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[xla:gpu] Allow address computation fusion where slice has several uses.
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[xla:gpu] Allow address computation fusion where slice has several uses.
As address fusion has zero cost, we should always perform it if possible, allowing the possibility to avoid materializing a slice with multiple uses.
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[XLA:SPMD] Support shard-as propagation with unspecified_dims.
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Make MSA more deterministic.
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PR #11981: [ROCM] fixing pjrt_c_api_gpu_test
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PR #11981: [ROCM] fixing pjrt_c_api_gpu_test
Imported from GitHub PR https://github.com/openxla/xla/pull/11981
Fixing platform name CUDA/ROCM
@xla-rotation: could you please have a look?
Copybara import of the project:
--
d114eb0deb691b5cf6907880f26d241a2813f26d by Pavel Emeliyanenko <[email protected]>:
fixing the test
Merging this change closes #11981
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11981 from ROCm:ci_pjrt_c_api_gpu_test_fix d114eb0deb691b5cf6907880f26d241a2813f26d
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[XLA:SPMD] support sharding barrier (1/N).
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[XLA:SPMD] support sharding barrier (1/N).
1. Add a two custom-call to allow barrier sharding propagation forward/backward unidirectionally.
2. Make shard_as/shard_like shard_barrier aware.
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[ReplicaGroupV2] Define compressed replica group list (IotaReplicaGroupList) and migrate collective generation in SPMD to use this format whenever possible.
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[ReplicaGroupV2] Define compressed replica group list (IotaReplicaGroupList) and migrate collective generation in SPMD to use this format whenever possible.
In final stage of the SPMD partitioner, the partitioner goes from a sharding to a set of replica groups. It generates replica groups by "merging" devices along certain dimensions of the original sharding. If sharding provided is of HloShardingV2 type, it is possible to generate replica groups in a compressed format via reshapes and transposes of the V2 sharding.
This change also speeds up cloning of collective instruction by migrating CollectiveDeviceList to store pointers to replica groups.
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PR #11651: fix GpuPerformanceWithCollectiveModel: communication_time should be M…
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PR #11651: fix GpuPerformanceWithCollectiveModel: communication_time should be M…
Imported from GitHub PR https://github.com/openxla/xla/pull/11651
fix GpuPerformanceWithCollectiveModel: communication_time should be Milliseconds
#11650
Copybara import of the project:
--
cbdde983ab780971f384c821837b2ebda5de3ff1 by zjjott <[email protected]>:
fix GpuPerformanceWithCollectiveModel: communication_time should be Milliseconds
Merging this change closes #11651
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Sharding custom calls cannot be hoisted individually in `WhileLoopInvariantCodeMotion`.
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Sharding custom calls cannot be hoisted individually in `WhileLoopInvariantCodeMotion`.
A sharding custom call annotates sharding to its operand. If we move a single sharding custom call out of the while body, the sharding annotation may not take effect as expected.
Taking the follow while body as an example,
```
body {
p_body = (f32[2], f32[2], s32[]) parameter(0)
gte.0 = f32[2] get-tuple-element(p_body), index=0
gte.1 = f32[2] get-tuple-element(p_body), index=1
sharding.0 = f32[2] custom-call(gte.0), custom_call_target="Sharding", sharding={devices=[2]<=[2]}
sharding.1 = f32[2] custom-call(gte.1), custom_call_target="Sharding", sharding={replicated}
add.0 = f32[2] add(sharding.0, sharding.1)
gte.2 = s32[] get-tuple-element(p_body), index=2
const = s32[] constant(1)
add.1 = s32[] add(gte.2, const)
ROOT root = (f32[2], f32[2], s32[]) tuple(gte.0, add.0, add.1)
}
```
Before this cl, `WhileLoopInvariantCodeMotion` moves `sharding.0` out of the body and keeps `sharding.1` in the body. With this cl, `WhileLoopInvariantCodeMotion` does not modify this body.
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[
"Hi @LongZE666 ,\r\n\r\nThanks for reporting. I have reproduced the reported behaviour and attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/b454d163eb8d9e42ef83b4ed9d2dee9d/66768.ipynb) here.\r\n\r\nThe checkfail happening with empty inputs for `indices` and `updates` .\r\n\r\nAttaching snapshot of same below.\r\n\r\n<img width=\"1499\" alt=\"Screenshot 2024-05-02 at 9 28 41 AM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/7f58dd18-f0f2-44dd-a6a7-3950cf56de8e\">\r\n"
] | 2024-05-01T04:08:30 | 2024-05-02T04:03:11 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
The dimensions of `indices` and `updates` must be equal.If not equal,`tf.raw
_ops.TensorScatterSub,tf.raw
_ops.TensorScatterAdd,tf.raw
_ops.TensorScatterMin,tf.raw
_ops.TensorScatterMax,tf.raw
_ops.TensorScatterUpdate ` will crash.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
tensor = tf.constant(0, shape=[101,32], dtype=tf.float32)
indices = tf.constant([], shape=[0,0,0,6], dtype=tf.int32)
updates = tf.constant([], shape=[0,0], dtype=tf.float32)
tf.raw_ops.TensorScatterAdd(tensor=tensor, indices=indices, updates=updates)
```
```
import tensorflow as tf
tensor = tf.constant(0, shape=[101,32], dtype=tf.float32)
indices = tf.constant([], shape=[0,0,0,6], dtype=tf.int32)
updates = tf.constant([], shape=[0,0], dtype=tf.float32)
tf.raw_ops.TensorScatterMax(tensor=tensor, indices=indices, updates=updates)
```
```
import tensorflow as tf
tensor = tf.constant(0, shape=[101,32], dtype=tf.float32)
indices = tf.constant([], shape=[0,0,0,6], dtype=tf.int32)
updates = tf.constant([], shape=[0,0], dtype=tf.float32)
tf.raw_ops.TensorScatterMin(tensor=tensor, indices=indices, updates=updates)
```
```
import tensorflow as tf
tensor = tf.constant(0, shape=[101,32], dtype=tf.float32)
indices = tf.constant([], shape=[0,0,0,6], dtype=tf.int32)
updates = tf.constant([], shape=[0,0], dtype=tf.float32)
tf.raw_ops.TensorScatterSub(tensor=tensor, indices=indices, updates=updates)
```
```
import tensorflow as tf
tensor = tf.constant(0, shape=[101,32], dtype=tf.float32)
indices = tf.constant([], shape=[0,0,0,6], dtype=tf.int32)
updates = tf.constant([], shape=[0,0], dtype=tf.float32)
tf.raw_ops.TensorScatterUpdate(tensor=tensor, indices=indices, updates=updates)
```
```
### Relevant log output
```shell
2024-05-01 04:00:48.333921: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (2 vs. 2)
Aborted (core dumped)
```
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[
"@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/b62989f322e54bf8c02bc03dbad12c2b/66758.ipynb#scrollTo=pHh-zXQZBis2). Thank you!"
] | 2024-05-01T03:53:11 | 2024-05-14T08:03:54 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Multiplication of `dense_shape` and `size` causes integer overflow
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
binary_output = True
indices = tf.constant(0, shape=[3456,2], dtype=tf.int64)
values = tf.constant(-536870912, shape=[3456], dtype=tf.int32)
dense_shape = tf.constant([1250999896764,1250999896764], shape=[2], dtype=tf.int64)
size = tf.constant(305032608, shape=[], dtype=tf.int32)
weights = tf.constant(51, shape=[3456], dtype=tf.int32)
tf.raw_ops.SparseBincount(indices=indices, values=values, dense_shape=dense_shape, size=size, weights=weights, binary_output=binary_output)
```
### Relevant log output
```shell
2024-05-01 03:51:04.482001: F tensorflow/core/framework/tensor_shape.cc:201] Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Encountered overflow when multiplying 1250999896764 with 305032608, result: -1
Aborted (core dumped)
```
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I_kwDOArmXAs6HeWQr
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Aborted (core dumped) in `tf.raw_ops.NearestNeighbors`
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[
"Hi **@LongZE666** ,\r\n\r\n- I tried to run your code on colab using TF v2.16.1, nightly and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/922b66996b0dc68d2a781f9dac3ea427/66765_2-16-nighlty-v.ipynb) here for reference.\r\n\r\nThank you!"
] | 2024-05-01T03:50:23 | 2024-06-12T06:15:50 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Parameter k being set to a negative number will cause a crash.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
points = tf.constant(0.554979503, shape=[1000,100], dtype=tf.float32)
centers = tf.constant(0.554979503, shape=[1000,100], dtype=tf.float32)
k = tf.constant(-1250999896764, shape=[], dtype=tf.int64)
tf.raw_ops.NearestNeighbors(points=points, centers=centers, k=k)
```
### Relevant log output
```shell
2024-05-01 03:49:00.796702: F tensorflow/core/framework/tensor_shape.cc:201] Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Expected shape dimensions to be non-negative, got -1250999896764
Aborted (core dumped)
```
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Check fail in `tf.raw_ops.MaxPoolGradWithArgmax`
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[
"@LongZE666,\r\nThank you for reporting the issue. I was able to reproduce the issue on tensorflow v2.15, v2.16 and tf-nightly. Kindly find the gist of it [here](https://colab.sandbox.google.com/gist/tilakrayal/e4624af1befe303c784951661e836fdd/untitled1879.ipynb) and the screenshot of the check fail for the reference.\r\n\r\n\r\n\r\n"
] | 2024-05-01T03:46:46 | 2024-05-07T04:02:31 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When argmax is set to a maximum value， `tf.raw_ops.MaxPoolGradWithArgmax` encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
ksize = [1, 1, 1, 1]
strides = [1, 1, 1, 1]
padding = "VALID"
include_batch_in_index = False
input = tf.constant(1, shape=[1,1,1,1], dtype=tf.float32)
grad = tf.constant(1, shape=[1,1,1,1], dtype=tf.float32)
argmax = tf.constant(1250999896764, shape=[1,1,1,1], dtype=tf.int64)
tf.raw_ops.MaxPoolGradWithArgmax(input=input, grad=grad, argmax=argmax, ksize=ksize, strides=strides, padding=padding, include_batch_in_index=include_batch_in_index)
```
### Relevant log output
```shell
2024-05-01 03:42:17.994627: F tensorflow/core/kernels/maxpooling_op.cc:1081] Check failed: grad_out_index >= output_start && grad_out_index < output_end Invalid output gradient index: 1250999896764, 0, 1
Aborted (core dumped)
```
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I_kwDOArmXAs6HeUoL
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[
"Hi @LongZE666 ,\r\n\r\nThanks for reporting. I have reproduced the reported behaviour and attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/0442a0b113eeb076db37df0f020e80fa/66763.ipynb) here.\r\n\r\nThe checkfail is happening with 1D input to the argument `x` .\r\n\r\nAttaching also snapshot of same below.\r\n\r\n<img width=\"1510\" alt=\"Screenshot 2024-05-02 at 9 41 05 AM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/e4171cf5-906e-47ba-b245-bddbe1d7b0d8\">\r\n\r\n"
] | 2024-05-01T03:41:25 | 2024-05-02T04:12:05 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
The parameter x is expected to be two-dimensional, and an error will occur when x is one-dimensional
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
use_peephole = False
x = tf.constant(0, shape=[17], dtype=tf.float32)
cs_prev = tf.constant(0.837607, shape=[28,29], dtype=tf.float32)
h_prev = tf.constant(0.837607, shape=[28,29], dtype=tf.float32)
w = tf.constant(0.837607, shape=[28,29], dtype=tf.float32)
wci = tf.constant(0.837607, shape=[28,29], dtype=tf.float32)
wcf = tf.constant(0.837607, shape=[28,29], dtype=tf.float32)
wco = tf.constant(0.837607, shape=[28,29], dtype=tf.float32)
b = tf.constant(0.837607, shape=[28,29], dtype=tf.float32)
i = tf.constant(0.837607, shape=[28,29], dtype=tf.float32)
cs = tf.constant(0.837607, shape=[28,29], dtype=tf.float32)
f = tf.constant(0.837607, shape=[28,29], dtype=tf.float32)
o = tf.constant(0.837607, shape=[28,29], dtype=tf.float32)
ci = tf.constant(0.837607, shape=[28,29], dtype=tf.float32)
co = tf.constant(0.837607, shape=[28,29], dtype=tf.float32)
cs_grad = tf.constant(0.837607, shape=[28,29], dtype=tf.float32)
h_grad = tf.constant(0.837607, shape=[28,29], dtype=tf.float32)
tf.raw_ops.LSTMBlockCellGrad(x=x, cs_prev=cs_prev, h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b, i=i, cs=cs, f=f, o=o, ci=ci, co=co, cs_grad=cs_grad, h_grad=h_grad, use_peephole=use_peephole)
```
### Relevant log output
```shell
2024-05-01 03:41:18.804132: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (1 vs. 1)
Aborted (core dumped)
```
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I_kwDOArmXAs6HeT7T
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Aborted (core dumped) with `tf.raw_ops.LSTMBlockCell`
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[
"@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/99786af3d07690398bc1663c4ae8cf5f/66758.ipynb#scrollTo=jMvP_0CxGr5r). Thank you!"
] | 2024-05-01T03:37:18 | 2024-05-14T08:00:10 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Under specific input, `tf.raw_ops.LSTMBlockCell` encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
forget_bias = 1
cell_clip = 0
use_peephole = False
x = tf.constant([], shape=[0], dtype=tf.float32)
cs_prev = tf.constant([], shape=[0], dtype=tf.float32)
h_prev = tf.constant([], shape=[0,0], dtype=tf.float32)
w = tf.constant([], shape=[0], dtype=tf.float32)
wci = tf.constant([], shape=[0,0], dtype=tf.float32)
wcf = tf.constant(0.837607, shape=[28,29], dtype=tf.float32)
wco = tf.constant(0.837607, shape=[28,29], dtype=tf.float32)
b = tf.constant(0.837607, shape=[28,29], dtype=tf.float32)
tf.raw_ops.LSTMBlockCell(x=x, cs_prev=cs_prev, h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b, forget_bias=forget_bias, cell_clip=cell_clip, use_peephole=use_peephole)
```
### Relevant log output
```shell
2024-05-01 03:36:12.005999: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (1 vs. 1)
Aborted (core dumped)
```
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I_kwDOArmXAs6HeTNk
| 66,761 |
Aborted (core dumped) with `tf.raw_ops.LRNGrad`
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[
"Hi **@LongZE666** ,\r\n\r\nI tried to run your code on colab using TF v2.16.1, nightly and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/3e61e4933bd91cf66a9d0dfa8e747b96/66761_2-16-1-nightly.ipynb) here for reference.\r\n\r\n\r\nThank you!"
] | 2024-05-01T03:33:03 | 2024-05-22T06:14:53 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
If the dimension of `output_image` is less than `input_grads` and `input_image`, tf.raw_ops.tf.raw_ops.LoadAndRemapMatrix encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
# LRNGradOp
import tensorflow as tf
depth_radius = 1
bias = 1.59018219
alpha = 0.117728651
beta = 0.404427052
input_grads = tf.constant([], shape=[0,0,0,0], dtype=tf.float32)
input_image = tf.constant([], shape=[0,0,0,0], dtype=tf.float32)
output_image = tf.constant([], shape=[0], dtype=tf.float32)
tf.raw_ops.LRNGrad(input_grads=input_grads, input_image=input_image, output_image=output_image, depth_radius=depth_radius, bias=bias, alpha=alpha, beta=beta)
```
### Relevant log output
```shell
2024-05-01 03:28:07.055077: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (1 vs. 1)
Aborted (core dumped)
```
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I_kwDOArmXAs6HeSRW
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Segmentation fault (core dumped) in `tf.raw_ops.FractionalMaxPoolGrad`
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[
"@LongZE666,\r\nThank you for reporting the issue. I was able to reproduce the issue on tensorflow v2.15, v2.16 and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/94914e6fd53e058e0bf639fd22902b51/untitled.ipynb) and the screenshot of the check fail for the reference.\r\n\r\n\r\n\r\n",
"@LongZE666,\r\nI tried to execute the mentioned code on the Tensorflow v2.16 in the alternative approach and it was failing with the error which was intended. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/33134a8036a62d0000e58368ae0f85d3/untitled1937.ipynb). \r\n\r\nrow_pooling_sequence--A Tensor of type int64. row pooling sequence, form pooling region with col_pooling_sequence\r\n\r\nThank you!"
] | 2024-05-01T03:27:14 | 2024-06-05T11:31:46 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
If `row_pooling_sequence` is a minimal negative number, `tf.raw_ops.FractionalMaxPoolGrad` encounters "Segmentation fault (core dumped)".
### Standalone code to reproduce the issue
```shell
# FractionalMaxPoolGradOp
import tensorflow as tf
overlapping = True
orig_input = tf.constant(100, shape=[1,4,7,1], dtype=tf.float32)
orig_output = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32)
out_backprop = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32)
row_pooling_sequence = tf.constant(-1250999896764, shape=[5], dtype=tf.int64)
col_pooling_sequence = tf.constant(0, shape=[5], dtype=tf.int64)
tf.raw_ops.FractionalMaxPoolGrad(orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop, row_pooling_sequence=row_pooling_sequence, col_pooling_sequence=col_pooling_sequence, overlapping=overlapping)
```
### Relevant log output
```shell
ASAN Report:
'''
AddressSanitizer:DEADLYSIGNAL
=================================================================
==870==ERROR: AddressSanitizer: SEGV on unknown address 0x412468dbc9b0 (pc 0x7fe87681d217 bp 0x7ffc4041d560 sp 0x7ffc4041c680 T0)
==870==The signal is caused by a READ memory access.
#0 0x7fe87681d217 in tensorflow::FractionalMaxPoolGradOp<float>::Compute(tensorflow::OpKernelContext*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x4139e217)
#1 0x7fe89f4e714a in tensorflow::ThreadPoolDevice::Compute(tensorflow::OpKernel*, tensorflow::OpKernelContext*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x1cdc14a)
#2 0x7fe89f06dfe6 in tensorflow::(anonymous namespace)::SingleThreadedExecutorImpl::Run(tensorflow::Executor::Args const&) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x1862fe6)
#3 0x7fe89ef4a306 in tensorflow::FunctionLibraryRuntimeImpl::RunSync(tensorflow::FunctionLibraryRuntime::Options, unsigned long, absl::lts_20230802::Span<tensorflow::Tensor const>, std::vector<tensorflow::Tensor, std::allocator<tensorflow::Tensor> >*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x173f306)
#4 0x7fe89efa7f74 in tensorflow::ProcessFunctionLibraryRuntime::RunMultiDeviceSync(tensorflow::FunctionLibraryRuntime::Options const&, unsigned long, std::vector<std::variant<tensorflow::Tensor, tensorflow::TensorShape>, std::allocator<std::variant<tensorflow::Tensor, tensorflow::TensorShape> > >*, std::function<absl::lts_20230802::Status (tensorflow::ProcessFunctionLibraryRuntime::ComponentFunctionData const&, tensorflow::ProcessFunctionLibraryRuntime::InternalArgs*)>) const (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x179cf74)
#5 0x7fe89efb58b1 in tensorflow::ProcessFunctionLibraryRuntime::RunSync(tensorflow::FunctionLibraryRuntime::Options const&, unsigned long, absl::lts_20230802::Span<tensorflow::Tensor const>, std::vector<tensorflow::Tensor, std::allocator<tensorflow::Tensor> >*) const (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x17aa8b1)
#6 0x7fe8670d72eb in tensorflow::KernelAndDeviceFunc::Run(tensorflow::ScopedStepContainer*, tensorflow::EagerKernelArgs const&, std::vector<std::variant<tensorflow::Tensor, tensorflow::TensorShape>, std::allocator<std::variant<tensorflow::Tensor, tensorflow::TensorShape> > >*, tsl::CancellationManager*, std::optional<tensorflow::EagerFunctionParams> const&, std::optional<tensorflow::ManagedStackTrace> const&, tsl::CoordinationServiceAgent*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31c582eb)
AddressSanitizer:DEADLYSIGNAL
AddressSanitizer: nested bug in the same thread, aborting.
'''
```
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I_kwDOArmXAs6HePfm
| 66,759 |
Aborted (core dumped) with `tf.raw_ops.FakeQuantWithMinMaxVarsPerChannelGradient`
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[
"Hi @LongZE666 ,\r\n\r\nThanks for reporting. I have reproduced the reported behaviour and attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/1a801be5879be2d1c7484eb265478542/66759.ipynb) here.\r\n\r\nThe check fail observed when the `inputs` arg `shape` is empty.\r\n\r\nAttaching also snapshot of same below.\r\n\r\n<img width=\"1504\" alt=\"Screenshot 2024-05-02 at 9 44 04 AM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/34a24455-787c-471d-8fdf-9760c7d7b18f\">\r\n"
] | 2024-05-01T03:16:02 | 2024-06-05T11:36:00 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Check Failed in `tf.raw_ops.FakeQuantWithMinMaxVarsPerChannelGradient` when the input of inputs is scalar, which causes the program to crash.
### Standalone code to reproduce the issue
```shell
# FakeQuantWithMinMaxVarsPerChannelGradientOp
import tensorflow as tf
num_bits = 8
narrow_range = False
gradients = tf.constant(0, shape=[], dtype=tf.float32)
inputs = tf.constant(0, shape=[], dtype=tf.float32)
min = tf.constant(124, shape=[3,1], dtype=tf.float32)
max = tf.constant(124, shape=[3,1], dtype=tf.float32)
tf.raw_ops.FakeQuantWithMinMaxVarsPerChannelGradient(gradients=gradients, inputs=inputs, min=min, max=max, num_bits=num_bits, narrow_range=narrow_range)
```
### Relevant log output
```shell
2024-05-01 03:10:46.669162: F tensorflow/core/framework/tensor_shape.cc:356] Check failed: d >= 0 (0 vs. -1)
Aborted (core dumped)
```
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I_kwDOArmXAs6HeNMi
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[
"@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/b62989f322e54bf8c02bc03dbad12c2b/66758.ipynb). Thank you!"
] | 2024-05-01T03:05:19 | 2024-05-14T07:55:32 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Under specific input, `tf.raw_ops.Dilation2DBackpropFilter` encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
strides = [1, 1, 1, 1]
rates = [1, 1, 1, 1]
padding = "VALID"
input = tf.constant([], shape=[0,0,0,0], dtype=tf.float32)
filter = tf.constant([], shape=[0,0,0], dtype=tf.float32)
out_backprop = tf.constant([], shape=[0], dtype=tf.float32)
tf.raw_ops.Dilation2DBackpropFilter(input=input, filter=filter, out_backprop=out_backprop, strides=strides, rates=rates, padding=padding)
```
### Relevant log output
```shell
2024-05-01 03:00:39.549898: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (1 vs. 1)
Aborted (core dumped)
```
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I_kwDOArmXAs6HeMLF
| 66,757 |
Aborted (core dumped) with `tf.raw_ops.CombinedNonMaxSuppression`
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[
"Hi **@LongZE666** ,\r\n\r\nI tried to run your code on colab using TF v2.16.1, nightly and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/94ced89b9c0ea9b2060ef2502c83531c/66757_2-16-1-nightly.ipynb) here for reference.\r\n\r\nThank you!"
] | 2024-05-01T02:59:49 | 2024-05-22T04:10:28 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
If `max_total_size` is too large , `tf.raw_ops.CombinedNonMaxSuppression` encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
pad_per_class = True
clip_boxes = False
boxes = tf.constant(3.5e+35, shape=[], dtype=tf.float32)
scores = tf.constant(0, shape=[], dtype=tf.float32)
max_output_size_per_class = tf.constant(8, shape=[], dtype=tf.int32)
max_total_size = tf.constant(1879048192, shape=[], dtype=tf.int32)
iou_threshold = tf.constant(0.5, shape=[], dtype=tf.float32)
score_threshold = tf.constant(0.5, shape=[], dtype=tf.float32)
tf.raw_ops.CombinedNonMaxSuppression(boxes=boxes, scores=scores, max_output_size_per_class=max_output_size_per_class, max_total_size=max_total_size, iou_threshold=iou_threshold, score_threshold=score_threshold, pad_per_class=pad_per_class, clip_boxes=clip_boxes)
```
### Relevant log output
```shell
2024-05-01 02:52:31.372751: W tensorflow/core/kernels/image/non_max_suppression_op.cc:998] Detected a large value for `max_total_size`. This may cause OOM error. (max_total_size: 1879048192)
2024-05-01 02:52:31.372808: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (2 vs. 0)
Aborted (core dumped)
```
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[xla:gpu] Add a GemmDegenerateDimRemover pass
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[] | 2024-05-01T01:49:18 | 2024-05-01T03:32:24 | 2024-05-01T03:32:23 |
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[xla:gpu] Add a GemmDegenerateDimRemover pass
This pass removes the degenerate dimension introduced by GemvRewriter. We should remove degenerate dimensions after we run GemmFusion.
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[tsl:concurrency] Correctly handle typed indirect async values that become errors
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[tsl:concurrency] Correctly handle typed indirect async values that become errors
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Fix HloAyncStartInstruction::ClassOf
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Fix HloAyncStartInstruction::ClassOf
Ignore verifying computation parameter/result shapes for custom-call async ops
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Fix a bug in flatbuffer_operator.cc when importing vhlo.float_v1 attribute.
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Fix a bug in flatbuffer_operator.cc when importing vhlo.float_v1 attribute.
FloatV1Attr expects an vhlo type instead of a regelar mlir type.
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[xla:gpu] NFC: Delete unused external allocations and allocate/free command buffer commands
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[xla:gpu] NFC: Delete unused external allocations and allocate/free command buffer commands
Run time memory allocation inside command buffers does not compose well with XLA static memory planning and was never fully implemented. Instead we should build it around memory spaces and introduce a special memory space for virtual allocations (via direct use of virtual memory allocation APIs or CUDA graphs).
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[XLA] Account for private stack allocations in HloRematerialization
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[XLA] Account for private stack allocations in HloRematerialization
Buffer assignment allocates a single stack for all asynchronous computations, which persists for the entire duration of the program. We need to account for this by adjusting the memory limit.
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tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
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"@Fchaubard,\r\nI suspect you are trying to install the every tensorflow version on CUDA 12.4 only which is not compatible. Could you please uninstall the installed libraries and try to install Clang 16.0.0, Bazel 5.3.0, cuDNN - 8.6,\tCUDA - 11.8 for the tensorflow v2.13 and check the below process.\r\n\r\n```python\r\n- Make sure cuda and cudnn are installed correctly.\r\n- Make sure $Env:CUDA_PATH is given correctly\r\n- Make sure $Env:LD_LIBRARY_PATH is given correctly\r\n- Make sure $Env:TF_CUDA_PATHS is given correctly\r\n```\r\nhttps://www.tensorflow.org/install/source#gpu\r\n\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66750\">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/66750\">No</a>\n"
] | 2024-04-30T23:43:13 | 2024-05-18T01:48:21 | 2024-05-18T01:48:19 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.13.1
### Custom code
No
### OS platform and distribution
Ubuntu 20.04.6
### Mobile device
_No response_
### Python version
3.8.10
### Bazel version
_No response_
### GCC/compiler version
9.4.0
### CUDA/cuDNN version
CUDA 12.4
### GPU model and memory
GEFORCE GTX 4090
### Current behavior?
tensorflow does not detect the CUDA drivers but they are there and added to PATH.
---
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
2024-04-30 16:29:17.328463: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2024-04-30 16:29:17.362495: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2024-04-30 16:29:17.362915: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-30 16:29:18.094331: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-04-30 16:29:18.749023: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1960] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
[]
---
nvidia-smi returns this suggesting the drivers are there and working:
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.78 Driver Version: 550.78 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 4090 Off | 00000000:03:00.0 Off | Off |
| 0% 36C P8 12W / 450W | 29MiB / 24564MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 0 N/A N/A 1089 G /usr/lib/xorg/Xorg 9MiB |
| 0 N/A N/A 1142 G /usr/bin/gnome-shell 8MiB |
+-----------------------------------------------------------------------------------------+
Even pytorch recognizes the drivers without issue:
---
Python 3.8.10 (default, Nov 22 2023, 10:22:35)
[GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> torch.cuda.device_count()
1
>>> torch.cuda.get_device_name(0)
'NVIDIA GeForce RTX 4090'
---
But not tensorflow? Please help!
### Standalone code to reproduce the issue
```shell
You will need to recreate the environment and then:
pip install tensorflow
```
### Relevant log output
_No response_
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PR_kwDOArmXAs5uNLSd
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Update Eigen to commit:c1d637433e3b3f9012b226c2c9125c494b470ae6
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Update Eigen to commit:c1d637433e3b3f9012b226c2c9125c494b470ae6
CHANGELOG
=========
c1d637433 - Judge unitary-ness relative to scaling.
9000b3767 - Fix new generic nearest integer ops on GPU.
0ee5c90aa - Eigen transpose product
fb95e90f7 - Add truncation op
d5524fc57 - Remove unnecessary semicolons.
ae5280aa8 - Fix more hard-coded magic bounds.
a5e147305 - Fix undefined behavior for generating inputs to the predux_mul test.
dcceb9afe - Unbork avx512 preduce_mul on MSVC.
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Cleanup DotAttention XNNPACK node.
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Cleanup DotAttention XNNPACK node.
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mlir to elf file
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PR_kwDOArmXAs5uNFrO
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Remove references to internal Eigen symbols HasCeil, HasFloor, and HasRint, which have been removed and will implied by HasRound in the future. The corresponding ops are all backed by generic SIMD implementations, which will be used unless a back-specific overload is defined.
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Remove references to internal Eigen symbols HasCeil, HasFloor, and HasRint, which have been removed and will implied by HasRound in the future. The corresponding ops are all backed by generic SIMD implementations, which will be used unless a back-specific overload is defined.
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PR_kwDOArmXAs5uNFFk
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Return error early if barrier is requested after coord service has shut down.
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Return error early if barrier is requested after coord service has shut down.
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PR_kwDOArmXAs5uNCo4
| 66,744 |
Add constants for decode and prefill cost.
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Add constants for decode and prefill cost.
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Make StreamExecutorMemoryAllocator work in terms of StreamExecutorInterface instead of StreamExecutor.
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[xla:gpu] NFC: Move async comm streams to CollectiveExecuteParams
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[xla:gpu] NFC: Move async comm streams to CollectiveExecuteParams
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I_kwDOArmXAs6HdNJe
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GPUv2 numerical inaccuracy in simple Add + Mul
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[
"Hi @gustavla ,\r\n\r\nI am in the process of replicating your issue . I will get back to you as soon as poosible. ",
"Hi @gustavla ,\r\n\r\nI replicated your issue and i got the same result as yours . I will try to replicate the issue through an android app and will let you know . ",
"Hi @gustavla ,\r\n\r\nCan you please reproduce this error through an Android app please and let me know? That way it will be easier for me to debug the issue.",
"@sawantkumar I do not have a stand-alone app with a repro. How do you usually feed in data to TFLite for the purpose setting up repros? It is easy to dump out the inputs from numpy to another format you need (happy to show how).",
"Hi @gustavla ,\r\n\r\nUsually its either images or video data which I use as input . But here i need to create .npy files or put the inputs in a text file and then through the java code i need to load and do inference on it. I am having error loading .npy files in Java/Kotlin in Android. "
] | 2024-04-30T22:23:44 | 2024-06-11T14:33:12 | null |
CONTRIBUTOR
| null | null | null |
**System information**
- Google Pixel 7 / Android 14 / Google Tensor G2
- TensorFlow 2.16.1
Running a simple model with a Add + Mul on both GPU and CPU gives vastly different results (on device CPU confirmed separately to be correct):
```
Arrays are not almost equal to 7 decimals
Mismatched elements: 392911 / 393216 (99.9%)
Max absolute difference: 2.719718
Max relative difference: 0.99997413
x: array([[[[0.408207 , 0.2999736, 0.1134637, ..., 0.1838855, 0.1379557,
0.2322327],
[1.3259705, 0.9743981, 0.3685618, ..., 0.5973114, 0.4481186,...
y: array([[[[0.4082031, 0. , 0. , ..., 0. , 0. ,
1.2128906],
[1.3251953, 0. , 0. , ..., 0. , 0. ,...
```
**Standalone code to reproduce the issue**
* Model asset: [tflite_66740_add_mul_gpu_numerically_incorrect.tflite](https://qaihub-public-issues.s3.us-west-2.amazonaws.com/tflite/tflite_66740_add_mul_gpu_numerically_incorrect.tflite)
* Generate some data and run on device on GPU (and optionally CPU for baseline).
* This results in the above severe numerical discrepancy
**Inference repro**
I reproduced this using https://aihub.qualcomm.com/ out of convenience (but I'm sure it will repro through other means as well), so I'm attaching the script here.
```python
import numpy as np
import qai_hub as hub
model = hub.upload_model("tflite_66740_add_mul_gpu_numerically_incorrect.tflite")
rng = np.random.RandomState(1234)
x = rng.uniform(size=(1, 64, 64, 1)).astype(np.float32)
inputs = hub.upload_dataset({"model_5/0/body/0/0/norm/LayerNormalization/batchnorm/Rsqrt": [x]})
device = hub.Device("Google Pixel 7")
cpu_job = hub.submit_inference_job(
model,
device=device,
inputs=inputs,
options="--compute_unit cpu",
)
gpu_job = hub.submit_inference_job(
model,
device=device,
inputs=inputs,
options="--compute_unit gpu",
)
cpu_output = cpu_job.download_output_data()
gpu_output = gpu_job.download_output_data()
np.testing.assert_almost_equal(cpu_output["output_0"][0], gpu_output["output_0"][0])
```
**Model visualization**
<img width="191" alt="image" src="https://github.com/tensorflow/tensorflow/assets/902935/3d9350cb-f368-4a88-aef0-6d9c9e5e8721">
**Any other info / logs**
<!-- cpu job: https://dev.aihub.qualcomm.com/jobs/j7gjjmy1g/ gpu job: https://dev.aihub.qualcomm.com/jobs/jlpej1x85/ -->
Logs from the GPU inference job (via https://aihub.qualcomm.com/):
```
[01/May/2024:08:11:15 +10:00: profiler/info] Android system property: ro.board.platform = gs201
[01/May/2024:08:11:15 +10:00: profiler/info] Android system property: ro.boot.hardware = panther
[01/May/2024:08:11:15 +10:00: profiler/info] Android system property: ro.boot.hardware.platform = gs201
[01/May/2024:08:11:15 +10:00: profiler/info] Android system property: ro.system.build.id = UP1A.231005.007.A1
[01/May/2024:08:11:15 +10:00: profiler/info] Android system property: ro.system.build.version.release = 14
[01/May/2024:08:11:15 +10:00: profiler/info] Android system property: ro.hardware = panther
[01/May/2024:08:11:15 +10:00: profiler/info] Android system property: ro.hardware.chipname =
[01/May/2024:08:11:15 +10:00: profiler/info] Android system property: ro.product.board = panther
[01/May/2024:08:11:15 +10:00: profiler/info] Android system property: ro.product.brand = google
[01/May/2024:08:11:15 +10:00: profiler/info] Android system property: ro.product.device = panther
[01/May/2024:08:11:15 +10:00: profiler/info] Android system property: ro.product.build.fingerprint = google/panther/panther:14/UP1A.231005.007.A1/10762838:user/release-keys
[01/May/2024:08:11:15 +10:00: profiler/info] Android system property: ro.product.manufacturer = Google
[01/May/2024:08:11:15 +10:00: profiler/info] Android system property: ro.product.model = Pixel 7
[01/May/2024:08:11:15 +10:00: profiler/info] Android system property: ro.product.name = panther
[01/May/2024:08:11:15 +10:00: profiler/info] Android system property: ro.soc.manufacturer = Google
[01/May/2024:08:11:15 +10:00: profiler/info] Android system property: ro.soc.model = GS201
[01/May/2024:08:11:15 +10:00: profiler/info] [Manager] DeviceManager::DeviceManager
[01/May/2024:08:11:15 +10:00: profiler/info] [Manager] findAvailableDevices
[01/May/2024:08:11:15 +10:00: profiler/info] [Manager] Found interface google-edgetpu (version = 2.0)
[01/May/2024:08:11:15 +10:00: profiler/info] [Manager] Found interface google-armnn (version = ArmNN)
[01/May/2024:08:11:15 +10:00: profiler/info] NNAPI devices: google-edgetpu,google-armnn,nnapi-reference
[01/May/2024:08:11:15 +10:00: profiler/info] GPU device: ARM Mali-G710
[01/May/2024:08:11:15 +10:00: profiler/info] OpenGL Version: OpenGL ES 3.2 v1.r43p0-01eac0.ff5c643eda65d3f5ba9886fdffb12673
[01/May/2024:08:11:15 +10:00: profiler/info] OpenCL Version: OpenCL C 1.2 v1.r43p0-01eac0.ff5c643eda65d3f5ba9886fdffb12673
[01/May/2024:08:11:15 +10:00: profiler/info] -=- Tungsten Running Task: Loading -=-
[01/May/2024:08:11:15 +10:00: profiler/info] Detected chipset 3101, made by 3000.
[01/May/2024:08:11:15 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] Loading tflite model Models/model.tflite
[01/May/2024:08:11:15 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] Malloc VM size before: 28600.0 kB, allocated: 18032.4 kB, slack: 10567.6 kB.
[01/May/2024:08:11:15 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] Current memory baseline range: 61792.4-72360.0 kB.
[01/May/2024:08:11:15 +10:00: profiler/debug] [job_id: jlpej1x85] [model.tflite] Runtime metadata not found in Models/model.tflite/trt_metadata.json or Models/model.tflite/trt_metadata.pb
[01/May/2024:08:11:15 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] TF Lite version 2.16.1. Loading model from Models/model.tflite.
[01/May/2024:08:11:15 +10:00: profiler/debug] [job_id: jlpej1x85] [model.tflite] Mapping resource file in Models/model.tflite
[01/May/2024:08:11:15 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] Loaded model. Minimum TF Lite version = 1.5.0.
[01/May/2024:08:11:15 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] No delegates specified; using compute unit=cpu_and_gpu.
[01/May/2024:08:11:15 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] [tflite] Initialized TensorFlow Lite runtime.
[01/May/2024:08:11:15 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] GPUV2 delegate requested. OpenCL detected.
[01/May/2024:08:11:15 +10:00: profiler/debug] [job_id: jlpej1x85] [model.tflite] Enabling delegate cache in dir=/data/user/0/ai.tetra.tungsten/cache/1714515075350/ai.tetra.runtime/0.6.0/model.tflite_8945450969824422876_1714515066045/gpuv2.
[01/May/2024:08:11:15 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] [tflite] Created TensorFlow Lite delegate for GPU.
[01/May/2024:08:11:15 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] [tflite] Replacing 2 out of 2 node(s) with delegate (TfLiteGpuDelegateV2) node, yielding 1 partitions for the whole graph.
[01/May/2024:08:11:15 +10:00: profiler/warning] [job_id: jlpej1x85] [model.tflite] [tflite] File /data/user/0/ai.tetra.tungsten/cache/1714515075350/ai.tetra.runtime/0.6.0/model.tflite_8945450969824422876_1714515066045/gpuv2/gpuv2_5609552586171313032.bin couldn't be opened for reading: No such file or directory
[01/May/2024:08:11:15 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] [tflite] Initialized OpenCL-based API.
[01/May/2024:08:11:15 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] [tflite] Created 1 GPU delegate kernels.
[01/May/2024:08:11:15 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] Applied 1 delegates: GPUV2/OpenCL. Model is fully delegated=true.
[01/May/2024:08:11:15 +10:00: profiler/debug] [job_id: jlpej1x85] [model.tflite] Saving delegate selection for subsequent steps.
[01/May/2024:08:11:15 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] Malloc VM size after: 36240.0 kB, allocated: 25209.7 kB, slack: 11030.3 kB.
[01/May/2024:08:11:15 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] Status Successfully Loaded Cold with t = 132027 us and usage: before = 72360.0 kB; peakBefore = 72360.0 kB; mallocUnusedBefore = 10567.6 kB; after = 81780.0 kB; peakAfter = 81744.0 kB; mallocUnusedAfter = 11030.3 kB; increase = 0.0-8957.3 kB; peak = 9384.0-19951.6 kB
[01/May/2024:08:11:15 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] Saving results to /storage/emulated/0/Android/data/ai.tetra.tungsten/files/Results/job_jlpej1x85/job_jlpej1x85_results.bin
[01/May/2024:08:11:16 +10:00: profiler/info] -=- Tungsten Running Task: Loading -=-
[01/May/2024:08:11:16 +10:00: profiler/info] Detected chipset 3101, made by 3000.
[01/May/2024:08:11:16 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] Loading previously saved results in /storage/emulated/0/Android/data/ai.tetra.tungsten/files/Results/job_jlpej1x85/job_jlpej1x85_results.bin
[01/May/2024:08:11:16 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] Loading tflite model Models/model.tflite
[01/May/2024:08:11:16 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] Malloc VM size before: 30640.0 kB, allocated: 17338.9 kB, slack: 13301.1 kB.
[01/May/2024:08:11:16 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] Current memory baseline range: 54914.9-68216.0 kB.
[01/May/2024:08:11:16 +10:00: profiler/debug] [job_id: jlpej1x85] [model.tflite] Runtime metadata not found in Models/model.tflite/trt_metadata.json or Models/model.tflite/trt_metadata.pb
[01/May/2024:08:11:16 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] TF Lite version 2.16.1. Loading model from Models/model.tflite.
[01/May/2024:08:11:16 +10:00: profiler/debug] [job_id: jlpej1x85] [model.tflite] Mapping resource file in Models/model.tflite
[01/May/2024:08:11:16 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] Loaded model. Minimum TF Lite version = 1.5.0.
[01/May/2024:08:11:16 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] GPUV2 delegate requested. OpenCL detected.
[01/May/2024:08:11:16 +10:00: profiler/debug] [job_id: jlpej1x85] [model.tflite] Enabling delegate cache in dir=/data/user/0/ai.tetra.tungsten/cache/1714515075350/ai.tetra.runtime/0.6.0/model.tflite_8945450969824422876_1714515066045/gpuv2.
[01/May/2024:08:11:16 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] [tflite] Replacing 2 out of 2 node(s) with delegate (TfLiteGpuDelegateV2) node, yielding 1 partitions for the whole graph.
[01/May/2024:08:11:16 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] [tflite] Found serialized data for model gpuv2 (790680 B) at /data/user/0/ai.tetra.tungsten/cache/1714515075350/ai.tetra.runtime/0.6.0/model.tflite_8945450969824422876_1714515066045/gpuv2/gpuv2_5609552586171313032.bin
[01/May/2024:08:11:16 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] [tflite] Initialized OpenCL-based API from serialized data.
[01/May/2024:08:11:16 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] [tflite] Created 1 GPU delegate kernels.
[01/May/2024:08:11:16 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] Applied 1 delegates: GPUV2/OpenCL. Model is fully delegated=true.
[01/May/2024:08:11:16 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] Malloc VM size after: 36496.0 kB, allocated: 25417.5 kB, slack: 11078.5 kB.
[01/May/2024:08:11:16 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] Status Successfully Loaded Warm with t = 94180 us and usage: before = 68216.0 kB; peakBefore = 68216.0 kB; mallocUnusedBefore = 13301.1 kB; after = 78828.0 kB; peakAfter = 78076.0 kB; mallocUnusedAfter = 11078.5 kB; increase = 0.0-12834.6 kB; peak = 9860.0-23161.1 kB
[01/May/2024:08:11:16 +10:00: profiler/info] [job_id: jlpej1x85] [model.tflite] Saving results to /storage/emulated/0/Android/data/ai.tetra.tungsten/files/Results/job_jlpej1x85/job_jlpej1x85_results.bin
```
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PR_kwDOArmXAs5uM4YQ
| 66,739 |
[xla:ffi] Revised the list of supported attribute types and added tests
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[] | 2024-04-30T22:12:17 | 2024-05-01T11:03:02 | 2024-05-01T11:03:01 |
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[xla:ffi] Revised the list of supported attribute types and added tests
Turns out the previous commit did not update the runtime, this the FFI failed
with an error when constructing a frame with e.g. bool attributes.
Here I decided to
* Only support signed integer attributes, i.e. leave out unsigned integer
types until we have a use-case which needs them.
* Drop support for boolean arrays as attributes. The reasons are purely
techincal: MLIR stores them as std::vector<bool> which has
implementation-defined storage. Thus, we have to copy the vector to be able
to expose it as XLA_FFI_Array.
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[xla:cpu] NFC: Remove deprecated XLA:CPU mlir based codegen part #6
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[xla:cpu] NFC: Remove deprecated XLA:CPU mlir based codegen part #6
Reverts changelist 525613555
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Rollback
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Rollback
Reverts 838b2f22e36d483be7fd6cccbedd30d46497c7f3
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I_kwDOArmXAs6Hc5Y7
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2.12.0: memory leak in TFLite's tflite::Interpreter::Invoke()
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[
"I am able to reproduce this in the benchmark android_aarch64_benchmark_model of the tf nightly build",
"@sushreebarsa can you take a look?",
"@gestalone Could you please try to upgrade to the latest TF version as memory leak issues are often addressed in subsequent versions. Kindly let us know if it is appearing in the latest and try to explore if your stable delegate library supports alternative back-ends besides OpenCL?\r\nThank you!",
"Hi @sushreebarsa, I was able to reproduce it with the benchmark 2.16 version. I tried the opengl backend of the gpu delegate, but unfortunately is not working. \r\nit's quite easy to test, just use the android benchmark, with any tensorflow approved model using the gpu delegate. \r\nThe tutorial can be followed from here. \r\nhttps://www.tensorflow.org/lite/performance/measurement\r\nI think should be fixed, as memory leaks give quite a lot of issues.",
"@sawantkumar, any help here?",
"Hi @gestalone ,\r\n\r\nSure thing, let me replicate your issue . However gpu delegates today primarily use \"openCL\" as their backend instead of \"openGL\" . I will get back to you .",
"Hi @sawantkumar!\r\nThe issue I had was using opencl backend, i was not able to use opengl",
"Hi @gestalone ,\r\n\r\nI used \"android_aarch64_benchmark_model\" on pixel 6a to test a tflite model using the below command \r\n\r\n```\r\nadb shell am start -S \\\r\n -n org.tensorflow.lite.benchmark/.BenchmarkModelActivity \\\r\n --es args '\"--graph=/data/local/tmp/efficientnet.tflite \\\r\n --use_gpu=true \\\r\n --num_runs=1000\\\r\n --report_peak_memory_footprint=true\\\r\n —max_secs=30\\\r\n —gpu_backend= “cl”\\\r\n --num_threads=4\"'\r\n```\r\n \r\n \r\n \r\n\r\n<img width=\"1728\" alt=\"Screenshot 2024-05-23 at 2 15 45 PM\" src=\"https://github.com/tensorflow/tensorflow/assets/166358452/58151178-4530-43ad-869a-f9293b0497ea\">\r\n\r\n\r\n I used android stuido pofiler to check the memory used by the \"tflite benchmark activity \" process and it didn't show any memory leaks . The memory usage spiked up to 130 MB when using the benchmark tool but it came back to normal once the benchmarking was complete. Can you please try out your code on a different phone and let me know if you are able to replicate this issue on a different phone. Also if possible , can you provide your tflite model for easier debugging for me.\r\n",
"\r\n\r\n\r\nHi! I am using a different device and I am not able to reproduce, then I guess is device related. MAybe the opencl drivers? I will check.\r\nboth run with the same command:\r\n\r\n",
"I was looking a bit and I found this:\r\nhttps://developer.qualcomm.com/forum/qdn-forums/software/adreno-gpu-sdk/35473\r\nfor the model that i Found, I will try to change the buildoptions of opencl for adreno 530.\r\nI will let you know",
"@sawantkumar Hi!\r\nI found the issue. Seems the snapdragon profiler program that i use to check the memory make a bad interaction with the opencl thing. I check it with a different method to get the memory and is not reproducible. Then I guess the issue was to run the benchmark and my code with the snapdragon profiler. \r\nreally strange interaction, should be reporte to qualcomm. \r\n\r\nBest you can close and thanks for all the help",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66736\">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/66736\">No</a>\n"
] | 2024-04-30T21:44:09 | 2024-05-24T07:34:47 | 2024-05-24T07:34:45 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.12.0
### Custom code
Yes
### OS platform and distribution
Cross-build from 'Windows:x86_64' to 'Android:armv8'
### Mobile device
Android with Snapdragon 820
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
CXX compiler identification is Clang 14.0.7
### CUDA/cuDNN version
no
### GPU model and memory
Snapdragon 820 with Adreno 530
### Current behavior?
Running the invoke for a tflite model using the gpu delegate, with opencl backend.
It goes fast and well, the problem is that exist a memory leak, that is increasing, not sure how to fix it. Not sure if it's an error on the opencl implementation, on the drivers of the adreno gpu or in the delegate implementation.
### Standalone code to reproduce the issue
```shell
#include <tensorflow/lite/model.h>
#include <tensorflow/lite/interpreter.h>
#include <tensorflow/lite/delegates/gpu/delegate.h>
#include <tensorflow/lite/c/common.h>
std::unique_ptr<tflite::FlatBufferModel> m_model;
std::unique_ptr<tflite::Interpreter> m_interpreter;
m_model = tflite::FlatBufferModel::BuildFromBuffer(m_modelName, m_bufferSize);
tflite::ops::builtin::BuiltinOpResolver resolver;
tflite::InterpreterBuilder(*m_model.get(), resolver)(&m_interpreter);
auto delegategpu = tflite::Interpreter::TfLiteDelegatePtr(TfLiteGpuDelegateV2Create(&gpu_options), &TfLiteGpuDelegateV2Delete);
m_interpreter->ModifyGraphWithDelegate(std::move(delegategpu))
m_interpreter->AllocateTensors()
for(int i = 0; i< 1000 ;i ++)
{
m_interpreter->Invoke() != TfLiteStatus::kTfLiteOk)
}
```
### Relevant log output
_No response_
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PR_kwDOArmXAs5uMl-Q
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Integrate LLVM at llvm/llvm-project@a7b968a57834
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Integrate LLVM at llvm/llvm-project@a7b968a57834
Updates LLVM usage to match
[a7b968a57834](https://github.com/llvm/llvm-project/commit/a7b968a57834)
Reverts changelist 473051719
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Adds tool path to visibility list.
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NONE
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Adds tool path to visibility list.
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PR_kwDOArmXAs5uMeS8
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Internal change only.
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Internal change only.
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| 66,732 |
Update version of nsync used by TensorFlow.
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Update version of nsync used by TensorFlow.
TensorFlow previously imported nsync 1.25.0.
The changes to nsync since then are:
- a fix for a bug that can affect systems with weak memory
ordering, such as Arm (the bug does not impact x86);
- a fix to a build problem when using both cmake on
Windows with a compiler other than MSVC
(previously, CMakeLists.txt would erroneously pass MSVC-specific
warning-suppression flags to a non-MSVC compiler);
- changes to help nsync work with thread sanitizer; and
- native atomic operation support for loongarch.
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I_kwDOArmXAs6Hcmii
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TextVectorization does not convert Cyrillic characters to lowercase
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[
"Hi **@Ybisalt** ,\r\n- Thank you for your issue, I replicated the issue in colab 2.16.1 and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/14f248fedb26961b4b4dd367853bc4c9/66731_2-16-1.ipynb) for reference.\r\n- Please post this issue on [keras-team/keras repo.](https://github.com/keras-team/keras/issues)\r\nTo know more see;\r\n[https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999](https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999).\r\n\r\nThank you!",
"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/66731\">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/66731\">No</a>\n"
] | 2024-04-30T20:46:06 | 2024-05-18T01:48:24 | 2024-05-18T01:48:20 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.8
### Custom code
Yes
### OS platform and distribution
Windows 10
### 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?
keras.layers.TextVectorization does not convert Cyrillic characters to lowercase with 'lower_and_strip_punctuation'.
Deprecated keras.preprocessing.text.Tokenizer does this.
### Standalone code to reproduce the issue
```shell
from tensorflow.keras.layers import TextVectorization
tokenizer = TextVectorization(split='character', standardize='lower_and_strip_punctuation')
tokenizer.adapt(["Zz, Aa"]) # Latin
vocab = tokenizer.get_vocabulary()
print(vocab) # ['', '[UNK]', 'z', 'a', ' ']
tokenizer.adapt(["Яя, Аа"]) # Cyrillic
vocab = tokenizer.get_vocabulary()
print(vocab) # ['', '[UNK]', 'я', 'а', 'Я', 'А', ' ']
from tensorflow.keras.preprocessing.text import Tokenizer # deprecated
tokenizer = Tokenizer(char_level=True)
tokenizer.fit_on_texts(["Яя, Аа"]) # Cyrillic
print(tokenizer.index_word) # {1: 'я', 2: 'а', 3: ',', 4: ' '}
```
### Relevant log output
```shell
['', '[UNK]', 'z', 'a', ' ']
['', '[UNK]', 'я', 'а', 'Я', 'А', ' ']
{1: 'я', 2: 'а', 3: ',', 4: ' '}
```
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Add `--remote_download_minimal` to RBE cross-compile Mac config
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Add `--remote_download_minimal` to RBE cross-compile Mac config
Fixes https://github.com/bazelbuild/bazel/issues/21568
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TFLiteConverter produces model that doesn't conform to GPUv2 (TfLiteGpuDelegate Init: FULLY_CONNECTED: Amount of input channels should match weights width)
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"hi @gustavla ,\r\n\r\nI tired to create the tflite model from the code you provided but i ran into this error and no tflite model was created.\r\n\r\n\r\n\r\n\r\n---------------------------------------------------------------------------\r\nValueError Traceback (most recent call last)\r\nCell In[1], line 9\r\n 5 output_shape = [1, 21]\r\n 7 tf_input = keras.Input(input_shape[1:], batch_size=input_shape[0])\r\n----> 9 tf_z = tf.reshape(tf_input, (1, 7))\r\n 10 tf_output = tf.keras.layers.Dense(21)(tf_z)\r\n 12 model = keras.Model(inputs=[tf_input], outputs=[tf_output])\r\n\r\nFile ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\ops\\weak_tensor_ops.py:88, in weak_tensor_unary_op_wrapper.<locals>.wrapper(*args, **kwargs)\r\n 86 def wrapper(*args, **kwargs):\r\n 87 if not ops.is_auto_dtype_conversion_enabled():\r\n---> 88 return op(*args, **kwargs)\r\n 89 bound_arguments = signature.bind(*args, **kwargs)\r\n 90 bound_arguments.apply_defaults()\r\n\r\nFile ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:153, in filter_traceback.<locals>.error_handler(*args, **kwargs)\r\n 151 except Exception as e:\r\n 152 filtered_tb = _process_traceback_frames(e.__traceback__)\r\n--> 153 raise e.with_traceback(filtered_tb) from None\r\n 154 finally:\r\n 155 del filtered_tb\r\n\r\nFile ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\backend\\common\\keras_tensor.py:91, in KerasTensor.__tf_tensor__(self, dtype, name)\r\n 90 def __tf_tensor__(self, dtype=None, name=None):\r\n---> 91 raise ValueError(\r\n 92 \"A KerasTensor cannot be used as input to a TensorFlow function. \"\r\n 93 \"A KerasTensor is a symbolic placeholder for a shape and dtype, \"\r\n 94 \"used when constructing Keras Functional models \"\r\n 95 \"or Keras Functions. You can only use it as input to a Keras layer \"\r\n 96 \"or a Keras operation (from the namespaces `keras.layers` \"\r\n 97 \"and `keras.operations`). \"\r\n 98 \"You are likely doing something like:\\n\\n\"\r\n 99 \"```\\n\"\r\n 100 \"x = Input(...)\\n\"\r\n 101 \"...\\n\"\r\n 102 \"tf_fn(x) # Invalid.\\n\"\r\n 103 \"```\\n\\n\"\r\n 104 \"What you should do instead is wrap `tf_fn` in a layer:\\n\\n\"\r\n 105 \"```\\n\"\r\n 106 \"class MyLayer(Layer):\\n\"\r\n 107 \" def call(self, x):\\n\"\r\n 108 \" return tf_fn(x)\\n\\n\"\r\n 109 \"x = MyLayer()(x)\\n\"\r\n 110 \"```\\n\"\r\n 111 )\r\n\r\nValueError: A KerasTensor cannot be used as input to a TensorFlow function. A KerasTensor is a symbolic placeholder for a shape and dtype, used when constructing Keras Functional models or Keras Functions. You can only use it as input to a Keras layer or a Keras operation (from the namespaces `keras.layers` and `keras.operations`). You are likely doing something like:\r\n\r\n```\r\nx = Input(...)\r\n...\r\ntf_fn(x) # Invalid.\r\n```\r\n\r\nWhat you should do instead is wrap `tf_fn` in a layer:\r\n\r\n```\r\nclass MyLayer(Layer):\r\n def call(self, x):\r\n return tf_fn(x)\r\n\r\nx = MyLayer()(x)\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.",
"I wrote the repro script against tensorflow 2.15 (even though I ran on TFLite 2.16 runtime).\r\n\r\nModel: https://qaihub-public-issues.s3.us-west-2.amazonaws.com/tflite/66729_model.tflite",
"Hi @gustavla ,\r\n\r\nThe script that you used to create the tflite model seems to be the issue. The \"input_shape\" and \"output_shape\" should be a tuple when you are passing it to the input layer. Also because of an issue keras tensors are incompatible with tf tensors. I will suggest you to use the below script to create the model. The tflite file created with the below script should run perfectly fine using the gpu delegate.\r\n\r\nPlease run [this](https://colab.sandbox.google.com/drive/1xyYQw2WmF8MpSso4_wCcgyBKpt3B6L4y#scrollTo=G2KVTUkeaBo8) colab notebook to create your tflite model\r\n\r\n",
"@sawantkumar Thanks! I tried changing to tuples, and that doesn't seem to make any difference. As for keras vs tflite tensors, I am unable to view your colab link (I requested access).",
"Hi @gustavla ,\r\n\r\nI have given you the access to view the colab notebook, Please review it and let me know if the tflite model created through this colab link solves your problem.",
"TF 2.16 uses keras 3, which results in the above error. add \r\n\r\n```\r\nimport os\r\nos.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\"\r\n```\r\n\r\nto the above script to make it compile"
] | 2024-04-30T20:20:59 | 2024-06-11T14:33:33 | null |
CONTRIBUTOR
| null | null | null |
**System information**
- Compilation: Ubuntu Linux
- Execution: Google Pixel 7 / Android 14 / Google Tensor G2
- Tensorflow 2.16.1
**Standalone code to reproduce the issue**
* Generate model with script below (note, this fails with the reshape, but the TFLiteConverter ends up with a model without the reshape)
* Run on GPUv2 on device (for instance via benchmark tool)
* Falls back to run 100% on CPU
<img width="376" alt="image" src="https://github.com/tensorflow/tensorflow/assets/902935/e0997e28-a98d-4688-bacb-2b0fc2ddd7d3">
**Generate model script**
```
import tensorflow as tf
import keras
input_shape = [7]
output_shape = [1, 21]
tf_input = keras.Input(input_shape[1:], batch_size=input_shape[0])
tf_z = tf.reshape(tf_input, (1, 7))
tf_output = tf.keras.layers.Dense(21)(tf_z)
model = keras.Model(inputs=[tf_input], outputs=[tf_output])
# Convert the model.
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
# Save the model.
with open('model.tflite', 'wb') as f:
f.write(tflite_model)
```
**Any other info / logs**
Runtime log (executed by https://aihub.qualcomm.com/)
<!-- job id: https://dev.aihub.qualcomm.com/jobs/j1gljz825/ -->
```
[01/May/2024:07:48:20 +10:00: profiler/info] Android system property: ro.board.platform = gs201
[01/May/2024:07:48:20 +10:00: profiler/info] Android system property: ro.boot.hardware = panther
[01/May/2024:07:48:20 +10:00: profiler/info] Android system property: ro.boot.hardware.platform = gs201
[01/May/2024:07:48:20 +10:00: profiler/info] Android system property: ro.system.build.id = UP1A.231005.007.A1
[01/May/2024:07:48:20 +10:00: profiler/info] Android system property: ro.system.build.version.release = 14
[01/May/2024:07:48:20 +10:00: profiler/info] Android system property: ro.hardware = panther
[01/May/2024:07:48:20 +10:00: profiler/info] Android system property: ro.hardware.chipname =
[01/May/2024:07:48:20 +10:00: profiler/info] Android system property: ro.product.board = panther
[01/May/2024:07:48:20 +10:00: profiler/info] Android system property: ro.product.brand = google
[01/May/2024:07:48:20 +10:00: profiler/info] Android system property: ro.product.device = panther
[01/May/2024:07:48:20 +10:00: profiler/info] Android system property: ro.product.build.fingerprint = google/panther/panther:14/UP1A.231005.007.A1/10762838:user/release-keys
[01/May/2024:07:48:20 +10:00: profiler/info] Android system property: ro.product.manufacturer = Google
[01/May/2024:07:48:20 +10:00: profiler/info] Android system property: ro.product.model = Pixel 7
[01/May/2024:07:48:20 +10:00: profiler/info] Android system property: ro.product.name = panther
[01/May/2024:07:48:20 +10:00: profiler/info] Android system property: ro.soc.manufacturer = Google
[01/May/2024:07:48:20 +10:00: profiler/info] Android system property: ro.soc.model = GS201
[01/May/2024:07:48:20 +10:00: profiler/info] [Manager] DeviceManager::DeviceManager
[01/May/2024:07:48:20 +10:00: profiler/info] [Manager] findAvailableDevices
[01/May/2024:07:48:20 +10:00: profiler/info] [Manager] Found interface google-edgetpu (version = 2.0)
[01/May/2024:07:48:20 +10:00: profiler/info] [Manager] Found interface google-armnn (version = ArmNN)
[01/May/2024:07:48:20 +10:00: profiler/info] NNAPI devices: google-edgetpu,google-armnn,nnapi-reference
[01/May/2024:07:48:20 +10:00: profiler/info] GPU device: ARM Mali-G710
[01/May/2024:07:48:20 +10:00: profiler/info] OpenGL Version: OpenGL ES 3.2 v1.r43p0-01eac0.ff5c643eda65d3f5ba9886fdffb12673
[01/May/2024:07:48:20 +10:00: profiler/info] OpenCL Version: OpenCL C 1.2 v1.r43p0-01eac0.ff5c643eda65d3f5ba9886fdffb12673
[01/May/2024:07:48:20 +10:00: profiler/info] -=- Tungsten Running Task: Loading -=-
[01/May/2024:07:48:20 +10:00: profiler/info] Detected chipset 3101, made by 3000.
[01/May/2024:07:48:20 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] Loading tflite model Models/model.tflite
[01/May/2024:07:48:20 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] Malloc VM size before: 28856.0 kB, allocated: 18204.7 kB, slack: 10651.3 kB.
[01/May/2024:07:48:20 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] Current memory baseline range: 61424.7-72076.0 kB.
[01/May/2024:07:48:20 +10:00: profiler/debug] [job_id: j1p3y37mg] [model.tflite] Runtime metadata not found in Models/model.tflite/trt_metadata.json or Models/model.tflite/trt_metadata.pb
[01/May/2024:07:48:20 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] TF Lite version 2.16.1. Loading model from Models/model.tflite.
[01/May/2024:07:48:20 +10:00: profiler/debug] [job_id: j1p3y37mg] [model.tflite] Mapping resource file in Models/model.tflite
[01/May/2024:07:48:20 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] Loaded model. Minimum TF Lite version = 1.5.0.
[01/May/2024:07:48:20 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] No delegates specified; using compute unit=cpu_and_gpu.
[01/May/2024:07:48:20 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] [tflite] Initialized TensorFlow Lite runtime.
[01/May/2024:07:48:20 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] GPUV2 delegate requested. OpenCL detected.
[01/May/2024:07:48:20 +10:00: profiler/debug] [job_id: j1p3y37mg] [model.tflite] Enabling delegate cache in dir=/data/user/0/ai.tetra.tungsten/cache/1714513700731/ai.tetra.runtime/0.6.0/model.tflite_8945450969824422876_1714513695446/gpuv2.
[01/May/2024:07:48:20 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] [tflite] Created TensorFlow Lite delegate for GPU.
[01/May/2024:07:48:20 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] [tflite] Replacing 1 out of 1 node(s) with delegate (TfLiteGpuDelegateV2) node, yielding 1 partitions for the whole graph.
[01/May/2024:07:48:20 +10:00: profiler/warning] [job_id: j1p3y37mg] [model.tflite] [tflite] TfLiteGpuDelegate Init: FULLY_CONNECTED: Amount of input channels should match weights width
[01/May/2024:07:48:20 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] [tflite] Created 0 GPU delegate kernels.
[01/May/2024:07:48:20 +10:00: profiler/warning] [job_id: j1p3y37mg] [model.tflite] [tflite] TfLiteGpuDelegate Prepare: delegate is not initialized
[01/May/2024:07:48:20 +10:00: profiler/warning] [job_id: j1p3y37mg] [model.tflite] [tflite] Node number 1 (TfLiteGpuDelegateV2) failed to prepare.
[01/May/2024:07:48:20 +10:00: profiler/warning] [job_id: j1p3y37mg] [model.tflite] [tflite] Restored original execution plan after delegate application failure.
[01/May/2024:07:48:20 +10:00: profiler/error] [job_id: j1p3y37mg] [model.tflite] Delegate GPUV2/OpenCL failed to prepare. Retrying without this delegate.
[01/May/2024:07:48:20 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] TfLite XNNPACK delegate requested.
[01/May/2024:07:48:20 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] [tflite] Created TensorFlow Lite XNNPACK delegate for CPU.
[01/May/2024:07:48:20 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] [tflite] Replacing 1 out of 1 node(s) with delegate (TfLiteXNNPackDelegate) node, yielding 1 partitions for the whole graph.
[01/May/2024:07:48:20 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] Applied 1 delegates: XNNPACK. Model is fully delegated=true.
[01/May/2024:07:48:20 +10:00: profiler/debug] [job_id: j1p3y37mg] [model.tflite] Saving delegate selection for subsequent steps.
[01/May/2024:07:48:20 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] Malloc VM size after: 28856.0 kB, allocated: 18272.2 kB, slack: 10583.8 kB.
[01/May/2024:07:48:20 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] Status Successfully Loaded Cold with t = 16935 us and usage: before = 72076.0 kB; peakBefore = 72076.0 kB; mallocUnusedBefore = 10651.3 kB; after = 72232.0 kB; peakAfter = 72144.0 kB; mallocUnusedAfter = 10583.8 kB; increase = 0.0-223.5 kB; peak = 68.0-10719.3 kB
[01/May/2024:07:48:21 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] Saving results to /storage/emulated/0/Android/data/ai.tetra.tungsten/files/Results/job_j1p3y37mg/job_j1p3y37mg_results.bin
[01/May/2024:07:48:21 +10:00: profiler/info] -=- Tungsten Running Task: Loading -=-
[01/May/2024:07:48:21 +10:00: profiler/info] Detected chipset 3101, made by 3000.
[01/May/2024:07:48:21 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] Loading previously saved results in /storage/emulated/0/Android/data/ai.tetra.tungsten/files/Results/job_j1p3y37mg/job_j1p3y37mg_results.bin
[01/May/2024:07:48:21 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] Loading tflite model Models/model.tflite
[01/May/2024:07:48:21 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] Malloc VM size before: 28336.0 kB, allocated: 17488.8 kB, slack: 10847.2 kB.
[01/May/2024:07:48:21 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] Current memory baseline range: 55556.8-66404.0 kB.
[01/May/2024:07:48:21 +10:00: profiler/debug] [job_id: j1p3y37mg] [model.tflite] Runtime metadata not found in Models/model.tflite/trt_metadata.json or Models/model.tflite/trt_metadata.pb
[01/May/2024:07:48:21 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] TF Lite version 2.16.1. Loading model from Models/model.tflite.
[01/May/2024:07:48:21 +10:00: profiler/debug] [job_id: j1p3y37mg] [model.tflite] Mapping resource file in Models/model.tflite
[01/May/2024:07:48:21 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] Loaded model. Minimum TF Lite version = 1.5.0.
[01/May/2024:07:48:21 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] TfLite XNNPACK delegate requested.
[01/May/2024:07:48:21 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] [tflite] Replacing 1 out of 1 node(s) with delegate (TfLiteXNNPackDelegate) node, yielding 1 partitions for the whole graph.
[01/May/2024:07:48:21 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] Applied 1 delegates: XNNPACK. Model is fully delegated=true.
[01/May/2024:07:48:21 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] Malloc VM size after: 29368.0 kB, allocated: 18832.3 kB, slack: 10535.7 kB.
[01/May/2024:07:48:21 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] Status Successfully Loaded Warm with t = 5937 us and usage: before = 66404.0 kB; peakBefore = 66404.0 kB; mallocUnusedBefore = 10847.2 kB; after = 66848.0 kB; peakAfter = 66644.0 kB; mallocUnusedAfter = 10535.7 kB; increase = 0.0-755.5 kB; peak = 240.0-11087.2 kB
[01/May/2024:07:48:21 +10:00: profiler/info] [job_id: j1p3y37mg] [model.tflite] Saving results to /storage/emulated/0/Android/data/ai.tetra.tungsten/files/Results/job_j1p3y37mg/job_j1p3y37mg_results.bin
```
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PR_kwDOArmXAs5uMNF7
| 66,728 |
Mark dots to be unpropogatable on space-to-batch conversion. Delaying the conversion from dots to convs to occur post layout assignment caused this.
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Mark dots to be unpropogatable on space-to-batch conversion. Delaying the conversion from dots to convs to occur post layout assignment caused this.
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I_kwDOArmXAs6HcV1B
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Op support request: Matmul with constant left hand side
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"Hi @gustavla ,\r\n\r\nI ran the code you provided to create the tflite model and i am getting the below error. Can you share me the tflite model itself.\r\n\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\nCell In[6], line 21\r\n 19 # Convert the model.\r\n 20 converter = tf.lite.TFLiteConverter.from_keras_model(model)\r\n---> 21 tflite_model = converter.convert()\r\n 23 # Save the model.\r\n 24 with open('model.tflite', 'wb') as f:\r\n\r\nFile ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\lite\\python\\lite.py:1175, in _export_metrics.<locals>.wrapper(self, *args, **kwargs)\r\n 1172 @functools.wraps(convert_func)\r\n 1173 def wrapper(self, *args, **kwargs):\r\n 1174 # pylint: disable=protected-access\r\n-> 1175 return self._convert_and_export_metrics(convert_func, *args, **kwargs)\r\n\r\nFile ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\lite\\python\\lite.py:1129, in TFLiteConverterBase._convert_and_export_metrics(self, convert_func, *args, **kwargs)\r\n 1127 self._save_conversion_params_metric()\r\n 1128 start_time = time.process_time()\r\n-> 1129 result = convert_func(self, *args, **kwargs)\r\n 1130 elapsed_time_ms = (time.process_time() - start_time) * 1000\r\n 1131 if result:\r\n\r\nFile ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\lite\\python\\lite.py:1641, in TFLiteKerasModelConverterV2.convert(self)\r\n 1637 if saved_model_convert_result:\r\n 1638 return saved_model_convert_result\r\n 1640 graph_def, input_tensors, output_tensors, frozen_func = (\r\n-> 1641 self._freeze_keras_model()\r\n 1642 )\r\n 1644 graph_def = self._optimize_tf_model(\r\n 1645 graph_def, input_tensors, output_tensors, frozen_func\r\n 1646 )\r\n 1648 return super(TFLiteKerasModelConverterV2, self).convert(\r\n 1649 graph_def, input_tensors, output_tensors\r\n 1650 )\r\n\r\nFile ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\lite\\python\\convert_phase.py:215, in convert_phase.<locals>.actual_decorator.<locals>.wrapper(*args, **kwargs)\r\n 213 except Exception as error:\r\n 214 report_error_message(str(error))\r\n--> 215 raise error from None\r\n\r\nFile ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\lite\\python\\convert_phase.py:205, in convert_phase.<locals>.actual_decorator.<locals>.wrapper(*args, **kwargs)\r\n 202 @functools.wraps(func)\r\n 203 def wrapper(*args, **kwargs):\r\n 204 try:\r\n--> 205 return func(*args, **kwargs)\r\n 206 except ConverterError as converter_error:\r\n 207 if converter_error.errors:\r\n\r\nFile ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\lite\\python\\lite.py:1582, in TFLiteKerasModelConverterV2._freeze_keras_model(self)\r\n 1573 # If the model's call is not a `tf.function`, then we need to first get its\r\n 1574 # input signature from `model_input_signature` method. We can't directly\r\n 1575 # call `trace_model_call` because otherwise the batch dimension is set\r\n 1576 # to None.\r\n 1577 # Once we have better support for dynamic shapes, we can remove this.\r\n 1578 if not isinstance(self._keras_model.call, _def_function.Function):\r\n 1579 # Pass `keep_original_batch_size=True` will ensure that we get an input\r\n 1580 # signature including the batch dimension specified by the user.\r\n 1581 # TODO(b/169898786): Use the Keras public API when TFLite moves out of TF\r\n-> 1582 input_signature = _model_input_signature(\r\n 1583 self._keras_model, keep_original_batch_size=True\r\n 1584 )\r\n 1586 # TODO(b/169898786): Use the Keras public API when TFLite moves out of TF\r\n 1587 func = _trace_model_call(self._keras_model, input_signature)\r\n\r\nFile ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\lite\\python\\tflite_keras_util.py:84, in model_input_signature(model, keep_original_batch_size)\r\n 82 input_specs = input_specs[0][0]\r\n 83 else:\r\n---> 84 input_specs = model._get_save_spec( # pylint: disable=protected-access\r\n 85 dynamic_batch=not keep_original_batch_size)\r\n 86 if input_specs is None:\r\n 87 return None\r\n\r\nAttributeError: 'Functional' object has no attribute '_get_save_spec'",
"Sorry, I think the repro script requires tensorflow 2.15.\r\n\r\nHere is the model: https://qaihub-public-issues.s3.us-west-2.amazonaws.com/tflite/66727_model.tflite"
] | 2024-04-30T19:58:24 | 2024-06-11T14:33:42 | null |
CONTRIBUTOR
| null | null | null |
**System information**
- Samsung Galaxy S23 / Android 13 / Snapdragon® 8 Gen 2 | SM8550
- TFLite 2.16.1 (stock)
**Standalone code to reproduce the issue**
Code to generate model:
```
import tensorflow as tf
import keras
input_shape = [16, 1]
output_shape = [13, 1]
tf_input = keras.Input(input_shape[1:], batch_size=input_shape[0])
class MyMatMul(keras.layers.Layer):
def call(self, tf_input):
tf_output = tf.matmul(tf.ones((13, 16)), tf_input)
return tf_output
tf_output = MyMatMul()(tf_input)
model = keras.Model(inputs=[tf_input], outputs=[tf_output])
# Convert the model.
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
# Save the model.
with open('model.tflite', 'wb') as f:
f.write(tflite_model)
```
**Any other info / logs**
Runtime log (executed on https://aihub.qualcomm.com/):
<!-- Job id: https://dev.aihub.qualcomm.com/jobs/jz5wjvy4g/ -->
```
[01/May/2024:05:54:09 +10:00: profiler/warning] [job_id: jz5wjvy4g] [model.tflite] [tflite] TfLiteGpuDelegate Init: BATCH_MATMUL: Not supported batched mat mul case
```
Full log:
```
[01/May/2024:05:54:08 +10:00: profiler/info] -=- Tungsten Initializing -=-
[01/May/2024:05:54:08 +10:00: profiler/info] Android system property: ro.board.platform = kalama
[01/May/2024:05:54:08 +10:00: profiler/info] Android system property: ro.boot.hardware = qcom
[01/May/2024:05:54:08 +10:00: profiler/info] Android system property: ro.boot.hardware.platform =
[01/May/2024:05:54:08 +10:00: profiler/info] Android system property: ro.system.build.id = TP1A.220624.014
[01/May/2024:05:54:08 +10:00: profiler/info] Android system property: ro.system.build.version.release = 13
[01/May/2024:05:54:08 +10:00: profiler/info] Android system property: ro.hardware = qcom
[01/May/2024:05:54:08 +10:00: profiler/info] Android system property: ro.hardware.chipname =
[01/May/2024:05:54:08 +10:00: profiler/info] Android system property: ro.product.board = kalama
[01/May/2024:05:54:08 +10:00: profiler/info] Android system property: ro.product.brand = samsung
[01/May/2024:05:54:08 +10:00: profiler/info] Android system property: ro.product.device = dm1q
[01/May/2024:05:54:08 +10:00: profiler/info] Android system property: ro.product.build.fingerprint = samsung/dm1quew/qssi:13/TP1A.220624.014/S911U1UES1AWC9:user/release-keys
[01/May/2024:05:54:08 +10:00: profiler/info] Android system property: ro.product.manufacturer = samsung
[01/May/2024:05:54:08 +10:00: profiler/info] Android system property: ro.product.model = SM-S911U1
[01/May/2024:05:54:08 +10:00: profiler/info] Android system property: ro.product.name = dm1quew
[01/May/2024:05:54:08 +10:00: profiler/info] Android system property: ro.soc.manufacturer = QTI
[01/May/2024:05:54:08 +10:00: profiler/info] Android system property: ro.soc.model = SM8550
[01/May/2024:05:54:09 +10:00: profiler/info] [Manager] DeviceManager::DeviceManager
[01/May/2024:05:54:09 +10:00: profiler/info] [Manager] findAvailableDevices
[01/May/2024:05:54:09 +10:00: profiler/info] NNAPI devices: nnapi-reference
[01/May/2024:05:54:09 +10:00: profiler/info] GPU device: Qualcomm Adreno (TM) 740
[01/May/2024:05:54:09 +10:00: profiler/info] OpenGL Version: OpenGL ES 3.2 [email protected] (GIT@9ab6a2b2d8, Ifc633ebcef, 1674564387) (Date:01/24/23)
[01/May/2024:05:54:09 +10:00: profiler/info] OpenCL Version: OpenCL C 3.0 Adreno(TM) 740
[01/May/2024:05:54:09 +10:00: profiler/info] -=- Tungsten Running Task: Loading -=-
[01/May/2024:05:54:09 +10:00: profiler/info] Detected chipset 2807, made by 2000.
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] Loading tflite model Models/model.tflite
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] Malloc VM size before: 16452.0 kB, allocated: 14059.8 kB, slack: 2392.2 kB.
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] Current memory baseline range: 58267.8-60660.0 kB.
[01/May/2024:05:54:09 +10:00: profiler/debug] [job_id: jz5wjvy4g] [model.tflite] Runtime metadata not found in Models/model.tflite/trt_metadata.json or Models/model.tflite/trt_metadata.pb
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] TF Lite version 2.16.1. Loading model from Models/model.tflite.
[01/May/2024:05:54:09 +10:00: profiler/debug] [job_id: jz5wjvy4g] [model.tflite] Mapping resource file in Models/model.tflite
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] Loaded model. Minimum TF Lite version = 2.3.0.
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] No delegates specified; using compute unit=cpu_and_gpu.
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] [tflite] Initialized TensorFlow Lite runtime.
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] GPUV2 delegate requested. OpenCL detected.
[01/May/2024:05:54:09 +10:00: profiler/debug] [job_id: jz5wjvy4g] [model.tflite] Enabling delegate cache in dir=/data/user/0/ai.tetra.tungsten/cache/1714506848918/ai.tetra.runtime/0.6.0/model.tflite_8945450969824422876_1714506836981/gpuv2.
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] [tflite] Created TensorFlow Lite delegate for GPU.
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] [tflite] Replacing 1 out of 1 node(s) with delegate (TfLiteGpuDelegateV2) node, yielding 1 partitions for the whole graph.
[01/May/2024:05:54:09 +10:00: profiler/warning] [job_id: jz5wjvy4g] [model.tflite] [tflite] TfLiteGpuDelegate Init: BATCH_MATMUL: Not supported batched mat mul case
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] [tflite] Created 0 GPU delegate kernels.
[01/May/2024:05:54:09 +10:00: profiler/warning] [job_id: jz5wjvy4g] [model.tflite] [tflite] TfLiteGpuDelegate Prepare: delegate is not initialized
[01/May/2024:05:54:09 +10:00: profiler/warning] [job_id: jz5wjvy4g] [model.tflite] [tflite] Node number 1 (TfLiteGpuDelegateV2) failed to prepare.
[01/May/2024:05:54:09 +10:00: profiler/warning] [job_id: jz5wjvy4g] [model.tflite] [tflite] Restored original execution plan after delegate application failure.
[01/May/2024:05:54:09 +10:00: profiler/error] [job_id: jz5wjvy4g] [model.tflite] Delegate GPUV2/OpenCL failed to prepare. Retrying without this delegate.
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] TfLite XNNPACK delegate requested.
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] [tflite] Created TensorFlow Lite XNNPACK delegate for CPU.
[01/May/2024:05:54:09 +10:00: profiler/warning] [job_id: jz5wjvy4g] [model.tflite] [tflite] failed to delegate BATCH_MATMUL node #0. Delegation of latest operators must be enabled
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] Applied 0 delegates. Model will run using built-in kernels.
[01/May/2024:05:54:09 +10:00: profiler/debug] [job_id: jz5wjvy4g] [model.tflite] Saving delegate selection for subsequent steps.
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] Malloc VM size after: 16472.0 kB, allocated: 14339.7 kB, slack: 2132.3 kB.
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] Status Successfully Loaded Cold with t = 30997 us and usage: before = 60660.0 kB; peakBefore = 60660.0 kB; mallocUnusedBefore = 2392.2 kB; after = 61108.0 kB; peakAfter = 60808.0 kB; mallocUnusedAfter = 2132.3 kB; increase = 0.0-707.9 kB; peak = 148.0-2540.2 kB
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] Saving results to /storage/emulated/0/Android/data/ai.tetra.tungsten/files/Results/job_jz5wjvy4g/job_jz5wjvy4g_results.bin
[01/May/2024:05:54:09 +10:00: profiler/info] -=- Tungsten Running Task: Loading -=-
[01/May/2024:05:54:09 +10:00: profiler/info] Detected chipset 2807, made by 2000.
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] Loading previously saved results in /storage/emulated/0/Android/data/ai.tetra.tungsten/files/Results/job_jz5wjvy4g/job_jz5wjvy4g_results.bin
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] Loading tflite model Models/model.tflite
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] Malloc VM size before: 16028.0 kB, allocated: 14825.3 kB, slack: 1202.7 kB.
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] Current memory baseline range: 61873.3-63076.0 kB.
[01/May/2024:05:54:09 +10:00: profiler/debug] [job_id: jz5wjvy4g] [model.tflite] Runtime metadata not found in Models/model.tflite/trt_metadata.json or Models/model.tflite/trt_metadata.pb
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] TF Lite version 2.16.1. Loading model from Models/model.tflite.
[01/May/2024:05:54:09 +10:00: profiler/debug] [job_id: jz5wjvy4g] [model.tflite] Mapping resource file in Models/model.tflite
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] Loaded model. Minimum TF Lite version = 2.3.0.
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] Applied 0 delegates. Model will run using built-in kernels.
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] Malloc VM size after: 16480.0 kB, allocated: 15353.9 kB, slack: 1126.1 kB.
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] Status Successfully Loaded Warm with t = 4159 us and usage: before = 63076.0 kB; peakBefore = 63076.0 kB; mallocUnusedBefore = 1202.7 kB; after = 63480.0 kB; peakAfter = 63384.0 kB; mallocUnusedAfter = 1126.1 kB; increase = 0.0-480.7 kB; peak = 308.0-1510.7 kB
[01/May/2024:05:54:09 +10:00: profiler/info] [job_id: jz5wjvy4g] [model.tflite] Saving results to /storage/emulated/0/Android/data/ai.tetra.tungsten/files/Results/job_jz5wjvy4g/job_jz5wjvy4g_results.bin
```
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| 2,272,249,548 |
PR_kwDOArmXAs5uLxgK
| 66,726 |
Add `WaitAtBarrier` with subset of nodes to `DistributedRuntimeCoordinationServiceClient` and plumb that up to the JAX API layer.
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Add `WaitAtBarrier` with subset of nodes to `DistributedRuntimeCoordinationServiceClient` and plumb that up to the JAX API layer.
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PR_kwDOArmXAs5uLpd0
| 66,725 |
Add support for the PartialReduce custom call op in auto-sharding.
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Add support for the PartialReduce custom call op in auto-sharding.
Reverts 5445d4bf8ea2e8566741926de63a891972ede491
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PR_kwDOArmXAs5uLhtf
| 66,724 |
Fix typo in shape formatter.
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Fix typo in shape formatter.
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[pjrt] NFC: Make absl::StatusOr<T> implicit part of PjRtFuture<T>
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[pjrt] NFC: Make absl::StatusOr<T> implicit part of PjRtFuture<T>
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Move `gpu_plugin_deps` inside the `if_google` condition of `xla_extension`.
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Move `gpu_plugin_deps` inside the `if_google` condition of `xla_extension`.
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I_kwDOArmXAs6Hbfb2
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GPUv2 segfaults on split-head attention CLIP model
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[
"Hi @gustavla ,\r\n\r\nDid you try out this model on other devices? Also can you please provide me the inference code you used on qualcom ai hub ",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"I just tried it on a few devices and it only repros on the Pixels that I tried:\r\n\r\n* Google Pixel 7: Segfaults\r\n* Google Pixel 8: Segfaults\r\n* Samsung Galaxy S23: OK (100% GPUV2 (OpenCL), 81.6 ms / 0 - 366 MB peak memory)\r\n* Xiaomi Redmi Note 10 5G: OK (100% GPUv2 (OpenCL), 376.2 ms, 0 - 352 MB peak memory)\r\n\r\n\r\n<!--\r\nhttps://dev.aihub.qualcomm.com/jobs/j1pvxe7j5/\r\nhttps://dev.aihub.qualcomm.com/jobs/j7gj4oqxg/\r\nhttps://dev.aihub.qualcomm.com/jobs/jlpe38y1g/\r\n-->\r\n\r\nHere is the submission:\r\n```\r\nqai-hub submit-profile-job --model tflite_66721_sha_clip_gpuv2_segfault.tflite --device \"Google Pixel 7\" --profile_options \" --compute_unit gpu\"\r\n```\r\nOr\r\n```\r\nimport qai_hub as hub\r\nhub.submit_profile_job(\r\n \"tflite_66721_sha_clip_gpuv2_segfault.tflite\", \r\n device=hub.Device(\"Google Pixel 7\"), \r\n options=\"--compute_unit gpu\")\r\n```"
] | 2024-04-30T17:44:14 | 2024-06-11T14:33:58 | null |
CONTRIBUTOR
| null | null | null |
**System information**
- Google Pixel 7 / Android 13 / Google Tensor G2
- TFLite 2.16.1 (stock)
**Standalone code to reproduce the issue**
Model asset: [tflite_66721_sha_clip_gpuv2_segfault.tflite](https://qaihub-public-issues.s3.us-west-2.amazonaws.com/tflite/tflite_66721_sha_clip_gpuv2_segfault.tflite)
Run model through TFLite (GPUv2) on an Android device (for instance through benchmark tool).
**Any other info / logs**
Runtime log (executed on https://aihub.qualcomm.com/)
<!-- Job id: https://dev.aihub.qualcomm.com/jobs/jygz19nxp/ -->
```
[30/Apr/2024:10:26:55 -07:00: profiler/info] -=- Tungsten Initializing -=-
[30/Apr/2024:10:26:55 -07:00: profiler/info] Android system property: ro.board.platform = gs201
[30/Apr/2024:10:26:55 -07:00: profiler/info] Android system property: ro.boot.hardware = panther
[30/Apr/2024:10:26:55 -07:00: profiler/info] Android system property: ro.boot.hardware.platform = gs201
[30/Apr/2024:10:26:55 -07:00: profiler/info] Android system property: ro.system.build.id = TQ1A.221205.011
[30/Apr/2024:10:26:55 -07:00: profiler/info] Android system property: ro.system.build.version.release = 13
[30/Apr/2024:10:26:55 -07:00: profiler/info] Android system property: ro.hardware = panther
[30/Apr/2024:10:26:55 -07:00: profiler/info] Android system property: ro.hardware.chipname =
[30/Apr/2024:10:26:55 -07:00: profiler/info] Android system property: ro.product.board = panther
[30/Apr/2024:10:26:55 -07:00: profiler/info] Android system property: ro.product.brand = google
[30/Apr/2024:10:26:55 -07:00: profiler/info] Android system property: ro.product.device = panther
[30/Apr/2024:10:26:55 -07:00: profiler/info] Android system property: ro.product.build.fingerprint = google/panther/panther:13/TQ1A.221205.011/9244662:user/release-keys
[30/Apr/2024:10:26:55 -07:00: profiler/info] Android system property: ro.product.manufacturer = Google
[30/Apr/2024:10:26:55 -07:00: profiler/info] Android system property: ro.product.model = Pixel 7
[30/Apr/2024:10:26:55 -07:00: profiler/info] Android system property: ro.product.name = panther
[30/Apr/2024:10:26:55 -07:00: profiler/info] Android system property: ro.soc.manufacturer = Google
[30/Apr/2024:10:26:55 -07:00: profiler/info] Android system property: ro.soc.model = GS201
[30/Apr/2024:10:26:55 -07:00: profiler/info] [Manager] DeviceManager::DeviceManager
[30/Apr/2024:10:26:55 -07:00: profiler/info] [Manager] findAvailableDevices
[30/Apr/2024:10:26:55 -07:00: profiler/info] [Manager] Found interface google-edgetpu (version = 2.0)
[30/Apr/2024:10:26:55 -07:00: profiler/info] [Manager] Found interface google-armnn (version = ArmNN)
[30/Apr/2024:10:26:55 -07:00: profiler/info] NNAPI devices: google-edgetpu,google-armnn,nnapi-reference
[30/Apr/2024:10:26:55 -07:00: profiler/info] GPU device: ARM Mali-G710
[30/Apr/2024:10:26:55 -07:00: profiler/info] OpenGL Version: OpenGL ES 3.2 v1.r36p0-01eac0.1f36dec337e44918d811de9a8a2acf4d
[30/Apr/2024:10:26:55 -07:00: profiler/info] OpenCL Version: OpenCL C 1.2 v1.r36p0-01eac0.1f36dec337e44918d811de9a8a2acf4d
[30/Apr/2024:10:26:55 -07:00: profiler/info] -=- Tungsten Running Task: Loading -=-
[30/Apr/2024:10:26:55 -07:00: profiler/info] Detected chipset 3101, made by 3000.
[30/Apr/2024:10:26:55 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Loading tflite model Models/model.tflite
[30/Apr/2024:10:26:55 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Malloc VM size before: 24632.0 kB, allocated: 13796.0 kB, slack: 10836.0 kB.
[30/Apr/2024:10:26:55 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Current memory baseline range: 57552.0-68388.0 kB.
[30/Apr/2024:10:26:55 -07:00: profiler/debug] [job_id: jygz19nxp] [model.tflite] Runtime metadata not found in Models/model.tflite/trt_metadata.json or Models/model.tflite/trt_metadata.pb
[30/Apr/2024:10:26:55 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] TF Lite version 2.16.1. Loading model from Models/model.tflite.
[30/Apr/2024:10:26:55 -07:00: profiler/debug] [job_id: jygz19nxp] [model.tflite] Mapping resource file in Models/model.tflite
[30/Apr/2024:10:26:55 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Loaded model. Minimum TF Lite version = 2.3.0.
[30/Apr/2024:10:26:55 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] No delegates specified; using compute unit=cpu_and_gpu.
[30/Apr/2024:10:26:55 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] [tflite] Initialized TensorFlow Lite runtime.
[30/Apr/2024:10:26:55 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] GPUV2 delegate requested. OpenCL detected.
[30/Apr/2024:10:26:55 -07:00: profiler/debug] [job_id: jygz19nxp] [model.tflite] Enabling delegate cache in dir=/data/user/0/ai.tetra.tungsten/cache/1714498015468/ai.tetra.runtime/0.6.0/model.tflite_8945450969824422876_1714498006500/gpuv2.
[30/Apr/2024:10:26:55 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] [tflite] Created TensorFlow Lite delegate for GPU.
[30/Apr/2024:10:26:55 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] [tflite] Replacing 2003 out of 2003 node(s) with delegate (TfLiteGpuDelegateV2) node, yielding 1 partitions for the whole graph.
[30/Apr/2024:10:26:56 -07:00: profiler/warning] [job_id: jygz19nxp] [model.tflite] [tflite] File /data/user/0/ai.tetra.tungsten/cache/1714498015468/ai.tetra.runtime/0.6.0/model.tflite_8945450969824422876_1714498006500/gpuv2/gpuv2_1297717803319390986.bin couldn't be opened for reading: No such file or directory
[30/Apr/2024:10:27:00 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] [tflite] Initialized OpenCL-based API.
[30/Apr/2024:10:27:01 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] [tflite] Created 1 GPU delegate kernels.
[30/Apr/2024:10:27:01 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Applied 1 delegates: GPUV2/OpenCL. Model is fully delegated=true.
[30/Apr/2024:10:27:01 -07:00: profiler/debug] [job_id: jygz19nxp] [model.tflite] Saving delegate selection for subsequent steps.
[30/Apr/2024:10:27:01 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Malloc VM size after: 256412.0 kB, allocated: 233690.0 kB, slack: 22722.0 kB.
[30/Apr/2024:10:27:01 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Status Successfully Loaded Cold with t = 5381726 us and usage: before = 68388.0 kB; peakBefore = 68388.0 kB; mallocUnusedBefore = 10836.0 kB; after = 291732.0 kB; peakAfter = 805160.0 kB; mallocUnusedAfter = 22722.0 kB; increase = 200622.0-211458.0 kB; peak = 736772.0-747608.0 kB
[30/Apr/2024:10:27:01 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Saving results to /storage/emulated/0/Android/data/ai.tetra.tungsten/files/Results/job_jygz19nxp/job_jygz19nxp_results.bin
[30/Apr/2024:10:27:01 -07:00: profiler/info] -=- Tungsten Running Task: Loading -=-
[30/Apr/2024:10:27:01 -07:00: profiler/info] Detected chipset 3101, made by 3000.
[30/Apr/2024:10:27:01 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Loading previously saved results in /storage/emulated/0/Android/data/ai.tetra.tungsten/files/Results/job_jygz19nxp/job_jygz19nxp_results.bin
[30/Apr/2024:10:27:01 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Loading tflite model Models/model.tflite
[30/Apr/2024:10:27:01 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Malloc VM size before: 77880.0 kB, allocated: 16704.4 kB, slack: 61175.6 kB.
[30/Apr/2024:10:27:01 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Current memory baseline range: 25732.4-86908.0 kB.
[30/Apr/2024:10:27:01 -07:00: profiler/debug] [job_id: jygz19nxp] [model.tflite] Runtime metadata not found in Models/model.tflite/trt_metadata.json or Models/model.tflite/trt_metadata.pb
[30/Apr/2024:10:27:01 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] TF Lite version 2.16.1. Loading model from Models/model.tflite.
[30/Apr/2024:10:27:01 -07:00: profiler/debug] [job_id: jygz19nxp] [model.tflite] Mapping resource file in Models/model.tflite
[30/Apr/2024:10:27:01 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Loaded model. Minimum TF Lite version = 2.3.0.
[30/Apr/2024:10:27:01 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] GPUV2 delegate requested. OpenCL detected.
[30/Apr/2024:10:27:01 -07:00: profiler/debug] [job_id: jygz19nxp] [model.tflite] Enabling delegate cache in dir=/data/user/0/ai.tetra.tungsten/cache/1714498015468/ai.tetra.runtime/0.6.0/model.tflite_8945450969824422876_1714498006500/gpuv2.
[30/Apr/2024:10:27:01 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] [tflite] Replacing 2003 out of 2003 node(s) with delegate (TfLiteGpuDelegateV2) node, yielding 1 partitions for the whole graph.
[30/Apr/2024:10:27:02 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] [tflite] Found serialized data for model gpuv2 (175507208 B) at /data/user/0/ai.tetra.tungsten/cache/1714498015468/ai.tetra.runtime/0.6.0/model.tflite_8945450969824422876_1714498006500/gpuv2/gpuv2_1297717803319390986.bin
[30/Apr/2024:10:27:02 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] [tflite] Initialized OpenCL-based API from serialized data.
[30/Apr/2024:10:27:02 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] [tflite] Created 1 GPU delegate kernels.
[30/Apr/2024:10:27:02 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Applied 1 delegates: GPUV2/OpenCL. Model is fully delegated=true.
[30/Apr/2024:10:27:02 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Malloc VM size after: 252240.0 kB, allocated: 225091.0 kB, slack: 27149.0 kB.
[30/Apr/2024:10:27:02 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Status Successfully Loaded Warm with t = 1281645 us and usage: before = 86908.0 kB; peakBefore = 86908.0 kB; mallocUnusedBefore = 61175.6 kB; after = 283312.0 kB; peakAfter = 785988.0 kB; mallocUnusedAfter = 27149.0 kB; increase = 169255.0-230430.6 kB; peak = 699080.0-760255.6 kB
[30/Apr/2024:10:27:03 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Saving results to /storage/emulated/0/Android/data/ai.tetra.tungsten/files/Results/job_jygz19nxp/job_jygz19nxp_results.bin
[30/Apr/2024:10:27:03 -07:00: profiler/info] -=- Tungsten Running Task: Performing inference by layer -=-
[30/Apr/2024:10:27:03 -07:00: profiler/info] Detected chipset 3101, made by 3000.
[30/Apr/2024:10:27:03 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Loading previously saved results in /storage/emulated/0/Android/data/ai.tetra.tungsten/files/Results/job_jygz19nxp/job_jygz19nxp_results.bin
[30/Apr/2024:10:27:03 -07:00: profiler/debug] [job_id: jygz19nxp] [model.tflite] Starting profiler
[30/Apr/2024:10:27:03 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Loading tflite model Models/model.tflite
[30/Apr/2024:10:27:03 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Malloc VM size before: 77880.0 kB, allocated: 16961.8 kB, slack: 60918.2 kB.
[30/Apr/2024:10:27:03 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Current memory baseline range: 45341.8-106260.0 kB.
[30/Apr/2024:10:27:03 -07:00: profiler/debug] [job_id: jygz19nxp] [model.tflite] Runtime metadata not found in Models/model.tflite/trt_metadata.json or Models/model.tflite/trt_metadata.pb
[30/Apr/2024:10:27:03 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] TF Lite version 2.16.1. Loading model from Models/model.tflite.
[30/Apr/2024:10:27:03 -07:00: profiler/debug] [job_id: jygz19nxp] [model.tflite] Mapping resource file in Models/model.tflite
[30/Apr/2024:10:27:03 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Loaded model. Minimum TF Lite version = 2.3.0.
[30/Apr/2024:10:27:03 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] GPUV2 delegate requested. OpenCL detected.
[30/Apr/2024:10:27:03 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] [tflite] Replacing 2003 out of 2003 node(s) with delegate (TfLiteGpuDelegateV2) node, yielding 1 partitions for the whole graph.
[30/Apr/2024:10:27:07 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] [tflite] Created 1 GPU delegate kernels.
[30/Apr/2024:10:27:07 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Applied 1 delegates: GPUV2/OpenCL. Model is fully delegated=true.
[30/Apr/2024:10:27:07 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Malloc VM size after: 259724.0 kB, allocated: 243057.6 kB, slack: 16666.4 kB.
[30/Apr/2024:10:27:07 -07:00: profiler/info] [job_id: jygz19nxp] [model.tflite] Status Successfully Loaded Warm with t = 3769509 us and usage: before = 106260.0 kB; peakBefore = 106260.0 kB; mallocUnusedBefore = 60918.2 kB; after = 300360.0 kB; peakAfter = 635204.0 kB; mallocUnusedAfter = 16666.4 kB; increase = 177433.6-238351.8 kB; peak = 528944.0-589862.2 kB
The process ended because of a segmentation fault. Consult the runtime log for more details.
The following is the suspected stack trace.
* ? (/vendor/lib64/egl/libGLES_mali.so)
* ? (/vendor/lib64/egl/libGLES_mali.so)
* ? (/vendor/lib64/egl/libGLES_mali.so)
* ? (/vendor/lib64/egl/libGLES_mali.so)
* ? (/vendor/lib64/egl/libGLES_mali.so)
* clEnqueueNDRangeKernel (/vendor/lib64/egl/libGLES_mali.so)
* tflite::gpu::cl::CLCommandQueue::Dispatch() (/data/app/~~58txPSlH_K9lY1T48CQEEw==/ai.tetra.tungsten-qGxzJmjd0hwWY5NXpr8n5A==/lib/arm64/libtungsten-native-bridge.so)
* tflite::gpu::cl::ProfilingCommandQueue::DispatchNTimes() (/data/app/~~58txPSlH_K9lY1T48CQEEw==/ai.tetra.tungsten-qGxzJmjd0hwWY5NXpr8n5A==/lib/arm64/libtungsten-native-bridge.so)
* tflite::gpu::cl::InferenceContext::ProfileTime() (/data/app/~~58txPSlH_K9lY1T48CQEEw==/ai.tetra.tungsten-qGxzJmjd0hwWY5NXpr8n5A==/lib/arm64/libtungsten-native-bridge.so)
* tflite::gpu::cl::InferenceContext::Profile() (/data/app/~~58txPSlH_K9lY1T48CQEEw==/ai.tetra.tungsten-qGxzJmjd0hwWY5NXpr8n5A==/lib/arm64/libtungsten-native-bridge.so)
* ? (/data/app/~~58txPSlH_K9lY1T48CQEEw==/ai.tetra.tungsten-qGxzJmjd0hwWY5NXpr8n5A==/lib/arm64/libtungsten-native-bridge.so)
* ? (/data/app/~~58txPSlH_K9lY1T48CQEEw==/ai.tetra.tungsten-qGxzJmjd0hwWY5NXpr8n5A==/lib/arm64/libtungsten-native-bridge.so)
* ? (/data/app/~~58txPSlH_K9lY1T48CQEEw==/ai.tetra.tungsten-qGxzJmjd0hwWY5NXpr8n5A==/lib/arm64/libtungsten-native-bridge.so)
* tflite::Subgraph::InvokeImpl() (/data/app/~~58txPSlH_K9lY1T48CQEEw==/ai.tetra.tungsten-qGxzJmjd0hwWY5NXpr8n5A==/lib/arm64/libtungsten-native-bridge.so)
* tflite::Subgraph::Invoke() (/data/app/~~58txPSlH_K9lY1T48CQEEw==/ai.tetra.tungsten-qGxzJmjd0hwWY5NXpr8n5A==/lib/arm64/libtungsten-native-bridge.so)
* tflite::impl::Interpreter::Invoke() (/data/app/~~58txPSlH_K9lY1T48CQEEw==/ai.tetra.tungsten-qGxzJmjd0hwWY5NXpr8n5A==/lib/arm64/libtungsten-native-bridge.so)
* backend::tflite::TfLiteModel::Run() (/data/app/~~58txPSlH_K9lY1T48CQEEw==/ai.tetra.tungsten-qGxzJmjd0hwWY5NXpr8n5A==/lib/arm64/libtungsten-native-bridge.so)
* tungsten::Profiler::ProfileOrValidate() (/data/app/~~58txPSlH_K9lY1T48CQEEw==/ai.tetra.tungsten-qGxzJmjd0hwWY5NXpr8n5A==/lib/arm64/libtungsten-native-bridge.so)
* tungsten::ProfilerRunner::ProfileModels() (/data/app/~~58txPSlH_K9lY1T48CQEEw==/ai.tetra.tungsten-qGxzJmjd0hwWY5NXpr8n5A==/lib/arm64/libtungsten-native-bridge.so)
* tungsten::ProfilerRunner::RunTask() (/data/app/~~58txPSlH_K9lY1T48CQEEw==/ai.tetra.tungsten-qGxzJmjd0hwWY5NXpr8n5A==/lib/arm64/libtungsten-native-bridge.so)
* Java_ai_tetra_tungsten_ProfilerRunner_profileModels (/data/app/~~58txPSlH_K9lY1T48CQEEw==/ai.tetra.tungsten-qGxzJmjd0hwWY5NXpr8n5A==/lib/arm64/libtungsten-native-bridge.so)
* ? (/apex/com.android.art/lib64/libart.so)
* ? (/apex/com.android.art/lib64/libart.so)
* ? (/apex/com.android.art/lib64/libart.so)
```
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| 2,272,122,825 |
PR_kwDOArmXAs5uLVrf
| 66,720 |
Fix missing resource name in a default container
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Fix missing resource name in a default container
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[XLA] Add an option to dump large constants.
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[XLA] Add an option to dump large constants.
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Accounts for ParallelMap's initial parallelism maximum buffered bytes
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PR #11597: Compile F32, BF16 and S32 constants in cuDNN graph.
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PR #11597: Compile F32, BF16 and S32 constants in cuDNN graph.
Imported from GitHub PR https://github.com/openxla/xla/pull/11597
Support compiling F32, BF16 and S32 constants in cuDNN graph.
This PR is blocked until cuDNN frontend dependency is upgraded to v1.3.
Copybara import of the project:
--
e8c6a3f4186242d812a00e8849350febd204f867 by Elfie Guo <[email protected]>:
Compile F32, BF16 and S32 constants in cuDNN graph.
Merging this change closes #11597
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11597 from elfiegg:cudnn_scalar_constants_2 e8c6a3f4186242d812a00e8849350febd204f867
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XLA related ptxas version error when changing batch size
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[
"Hi **@andremfreitas** ,\r\n- Ensure that you have CUDA Toolkit version 11.8 or higher installed on your system. You can download the latest version from the NVIDIA website and follow the installation instructions specific to your operating system and CUDA version.\r\n- Verify that your TensorFlow version is compatible with the CUDA Toolkit version you are using. Some TensorFlow versions may require specific CUDA Toolkit versions for full compatibility.\r\n- Here i am providing tensorflow [documentation](https://www.tensorflow.org/install/source) for your reference.\r\n\r\nThank you!",
"CUDA 12.3\r\ncuDNN 8.9 \r\n\r\nThese versions should be compatible with tensorflow 2.16.\r\n",
"Turns out it was an issue with a specific node of the cluster ... sorry about that",
"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/66716\">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/66716\">No</a>\n",
"Hey @andremfreitas, could you go a bit more into what you did to solve the issue? I'm having the exact same issue but not sure what the issue with the node in the cluster you're talking about it, or how to even begin solving an issue like this.\r\n\r\nAny help is appreciated!",
"Hi, having the same issue here! I also do not understand what issue with the node you're talking about. Any indication on how you solved the issue would really be appreciated!",
"@LeonardoPaccianiMori while this isn't really an answer, I just re-implemented the network in PyTorch and it works like a charm!",
"@LeonardoPaccianiMori @trevorcarrell In my case, one of the nodes of the cluster had an older cuda version, which caused me to mislead the error by the change of the batch size and not the allocation of this specific node.\n\nAfter avoiding this node I no longer had the problem with any batch size."
] | 2024-04-30T17:20:16 | 2024-05-26T10:16:26 | 2024-05-03T11:26:26 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
A100 40GB
### Current behavior?
I have a custom training loop that calls functions that are jit compiled. I got this error message you see below when using a batch size of 512. However, if I change the batch size to 256 for example (or 128), I no longer get this error. This is very weird, because the error about the ptxas version (which from my understanding is related with the CUDA toolkit version) has nothing to do with the batch size. So, I think the batch size of 512 may be causing another error (possibly some memory issue?) and the wrong error is being thrown ... I am not sure, but let me know what you think.
Thanks and sorry for not being able to provide MWE.
### Standalone code to reproduce the issue
```shell
Cannot build a MWE unfortunately.
```
### Relevant log output
```shell
2024-04-30 16:37:32.740441: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at xla_ops.cc:580 : INTERNAL: XLA requires ptxas version 11.8 or higher
2024-04-30 16:37:32.740516: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: INTERNAL: XLA requires ptxas version 11.8 or higher
[[{{node PartitionedCall}}]]
Traceback (most recent call last):
File "/home/ids/afreitas/april/cnn_test/train_traj.py", line 281, in <module>
loss = training_loop(ic, gt, msteps_sched[j])
File "/home/ids/afreitas/my_tf/lib/python3.10/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/ids/afreitas/my_tf/lib/python3.10/site-packages/tensorflow/python/eager/execute.py", line 53, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InternalError: Graph execution error:
Detected at node PartitionedCall defined at (most recent call last):
File "/home/ids/afreitas/april/cnn_test/train_traj.py", line 281, in <module>
File "/home/ids/afreitas/april/cnn_test/train_traj.py", line 191, in training_loop
File "/home/ids/afreitas/april/cnn_test/train_traj.py", line 192, in training_loop
XLA requires ptxas version 11.8 or higher
[[{{node PartitionedCall}}]] [Op:__inference_training_loop_26507]
```
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PR_kwDOArmXAs5uLIj3
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[xla:gpu] Extend the collective pipeliner to support pipelining collective
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[] | 2024-04-30T17:02:39 | 2024-05-01T15:12:34 | 2024-05-01T15:12:33 |
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[xla:gpu] Extend the collective pipeliner to support pipelining collective
operations with control dependencies.
Add should_allow_control_dependencies to the collective pipeliner configuration
to allow pipelining collective operations with control dependencies. The
control dependencies will be dropped when the operation is pipelined. This is
currently only used in kBackward pipelining.
In the GPU compiler, we make Send as a control predecessor of its corresponding
RecvDone, to prevent certain optimizations, in particular, the fusion of the
computation that calculates the Send data and the computation that uses the
Recv result. This extension to the collective pipeliner allows the GPU compiler
to pipeline RecvDone operations with a Send as their control predecessor.
Modify an existing test to test the situation.
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Make OP_REQUIRES_OK* macros work correctly regardless of lifetime of the status argument.
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Make OP_REQUIRES_OK* macros work correctly regardless of lifetime of the status argument.
This corrects earlier changes to OP_REQUIES_OK and OP_REQUIES_OK_ASYNC (e.g. CL/330823151), which took a reference but did not extend the lifetime of potential intermediate temporaries. This would cause problems in situations where a short-lived status was passed in as something other than a prvalue, for example:
struct T {
const absl::Status& f() const { return s; }
absl::Status s;
};
OP_REQUIRES_OK(T().f());
This becomes
const absl::Status& _s(T().f());
which is instantly a dangling reference.
To fix this, we move operations on user-provided arguments into a helper function, and ensure that the user-provided arguments are only used inside a single function call expression. All temporaries remain alive for the duration of the function call.
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[XLA:GPU] Update FindNonTrivialHero to work with HloInstructionAdaptor.
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[XLA:GPU] Update FindNonTrivialHero to work with HloInstructionAdaptor.
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GPUv2 syntax error in shader generation in broadcast with different batch sizes
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[
"Hi @gustavla ,\r\n\r\nHave you tried replicating this particular issue on other android devices or this happens only on galaxy s23?",
"@sawantkumar I also tried it on a Google Pixel 7 and it also falls back to CPU.",
"Hi @gustavla ,\r\n\r\nI ran the code to create the tflite model but it gave me the below error:\r\n\r\n\r\n\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\nCell In[8], line 23\r\n 21 # Convert the model.\r\n 22 converter = tf.lite.TFLiteConverter.from_keras_model(model)\r\n---> 23 tflite_model = converter.convert()\r\n 25 # Save the model.\r\n 26 with open('model.tflite', 'wb') as f:\r\n\r\nFile ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\lite\\python\\lite.py:1175, in _export_metrics.<locals>.wrapper(self, *args, **kwargs)\r\n 1172 @functools.wraps(convert_func)\r\n 1173 def wrapper(self, *args, **kwargs):\r\n 1174 # pylint: disable=protected-access\r\n-> 1175 return self._convert_and_export_metrics(convert_func, *args, **kwargs)\r\n\r\nFile ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\lite\\python\\lite.py:1129, in TFLiteConverterBase._convert_and_export_metrics(self, convert_func, *args, **kwargs)\r\n 1127 self._save_conversion_params_metric()\r\n 1128 start_time = time.process_time()\r\n-> 1129 result = convert_func(self, *args, **kwargs)\r\n 1130 elapsed_time_ms = (time.process_time() - start_time) * 1000\r\n 1131 if result:\r\n\r\nFile ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\lite\\python\\lite.py:1641, in TFLiteKerasModelConverterV2.convert(self)\r\n 1637 if saved_model_convert_result:\r\n 1638 return saved_model_convert_result\r\n 1640 graph_def, input_tensors, output_tensors, frozen_func = (\r\n-> 1641 self._freeze_keras_model()\r\n 1642 )\r\n 1644 graph_def = self._optimize_tf_model(\r\n 1645 graph_def, input_tensors, output_tensors, frozen_func\r\n 1646 )\r\n 1648 return super(TFLiteKerasModelConverterV2, self).convert(\r\n 1649 graph_def, input_tensors, output_tensors\r\n 1650 )\r\n\r\nFile ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\lite\\python\\convert_phase.py:215, in convert_phase.<locals>.actual_decorator.<locals>.wrapper(*args, **kwargs)\r\n 213 except Exception as error:\r\n 214 report_error_message(str(error))\r\n--> 215 raise error from None\r\n\r\nFile ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\lite\\python\\convert_phase.py:205, in convert_phase.<locals>.actual_decorator.<locals>.wrapper(*args, **kwargs)\r\n 202 @functools.wraps(func)\r\n 203 def wrapper(*args, **kwargs):\r\n 204 try:\r\n--> 205 return func(*args, **kwargs)\r\n 206 except ConverterError as converter_error:\r\n 207 if converter_error.errors:\r\n\r\nFile ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\lite\\python\\lite.py:1582, in TFLiteKerasModelConverterV2._freeze_keras_model(self)\r\n 1573 # If the model's call is not a `tf.function`, then we need to first get its\r\n 1574 # input signature from `model_input_signature` method. We can't directly\r\n 1575 # call `trace_model_call` because otherwise the batch dimension is set\r\n 1576 # to None.\r\n 1577 # Once we have better support for dynamic shapes, we can remove this.\r\n 1578 if not isinstance(self._keras_model.call, _def_function.Function):\r\n 1579 # Pass `keep_original_batch_size=True` will ensure that we get an input\r\n 1580 # signature including the batch dimension specified by the user.\r\n 1581 # TODO(b/169898786): Use the Keras public API when TFLite moves out of TF\r\n-> 1582 input_signature = _model_input_signature(\r\n 1583 self._keras_model, keep_original_batch_size=True\r\n 1584 )\r\n 1586 # TODO(b/169898786): Use the Keras public API when TFLite moves out of TF\r\n 1587 func = _trace_model_call(self._keras_model, input_signature)\r\n\r\nFile ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tensorflow\\lite\\python\\tflite_keras_util.py:84, in model_input_signature(model, keep_original_batch_size)\r\n 82 input_specs = input_specs[0][0]\r\n 83 else:\r\n---> 84 input_specs = model._get_save_spec( # pylint: disable=protected-access\r\n 85 dynamic_batch=not keep_original_batch_size)\r\n 86 if input_specs is None:\r\n 87 return None\r\n\r\nAttributeError: 'Functional' object has no attribute '_get_save_spec'\r\n",
"Just like in the other issue, I think the repro only works on tensorflow 2.15.\r\n\r\nHere is the model: https://qaihub-public-issues.s3.us-west-2.amazonaws.com/tflite/66712_model.tflite",
"Hi @gustavla ,\r\n\r\nCan you please use the code that i provided for model conversion in this [issue](https://github.com/tensorflow/tensorflow/issues/66729) and let me know if it resolved this issue.",
"@sawantkumar I do not have access to the colab that you posted in that issue.",
"Hi @gustavla ,\r\n\r\nGiven you the access."
] | 2024-04-30T16:15:55 | 2024-06-04T06:33:09 | null |
CONTRIBUTOR
| null | null | null |
**System information**
- Samsung Galaxy S23 / Android 13 / Snapdragon® 8 Gen 2 | SM8550
- TFLite 2.16.1 (also repros on many older versions)
**Standalone code to reproduce the issue**
Compile the model that fails:
```
import tensorflow as tf
import keras
input0_shape = [2, 2, 1]
input1_shape = [1, 2, 2, 1]
output_shape = [1, 2, 2, 1]
tf_input0 = keras.Input(input0_shape[1:], batch_size=input0_shape[0])
tf_input1 = keras.Input(input1_shape[1:], batch_size=input1_shape[0])
class MyMatMul(keras.layers.Layer):
def call(self, tf_input0, tf_input1):
tf_output = tf_input0 * tf_input1
return tf_output
tf_output = MyMatMul()(tf_input0, tf_input1)
model = keras.Model(inputs=[tf_input0, tf_input1], outputs=[tf_output])
# Convert the model.
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
# Save the model.
with open('model.tflite', 'wb') as f:
f.write(tflite_model)
```
Execute on GPUv2 (for instance through benchmark_tool). It falls back to CPU and executes without a problem there.
**Any other info / logs**
Runtime log (executed through https://aihub.qualcomm.com/):
```
[30/Apr/2024:21:28:08 +05:30: profiler/info] Detected chipset 2807, made by 2000.
[30/Apr/2024:21:28:08 +05:30: profiler/info] [job_id: jqp4zo91g] [model.tflite] Loading tflite model Models/model.tflite
[30/Apr/2024:21:28:08 +05:30: profiler/info] [job_id: jqp4zo91g] [model.tflite] Malloc VM size before: 16488.0 kB, allocated: 14040.6 kB, slack: 2447.4 kB.
[30/Apr/2024:21:28:08 +05:30: profiler/info] [job_id: jqp4zo91g] [model.tflite] Current memory baseline range: 58204.6-60652.0 kB.
[30/Apr/2024:21:28:08 +05:30: profiler/debug] [job_id: jqp4zo91g] [model.tflite] Runtime metadata not found in Models/model.tflite/trt_metadata.json or Models/model.tflite/trt_metadata.pb
[30/Apr/2024:21:28:08 +05:30: profiler/info] [job_id: jqp4zo91g] [model.tflite] TF Lite version 2.16.1. Loading model from Models/model.tflite.
[30/Apr/2024:21:28:08 +05:30: profiler/debug] [job_id: jqp4zo91g] [model.tflite] Mapping resource file in Models/model.tflite
[30/Apr/2024:21:28:08 +05:30: profiler/info] [job_id: jqp4zo91g] [model.tflite] Loaded model. Minimum TF Lite version = .
[30/Apr/2024:21:28:08 +05:30: profiler/info] [job_id: jqp4zo91g] [model.tflite] No delegates specified; using compute unit=cpu_and_gpu.
[30/Apr/2024:21:28:08 +05:30: profiler/info] [job_id: jqp4zo91g] [model.tflite] [tflite] Initialized TensorFlow Lite runtime.
[30/Apr/2024:21:28:08 +05:30: profiler/info] [job_id: jqp4zo91g] [model.tflite] GPUV2 delegate requested. OpenCL detected.
[30/Apr/2024:21:28:08 +05:30: profiler/debug] [job_id: jqp4zo91g] [model.tflite] Enabling delegate cache in dir=/data/user/0/ai.tetra.tungsten/cache/1714492688301/ai.tetra.runtime/0.6.0/model.tflite_8945450969824422876_1714492678194/gpuv2.
[30/Apr/2024:21:28:08 +05:30: profiler/info] [job_id: jqp4zo91g] [model.tflite] [tflite] Created TensorFlow Lite delegate for GPU.
[30/Apr/2024:21:28:08 +05:30: profiler/info] [job_id: jqp4zo91g] [model.tflite] [tflite] Replacing 1 out of 1 node(s) with delegate (TfLiteGpuDelegateV2) node, yielding 1 partitions for the whole graph.
[30/Apr/2024:21:28:08 +05:30: profiler/warning] [job_id: jqp4zo91g] [model.tflite] [tflite] File /data/user/0/ai.tetra.tungsten/cache/1714492688301/ai.tetra.runtime/0.6.0/model.tflite_8945450969824422876_1714492678194/gpuv2/gpuv2_11394406828873984768.bin couldn't be opened for reading: No such file or directory
[30/Apr/2024:21:28:08 +05:30: profiler/warning] [job_id: jqp4zo91g] [model.tflite] [tflite] Failed to build program executable - Build program failureBC-src-code:64:105: error: expected expression
{half4 second_value = read_imageh(src_tensor_1_image2d, smp_zero, (int2)((((X)) * shared_int4_0.w + (())), (0)));
^
1 diagnostic(s) generated.
```
There are many input shapes that will trigger this (NG), but some execute just fine (OK):
```
# OK
# (5, 352, 352, 3), (352, 352, 3) -> (5, 352, 352, 3)
# (2, 1, 1, 3), (1, 3) -> (2, 1, 1, 3)
# (2, 1, 1, 3), (2, 3) -> (2, 1, 2, 3)
# (2, 1, 1, 3), (3, 3) -> (2, 1, 3, 3)
# (2, 2, 2, 1), (2, 1) -> (2, 2, 2, 1)
# (2, 2, 1), (2, 1) -> (2, 2, 1)
# (3, 2), (2,) -> (3, 2)
# (3,) -> (1, 2, 2, 3) -> (1, 2, 2, 3)
# (2,), (1, 2) -> (1, 2)
# (2, 2), (1, 2) -> (2, 2)
# NG
# (1, 2, 2, 3), (3,) -> (1, 2, 2, 3)
# (1, 2, 2, 1), (2, 2, 1) -> (1, 2, 2, 1)
# (2, 2, 1), (1, 2, 2, 1) -> (1, 2, 2, 1)
# (2, 1), (1, 2, 1) -> (1, 2, 1)
# (1, 2, 2, 1), (2, 1) -> (1, 2, 2, 1)
# (1, 2, 1), (2, 1) -> (1, 2, 1)
# (1, 2), (2,) -> (1, 2)
```
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PR_kwDOArmXAs5uKgat
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Integrate LLVM at llvm/llvm-project@f4843acd839f
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Integrate LLVM at llvm/llvm-project@f4843acd839f
Updates LLVM usage to match
[f4843acd839f](https://github.com/llvm/llvm-project/commit/f4843acd839f)
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[xla:ffi] Added attribute decoders for most FFI data types
The only exception is bfloat16, because I wans't sure what is the right native
type to use for it.
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[XLA:GPU] Add hero and root accessors by index to HloFusionAnalysis.
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[XLA:GPU] Add hero and root accessors by index to HloFusionAnalysis.
`fusion_root(i)` and `fusion_hero(i)` return `HloInstructionAdaptor` as a preparation to store heros and fusions as adaptors.
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I_kwDOArmXAs6HZ9H4
| 66,708 |
404 - page not found
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[
"Hi @AS005209 ,\r\n\r\nPlease check this [link](https://github.com/tensorflow/examples/tree/master/lite/examples/audio_classification/android) for the audio classification android example and let me know if this is the one you were looking for. ",
"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."
] | 2024-04-30T14:56:50 | 2024-06-07T01:51:01 | 2024-06-07T01:51:00 |
NONE
| null | null | null |
I couldn't find anywhere else to post, so I'll just put it here.
Good afternoon
I am a 2nd year student, studying to be a software engineer
I decided to study the topic of sound recognition, I would like to see and parse a ready project to work with tflite, which is offered by teachablemachine. Here is the link it gives, but unfortunately the empty example has been removed from the github thread, is there any way to get it or put it back in the thread.
https://github.com/tensorflow/examples/tree/master/lite/examples/sound_classification/android
I have also looked at more complex projects, but have not been able to fully understand them yet.
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Remove unused function GetContextHandle from stream_executor
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Remove unused function GetContextHandle from stream_executor
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[XLA:GPU] Add HloAnyOf to hlo_traversal.h
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[XLA:GPU] Add HloAnyOf to hlo_traversal.h
This is a variant of HloFindIf that doesn't require instruction and fusion adaptors.
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FarthestPointSimple issue when importing meta graph
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"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66705\">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/66705\">No</a>\n"
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NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 1.14
### Custom code
Yes
### OS platform and distribution
Ubuntu 16.04
### Mobile device
_No response_
### Python version
3.6
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
cuda 11.1
### GPU model and memory
_No response_
### Current behavior?
when loading the pretrained model 'R-PointNet' from repository [https://github.com/ericyi/GSPN](url) ,it has the problem when running code `saver = tf.compat.v1.train.import_meta_graph('gspn/GSPN-master/models/model.ckpt.meta', clear_devices=True)`,
I've tried to switch several tf versions including 1.x and 2.x ,but still have the similar problem, when using tf 1.14 the traceback is KeyError, full traceback is provided below
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tf.test.is_gpu_available()
tf.test.is_built_with_cuda()
saver = tf.compat.v1.train.import_meta_graph('gspn/GSPN-master/models/model.ckpt.meta', clear_devices=True)
# for op in tf.compat.v1.get_default_graph().get_operations():
# print(op.name)
```
### Relevant log output
```shell
/home/lingyr/miniconda3/bin/conda run -n gspn --no-capture-output python /home/lingyr/hongzhan/22_1_GetModelTensorName.py
/home/lingyr/miniconda3/envs/gspn/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/lingyr/miniconda3/envs/gspn/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/lingyr/miniconda3/envs/gspn/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/lingyr/miniconda3/envs/gspn/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/lingyr/miniconda3/envs/gspn/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/lingyr/miniconda3/envs/gspn/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
/home/lingyr/miniconda3/envs/gspn/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/lingyr/miniconda3/envs/gspn/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/lingyr/miniconda3/envs/gspn/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/lingyr/miniconda3/envs/gspn/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/lingyr/miniconda3/envs/gspn/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/lingyr/miniconda3/envs/gspn/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
2024-04-30 20:36:20.508145: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2024-04-30 20:36:20.534394: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2024-04-30 20:36:21.354329: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55dc1c2b0870 executing computations on platform CUDA. Devices:
2024-04-30 20:36:21.354405: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): NVIDIA GeForce RTX 3090, Compute Capability 8.6
2024-04-30 20:36:21.354426: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (1): NVIDIA GeForce RTX 3090, Compute Capability 8.6
2024-04-30 20:36:21.354443: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (2): NVIDIA GeForce RTX 3090, Compute Capability 8.6
2024-04-30 20:36:21.354462: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (3): NVIDIA GeForce RTX 3090, Compute Capability 8.6
2024-04-30 20:36:21.358802: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2499865000 Hz
2024-04-30 20:36:21.361254: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55dc1a4cb470 executing computations on platform Host. Devices:
2024-04-30 20:36:21.361374: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): <undefined>, <undefined>
2024-04-30 20:36:21.362114: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: NVIDIA GeForce RTX 3090 major: 8 minor: 6 memoryClockRate(GHz): 1.695
pciBusID: 0000:08:00.0
2024-04-30 20:36:21.362895: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 1 with properties:
name: NVIDIA GeForce RTX 3090 major: 8 minor: 6 memoryClockRate(GHz): 1.695
pciBusID: 0000:09:00.0
2024-04-30 20:36:21.363591: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 2 with properties:
name: NVIDIA GeForce RTX 3090 major: 8 minor: 6 memoryClockRate(GHz): 1.695
pciBusID: 0000:88:00.0
2024-04-30 20:36:21.364237: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 3 with properties:
name: NVIDIA GeForce RTX 3090 major: 8 minor: 6 memoryClockRate(GHz): 1.695
pciBusID: 0000:89:00.0
2024-04-30 20:36:21.364614: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcudart.so.10.0'; dlerror: libcudart.so.10.0: cannot open shared object file: No such file or directory
2024-04-30 20:36:21.364758: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcublas.so.10.0'; dlerror: libcublas.so.10.0: cannot open shared object file: No such file or directory
2024-04-30 20:36:21.367031: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcufft.so.10.0'; dlerror: libcufft.so.10.0: cannot open shared object file: No such file or directory
2024-04-30 20:36:21.367153: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcurand.so.10.0'; dlerror: libcurand.so.10.0: cannot open shared object file: No such file or directory
2024-04-30 20:36:21.367257: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcusolver.so.10.0'; dlerror: libcusolver.so.10.0: cannot open shared object file: No such file or directory
2024-04-30 20:36:21.367359: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcusparse.so.10.0'; dlerror: libcusparse.so.10.0: cannot open shared object file: No such file or directory
2024-04-30 20:36:21.367464: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcudnn.so.7'; dlerror: libcudnn.so.7: cannot open shared object file: No such file or directory
2024-04-30 20:36:21.367489: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1663] Cannot dlopen some GPU libraries. Skipping registering GPU devices...
2024-04-30 20:36:21.367536: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2024-04-30 20:36:21.367556: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187] 0 1 2 3
2024-04-30 20:36:21.367573: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0: N N N N
2024-04-30 20:36:21.367588: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 1: N N N N
2024-04-30 20:36:21.367602: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 2: N N N N
2024-04-30 20:36:21.367617: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 3: N N N N
Traceback (most recent call last):
File "/home/lingyr/hongzhan/22_1_GetModelTensorName.py", line 5, in <module>
saver = tf.compat.v1.train.import_meta_graph('gspn/GSPN-master/models/model.ckpt.meta', clear_devices=True)
File "/home/lingyr/miniconda3/envs/gspn/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1449, in import_meta_graph
**kwargs)[0]
File "/home/lingyr/miniconda3/envs/gspn/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1473, in _import_meta_graph_with_return_elements
**kwargs))
File "/home/lingyr/miniconda3/envs/gspn/lib/python3.6/site-packages/tensorflow/python/framework/meta_graph.py", line 857, in import_scoped_meta_graph_with_return_elements
return_elements=return_elements)
File "/home/lingyr/miniconda3/envs/gspn/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "/home/lingyr/miniconda3/envs/gspn/lib/python3.6/site-packages/tensorflow/python/framework/importer.py", line 400, in import_graph_def
_RemoveDefaultAttrs(op_dict, producer_op_list, graph_def)
File "/home/lingyr/miniconda3/envs/gspn/lib/python3.6/site-packages/tensorflow/python/framework/importer.py", line 160, in _RemoveDefaultAttrs
op_def = op_dict[node.op]
KeyError: 'FarthestPointSample'
ERROR conda.cli.main_run:execute(124): `conda run python /home/lingyr/hongzhan/22_1_GetModelTensorName.py` failed. (See above for error)
Process finished with exit code 1
```
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Ported threefry2x32 for GPU to the typed XLA FFI
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Ported threefry2x32 for GPU to the typed XLA FFI
This allows lowering of threefry2x32 for GPU even on a machine without GPUs.
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[XLA:GPU][IndexAnalysis] Add a method to remove unused dims and symbols.
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[XLA:GPU][IndexAnalysis] Add a method to remove unused dims and symbols.
Both RemoveUnusedSymbols and RemoveUnusedDims find unused dims and symbols. Therefore, it would not be efficient to run this part twice when we want to remove both symbols and dimensions.
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Tensorflow lite GPU delegate can't work well and NNAPI can't make the model faster
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[
"Hi @Raphaelzrf ,\r\n\r\nI don't have a vivo x100 to replicate this issue , but i have vivo x80 and the GPU delegate works fine on that device. What i can suggest you is to use is tflite benchmark tool [tflite benchmark tool](https://www.tensorflow.org/lite/performance/measurement) and see if there is a difference between the timings on NNAPI and GPU delegate. This will confirm the GPU support on your device. Let me know once you are able to do it.",
"@sawantkumar Thank you for your reply. I have tried to use tflite benchmark tool to run Mobilenet and there was a difference between the timings on NNAPI and GPU delegate. So I 'm sure that there is the GPU supported on my device. But compatList.isDelegateSupportedOnThisDevice() return false. How did you use the GPU delegate works on vivo x80? tflite benchmark tool? Have you tried the GPU delegate on an App?If you have, can you share your code?",
"Hi @Raphaelzrf ,\r\n\r\nYes i was able to access the GPU delegate in an android app , also i was able to access the neuron delegate which is an AI accelerator for your int8 models . The reason behind no speedup could be model specific also. Maybe you can try out any of [examples](https://github.com/tensorflow/examples/tree/master/lite/examples) and see for yourself if you are able to access the GPU delegate in these examples.",
"@sawantkumar As I said above, compatList.isDelegateSupportedOnThisDevice() return false. So it has nothing to do with the model. Neuron delegate? I don't konw that. Do you mean using NNAPI to access NPU？",
"@Raphaelzrf Yes, i meant using NNAPI to access NPU . Looking at all the sources x100 has a relatively powerful gpu as you can see from [here ](https://ai-benchmark.com/ranking_processors.html) . Can you please check if you have any updates available on your phone ?",
"@sawantkumar My device is already up to date. Can you tell me the version of tensorflow-lite and tensorflow-lite-gpu that you use?",
"@Raphaelzrf i can't recall very clearly what version of tflite it was, but i think it was 2.4.0 . I am looking into your issue btw and will let you know if i come up with something .",
"Hi @Raphaelzrf , \r\n\r\nWere you able to run your model on the vivo x100 (MediaTek Dimensity 9300 GPU) ? Please let me know if you are still facing this issue.",
"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.",
"@sawantkumar Yes. I still face this issue."
] | 2024-04-30T12:02:37 | 2024-05-31T03:15:17 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tensorflow-lite-task-text:0.3.0 tensorflow-lite-gpu-delegate-plugin:0.4.4 tensorflow-lite-gpu:2.13.0 tensorflow-lite:2.14.0 tensorflow-lite-support:0.4.4
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
Vivo x100: cpu: MediaTek Dimensity 9300 GPU:Immortalis-G720
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I used TensorFlow Lite Model Maker to fine-tune BERT to get a text classification model. I want to use the GPU to speed up my model. I tried two methods. One is to use NNAPI and the other is to use GPU delegate. But it turned out that NNAPI didn't make my model faster, it was much slower. Before using GPU delegate for acceleration, I used compatList.isDelegateSupportedOnThisDevice() to detect if there is a supported GPU on the device, and the result is no. But in fact there is.
### Standalone code to reproduce the issue
```shell
fun classifyGpu(text: String){
val baseOptionsBuilder = BaseOptions.builder()
lateinit var bertClassifierGpu: BertNLClassifier
baseOptionsBuilder.useNnapi()
val baseOptions = baseOptionsBuilder.build()
val options = BertNLClassifier.BertNLClassifierOptions
.builder()
.setBaseOptions(baseOptions)
.build()
bertClassifierGpu = BertNLClassifier.createFromFileAndOptions(
context,
MOBILEBERT,
options)
results = bertClassifierGpu.classify(text)
}
```
### Relevant log output
_No response_
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[xla:cpu] Add new file `runtime_handle_ffi_call.h/cc` to support typed FFI
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[xla:cpu] Add new file `runtime_handle_ffi_call.h/cc` to support typed FFI
Part of #10056
Co-authored-by: pparuzel [email protected]
Co-authored-by: Adam-Banas [email protected]
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Fix CompileMemoryStats::output_size_in_bytes field deserialization from proto.
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Doc(Transfer learning and fine-tuning) is quite different from real executive result.
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[
"@lida2003,\r\nCould you please confirm whether the difference in Accuracy & Loss has happened in GPU/CPU with both tensorflow v2.15, v2.16? Also I will also try to debug more on this issue and provide the resolution. Thank you!",
"> @lida2003,\r\n> Could you please confirm whether the difference in Accuracy & Loss has happened in GPU/CPU with both tensorflow v2.15, v2.16? \r\n\r\n\r\n- NVIDIA 2.15.0+nv24.03 GPU version faild 100%\r\n- Colab is consistent with tensorflow document (But I don't know the version)\r\n\r\nPS: Jetson Orin Nano 8GB, CPU&GPU shared memory\r\n\r\n\r\n\r\n> v2.16\r\n\r\nWell, on the very begining, I have installed (pip binary installation) 2.16 CPU version on Jetson Orin Nano. Runing [Keras-Fine-Tuning-Pre-Trained-Models](https://github.com/spmallick/learnopencv/tree/master/Keras-Fine-Tuning-Pre-Trained-Models) without any resource warning. It might be the way CPU using swap area.\r\n\r\nThe result is also different from the document, See link below:\r\n\r\n- [Multiple executive warnings after switching tensorflow from 2.16.1 CPU to v60dp tensorflow==2.15.0+nv24.03 GPU version](https://forums.developer.nvidia.com/t/multiple-executive-warnings-after-switching-tensorflow-from-2-16-1-cpu-to-v60dp-tensorflow-2-15-0-nv24-03-gpu-version/291208/4)\r\n\r\nWhen I switched to NVIDIA 2.15.0+nv24.03 GPU version: [Tensorflow v2.16.1 GPU version local build on Jetson Orin Nano failed](https://forums.developer.nvidia.com/t/tensorflow-v2-16-1-gpu-version-local-build-on-jetson-orin-nano-failed/291195/9)\r\n\r\n- [The resource issue, we have confirmed with NVIDIA.](https://forums.developer.nvidia.com/t/multiple-executive-warnings-after-switching-tensorflow-from-2-16-1-cpu-to-v60dp-tensorflow-2-15-0-nv24-03-gpu-version/291208)\r\n\r\n> Also I will also try to debug more on this issue and provide the resolution. Thank you!\r\n\r\nThere are also a copule of other things might be a clue for you. Here is a link on NVIDIA forum: \r\n- [Tensorflow v2.16.1 GPU version local build on Jetson Orin Nano failed](https://forums.developer.nvidia.com/t/tensorflow-v2-16-1-gpu-version-local-build-on-jetson-orin-nano-failed/291195/7)\r\n- [Multiple executive warnings after switching tensorflow from 2.16.1 CPU to v60dp tensorflow==2.15.0+nv24.03 GPU version](https://forums.developer.nvidia.com/t/multiple-executive-warnings-after-switching-tensorflow-from-2-16-1-cpu-to-v60dp-tensorflow-2-15-0-nv24-03-gpu-version/291208/4)\r\n\r\nPlease take a look at those warnings and memory issue. I think we need a sanity check before software is packed for release (put on repo).\r\n\r\n\r\nEDIT: Keep sync with NVIDIA feedback.\r\n- [NVIDIA claimed their binary compiled following tensorflow guide.](https://forums.developer.nvidia.com/t/how-to-compile-tensorflow-for-jetson-orin-nano/291321/7)\r\n- [Those warnings seems OK with NVIDIA 2.15 binary version.](https://forums.developer.nvidia.com/t/inconsistency-of-nvidia-2-15-0-nv24-03-v-s-colab-v-s-tensorflow-documentation/291613/4)\r\n- [Inconsistency of NVIDIA 2.15.0+nv24.03 v.s. Colab v.s. Tensorflow Documentation](https://forums.developer.nvidia.com/t/inconsistency-of-nvidia-2-15-0-nv24-03-v-s-colab-v-s-tensorflow-documentation/291613)\r\n\r\n\r\n\r\n\r\n\r\n\r\n",
"@lida2003,\r\nAs you mentioned, I tried to execute the transfer learning code on the colab with the Tensorflow v2.16, v2.15 and observed that the loss and Accuracy is the same as the document. I suspect there might be an issue with the GPU. I will re-check and deep dive on the same. \r\n\r\n\r\n\r\nThank you!"
] | 2024-04-30T11:08:24 | 2024-06-11T13:13:20 | null |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.15.0+nv24.03 GPU version, check this link: https://forums.developer.nvidia.com/t/multiple-executive-warnings-after-switching-tensorflow-from-2-16-1-cpu-to-v60dp-tensorflow-2-15-0-nv24-03-gpu-version/291208
### Custom code
No
### OS platform and distribution
Jetson Orin Nano ubuntu 22.04 Jammy
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
CUDA12.2.140/cuDNN8.9.4.25
### GPU model and memory
sm90 8GB
### Current behavior?
The executive result (trend of curve and abosolute value) is quite different from document.
- My result: [transfer_learning.zip](https://github.com/tensorflow/tensorflow/files/15162333/transfer_learning.zip)
- Document (https://github.com/tensorflow/docs/blob/master/site/en/tutorials/images/transfer_learning.ipynb) says:
### Standalone code to reproduce the issue
```shell
100% reproducable
```
### Relevant log output
Also tried Colab, which is consistent with documentation:
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