self.constant_values})
return config
def compute_output_shape(self, input_shape):
"""
Formula to calculate the output shape
Suppose the input_shape is (None, N, W, C), the `paddings` is [[a, b], [c, d]].
The output_shape is (None, N+a+b, W+c+d, C).
"""
input_shape_list = list(input_shape)
padding = [[0, 0]] + self.padding
assert len(input_shape_list) == len(padding) # Two dimensions should match.
output_shape = [None]
for i, pad in zip(input_shape_list[1:], padding[1:]):
output_shape.append(i + pad[0] + pad[1])
return tuple(output_shape)
class CustomCropLayer(keras.layers.Layer):
def __init__(self, cropping, **kwargs):
self.cropping = cropping
super().__init__(**kwargs)
def build(self, input_shape):
super().build(input_shape)
def call(self, inputs, **kwargs):
input_shape = inputs.shape.as_list()
indices = [slice(None)]
cropping = [[0, 0]] + self.cropping # add [0,0] to padding so we will not change the shape of batch dimension
for shape, crop in zip(input_shape[1:], cropping[1:]):
indices.append(slice(0 + crop[0], shape - crop[1]))
return inputs[indices]
def get_config(self):
config = super().get_config()
config.update({"cropping": self.cropping})
return config
def compute_output_shape(self, input_shape):
"""
Formula to calculate the output shape
Suppose the input_shape is (None, N, W, C), the `cropping` is [[a, b], [c, d]].
The output_shape is (None, N-a-b, W-c-d, C).
"""
input_shape_list = list(input_shape)
cropping = [[0, 0]] + self.cropping
assert len(input_shape_list) == len(cropping) # Two dimensions should match.
output_shape = [None]
for i, crop in zip(input_shape[1:], cropping[1:]):
output_shape.append(i - crop[0] - crop[1])
return tuple(output_shape)
class CustomExpandLayer(keras.layers.Layer):
def __init__(self, axis=1, **kwargs):
self.axis = axis
super().__init__(**kwargs)
def build(self, input_shape):
super().build(input_shape)
def call(self, inputs, **kwargs):
return tf.expand_dims(inputs, self.axis)
def get_config(self):
config = super().get_config()
config.update({"axis": self.axis})
return config
def compute_output_shape(self, input_shape):
"""
Formula to calculate the output shape
Suppose the input_shape is [None, N, W, C]:
axis=0:
output_shape: [1, None, N, W, C]
axis=1 (default):
output_shape: [None, 1, N, W, C]
axis=2:
output_shape: [None, N, 1, W, C]
axis=3:
output_shape: [None, N, W, 1, C]
axis=4:
output_shape: [None, N, W, C, 1]
axis=5:
raise Exception
"""
input_shape_list = list(input_shape)
if self.axis > len(input_shape_list):
raise ValueError(f"axis {self.axis} should be smaller than input_shape + 1: {len(input_shape_list) + 1}")
output_shape = input_shape_list[0:self.axis] + [1] + input_shape_list[self.axis:]
return tuple(output_shape) # we should use tuple!!! not list !!!
class CustomDropDimLayer(keras.layers.Layer):
def __init__(self, axis=1, **kwargs):
self.axis = axis
super().__init__(**kwargs)
def build(self, input_shape):
super().build(input_shape)
def call(self, inputs, **kwargs):
"""
Something magic to automatically generate indices for array slicing.
To determine a specific axis, we can use slice(None) to replace `:`
"""
dim = len(inputs.shape)
if self.axis > dim - 1 or self.axis < 1:
raise ValueError(f"axis: {self.axis} should be within the range: [1, {dim - 1}] for {dim}D tensor")
indices = [slice(None) for i in range(dim)]
indices[self.axis] = 0
return inputs[indices]
def get_config(self):
config = super().get_config()
config.update({"axis": self.axis})
return config
def compute_output_shape(self, input_shape):
"""
Formula to calculate the output shape
Suppose the input_shape is [None, N, W, C]:
axis=0: # Although it is feasible, we don't allow this to happen
Raise Exception
axis=1 (default):
output_shape: [None, W, C]
axis=2:
output_shape: [None, N, C]
axis=3:
output_shape: [None, N, W]
axis=4:
Raise Exception
"""
input_shape_list = list(input_shape)
output_shape = input_shape_list[0:self.axis] + input_shape_list[self.axis + 1:]
return tuple(output_shape)
def custom_objects(mode="custom"):
def no_activation(x):
return x
def leakyrelu(x):
import keras.backend as K
return K.relu(x, alpha=0.01)
# objects = {}
objects = {'no_activation': no_activation, 'leakyrelu': leakyrelu}
if mode == "custom":
objects['CustomPadLayer'] = CustomPadLayer
objects['CustomCropLayer'] = CustomCropLayer
objects['CustomDropDimLayer'] = CustomDropDimLayer
objects['CustomExpandLayer'] = CustomExpandLayer
objects['CustomCastLayer'] = CustomCastLayer
return objects
model_path = "/data1/pzy/MUTANTS/LEMON/lenet5_fashion/lenet5-fashion-mnist_origin-NLAll30-LMerg38-Edge63-NLAll90-92/lenet5-fashion-mnist_origin-NLAll30-LMerg38-Edge63-NLAll90.h5"
model = tf.keras.models.load_model(model_path, custom_objects=custom_objects())
# model = tf.keras.models.load_model(model_path,)
model.save(f'tmp/lenet5_sb', save_format='tf')
```
### Relevant log output
```shell
Traceback (most recent call last):
File "issue_tf2onnx_001.py", line 207, in <module>
model.save(f'tmp/lenet5_sb', save_format='tf')
File "/opt/conda/envs/tf2onnx/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/opt/conda/envs/tf2onnx/lib/python3.7/contextlib.py", line 119, in __exit__
next(self.gen)
File "issue_tf2onnx_001.py", line 77, in call
indices.append(slice(0 + crop[0], shape - crop[1]))
TypeError: Exception encountered when calling layer "custom_crop_layer_2" " f"(type CustomCropLayer).
unsupported operand type(s) for -: 'NoneType' and 'int'
Call arguments received by layer "custom_crop_layer_2" " f"(type CustomCropLayer):
• args=('tf.Tensor(shape=(None, None, None, 32), dtype=float32)',)
• kwargs={'training': 'False'}
```
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PR_kwDOArmXAs5puDiB
| 63,787
|
Fix inconsistency in IsReadCoalescedHeuristic.
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[] | 2024-03-15T08:54:56
| 2024-03-15T10:38:11
| 2024-03-15T10:38:09
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|
Fix inconsistency in IsReadCoalescedHeuristic.
We considered a transpose of a broadcast to be coalesced, but only if it was
inside a fusion. Also, if we allow broadcasts, we should allow iota, too.
Add tests to cover these cases.
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PR_kwDOArmXAs5pt9w6
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Rollback of GpuTimer: improve kernel execution time measurement accuracy
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Rollback of GpuTimer: improve kernel execution time measurement accuracy
This breaks the gemm algorithm picker test on V100
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I_kwDOArmXAs6CaWu7
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Unable to retrieve BLAS factory in Tensorflow 2.16.1
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[
"Hi @yutkin ,\r\n\r\nJust FYIP, we are not currently supporting GOLANG. If you are looking for any support of it please contact the third party contributor as mentioned [here](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/go#readme).",
"> Hi @yutkin ,\n> \n> \n> \n> Just FYIP, we are not currently supporting GOLANG. If you are looking for any support of it please contact the third party contributor as mentioned [here](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/go#readme).\n\nHi! It's not a Golang problem. The errors come from libtensorflow. ",
"I also have this problem. All was working fine with 2.15 and became a problem with 2.16\r\nApplication is Blurexterminator plugin of PixInsight.\r\nI believe it is C++ application."
] | 2024-03-15T08:09:48
| 2024-04-26T01:26:21
| null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
Linux, Ubuntu 20.04
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
cuda:12.2.2-cudnn8
### GPU model and memory
NVIDIA T4
### Current behavior?
We tried to upgrade our current 2.13.0 `libtensorflow` binary in our Golang application to the latest 2.16.1. However, the new `libtensorflow` does not work and starts to produce errors during the model inference:
```
2024-03-14 20:31:02.259695: E external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:837] Unable to retrieve BLAS factory: No suitable BLAS plugin registered. Have you linked in a BLAS-providing plugin?
```
The only change we made is to replace the Google-distributed `libtensorflow` 2.13.0 with `libtensorflow` 2.16.1 from [here](https://github.com/tensorflow/tensorflow/issues/63384#issuecomment-1991794920) and change the base docker image:
```diff
- ARG CUDA_IMAGE=nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu20.04
+ ARG CUDA_IMAGE=nvcr.io/nvidia/cuda:12.2.2-cudnn8-devel-ubuntu20.04
```
### Standalone code to reproduce the issue
```shell
-
```
### Relevant log output
```shell
2024-03-14 20:31:02.259695: E external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:837] Unable to retrieve BLAS factory: No suitable BLAS plugin registered. Have you linked in a BLAS-providing plugin?
2024-03-14 20:31:02.259770: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: INTERNAL: No blas support for stream
[[{{function_node __inference__wrapped_model_57676}}{{node model/cross_0/dense/MatMul}}]]
2024-03-14 20:31:02.259787: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: INTERNAL: No blas support for stream
[[{{function_node __inference__wrapped_model_57676}}{{node model/cross_0/dense/MatMul}}]]
[[StatefulPartitionedCall_41/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall_2/StatefulPartitionedCall/model/mean/Sigmoid/_55]]
2024-03-14 20:31:02.259876: I tensorflow/core/framework/local_rendezvous.cc:422] Local rendezvous recv item cancelled. Key hash: 14129393403982148508
2024-03-14 20:31:02.259917: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: ABORTED: Stopping remaining executors.
{"level":"error","model_name":"cpi-model","model_version":20240314181330,"error":"failed to infer request during warmup: failed to run: failed to run session: 2 root error(s) found.\n (0) INTERNAL: No blas support for stream\n\t [[{{function_node __inference__wrapped_model_57676}}{{node model/cross_0/dense/MatMul}}]]\n\t [[StatefulPartitionedCall_41/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall_2/StatefulPartitionedCall/model/mean/Sigmoid/_55]]\n (1) INTERNAL: No blas support for stream\n\t [[{{function_node __inference__wrapped_model_57676}}{{node model/cross_0/dense/MatMul}}]]\n0 successful operations.\n0 derived errors ignored.","time":"2024-03-14T20:31:02Z","message":"failed to warmup model"}
{"level":"info","model_name":"cpi-model","model_version":20240314181330,"time":"2024-03-14T20:31:02Z","message":"model warmup finished"}
```
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PR_kwDOArmXAs5ptr04
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Fix checkfail in ResourceSparseApplyKerasMomentum
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"> Thanks for the changes! I'm not sure if the current change is comprehensive enough and also a little unclear on what it might fix.\r\n\r\nActually as per Register_Op this Op Args `var` and `accum` are of same type `resource` which Should be from a Variable().\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/9c1b61a664d66b15d07c25e713d8d96f6bf347c8/tensorflow/core/ops/training_ops.cc#L765-L767\r\n\r\nI have checked and found that These args accepting only float32 dtype and with other dtypes (including float64) it checkfails with the error(eg below for complex64) like below.\r\n\r\n`\r\nCheck failed: dtype() == expected_dtype (8 vs. 1) float expected, got complex64\r\n`\r\n\r\n",
"Hi @sagunb Can you please review this PR ? Thank you!",
"Hi @sagunb Can you please review this PR ? Thank you!",
"Hi @sagunb Can you please review this PR ? Thank you!\r\n\r\n",
"Hi @sagunb Can you please review this PR ? Thank you!\r\n\r\n"
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The Op `ResourceSparseApplyKerasMomentum` checkfails if the arguments `var` or `accum` has dtype other than float32.
Proposed a validation check for same.
May Fixes #63720 .
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[xla:ffi] NFC: Use type tags to extract Stream and OwningScratchAllocator from FFI execution context
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[xla:ffi] NFC: Use type tags to extract Stream and OwningScratchAllocator from FFI execution context
For consistency with passing called computation use type tags to extract all internal execution context details
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Move the logic for populating default calibration options to `PopulateDefaults`.
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Move the logic for populating default calibration options to `PopulateDefaults`.
This change populates calibration method as part of `PopulateDefaults`. This is reused for ODML use cases. However, for ODML the value for `unpack_quantized_types` is explicitly set to `False` because ODML use cases require uniform quantized types to be left intact.
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Fix index out of range in LiftHashTableOpsAsArgsPass
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Fix index out of range in LiftHashTableOpsAsArgsPass
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Roll back the optimized version of `jax.block_until_ready` due to test breakage
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Roll back the optimized version of `jax.block_until_ready` due to test breakage
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Fix forward: address tsan issue in `prepare_quantize.cc`.
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Fix forward: address tsan issue in `prepare_quantize.cc`.
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PR_kwDOArmXAs5ps7xW
| 63,775
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Create the pass to lower tf.RestoreV2 op to tf.IfrtRestoreVariableOp
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Create the pass to lower tf.RestoreV2 op to tf.IfrtRestoreVariableOp
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PR_kwDOArmXAs5ps7Bc
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Renames _pywrap_utils in tensorflow/python/data/experimenta/service to _pywrap_utils_exp.
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Renames _pywrap_utils in tensorflow/python/data/experimenta/service to _pywrap_utils_exp.
This change is needed to prevent conflicts between _pywrap_utils from the following two targets when implementing the new solutions for addressing the linking issues:
tensorflow/python/util:_pywrap_utils
tensorflow/python/data/experimenta/service:_pywrap_utils
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PR_kwDOArmXAs5pspv2
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Add test cases for `PopulateDefaults`.
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Add test cases for `PopulateDefaults`.
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PR_kwDOArmXAs5psprI
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Fix index out of range in LiftHashTableOpsAsArgsPass
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Fix index out of range in LiftHashTableOpsAsArgsPass
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PR_kwDOArmXAs5psoWd
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Remove unused function `_populate_default_quantization_config`.
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Remove unused function `_populate_default_quantization_config`.
This has been replaced by the new function `pywrap_quantization.populate_default_configs`.
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[XLA:GPU] Enable Triton Codegen to emit matmuls with a trivial non-contracting dimension
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[XLA:GPU] Enable Triton Codegen to emit matmuls with a trivial non-contracting dimension
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PR #10489: [GPU] Fix command buffer support for cuDNN fusions.
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PR #10489: [GPU] Fix command buffer support for cuDNN fusions.
Imported from GitHub PR https://github.com/openxla/xla/pull/10489
CuDnnCmd is constructed before DnnGraph in CuDnnThunk is initialized so CuDnnCmd has to get unique_ptr\<DnnGraph\>& instead of DnnGraph& at initialization.
Accordingly cuDNN thunks have to be initialized before command buffer ones to initialize graphs before they get captured.
Test CommandBuffersAreSupported used to not demonstrate the use of command buffers because the corresponding command buffer call used to be inlined and no command buffers were created. This is now cleaned up and does work as expected with minimal CUDA graph size set to 1 with a flag.
Copybara import of the project:
--
8547c674f3e0858efca9763bed586f1d796184d7 by Ilia Sergachev <isergachev@nvidia.com>:
[GPU] Fix command buffer support for cuDNN fusions.
Merging this change closes #10489
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10489 from openxla:fix_cudnn_cmd_buffers 8547c674f3e0858efca9763bed586f1d796184d7
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lite: fix test failure
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lite: fix test failure
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[xla] Enhancement to the GPU latency hiding scheduler.
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[xla] Enhancement to the GPU latency hiding scheduler.
We extend the GPU latency hiding scheduler in two aspects. First, we add one
more stream resource for executing Send and Recv instructions respectively.
Second, we extend the scheduler to handle Send-done and Recv-done ordered
before Send and Recv in a computation for a pipelined while loop.
Add tests.
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Support host offloaded values as entry computation output
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Support host offloaded values as entry computation output
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PR_kwDOArmXAs5psNtE
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Remove references to Arm64 installer wheels from setup.py
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Remove references to Arm64 installer wheels from setup.py
Full Arm64 packages (Linux + Mac) are now directly uploaded to `tf-nightly/`tensorflow` so we don't need the "installer" wheels for them anymore.
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#tf-data Add a class to support random access iterators re-using `Dataset::Get`.
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#tf-data Add a class to support random access iterators re-using `Dataset::Get`.
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Insert a task to the low priority task queue when the criticality is one of the fixed list of low priority criticalities and support padding the high priority batch with the unbatched tasks given via the ProcessBatchCallBack.
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Insert a task to the low priority task queue when the criticality is one of the fixed list of low priority criticalities and support padding the high priority batch with the unbatched tasks given via the ProcessBatchCallBack.
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Error: numpy.core._exceptions._ArrayMemoryError: Unable to allocate 115. TiB for an array with shape (3983360, 3983360) and data type float64
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"@NOORLEICESTER `numpy.core._exceptions._ArrayMemoryError` on the HPC system but not on your laptop indicates a limitation in the available memory on the HPC cluster compared to your laptop. HPC environments often prioritize memory efficiency, so could you try switching the data type from float64 to float32 can significantly reduce memory usage.\r\n\r\nIn order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here. Thank you!",
"@sushreebarsa the same error\r\n",
"@NOORLEICESTER In order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63761\">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/63761\">No</a>\n"
] | 2024-03-14T22:59:46
| 2024-04-11T01:48:07
| 2024-04-11T01:48:04
|
NONE
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I am encountering an error: 'numpy.core._exceptions._ArrayMemoryError: Unable to allocate 115 TiB for an array with shape (3983360, 3983360) and data type float64' when I use HPC. However, I do not encounter any error when I use my personal laptop for the same code.
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[xla:ffi] Add support for passing scratch allocator to FFI handlers
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PR #10521: [ROCM] fixing gpu_pjrt_client crash and improved debug in rocm_driver
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PR #10521: [ROCM] fixing gpu_pjrt_client crash and improved debug in rocm_driver
Imported from GitHub PR https://github.com/openxla/xla/pull/10521
In this PR I fix xla/pjrt/gpu/se_gpu_pjrt_client.cc which causes a crash during the execution of se_gpu_pjrt_client_test.cc, namely the subtest **StreamExecutorGpuClientTest.CopyRawToHostFullBuffer**.
The crash was caused by taking a pointer of a unique_ptr in one function's argument and releasing that pointer in another one (not sure why it worked before: maybe on CUDA the compiler has different behaviour?). But I guess the order of evaluating the function arguments is not defined..
Besides, I also improve gpu_driver debug output.
@xla-rotation: can you please have a look ?
Copybara import of the project:
--
fc43f8e5eac8d0f74b2dda453f9e622d62c78896 by Pavel Emeliyanenko <pavel.emeliyanenko@amd.com>:
fixing gpu_pjrt_client crash and added improved debug output in rocm_driver
--
68e52b52d3c157d1d8f1d1eb50c2368dbed50198 by Pavel Emeliyanenko <pavel.emeliyanenko@amd.com>:
passing promise by value since the callback function is copied
--
65ab1d44bd83aff64776e1fdd66c42b48ce8acf2 by Pavel Emeliyanenko <pavel.emeliyanenko@amd.com>:
fixing buildifier warnings
Merging this change closes #10521
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10521 from ROCm:ci_pjrt_gpu_client_fix 65ab1d44bd83aff64776e1fdd66c42b48ce8acf2
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[xla:ffi] Add access to se::Stream to internal FFI API and remove ServiceExecutableOptions
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[xla:gpu][NFC] Add test for embedding CustomCallThunk inside AddressComputationThunk
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[xla:gpu][NFC] Add test for embedding CustomCallThunk inside AddressComputationThunk
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PR #10316: [NVIDIA GPU] Add a mechanism in GpuAsyncTracker to force delaying scheduling of an instruction.
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PR #10316: [NVIDIA GPU] Add a mechanism in GpuAsyncTracker to force delaying scheduling of an instruction.
Imported from GitHub PR https://github.com/openxla/xla/pull/10316
latency hiding scheduler has a property for each HloScheduleNode called SetForceDelay, setting this to true for a node will result in forcing LHS to ignore all cost info and schedule the instruction to the very beginning of the instruction sequence.
This is useful in manually enforcing an instruction to run before anything else in the scheduled graph.
Passes can set `should_force_delay` attribute in the GpuBackendConfig to instruct how this instruction will be scheduled. It's set to false by default.
An example usage:
```
original sequence:
allreduce-start
allreduce-done
add0, backend_config={should_force_delay=true}
add1
schedule after LHS:
add0, backend_config={should_force_delay=true}
allreduce-start
add1
allreduce-done
```
Copybara import of the project:
--
74267e60142c5161310580db5b760a3fe6d4f625 by TJ <tjx@nvidia.com>:
Add a mechanism in GpuAsyncTracker to force delaying scheduling
of an instruction.
--
0aac4cf8c42f2ca210450581f05239378d8a33c2 by TJ <tjx@nvidia.com>:
Fix failing backend config test
--
4deab8393ecb01ca010e0cf4ea30930ac448b815 by TJ <tjx@nvidia.com>:
Use force delay for windowed einsum loops
--
a1321e88fd336f83a4fcfcc89037b8d4a3d1903f by TJ <tjx@nvidia.com>:
Revert changes in gpu_windowed_einsum_handler, moving them to a separate
pr
Merging this change closes #10316
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10316 from Tixxx:tixxx/force_delay_lhs a1321e88fd336f83a4fcfcc89037b8d4a3d1903f
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PR_kwDOArmXAs5pr1tL
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Integrate LLVM at llvm/llvm-project@f9a14782000e
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Integrate LLVM at llvm/llvm-project@f9a14782000e
Updates LLVM usage to match
[f9a14782000e](https://github.com/llvm/llvm-project/commit/f9a14782000e)
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PR_kwDOArmXAs5pr1qL
| 63,754
|
Guard host transfers inside pure_callbacks from deadlocking the TPU.
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Guard host transfers inside pure_callbacks from deadlocking the TPU.
Also fix python/callback.cc to not swallow errors in numpy conversions.
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PR_kwDOArmXAs5pr0WJ
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PR #62817: [onednn] Enable auto_mixed_precision for fp16 on cpu
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PR #62817: [onednn] Enable auto_mixed_precision for fp16 on cpu
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/62817
This PR enables AMP FP16 on supporting Intel Xeon CPUs. If there is no GPU available to run FP16, it checks if FP16 can be executed on CPU.
Use config `auto_mixed_precision` to enable FP16 on CPUs.
Copybara import of the project:
--
fc1ee3bcc2d07d30bcd9480280796eb014bb0e2b by Gauri1 Deshpande <gauri1.deshpande@intel.com>:
[onednn] Enable auto_mixed_precision for fp16 on cpu
--
9a3c8d59eb65fac7253b8f9a0db92dc93e22e374 by Gauri1 Deshpande <gauri1.deshpande@intel.com>:
Address review comments - update comments as per guidelines.
--
27b1a9623f72a5acd13a864d1d00b47a85ca43fe by Gauri1 Deshpande <gauri1.deshpande@intel.com>:
Address review comments
--
9a3ef902974965aacfaf2192b3a7253dcd0609f8 by Gauri1 Deshpande <gauri1.deshpande@intel.com>:
Address review comments
--
0ab5b80f9bf952e3eb6f5fb1df0881be7cdd0959 by Gauri1 Deshpande <gauri1.deshpande@intel.com>:
minor change
--
182a17386f94f26fa7f9753eb03ea0b6263ef8b6 by Gauri1 Deshpande <gauri1.deshpande@intel.com>:
Address review comments
--
9780d75ad68275bf30b81c7cf02688fb87e5f1e2 by Gauri1 Deshpande <gauri1.deshpande@intel.com>:
Address re-review comments
Merging this change closes #62817
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/62817 from Intel-tensorflow:gaurides/amp_fp16 9780d75ad68275bf30b81c7cf02688fb87e5f1e2
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PR_kwDOArmXAs5prr0l
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#tf-data Graduate `file_locality_v2` experiment.
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#tf-data Graduate `file_locality_v2` experiment.
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PR_kwDOArmXAs5prrDg
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[xla:cpu] Add support for running XLA:CPU via multihost_hlo_runner
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[xla:cpu] Add support for running XLA:CPU via multihost_hlo_runner
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Rename GetFusionComputations within instruction fusion.
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Rename GetFusionComputations within instruction fusion.
The GetFusionComputations method returns computations that are candidates for performing fusion. These candidate computations are just non-fusion computations. As we are essentially just returning non-fusion computations, we can rename this function to GetNonFusionComputations.
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PR_kwDOArmXAs5pro-N
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hlo_dfs_reachability: reserve indices' storage at Build() time
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hlo_dfs_reachability: reserve indices' storage at Build() time
name old time/op new time/op delta
BM_HloDfsReachabilityBuild/1 105ns ± 1% 105ns ± 1% ~ (p=0.151 n=5+5)
BM_HloDfsReachabilityBuild/64 3.51µs ± 3% 2.38µs ± 2% -32.18% (p=0.008 n=5+5)
BM_HloDfsReachabilityBuild/128 6.93µs ± 2% 4.79µs ± 3% -30.82% (p=0.008 n=5+5)
BM_HloDfsReachabilityBuild/256 13.8µs ± 1% 9.5µs ± 1% -30.68% (p=0.008 n=5+5)
BM_HloDfsReachabilityBuild/512 28.0µs ± 1% 19.3µs ± 1% -30.86% (p=0.008 n=5+5)
BM_HloDfsReachabilityBuild/4096 268µs ± 1% 188µs ± 1% -29.90% (p=0.008 n=5+5)
BM_HloDfsReachabilityBuild/32768 2.74ms ± 3% 2.03ms ± 2% -25.99% (p=0.008 n=5+5)
BM_HloDfsReachabilityBuild/262144 43.8ms ± 2% 38.9ms ± 2% -11.15% (p=0.008 n=5+5)
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hlo_dfs_reachability: add tests + benchmark
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hlo_dfs_reachability: add tests + benchmark
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PR_kwDOArmXAs5prnwO
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When converting a graph to a module that came from MlirXlaOpKernel force the use of _output_shapes.
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When converting a graph to a module that came from MlirXlaOpKernel force the use of _output_shapes.
When an Op is to be lowered by MlirXlaOpKernel all the output tensor shapes have been thrown away (except in the attribute) - this hasn't been a problem for most kernels because they generate the output shape based on inputs or other features; however, some Ops (e.g. _XlaHostComputeMlir') do not and are broken by the go/tuba bridge changes. This change forces the graph to module conv...
***
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/9778 from shraiysh:flash-attn-2 77a644020c82939ea89c717c3490b5469d35e175
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PR_kwDOArmXAs5prnrR
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[XLA:CollectiveTransformationReorderer] Move all-reduce from the bottom of the reshape chain to the top as long as reshape has a single user and all-reduce is not layout constrained.
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[XLA:CollectiveTransformationReorderer] Move all-reduce from the bottom of the reshape chain to the top as long as reshape has a single user and all-reduce is not layout constrained.
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PR_kwDOArmXAs5prgjU
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Enable the modeling of memory costs when resharding tensors by default.
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Enable the modeling of memory costs when resharding tensors by default.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/9778 from shraiysh:flash-attn-2 77a644020c82939ea89c717c3490b5469d35e175
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PR_kwDOArmXAs5preGA
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#tfdata Set code-based default name for map and parallel map transformations.
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#tfdata Set code-based default name for map and parallel map transformations.
This is helpful for correlating tf.data maps in profiling traces with the code that generated them.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/9778 from shraiysh:flash-attn-2 77a644020c82939ea89c717c3490b5469d35e175
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To ensure that C++20 is fully supported in TensorFlow,
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To ensure that C++20 is fully supported in TensorFlow,
Ubuntu image used in TF SIG Build Dockerfile upgraded from 20.04 to 22.04 (LTS).
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PR_kwDOArmXAs5prN73
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Preserve Aliased Functions in StableHLO Quantizer
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| 2024-03-15T23:05:58
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Preserve Aliased Functions in StableHLO Quantizer
Aliased functions are represented as StatefulPartitionedCall in the TF graph. They will be marked tf._noinline=true so that Inliner pass will skip them. In this CL I:
* modified TF->StableHLO pass to skip lowering them to func.call op.
* modified ReplaceStablehloOpsInMainFunctionWithXlaCallModuleOpsPass. So that StableHLO graph in each StatefulPartitionedCall will be lifted.
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PR_kwDOArmXAs5prJ3M
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Fix a bug in which an absl hashtable is used with inconsistent hash/eq functors.
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Fix a bug in which an absl hashtable is used with inconsistent hash/eq functors.
`eq(k1, k2) -> hash(k1) == hash(k2)` must be true for hashtable usage to be valid.
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PR_kwDOArmXAs5prGKp
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Add XlaSplitND and XlaConcatND ops to tf_generated_ops.td.
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Add XlaSplitND and XlaConcatND ops to tf_generated_ops.td.
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PR_kwDOArmXAs5pq3oX
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make instructions more clear for building c dll with Windows
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"Hi @terryheo Can you please review this PR ? Thank you!",
"Closing, changes already committed here: https://github.com/tensorflow/tensorflow/commit/68c6031d03e2bce22e0b13da630171976c1ef65d"
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To more explicitly show how to build Release build for c dll on Windows.
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Add a knob to HloCSE to only sink scalar instructions.
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Add a knob to HloCSE to only sink scalar instructions.
HloCSE is executed to identifying unroallable loops.
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#shlo_ref Remove impossible storage/expressed type combinations from `DISPATCH_QUANTIZED`.
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#shlo_ref Remove impossible storage/expressed type combinations from `DISPATCH_QUANTIZED`.
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Rollback of GpuTimer: improve kernel execution time measurement accuracy
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Rollback of GpuTimer: improve kernel execution time measurement accuracy
This breaks the gemm algorithm picker test on V100
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PR_kwDOArmXAs5pqg5r
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Fix error messages mhlo_quant_legalize_to_int for illegal usages of per-axis quantization
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Fix error messages mhlo_quant_legalize_to_int for illegal usages of per-axis quantization
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Insert a task to the low priority task queue when the criticality is one of the fixed list of low priority criticalities and support padding the high priority batch with the unbatched tasks given via the ProcessBatchCallBack.
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Insert a task to the low priority task queue when the criticality is one of the fixed list of low priority criticalities and support padding the high priority batch with the unbatched tasks given via the ProcessBatchCallBack.
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PR_kwDOArmXAs5pqZZx
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Parameterize benchmark configuration for performance profiling
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Parameterize benchmark configuration for performance profiling
Parameterize num_tests, num_runs_per_test, and benchmark_command_buffer
for performance profiling. Update benchmark script to passthrough
additional arguments.
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PR_kwDOArmXAs5pqWZO
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Remove lazy caching of backend config
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Remove lazy caching of backend config
The current implementation is not thread-compatible which already
led to various intermittent threading bugs.
Let's just remove the lazy caching. There is no evidence that
it brings any improvements in the first place.
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PR #10372: [GPU] Change cuDNN GEMM fusion flag type to an integer.
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PR #10372: [GPU] Change cuDNN GEMM fusion flag type to an integer.
Imported from GitHub PR https://github.com/openxla/xla/pull/10372
This will allow enabling more fusions in parallel with rolling them out by default.
Copybara import of the project:
--
4df569ff5e204f1b2025863f5e1f8dba2a5d8b95 by Ilia Sergachev <isergachev@nvidia.com>:
[GPU] Change cuDNN GEMM fusion flag type to an integer.
This will allow enabling more fusions in parallel with rolling them out by default.
Merging this change closes #10372
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10372 from openxla:cudnn_fusion_level 4df569ff5e204f1b2025863f5e1f8dba2a5d8b95
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PR #62817: [onednn] Enable auto_mixed_precision for fp16 on cpu
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PR #62817: [onednn] Enable auto_mixed_precision for fp16 on cpu
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/62817
This PR enables AMP FP16 on supporting Intel Xeon CPUs. If there is no GPU available to run FP16, it checks if FP16 can be executed on CPU.
Use config `auto_mixed_precision` to enable FP16 on CPUs.
Copybara import of the project:
--
fc1ee3bcc2d07d30bcd9480280796eb014bb0e2b by Gauri1 Deshpande <gauri1.deshpande@intel.com>:
[onednn] Enable auto_mixed_precision for fp16 on cpu
--
9a3c8d59eb65fac7253b8f9a0db92dc93e22e374 by Gauri1 Deshpande <gauri1.deshpande@intel.com>:
Address review comments - update comments as per guidelines.
--
27b1a9623f72a5acd13a864d1d00b47a85ca43fe by Gauri1 Deshpande <gauri1.deshpande@intel.com>:
Address review comments
--
9a3ef902974965aacfaf2192b3a7253dcd0609f8 by Gauri1 Deshpande <gauri1.deshpande@intel.com>:
Address review comments
--
0ab5b80f9bf952e3eb6f5fb1df0881be7cdd0959 by Gauri1 Deshpande <gauri1.deshpande@intel.com>:
minor change
--
182a17386f94f26fa7f9753eb03ea0b6263ef8b6 by Gauri1 Deshpande <gauri1.deshpande@intel.com>:
Address review comments
--
9780d75ad68275bf30b81c7cf02688fb87e5f1e2 by Gauri1 Deshpande <gauri1.deshpande@intel.com>:
Address re-review comments
Merging this change closes #62817
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/62817 from Intel-tensorflow:gaurides/amp_fp16 9780d75ad68275bf30b81c7cf02688fb87e5f1e2
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PR_kwDOArmXAs5pqOY3
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Integrate LLVM at llvm/llvm-project@3b5e7c83a6e2
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Integrate LLVM at llvm/llvm-project@3b5e7c83a6e2
Updates LLVM usage to match
[3b5e7c83a6e2](https://github.com/llvm/llvm-project/commit/3b5e7c83a6e2)
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PR #10507: [GPU][NFC] Rename GEMM fusion pass.
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PR #10507: [GPU][NFC] Rename GEMM fusion pass.
Imported from GitHub PR https://github.com/openxla/xla/pull/10507
This is a follow-up to https://github.com/openxla/xla/pull/10252.
Copybara import of the project:
--
d93c2d3803d6e51dda0d1f64935647adef5326e2 by Ilia Sergachev <isergachev@nvidia.com>:
[GPU][NFC] Rename GEMM fusion pass.
Merging this change closes #10507
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10507 from openxla:rename_gemm_fusion d93c2d3803d6e51dda0d1f64935647adef5326e2
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PR_kwDOArmXAs5pqJcQ
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PR #9778: Added flash attention with new cudnn frontend
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PR #9778: Added flash attention with new cudnn frontend
Imported from GitHub PR https://github.com/openxla/xla/pull/9778
This is the second attempt at #9528.
Copybara import of the project:
--
77a644020c82939ea89c717c3490b5469d35e175 by Shraiysh Vaishay <svaishay@nvidia.com>:
Added flash attention with new cudnn frontend
Merging this change closes #9778
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/9778 from shraiysh:flash-attn-2 77a644020c82939ea89c717c3490b5469d35e175
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Rollback:
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Rollback:
Parameterize benchmark configuration for performance profiling
Parameterize num_tests, num_runs_per_test, and benchmark_command_buffer
for performance profiling. Update benchmark script to passthrough
additional arguments.
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Fix check fail in TensorScatterUpdate Op
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The Op `TensorScatterUpdate` checkfails if the rank of updates is lower than the rank of indices -1.
This will happen from this code below.
https://github.com/tensorflow/tensorflow/blob/0f7eb923815f4fd82321dda01bab45b493227d58/tensorflow/core/kernels/scatter_nd_op.cc#L169-L172
From above code the loop will iterate over `indices.shape().dims() - 1` times and checks whether `indices.shape().dim_size(i) == updates.shape().dim_size(i)`. Suppose if indices is a 3D and updates is 1D this will cause checkfail when i=1.
May fix #63575 .
|
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PR_kwDOArmXAs5pp7pB
| 63,724
|
Log checkpoint hashing error.
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Log checkpoint hashing error.
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PR_kwDOArmXAs5pp4zU
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Rename `TfLiteRegistrationExternal` as `TfLiteOperator`.
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Rename `TfLiteRegistrationExternal` as `TfLiteOperator`.
Likewise rename corresponding API functions (all experimental).
Rename (experimental) `TfLiteInterpreterOptionsAddRegistrationExternal` as `TfLiteInterpreterOptionsAddOperator`.
Rename (private) header registration_external.h as operator.h and
rename registration_external.cc as operator.cc.
Also add temporary backwards compatibility aliases that forward the old
names to the new names. These backwards compatibility aliases are needed
even though these names are experimental, because the old names are currently
referenced in MediaPipe, which is in a separate repository, and thus can't be
updated atomically.
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I_kwDOArmXAs6CVydm
| 63,722
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Aborted can be triggered in tf.raw_ops.TensorListConcat
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[
"Hi **@vwrewsge** ,\r\n\r\nI tried to run your code on colab using tf 2.15, 2.16 and tf-nightly versions faced same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/895584f0f6c534a30189918240c83056/63722_2-15-2-16-nightly.ipynb) here for reference. We need to work on it, In few days i will provide a solution for it.\r\n\r\nThank you!"
] | 2024-03-14T16:09:12
| 2024-03-18T09:38:48
| null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
The following code sometimes runs normally when calling `tf.raw_ops.TensorListConcat`, but sometimes encounters an "aborted" error.
To reproduce the issue, it may be necessary to execute several times.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
input_data_1 = tf.constant([[1, 2, 3], [4, 5, 6]])
input_data_2 = tf.constant([[7, 8, 9], [10, 11, 12]])
input_handle = tf.raw_ops.TensorListReserve(element_dtype=tf.int32, element_shape=[2, 3], num_elements=2)
input_handle = tf.raw_ops.TensorListSetItem(input_handle=input_handle, index=0, item=input_data_1)
input_handle = tf.raw_ops.TensorListSetItem(input_handle=input_handle, index=1, item=input_data_2)
concatenated_data, lengths = tf.raw_ops.TensorListConcat(input_handle=input_handle, element_dtype=tf.int32, element_shape=[6])
print(concatenated_data)
```
### Relevant log output
```shell
tf.Tensor([1 2 3 4], shape=(4,), dtype=int32)
malloc_consolidate(): invalid chunk size
Aborted (core dumped)
```
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I_kwDOArmXAs6CVtkP
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A check fail can be triggered in tf.raw_ops.StatefulPartitionedCall
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[
"@vwrewsge,\r\nCould you please confirm whether this check fail is happening with the specific input provided or for every input you are passing? When I tried with the another input it was executed without check fail. Thank you!",
"This check fail is happening for every input. This seems to be a lack of check on `Tout`. If I set `Tout ` to `[tf.int32]`, there shouldn't be any issues."
] | 2024-03-14T15:59:00
| 2024-04-03T08:34:14
| null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
In the latest version of TensorFlow, the following code can trigger a crash in `tf.raw_ops.StatefulPartitionedCall` due to check-fail.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
input_data = tf.constant([[1, 2, 3], [4, 5, 6]])
@tf.function
def my_function(inputs):
return tf.reduce_sum(inputs, axis=1)
callable_function = tf.function(my_function).get_concrete_function(tf.TensorSpec(shape=None, dtype=tf.int32))
output = tf.raw_ops.StatefulPartitionedCall(args=[input_data], Tout=None, f=callable_function)
print(output)
```
### Relevant log output
```shell
2024-03-14 15:58:52.804195: F tensorflow/core/framework/op_kernel.cc:989] Check failed: index < outputs_.size() (0 vs. 0)
Aborted (core dumped)
```
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I_kwDOArmXAs6CVsvd
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A check fail can be triggered in tf.raw_ops.ResourceSparseApplyKerasMomentum
|
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[] | 2024-03-14T15:57:19
| 2024-03-15T08:49:28
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NONE
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### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
In the latest version of TensorFlow, the following code can trigger a crash in `tf.raw_ops.ResourceSparseApplyKerasMomentum` due to check-fail.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
var = tf.Variable([[1.0, 2.0], [3.0, 4.0]], dtype=tf.complex64)
accum = tf.Variable([[0.1, 0.2], [0.3, 0.4]])
lr = 0.01
grad = tf.constant([[0.1, 0.2], [0.3, 0.4]])
indices = tf.constant([0, 1])
momentum = 0.9
result = tf.raw_ops.ResourceSparseApplyKerasMomentum(var=var.handle, accum=accum.handle, lr=lr, grad=grad, indices=indices, momentum=momentum)
print(result)
```
### Relevant log output
```shell
2024-03-14 15:57:08.452454: F tensorflow/core/framework/tensor.cc:844] Check failed: dtype() == expected_dtype (8 vs. 1) float expected, got complex64
Aborted (core dumped)
```
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[PJRT] Drop mentions of CPU support from the stream_executor client.
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[PJRT] Drop mentions of CPU support from the stream_executor client.
We never use the stream_executor client on CPU any more, since the TFRT CPU client is better in every way.
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Update SplitKGemmRewriter pass to support sparse dots
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Update SplitKGemmRewriter pass to support sparse dots
This pass runs after DotDecomposer, so the LHS input has the shape dimensions in the following order: batch, non-contracting, contracting.
The sparse dot operation can only be lowered through triton on NVidia GPUs (which have hardware support for it), and one of the restrictions is that sparsity is only supported for LHS input:
https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#matrix-multiply-accumulate-operation-using-mma-sp-instruction-with-sparse-matrix-a
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PR #10519: [GPU] Upgrade cuDNN frontend to v1.2.0.
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PR #10519: [GPU] Upgrade cuDNN frontend to v1.2.0.
Imported from GitHub PR https://github.com/openxla/xla/pull/10519
This will for instance allow fusion of scalar constants.
Copybara import of the project:
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Merging this change closes #10519
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Fix chakcfail of ResourceApplyProximalAdagrad Op
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The Op `ResourceApplyProximalAdagrad` can lead to checkfail if the inputs `lr`, `l1`, `l2` and `grad` don't passed with same dtype. Proposed validation for same.
Fixes #63697 .
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Another round of #include cleanups
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PR #10519: [GPU] Upgrade cuDNN frontend to v1.2.0.
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PR #10519: [GPU] Upgrade cuDNN frontend to v1.2.0.
Imported from GitHub PR https://github.com/openxla/xla/pull/10519
This will for instance allow fusion of scalar constants.
Copybara import of the project:
--
4695023e035516db05b707d79409a60eba9f47f7 by Ilia Sergachev <isergachev@nvidia.com>:
[GPU] Upgrade cuDNN frontend to v1.2.0.
Merging this change closes #10519
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10519 from openxla:cudnn_frontend_1_2_0 4695023e035516db05b707d79409a60eba9f47f7
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PR_kwDOArmXAs5pn-vK
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Integrate LLVM at llvm/llvm-project@3b5e7c83a6e2
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Integrate LLVM at llvm/llvm-project@3b5e7c83a6e2
Updates LLVM usage to match
[3b5e7c83a6e2](https://github.com/llvm/llvm-project/commit/3b5e7c83a6e2)
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Sparse dot meta now includes batch dimensions
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Sparse dot meta now includes batch dimensions
Before this change, sparse dot metadata didn't include batch dimensions (as it's the same across batches).
After this change, sparse dot metadata has the shape that is similar to the sparse operand (all dimensions are the same, except the contracting minor dimension).
This allows implementing split-k pass that splits the contracting dimension into batch/contracting, without losing the metadata.
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PR #10505: [ROCM] fixed build brake 2024-03-13
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PR #10505: [ROCM] fixed build brake 2024-03-13
Imported from GitHub PR https://github.com/openxla/xla/pull/10505
fixed the head error from https://github.com/openxla/xla/commit/f11b268db089df22adf604651b78d43f58f02952
@xla-rotation @sergeykozub
Copybara import of the project:
--
e7bd600b260cf6b4c301d62a1686053a5b456d18 by Chao Chen <cchen104@amd.com>:
pjrt_platform_id
--
751a1158904fbaf56495c7193aa8fc9a6cb76f12 by Chao Chen <cchen104@amd.com>:
fixed header error
--
ebffbcff915019214019782f5efd94ef4f5d9e2e by Chao Chen <cchen104@amd.com>:
roll back unintent changes
Merging this change closes #10505
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10505 from ROCm:ci_2024_0313 ebffbcff915019214019782f5efd94ef4f5d9e2e
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[XLA:GPU] In Multi-output fusion, use BFS to check reachability
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[XLA:GPU] In Multi-output fusion, use BFS to check reachability
Currently, MultiOutputFusion uses HloReachabilityMap, which builds reachability matrix on creation and element lookup on request. With multi-output fusion rebuilding the reachability after every step, that may take time.
This change makes MultiOutputFusion to use a simple breadth-first search reachability check, bounded by the post-order index. Namely,
* To build the "map", the instructions are sorted in the post order, and indices are stored.
* On the request, the BFS from the destination node is started, and only goes through nodes which have post-order index >= source node.
With this approach, the MultiOutputFusion pass of one example HLO module goes from 34 minutes to 4 minutes. There are more advanced data structures that help even more (0.5 minutes), this will be done in the subsequent changes.
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PR #10542: [GPU] Do not assign Triton configs to cuDNN fusions when autotuning is disabled.
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PR #10542: [GPU] Do not assign Triton configs to cuDNN fusions when autotuning is disabled.
Imported from GitHub PR https://github.com/openxla/xla/pull/10542
Copybara import of the project:
--
69b902f6f977acc7695a32cb228edacfa855646e by Ilia Sergachev <isergachev@nvidia.com>:
[GPU] Do not assign Triton configs to cuDNN fusions when autotuning is disabled.
Merging this change closes #10542
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10542 from openxla:cudnn_fix_autotuning 69b902f6f977acc7695a32cb228edacfa855646e
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PR #10510: [ROCm] Triton in XLA for ROCm - gemm rewriter triton related changes.
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PR #10510: [ROCm] Triton in XLA for ROCm - gemm rewriter triton related changes.
Imported from GitHub PR https://github.com/openxla/xla/pull/10510
First commit of the series for enabling Triton in XLA for ROCm .
Copybara import of the project:
--
832d7253db1f8972862252f034b216d1eed1da29 by Zoran Jovanovic <zjovanov@amd.com>:
[ROCm] Triton in XLA for ROCm - gemm rewriter triton related changes.
Merging this change closes #10510
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10510 from ROCm:rocm_triton_backend 832d7253db1f8972862252f034b216d1eed1da29
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AttributeError: type object 'TFLiteConverterV2' has no attribute 'from_frozen_graph'?
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"@selvakarna `AttributeError: type object 'TFLiteConverterV2' has no attribute` '`from_frozen_graph`' error because the from_frozen_graph method is not available in TensorFlow 2.x's TFLiteConverterV2 class.\r\nIf your model is a frozen graph saved as a .pb file, you'll need to use the TensorFlow 1.x compatibility layer (tf.compat.v1) to access the `from_frozen_graph` method.\r\nCould you please let us know which TF version you have used?\r\nThank you!",
"Thank you @sushreebarsa \r\nTF Version is 2.13.0...",
"@selvakarna Could you use tf.saved_model.load to load the frozen graph as a SavedModel and then convert it to TFLite?\r\nPlease let us know the outcome?\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further."
] | 2024-03-14T09:48:05
| 2024-04-11T01:48:06
| 2024-04-11T01:48:06
|
NONE
| null | null | null |
#### Questions:
I am try convert Frozen Graph.pb tensorflow model into TFLite light weigth model, I am getting following error?
#### Error
AttributeError: type object 'TFLiteConverterV2' has no attribute 'from_frozen_graph'
#### My Code:
import tensorflow as tf
frozen_graph_path="\model\md\saved_model.pb"
pbtxt_path="\model\md\saved_model.pbtxt"
input_shape=(1,224,224,3)
with tf.io.gfile.GFile(frozen_graph_path,"rb") as f:
graph_def=tf.compat.v1.GraphDef()
graph_def.ParseFromString(f.read())
print("The model graph loaded............")
converter=tf.lite.TFLiteConverter.from_frozen_graph(graph_def,input_arrays= ['image_tensor'],output_arrays=['Preprocessor/map/while/Identity'])
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Integrate LLVM at llvm/llvm-project@096ee4e16fd6
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[] | 2024-03-14T09:46:47
| 2024-04-05T13:01:13
| 2024-04-05T13:01:11
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Integrate LLVM at llvm/llvm-project@096ee4e16fd6
Updates LLVM usage to match
[096ee4e16fd6](https://github.com/llvm/llvm-project/commit/096ee4e16fd6)
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Could not load library cudnn_cnn_infer64_8.dll. Error code 126
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"Hi **@Tanishq-JM** ,\r\nSorry for the dealy, Confirm that TensorFlow-GPU is installed correctly by running a simple GPU-enabled TensorFlow script. You can use the following code snippet to check if TensorFlow can detect and use your GPU:\r\n```\r\nimport tensorflow as tf\r\nprint(\"Num GPUs Available:\", len(tf.config.experimental.list_physical_devices('GPU')))\r\n```\r\nIf this script doesn't detect your GPU, there may be underlying issues with your TensorFlow installation or GPU setup.\r\n\r\nHere i am providing the [documentation](https://www.tensorflow.org/install/source_windows) how to install. Please go through it once.\r\n\r\nHere i am providing updated [gist](https://colab.sandbox.google.com/gist/Venkat6871/5a7a97e09655679f39c3565ec176fd4a/63702_2-16-1-nightly.ipynb) with some corrections for your reference. Please check this too.\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Hi @Venkat6871 \r\nThank you for your time , i am still facing same issue and now i think i would go for the google colab instead it would me more easy for me to maintain and run it .\r\nThank you for your time and kind support 😃. ",
"Hi **@Tanishq-JM** ,\r\n- Could you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved.\r\n\r\nThank you!",
"Hello I am still facing this issue "
] | 2024-03-14T09:43:31
| 2024-06-11T23:46:55
| null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.10.0
### Custom code
No
### OS platform and distribution
windows 10
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.2 / 11.2(8.1 I think)
### GPU model and memory
Nvidia T2000 4GB
### Current behavior?
# Problem :
I expect this problem to be resolved soon. I am unable to use my GPU for training, as it remains at 0% usage.
When I try another version, it only uses my CPU, which takes days and causes my computer (laptop) to overheat.
### Error
I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8600
Could not load library cudnn_cnn_infer64_8.dll. Error code 126
Please make sure cudnn_cnn_infer64_8.dll is in your library path!
# Output
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import subprocess
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
def get_gpu_info():
try:
result = subprocess.run(['nvidia-smi'], stdout=subprocess.PIPE)
return result.stdout.decode('utf-8')
except FileNotFoundError:
return "nvidia-smi not found. Make sure NVIDIA drivers are installed."
from keras import layers, models, datasets
import time
(train_images, train_labels), (_, _) = datasets.mnist.load_data()
train_images = train_images.reshape(-1, 28, 28, 1).astype('float32') / 255.0
model = models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
start_time = time.time()
end_time = start_time + 30
while time.time() < end_time:
gpu_info = get_gpu_info()
print(gpu_info)
model.fit(train_images, train_labels, epochs=1, batch_size=64, verbose=0)
loss, accuracy = model.evaluate(train_images, train_labels, verbose=0)
print(f"Loss: {loss}, Accuracy: {accuracy}")
```
### Relevant log output
```shell
(ML) PS D:\python> & d:/python/ML/Scripts/python.exe d:/python/test.py
2024-03-14 11:50:53.845372: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-14 11:50:55.378833: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /device:GPU:0 with 2128 MB memory: -> device: 0, name: Quadro T2000, pci bus id: 0000:01:00.0, compute capability: 7.5
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 12036372220610172862
xla_global_id: -1
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 2232156160
locality {
bus_id: 1
links {
}
}
incarnation: 1063671899388078998
physical_device_desc: "device: 0, name: Quadro T2000, pci bus id: 0000:01:00.0, compute capability: 7.5"
xla_global_id: 416903419
]
2024-03-14 11:50:55.686954: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2128 MB memory: -> device: 0, name: Quadro T2000,
pci bus id: 0000:01:00.0, compute capability: 7.5
Thu Mar 14 11:50:55 2024
2024-03-14 11:50:57.159136: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8600
Could not load library cudnn_cnn_infer64_8.dll. Error code 126
Please make sure cudnn_cnn_infer64_8.dll is in your library path!
(ML) PS D:\python>
```
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Enormous memory usage after batched forward passes with TensorFlow 2.16.1
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"As a workaround (I just found), one can replace `model(images).numpy()` with `model.predict(images)`. This tames the memory usage.",
"@Dobiasd,\r\nAs you mentioned there was memory usage with tensorflow 2.16.1 while executing the mentioned code. When compared with 2.15, I can see the difference in memory usage. Could you please allow some time to check with the developer and provide the solution for the same. Meanwhile you can try using model.predict(images) where the memory is the same as 2.15.\r\nKindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/56402dc07a59baf4d0b694686851d799/untitled1761.ipynb).\r\n\r\n<details><summary>Click to expand for the screenshots!</summary>\r\n2.15:\r\n\r\n\r\n\r\n2.16:\r\n\r\n\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Thanks for reproducing the effect. I know that using `model.predict(images)` is a workaround for the problematic `model(images)` (wrote it already above). However, I don't feel that this is a reason to close this issue. `model(images)` is still broken.",
"@Dobiasd,\r\nBy default tensorflow v2.16 uses keras 3.0 version. I tried to import the keras as **import tf_keras as keras** and the code given execution time with the **model(images)** is similar as tensorflow v2.15. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/eb64aa5f382a486d1355a7bc2550309b/untitled1793.ipynb). \r\n\r\n```python\r\nMemory usage after 0 run(s) (in MiB): 1279.699\r\nMemory usage after 1 run(s) (in MiB): 1518.086\r\nMemory usage after 2 run(s) (in MiB): 1518.867\r\nMemory usage after 3 run(s) (in MiB): 1488.008\r\nMemory usage after 4 run(s) (in MiB): 1518.637\r\nMemory usage after 5 run(s) (in MiB): 1518.578\r\nMemory usage after 6 run(s) (in MiB): 1518.777\r\nMemory usage after 7 run(s) (in MiB): 1518.613\r\nMemory usage after 8 run(s) (in MiB): 1503.199\r\nMemory usage after 9 run(s) (in MiB): 1518.297\r\n```\r\nThank you!",
"Thanks! Does this mean this issue should be moved from the `tensorflow/tensorflow` repo to the `keras-team/keras` repo?",
"Btw., `model.predict(images)` also leaks a bit of memory:\r\n\r\n```python3\r\nimport numpy as np\r\nimport psutil\r\nimport tensorflow as tf\r\n\r\nmodel = tf.keras.applications.ResNet152V2()\r\nfor run in range(10000):\r\n images = np.zeros([run % 100 + 1, 224, 224, 3], dtype=np.uint8)\r\n memory_usage_in_MiB = psutil.Process().memory_info().rss / (1024 * 1024)\r\n print(f\"Memory usage after {run} run(s) (in MiB): {memory_usage_in_MiB:.3f}\", flush=True)\r\n model.predict(images, verbose=0)\r\n```\r\n\r\n```\r\nMemory usage after 0 run(s) (in MiB): 783.898\r\nMemory usage after 1 run(s) (in MiB): 843.965\r\nMemory usage after 2 run(s) (in MiB): 905.809\r\nMemory usage after 3 run(s) (in MiB): 926.613\r\nMemory usage after 4 run(s) (in MiB): 929.820\r\nMemory usage after 5 run(s) (in MiB): 988.938\r\nMemory usage after 6 run(s) (in MiB): 1022.199\r\nMemory usage after 7 run(s) (in MiB): 1030.844\r\nMemory usage after 8 run(s) (in MiB): 1035.547\r\nMemory usage after 9 run(s) (in MiB): 1075.227\r\nMemory usage after 10 run(s) (in MiB): 1080.125\r\nMemory usage after 11 run(s) (in MiB): 1085.094\r\nMemory usage after 12 run(s) (in MiB): 1064.848\r\nMemory usage after 13 run(s) (in MiB): 1082.512\r\nMemory usage after 14 run(s) (in MiB): 1094.887\r\nMemory usage after 15 run(s) (in MiB): 1102.629\r\nMemory usage after 16 run(s) (in MiB): 1122.195\r\nMemory usage after 17 run(s) (in MiB): 1101.715\r\nMemory usage after 18 run(s) (in MiB): 1118.234\r\nMemory usage after 19 run(s) (in MiB): 1120.234\r\nMemory usage after 20 run(s) (in MiB): 1136.480\r\n...\r\nMemory usage after 409 run(s) (in MiB): 1254.375\r\nMemory usage after 410 run(s) (in MiB): 1260.383\r\nMemory usage after 713 run(s) (in MiB): 1297.750\r\nMemory usage after 714 run(s) (in MiB): 1300.977\r\nMemory usage after 715 run(s) (in MiB): 1303.609\r\n...\r\nMemory usage after 3117 run(s) (in MiB): 1329.902\r\nMemory usage after 3118 run(s) (in MiB): 1357.523\r\n...\r\n```\r\n\r\n([Dockerfile](https://gist.github.com/Dobiasd/d37045cf3f4413457ff2427edeb69931) to reproduce)",
"@Dobiasd,\r\nLooks like this is also happening with the tensorflow v.2.16 & keras3.0. With the tensorflow v2.15 and tf-keras I was not able to find any issue with the memory. Kindly please raise the request in keras-team/keras repo. Thank you!",
"Thanks. As suggested, I've opened [this issue](https://github.com/keras-team/keras/issues/19500) in `keras-team/keras`.",
"The conclusion so far of the discussion in the Keras repo [is](https://github.com/keras-team/keras/issues/19500#issuecomment-2053705772), that it's a problem with TensorFlow, not with Keras.",
"This is what I get when running @Dobiasd's dockerfile image on my machine:\r\n```\r\nMemory usage after 0 run(s) (in MiB): 993.359\r\nMemory usage after 1 run(s) (in MiB): 1021.312\r\nMemory usage after 2 run(s) (in MiB): 1115.410\r\nMemory usage after 3 run(s) (in MiB): 1166.316\r\nMemory usage after 4 run(s) (in MiB): 1233.500\r\nMemory usage after 5 run(s) (in MiB): 1271.699\r\nMemory usage after 6 run(s) (in MiB): 1360.941\r\nMemory usage after 7 run(s) (in MiB): 1388.930\r\nMemory usage after 8 run(s) (in MiB): 1464.305\r\nMemory usage after 9 run(s) (in MiB): 1591.188\r\n...\r\nMemory usage after 37 run(s) (in MiB): 3212.566\r\nMemory usage after 38 run(s) (in MiB): 3280.664\r\nMemory usage after 39 run(s) (in MiB): 3320.461\r\nMemory usage after 40 run(s) (in MiB): 2003.023 <--- big drop\r\nMemory usage after 41 run(s) (in MiB): 2137.840\r\nMemory usage after 42 run(s) (in MiB): 2198.230\r\n...\r\nMemory usage after 67 run(s) (in MiB): 4361.066\r\nMemory usage after 68 run(s) (in MiB): 4424.414\r\nMemory usage after 69 run(s) (in MiB): 4457.262\r\nMemory usage after 70 run(s) (in MiB): 2415.262 <--- big drop\r\nMemory usage after 71 run(s) (in MiB): 2599.285\r\nMemory usage after 72 run(s) (in MiB): 2723.152\r\n...\r\n```\r\nThis pattern seems to repeat: slowly growing, then a drop. It never seems to monotonically increase: only up to some limit before dropping again.\r\n\r\nI'm not sure it's a leak, since it stays below a certain (perhaps machine-specific) limit before it drops down. Outside of docker, running locally on my machine, I see more stable results on CPU:\r\n```\r\nMemory usage after 0 run(s) (in MiB): 988.746\r\nMemory usage after 1 run(s) (in MiB): 1042.246\r\nMemory usage after 2 run(s) (in MiB): 1050.246\r\nMemory usage after 3 run(s) (in MiB): 1046.680\r\nMemory usage after 4 run(s) (in MiB): 1050.180\r\nMemory usage after 5 run(s) (in MiB): 1052.312\r\nMemory usage after 6 run(s) (in MiB): 1048.418\r\nMemory usage after 7 run(s) (in MiB): 1052.418\r\nMemory usage after 8 run(s) (in MiB): 1049.156\r\nMemory usage after 9 run(s) (in MiB): 1054.156\r\nMemory usage after 10 run(s) (in MiB): 1054.156\r\n...\r\nMemory usage after 96 run(s) (in MiB): 1074.281\r\nMemory usage after 97 run(s) (in MiB): 1072.508\r\nMemory usage after 98 run(s) (in MiB): 1076.008\r\nMemory usage after 99 run(s) (in MiB): 1070.641\r\n```\r\nwhich is very similar to @fchollet's [colab](https://colab.research.google.com/drive/1FesdhTGp65FmL1ZqNj28yJ8yqSPnG3Mt?usp=sharing).\r\n\r\nI don't even think it's a keras 3 vs legacy keras issue, since using [legacy `tf-keras`](https://keras.io/getting_started/#tensorflow--keras-2-backwards-compatibility) leads to the same memory profile for me both in docker and my local machine.\r\n\r\nMy guess is its just TensorFlow's [BFC allocator](https://github.com/google/tsl/blob/556552d6c0c97c7fdb8689cdc75768f0cfa0be7c/tsl/framework/bfc_allocator.h#L51) slowly growing. Something might have changed in TF 2.16 related to memory allocations that reduces memory re-use before the allocator consolidates freed blocks. If you have a lot of memory available, it might grow more before doing this on your particular machine.",
"Thanks for testing!\r\n\r\n> If you have a lot of memory available, it might grow more before doing this on your particular machine.\r\n\r\nThat's very unfortunate if you're running something like that in a Kubernetes pod with a (cgroups-managed) memory limit, and TensorFlow uses the memory as if it were the only process on the whole node. At least that's how I ran into this problem, i.e., my K8s pods were constantly OOMKilled.",
"> Thanks for testing!\r\n> \r\n> > If you have a lot of memory available, it might grow more before doing this on your particular machine.\r\n> \r\n> That's very unfortunate if you're running something like that in a Kubernetes pod with a (cgroups-managed) memory limit, and TensorFlow uses the memory as if it were the only process on the whole node. At least that's how I ran into this problem, i.e., my K8s pods were constantly OOMKilled.\r\n\r\nSorry, I made a mistake - using legacy keras _does_ fix the issue. Setting the environment variable:\r\n```\r\nexport TF_USE_LEGACY_KERAS=1\r\n```\r\n_does_ restore the original memory profile. So keras 3 is a contributing factor. I think it has to do with tracking information for training (since this is a trainable model). Note that the initial jump in memory use is just the model loading the weights for the first time, since you report your first memory usage _before_ your first predict call.\r\n\r\nI still don't think it's a \"leak\". Modifying your script a bit to use `tracemalloc` and recording memory use _after_ the first predict iteration:\r\n```python\r\nimport numpy as np\r\nimport psutil\r\nimport tensorflow as tf\r\nimport tracemalloc\r\nimport gc\r\n\r\ndef main():\r\n model = tf.keras.applications.ResNet152V2()\r\n images = np.zeros([20, 224, 224, 3], dtype=np.uint8)\r\n\r\n for run in range(20):\r\n model(images)\r\n gc.collect()\r\n memory_usage_in_MiB = psutil.Process().memory_info().rss / (1024 * 1024)\r\n print(f\"Memory usage after {run} run(s) (in MiB): {memory_usage_in_MiB:.3f}\", flush=True)\r\n\r\ntracemalloc.start()\r\nmain()\r\nsnapshot = tracemalloc.take_snapshot()\r\ntop_stats = snapshot.statistics('lineno')\r\nprint(\"[ Top 10 ]\")\r\nfor stat in top_stats[:10]:\r\n print(stat)\r\n```\r\nI get the following with Keras 3 by exporting `TF_USE_LEGACY_KERAS=0`:\r\n```\r\nMemory usage after 0 run(s) (in MiB): 6094.586\r\nMemory usage after 1 run(s) (in MiB): 7081.043\r\nMemory usage after 2 run(s) (in MiB): 7133.891\r\nMemory usage after 3 run(s) (in MiB): 7132.301\r\nMemory usage after 4 run(s) (in MiB): 7139.891\r\nMemory usage after 5 run(s) (in MiB): 7219.988\r\nMemory usage after 6 run(s) (in MiB): 7156.012\r\nMemory usage after 7 run(s) (in MiB): 7247.742\r\nMemory usage after 8 run(s) (in MiB): 7218.574\r\nMemory usage after 9 run(s) (in MiB): 7219.082\r\nMemory usage after 10 run(s) (in MiB): 7222.090\r\nMemory usage after 11 run(s) (in MiB): 7245.625\r\nMemory usage after 12 run(s) (in MiB): 7245.625\r\nMemory usage after 13 run(s) (in MiB): 7259.473\r\nMemory usage after 14 run(s) (in MiB): 7266.531\r\nMemory usage after 15 run(s) (in MiB): 7266.871\r\nMemory usage after 16 run(s) (in MiB): 7242.562\r\nMemory usage after 17 run(s) (in MiB): 7243.641\r\nMemory usage after 18 run(s) (in MiB): 7194.695\r\nMemory usage after 19 run(s) (in MiB): 7247.301\r\n[ Top 10 ]\r\n/tmp/venv/lib/python3.10/site-packages/tensorflow/python/framework/constant_op.py:96: size=188 MiB, count=1272, average=151 KiB\r\n/tmp/venv/lib/python3.10/site-packages/keras/src/utils/tracking.py:65: size=634 KiB, count=3405, average=191 B\r\n/tmp/venv/lib/python3.10/site-packages/tensorflow/python/trackable/autotrackable.py:70: size=444 KiB, count=1144, average=398 B\r\n/tmp/venv/lib/python3.10/site-packages/keras/src/utils/tracking.py:139: size=339 KiB, count=6676, average=52 B\r\n/tmp/venv/lib/python3.10/site-packages/tensorflow/python/ops/resource_variable_ops.py:2026: size=318 KiB, count=1631, average=200 B\r\n/tmp/venv/lib/python3.10/site-packages/keras/src/utils/tracking.py:95: size=235 KiB, count=3339, average=72 B\r\n/tmp/venv/lib/python3.10/site-packages/tensorflow/python/trackable/base.py:361: size=229 KiB, count=1085, average=216 B\r\n<frozen importlib._bootstrap_external>:672: size=226 KiB, count=2131, average=109 B\r\n/usr/local/google/home/cantonios/.pyenv/versions/3.10.12/lib/python3.10/abc.py:123: size=224 KiB, count=2834, average=81 B\r\n/tmp/venv/lib/python3.10/site-packages/keras/src/utils/tracking.py:100: size=191 KiB, count=874, average=224 B\r\n```\r\nThe `constant_op` is allocating the weight and shape tensors. Then there's a bunch of \"tracking\" allocations.\r\n\r\nIf I use legacy keras by exporting `TF_USE_LEGACY_KERAS=1`, I get:\r\n```\r\nMemory usage after 0 run(s) (in MiB): 1341.215\r\nMemory usage after 1 run(s) (in MiB): 1313.246\r\nMemory usage after 2 run(s) (in MiB): 1313.789\r\nMemory usage after 3 run(s) (in MiB): 1497.645\r\nMemory usage after 4 run(s) (in MiB): 1618.266\r\nMemory usage after 5 run(s) (in MiB): 1624.480\r\nMemory usage after 6 run(s) (in MiB): 1625.363\r\nMemory usage after 7 run(s) (in MiB): 1625.152\r\nMemory usage after 8 run(s) (in MiB): 1625.297\r\nMemory usage after 9 run(s) (in MiB): 1625.344\r\nMemory usage after 10 run(s) (in MiB): 1624.699\r\nMemory usage after 11 run(s) (in MiB): 1622.949\r\nMemory usage after 12 run(s) (in MiB): 1625.496\r\nMemory usage after 13 run(s) (in MiB): 1624.801\r\nMemory usage after 14 run(s) (in MiB): 1625.109\r\nMemory usage after 15 run(s) (in MiB): 1625.328\r\nMemory usage after 16 run(s) (in MiB): 1624.387\r\nMemory usage after 17 run(s) (in MiB): 1625.062\r\nMemory usage after 18 run(s) (in MiB): 1625.141\r\nMemory usage after 19 run(s) (in MiB): 1625.152\r\n[ Top 10 ]\r\n/tmp/venv/lib/python3.10/site-packages/tensorflow/python/framework/constant_op.py:96: size=107 MiB, count=1311, average=83.8 KiB\r\n/usr/local/google/home/cantonios/.pyenv/versions/3.10.12/lib/python3.10/abc.py:123: size=982 KiB, count=10222, average=98 B\r\n/tmp/venv/lib/python3.10/site-packages/tf_keras/src/engine/base_layer.py:3223: size=671 KiB, count=311, average=2208 B\r\n/tmp/venv/lib/python3.10/site-packages/tf_keras/src/engine/base_layer.py:375: size=330 KiB, count=4532, average=75 B\r\n/tmp/venv/lib/python3.10/site-packages/tensorflow/python/ops/resource_variable_ops.py:2026: size=319 KiB, count=1634, average=200 B\r\n/tmp/venv/lib/python3.10/site-packages/tf_keras/src/engine/base_layer.py:3134: size=314 KiB, count=822, average=392 B\r\n/tmp/venv/lib/python3.10/site-packages/tensorflow/python/trackable/base.py:361: size=269 KiB, count=1274, average=216 B\r\n/tmp/venv/lib/python3.10/site-packages/tf_keras/src/utils/object_identity.py:135: size=263 KiB, count=5608, average=48 B\r\n/tmp/venv/lib/python3.10/site-packages/tf_keras/src/utils/object_identity.py:141: size=254 KiB, count=313, average=830 B\r\n/tmp/venv/lib/python3.10/site-packages/tensorflow/python/framework/tensor_shape.py:830: size=158 KiB, count=2817, average=57 B\r\n```\r\n\r\nThe main difference to me looks like mostly `tracking`-related allocations in keras 3. Every keras layer is `Trackable`, so my guess is the increased memory usage lies there. I believe converting to a `tf.function` essentially disables this trainable tracking, so you get the similar results as your `model.predict(...)` call.\r\n\r\nI still don't get unbounded growth though, just vastly increased memory usage.\r\n\r\n---\r\n\r\nAs for what to do: can you live with legacy keras and set the environment variable `TF_USE_LEGACY_KERAS=1`? Can you freeze your model into an inference-only TF model and use that? Otherwise I think the keras team might be better at debugging.",
"Actually, rewriting your reproducer in terms of `keras` alone and setting it to use JAX as the backend, I get similar high memory usage:\r\n```python\r\nimport os\r\nos.environ[\"KERAS_BACKEND\"] = \"jax\"\r\n\r\nimport numpy as np\r\nimport psutil\r\nimport keras\r\nimport tracemalloc\r\nimport gc\r\n\r\ndef main():\r\n model = keras.applications.ResNet152V2()\r\n images = np.zeros([20, 224, 224, 3], dtype=np.uint8)\r\n\r\n for run in range(20):\r\n model(images)\r\n # model.predict(images)\r\n gc.collect()\r\n memory_usage_in_MiB = psutil.Process().memory_info().rss / (1024 * 1024)\r\n print(f\"Memory usage after {run} run(s) (in MiB): {memory_usage_in_MiB:.3f}\", flush=True)\r\n\r\ntracemalloc.start()\r\nmain()\r\nsnapshot = tracemalloc.take_snapshot()\r\ntop_stats = snapshot.statistics('lineno')\r\nprint(\"[ Top 10 ]\")\r\nfor stat in top_stats[:10]:\r\n print(stat)\r\n\r\n```\r\n```\r\nAn NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\r\nMemory usage after 0 run(s) (in MiB): 5612.281\r\nMemory usage after 1 run(s) (in MiB): 5612.195\r\nMemory usage after 2 run(s) (in MiB): 5610.016\r\nMemory usage after 3 run(s) (in MiB): 5611.594\r\nMemory usage after 4 run(s) (in MiB): 5609.293\r\nMemory usage after 5 run(s) (in MiB): 5611.176\r\nMemory usage after 6 run(s) (in MiB): 5611.828\r\nMemory usage after 7 run(s) (in MiB): 5609.570\r\nMemory usage after 8 run(s) (in MiB): 5611.676\r\nMemory usage after 9 run(s) (in MiB): 5612.074\r\nMemory usage after 10 run(s) (in MiB): 5611.574\r\nMemory usage after 11 run(s) (in MiB): 5610.383\r\nMemory usage after 12 run(s) (in MiB): 5613.406\r\nMemory usage after 13 run(s) (in MiB): 5610.793\r\nMemory usage after 14 run(s) (in MiB): 5610.465\r\nMemory usage after 15 run(s) (in MiB): 5610.535\r\nMemory usage after 16 run(s) (in MiB): 5611.539\r\nMemory usage after 17 run(s) (in MiB): 5612.266\r\nMemory usage after 18 run(s) (in MiB): 5610.262\r\nMemory usage after 19 run(s) (in MiB): 5611.531\r\n[ Top 10 ]\r\n/tmp/venv/lib/python3.10/site-packages/h5py/_hl/dataset.py:1057: size=230 MiB, count=2238, average=105 KiB\r\n/tmp/venv/lib/python3.10/site-packages/jax/_src/dispatch.py:333: size=1014 KiB, count=1632, average=636 B\r\n/tmp/venv/lib/python3.10/site-packages/keras/src/utils/tracking.py:65: size=634 KiB, count=3405, average=191 B\r\n<string>:1: size=480 KiB, count=6086, average=81 B\r\n/tmp/venv/lib/python3.10/site-packages/jax/_src/source_info_util.py:222: size=369 KiB, count=694, average=544 B\r\n/tmp/venv/lib/python3.10/site-packages/keras/src/layers/layer.py:1361: size=341 KiB, count=1146, average=305 B\r\n/tmp/venv/lib/python3.10/site-packages/jax/_src/config.py:208: size=312 KiB, count=1816, average=176 B\r\n<frozen importlib._bootstrap_external>:672: size=297 KiB, count=3060, average=99 B\r\n/tmp/venv/lib/python3.10/site-packages/keras/src/utils/tracking.py:139: size=250 KiB, count=4930, average=52 B\r\n<string>:3: size=244 KiB, count=4343, average=58 B\r\n```\r\nChanging the model to use `model.predict(...)` instead yields much lower memory usage:\r\n```\r\nAn NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 6s 6s/step\r\nMemory usage after 0 run(s) (in MiB): 656.660\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 4s 4s/step\r\nMemory usage after 1 run(s) (in MiB): 693.199\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 508ms/step\r\nMemory usage after 2 run(s) (in MiB): 697.055\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 492ms/step\r\nMemory usage after 3 run(s) (in MiB): 697.496\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 503ms/step\r\nMemory usage after 4 run(s) (in MiB): 697.465\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 499ms/step\r\nMemory usage after 5 run(s) (in MiB): 698.285\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 498ms/step\r\nMemory usage after 6 run(s) (in MiB): 698.219\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 508ms/step\r\nMemory usage after 7 run(s) (in MiB): 698.145\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 499ms/step\r\nMemory usage after 8 run(s) (in MiB): 698.293\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 501ms/step\r\nMemory usage after 9 run(s) (in MiB): 698.617\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 502ms/step\r\nMemory usage after 10 run(s) (in MiB): 698.617\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 505ms/step\r\nMemory usage after 11 run(s) (in MiB): 698.055\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 500ms/step\r\nMemory usage after 12 run(s) (in MiB): 698.348\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 501ms/step\r\nMemory usage after 13 run(s) (in MiB): 698.781\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 501ms/step\r\nMemory usage after 14 run(s) (in MiB): 698.602\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 500ms/step\r\nMemory usage after 15 run(s) (in MiB): 698.922\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 505ms/step\r\nMemory usage after 16 run(s) (in MiB): 698.758\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 492ms/step\r\nMemory usage after 17 run(s) (in MiB): 698.801\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 496ms/step\r\nMemory usage after 18 run(s) (in MiB): 698.977\r\n1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 503ms/step\r\nMemory usage after 19 run(s) (in MiB): 698.875\r\n[ Top 10 ]\r\n/tmp/venv/lib/python3.10/site-packages/h5py/_hl/dataset.py:1057: size=230 MiB, count=1335, average=176 KiB\r\n/tmp/venv/lib/python3.10/site-packages/jax/_src/source_info_util.py:222: size=1832 KiB, count=3449, average=544 B\r\n<string>:1: size=892 KiB, count=11325, average=81 B\r\n/tmp/venv/lib/python3.10/site-packages/jax/_src/dispatch.py:333: size=639 KiB, count=1029, average=636 B\r\n/tmp/venv/lib/python3.10/site-packages/keras/src/utils/tracking.py:65: size=634 KiB, count=3405, average=191 B\r\n/tmp/venv/lib/python3.10/site-packages/jax/_src/interpreters/partial_eval.py:1932: size=453 KiB, count=10520, average=44 B\r\n/tmp/venv/lib/python3.10/site-packages/jax/_src/interpreters/partial_eval.py:1931: size=452 KiB, count=10496, average=44 B\r\n/tmp/venv/lib/python3.10/site-packages/jax/_src/interpreters/partial_eval.py:1912: size=404 KiB, count=7370, average=56 B\r\n/tmp/venv/lib/python3.10/site-packages/keras/src/layers/layer.py:1361: size=341 KiB, count=1146, average=305 B\r\n<frozen importlib._bootstrap_external>:672: size=297 KiB, count=3060, average=99 B\r\n```\r\nwhich seems to confirm that all the memory is the keras model storing training information.\r\n\r\nSo I'm pretty confident this isn't a TensorFlow bug, and it may or may not be a Keras bug - depending on if that memory use for training is actually needed by keras.",
"> As for what to do: can you live with legacy keras and set the environment variable `TF_USE_LEGACY_KERAS=1`? Can you freeze your model into an inference-only TF model and use that? Otherwise I think the keras team might be better at debugging.\r\n\r\nCan conform, in my original minimal example, `TF_USE_LEGACY_KERAS=1` [helps](https://gist.github.com/Dobiasd/c13cfd6697010125e03143021189ce85). I'll try it in PROD next week. :+1: Thanks!",
"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/63701\">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/63701\">No</a>\n"
] | 2024-03-14T09:00:19
| 2024-05-17T19:56:50
| 2024-05-17T19:56:47
|
NONE
| null | null | null |
The following minimal example reproduces the effect. (No GPU, just CPU.)
```python3
import numpy as np
import psutil
import tensorflow as tf
model = tf.keras.applications.ResNet152V2()
images = np.zeros([20, 224, 224, 3], dtype=np.uint8)
for run in range(10):
memory_usage_in_MiB = psutil.Process().memory_info().rss / (1024 * 1024)
print(f"Memory usage after {run} run(s) (in MiB): {memory_usage_in_MiB:.3f}", flush=True)
model(images)
```
```
Memory usage after 0 run(s) (in MiB): 790.715
Memory usage after 1 run(s) (in MiB): 5969.941
Memory usage after 2 run(s) (in MiB): 7112.559
Memory usage after 3 run(s) (in MiB): 7787.625
Memory usage after 4 run(s) (in MiB): 7827.727
Memory usage after 5 run(s) (in MiB): 6785.316
Memory usage after 6 run(s) (in MiB): 5971.680
Memory usage after 7 run(s) (in MiB): 6615.105
Memory usage after 8 run(s) (in MiB): 7151.496
Memory usage after 9 run(s) (in MiB): 6679.250
```
([Dockerfile](https://gist.github.com/Dobiasd/4e6747f3c057b939cb2672d142f612fc) to reproduce)
And it's not just `tf.keras.applications.ResNet152V2()`. It also happens (for example) with `tf.keras.applications.inception_resnet_v2.InceptionResNetV2` and `tf.keras.applications.inception_v3.InceptionV3`. And it also happens when using Python `3.12.2` instead of `3.11.8`.
With TensorFlow `2.15.1` (instead of `2.16.1`), however, the memory usage does not explode:
```
Memory usage after 0 run(s) (in MiB): 1038.891
Memory usage after 1 run(s) (in MiB): 1154.062
Memory usage after 2 run(s) (in MiB): 1154.508
Memory usage after 3 run(s) (in MiB): 1154.684
Memory usage after 4 run(s) (in MiB): 1215.840
Memory usage after 5 run(s) (in MiB): 1154.664
Memory usage after 6 run(s) (in MiB): 1154.816
Memory usage after 7 run(s) (in MiB): 1155.059
Memory usage after 8 run(s) (in MiB): 1154.715
Memory usage after 9 run(s) (in MiB): 1155.367
```
([Dockerfile](https://gist.github.com/Dobiasd/0acf434d521cabf7fe3102b551bf36b0) to reproduce)
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Factor out model import/export functions from SRQ component for reusability
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Factor out model import/export functions from SRQ component for reusability
This refactoring is needed in order to reuse model import/export functions for weight-only quantization API.
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Reland PR #9757: GpuTimer: improve kernel execution time measurement accuracy
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Reland PR #9757: GpuTimer: improve kernel execution time measurement accuracy
Imported from GitHub PR https://github.com/openxla/xla/pull/9757
This PR changes how `GpuTimer` measures execution time, which should improve the measurement accuracy.
Quoting my comment in the code:
```c++
// When a timer is created it launches a delay kernel into the given stream and
// queues a start event immediately afterwards. This delay kernel blocks
// execution on the stream until GetElapsedDuration() is called, at which point
// an end event is queued and the delay kernel exits. This allows the device
// execution time of the tasks queued to the stream while the timer is active
// to be measured more accurately.
```
this should improve the accuracy of the measurements that are used to make auto-tuning decisions, especially for small kernels.
There are a couple of edge cases that have special treatment:
- if `CUDA_LAUNCH_BLOCKING=1` then the delay kernel will not achieve anything, so we don't launch it
- if any code in the timed region synchronises the device then the host will wait for the delay kernel to time out before actually launching the kernels to be measured. This will lead to a poor quality measurement, and an error message is printed. This condition can be met if one of the kernels being measured is lazily loaded, as lazy loading can trigger synchronisation. Best practice is to execute a warmup run (without timing enabled) before the timed execution.
Two parts of the autotuning code are updated to follow this best practice.
The `GpuTimer::Create*` signatures that were deprecated in https://github.com/openxla/xla/pull/9841 do not benefit from these accuracy improvements.
cc: @sergachev @nouiz
Copybara import of the project:
--
2b834f7154158ddc7294d45bb5481b8c38efdc61 by Olli Lupton <olupton@nvidia.com>:
GpuTimer: use delay kernel to improve accuracy
This ensures that all the device operations to be timed are queued to
the relevant stream before any of them are executed, resulting in a more
accurate measurement. This is skipped if CUDA_LAUNCH_BLOCKING=1 or
unified addressing is not available.
In addition, make sure that auto-tuning code paths have warm-up
executions of the kernels being measured. The warm-up executions should
not be inside GpuTimer regions.
With the example HLO:
parameter_0 = bf16[128,12,128]{2,1,0} parameter(0)
parameter_1 = bf16[12,128,128]{2,1,0} parameter(1)
ROOT dot.26 = bf16[12,128,128]{2,1,0} dot(parameter_0, parameter_1),
lhs_batch_dims={1}, lhs_contracting_dims={0}, rhs_batch_dims={0},
rhs_contracting_dims={2}
a cuBLAS kernel was selected with a CUPTI/NSys-measured runtime of
3.9µs and before this change, GpuTimer measured a runtime of 17.5µs.
With this change, GpuTimer measures a runtime of 8.1µs.
With the example HLO:
p0 = bf16[128,1536]{0,1} parameter(0)
p1 = s8[1536,12288]{0,1} parameter(1)
c = bf16[1536,12288]{0,1} convert(p1)
ROOT d = bf16[128,12288]{1,0} dot(p0, c), lhs_contracting_dims={1},
rhs_contracting_dims={0}
five cuDNN plans were auto-tuned, with CUPTI/Nsys-measured runtimes of
{34.9, 18.5, 30.9, 32.4, 31.5}µs. Before this change, GpuTimer measured
runtimes of {76.6, 40.0, 52.5, 55.7, 51.8}µs. With this change, GpuTimer
measures runtimes of {39.6, 23.0, 35.4, 35.1, 35.1}µs.
In summary, with this change, GpuTimer gives results that are much
closer to the CUPTI/Nsys measurements, with a ~uniform offset of ~4µs. A
constant offset doesn't matter for auto-tuning.
--
8f5ce0e7e309a46bf69c369bc4b1da8c26fd1579 by Olli Lupton <olupton@nvidia.com>:
Address CR
Merging this change closes #9757
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/9757 from olupton:gpu-timer-improvement 8f5ce0e7e309a46bf69c369bc4b1da8c26fd1579
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I_kwDOArmXAs6CRAHZ
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tf.raw_ops.ResourceApplyProximalAdagrad: Aborted (core dumped)
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[
"Hi @nonenone12135 ,\r\n\r\nThanks for reporting.Replicated the check fail as reported. Will let you know once it got fixed."
] | 2024-03-14T05:57:34
| 2024-03-14T13:50:22
| null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Under specific input, `tf.raw_ops.ResourceApplyProximalAdagrad` encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
var = tf.Variable([1.0, 2.0, 3.0])
accum = tf.Variable([0.1, 0.2, 0.3])
lr = 0.01
l1 = 0.1
l2 = 0.01
grad = tf.constant([0.1, 0.2, 0.3], dtype=tf.float64)
var_resource = tf.raw_ops.VarHandleOp(dtype=var.dtype, shape=var.shape)
accum_resource = tf.raw_ops.VarHandleOp(dtype=accum.dtype, shape=accum.shape)
assign_var = tf.raw_ops.AssignVariableOp(resource=var_resource, value=var)
assign_accum = tf.raw_ops.AssignVariableOp(resource=accum_resource, value=accum)
result = tf.raw_ops.ResourceApplyProximalAdagrad(var=var_resource, accum=accum_resource, lr=lr, l1=l1, l2=l2, grad=grad)
```
### Relevant log output
```shell
2024-03-14 05:57:20.787563: F tensorflow/core/framework/tensor.cc:844] Check failed: dtype() == expected_dtype (1 vs. 2) double expected, got float
Aborted (core dumped)
```
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I_kwDOArmXAs6CQ_kj
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tf.raw_ops.ResourceApplyMomentum: Aborted (core dumped)
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[
"@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/6734c009712e18a49cdc5b15cf7761a6/63696.ipynb).\r\nThank you!"
] | 2024-03-14T05:55:30
| 2024-04-29T07:36:40
| null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Under specific input, `tf.raw_ops.ResourceApplyMomentum` encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
var = tf.Variable([1.0, 2.0, 3.0])
accum = tf.Variable([0.1, 0.2, 0.3], dtype=tf.complex64)
lr = 0.01
grad = tf.constant([0.1, 0.2, 0.3])
momentum = 0.9
output = tf.raw_ops.ResourceApplyMomentum(var=var.handle, accum=accum.handle, lr=lr, grad=grad, momentum=momentum)
print(output)
```
### Relevant log output
```shell
2024-03-14 05:55:18.324852: F tensorflow/core/framework/tensor.cc:844] Check failed: dtype() == expected_dtype (8 vs. 1) float expected, got complex64
Aborted (core dumped)
```
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tf.raw_ops.ResourceApplyGradientDescent: Aborted (core dumped)
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[
"Hi **@nonenone12135** ,\r\n\r\nI tried to run your code on colab using tf 2.15 and tf-nightly versions faced same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/fdb6f1b3d8004ff20e3fdccfad9b2576/63695_2-15-2-16-nightly.ipynb) here for reference. We need to work on it, In few days i will provide a solution for it.\r\n\r\nThank you!",
"Hi **@vraebfdsb** ,\r\n\r\n- The tf.raw_ops.ResourceApplyGradientDescent function is designed for real-valued variables, not complex numbers. The alpha and delta parameters are expected to be real-valued, and when you provide a complex variable, it causes the operation to fail.\r\n- Here i tired alternative approach for this issue. I hope it will use for you. i am providing [gist](https://colab.research.google.com/gist/Venkat6871/90df5a5c3a72befd4773a64e81e72669/63695_2-16-1-v.ipynb) here for reference.\r\n\r\nThank you! "
] | 2024-03-14T05:53:42
| 2024-06-11T14:37:34
| null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Under specific input, `tf.raw_ops.ResourceApplyGradientDescent` encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
var = tf.Variable(3.0 + 1j, dtype=tf.complex64)
alpha = tf.constant(0.1, dtype=tf.float32)
delta = tf.constant(0.5, dtype=tf.float32)
result = tf.raw_ops.ResourceApplyGradientDescent(var=var.handle, alpha=alpha, delta=delta)
print(result)
```
### Relevant log output
```shell
2024-03-14 05:53:30.923509: F tensorflow/core/framework/tensor.cc:844] Check failed: dtype() == expected_dtype (8 vs. 1) float expected, got complex64
Aborted (core dumped)
```
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Grant internal visibility of variables lib.
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Grant internal visibility of variables lib.
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I_kwDOArmXAs6CQ-Mb
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tf.raw_ops.QuantizeAndDequantizeV3: Aborted (core dumped)
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[
"@nonenone12135,\r\nI was able to reproduce the issue on both tensorflow v2.15 and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/ddb3681c3a808fbd03695d8bde5bdc38/untitled1771.ipynb). Please allow some time to deep dive into the issue and provide the resolution. Thank you!"
] | 2024-03-14T05:50:58
| 2024-04-03T08:35:23
| null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Under specific input, `tf.raw_ops.QuantizeAndDequantizeV3` encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
input_data = tf.constant([1.5, 2.5, 3.5, 4.5])
input_min = tf.constant([[[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 0.0]]])
input_max = tf.constant(5.0)
num_bits = tf.constant(8)
signed_input = True
range_given = True
narrow_range = False
axis = -1
output_data = tf.raw_ops.QuantizeAndDequantizeV3(input=input_data, input_min=input_min, input_max=input_max, num_bits=num_bits, signed_input=signed_input, range_given=range_given, narrow_range=narrow_range, axis=axis)
print(output_data)
```
### Relevant log output
```shell
2024-03-14 05:50:03.159345: F tensorflow/core/framework/tensor.cc:852] Check failed: 1 == NumElements() (1 vs. 2)Must have a one element tensor
Aborted (core dumped)
```
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PR_kwDOArmXAs5plexi
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Make error message about disabling streaming enqueue more accurate by querying the environment variable
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Make error message about disabling streaming enqueue more accurate by querying the environment variable
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