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Update TFRT dependency to use revision
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Update TFRT dependency to use revision
http://github.com/tensorflow/runtime/commit/940356ccac805874a2043be2e10b79bbd7538b0f.
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[
"I have an update on this issue. When I use the `init(modelPath:delegates:)` initialiser it works. The `EXC_BAD_ACCESS` crash only happens when I use the `init(modelData:delegates:)` initialiser.",
"Hi @jakubdolejs, it looks like you're both loading the model and running inference on it asynchronously. Is it possible that they are somehow running out of order then? i.e. inference ends up running before the model is actually loaded. Do you need to load it asynchronously? I'm guessing you are calling init before run inference somewhere, perhaps change init to load it synchronously that way it has to initialize before you run inference on it.",
"Hi @pkgoogle,\r\n\r\nThanks for getting back to me. The queue is serial and it can't execute the two statements concurrently. The interpreter is created (asynchronously) in the class initialiser. The `runInference` method also dispatches the inference task on the same queue on which the interpreter was created. Since the queue is serial it can only execute one task at a time and the interpreter creation comes first.\r\n\r\nThe weird thing is the same task succeeds when I use the `init(modelPath:delegates:)` interpreter initialiser but it fails when I use the `init(modelData:delegates:)` one.",
"Hi @jakubdolejs, that's true, do you happen to have the code around where you create an Inference object and call runInference on it? Generally the more code/information you share, the easier it will be for us.",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"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/65990\">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/65990\">No</a>\n"
] | 2024-04-18T09:47:47 | 2024-05-29T01:51:00 | 2024-05-29T01:50:57 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
0.0.1-nightly.20240417
### Custom code
Yes
### OS platform and distribution
iOS 17
### Mobile device
iPhone 12 mini
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When I run `Interpreter.copy(data, toInputAt: 0)` or `Interpreter.input(at: 0)` the app crashes with `EXC_BAD_ACCESS` error. The error is thrown here: https://github.com/tensorflow/tensorflow/blob/911c3cce40c53816083d66850b8cbe4bd896c538/tensorflow/lite/swift/Sources/Interpreter.swift#L194
This only happens when I create the interpreter in a separate block than the inference. Please take a look at the code below. If I construct the class and run the `runInference(on:)` method I get `EXC_BAD_ACCESS`. However, running the `runInference(on:, usingModelAt:)` method succeeds. Note that the code to allocate tensors, copy the input and invoke the interpreter is identical.
I wouldn't worry about this and use the second method but creating the interpreter takes about 2 seconds and I'd like to keep it around for multiple inference runs.
### Standalone code to reproduce the issue
```shell
import Foundation
import TensorFlowLite
import TensorFlowLiteCCoreML
import TensorFlowLiteCMetal
class Inference {
var interpreter: Interpreter?
lazy var inferenceQueue = DispatchQueue(label: "tflite-inference")
init(modelFileUrl: URL) {
self.inferenceQueue.async {
do {
self.interpreter = try self.createInterpreter(modelFileUrl: modelFileUrl)
} catch {
}
}
}
func runInference(on data: Data) {
self.inferenceQueue.async {
do {
guard let interpreter = self.interpreter else {
return
}
try interpreter.allocateTensors()
try interpreter.copy(data, toInputAt: 0) // This is the call that causes the EXC_BAD_ACCESS error
try interpreter.invoke()
_ = try interpreter.output(at: 0)
} catch {
}
}
}
func createInterpreter(modelFileUrl: URL) throws -> Interpreter {
let modelData = try Data(contentsOf: modelFileUrl)
let delegates: [Delegate]
if let delegate = CoreMLDelegate() {
delegates = [delegate]
} else {
delegates = [MetalDelegate()]
}
return try Interpreter(modelData: modelData, delegates: delegates)
}
/** This method succeeds, no EXC_BAD_ACCESS */
func runInference(on data: Data, usingModelAt modelFileUrl: URL) throws -> Data {
let interpreter = try self.createInterpreter(modelFileUrl: modelFileUrl)
try interpreter.allocateTensors()
try interpreter.copy(data, toInputAt: 0) // This succeeds
try interpreter.invoke()
return try interpreter.output(at: 0).data
}
}
```
### Relevant log output
_No response_
|
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"@christinaburge The issue is present only in the nightly build, which is intended for testing new features and might contain bugs. Could you try with the stable version with a minimal example and let us know?\r\nThank you!",
"@penpornk @MichaelHudgins \r\n\r\nThis is an issue in our downstream CI, would you be able to advise please?",
"@d0k It seems https://github.com/tensorflow/tensorflow/commit/a4d7e978701feb9ed485b926b22676b9f7651d4e is still causing unit test failures on aarch64. Could you please help take a look? Thank you very much!",
"Hi, just wondering if anything has happened with this please?",
"Ah sorry, this fell through the cracks. It's a bug in LLVM's lowering to the bf16 hardware, but I don't have access to a machine with that instruction set.\r\n\r\nWould you mind running the test with `--test_env=XLA_FLAGS=--xla_dump_to=/tmp/some/directory` and attach the .ll files that produces? From there we can distill it into a bug report against LLVM.",
"[bug_files.tar.gz](https://github.com/user-attachments/files/15739690/bug_files.tar.gz)\r\nNo problem, here is the full output, let me know if you need anything else!",
"Thanks, created a reduced reproducer at https://github.com/llvm/llvm-project/issues/94951\r\n\r\nI'm not exactly sure it's the same issue, as this one requires SVE and your original error message had no SVE types in it."
] | 2024-04-18T09:41:03 | 2024-06-10T10:16:45 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17.0
### Custom code
No
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
We have the following unit test failures in Tensorflow github/nightly:
//tensorflow/compiler/tests:conv3d_test_cpu
//tensorflow/compiler/tests:conv3d_test_cpu_mlir_bridge_test
//tensorflow/compiler/tests:stateful_random_ops_test_cpu
//tensorflow/compiler/tests:stateless_random_ops_test_cpu
//tensorflow/compiler/tests:stateless_random_ops_test_cpu_mlir_bridge_test
//tensorflow/compiler/tests:stateful_random_ops_test_cpu_mlir_bridge_test
//tensorflow/compiler/tests:stochastic_cast_op_test_cpu
On investigation, the first commit where this issue is present is https://github.com/tensorflow/tensorflow/commit/a4d7e978701feb9ed485b926b22676b9f7651d4e, tests pass with the commit immediately prior to this.
The tests do not fail in the upstream CI because it uses N1 cores with no bf16 HW.
### Standalone code to reproduce the issue
```shell
From the directory tensorflow/ci/official, to reproduce the failure for e.g. //tensorflow/compiler/tests:conv3d_test_cpu we want to:
open any.sh and remove cd "$(dirname "$0")/../../" # tensorflow/
run:
TFCI=py311,linux_arm64 TF_ANY_MODE=test TF_ANY_TARGETS=//tensorflow/compiler/tests:conv3d_test_cpu ./any.sh
```
### Relevant log output
```shell
An example error from the stochastic_cast test:
FAIL: //tensorflow/compiler/tests:stochastic_cast_op_test_cpu (shard 13 of 20) (see /root/.cache/bazel/_bazel_root/574657b8af23672198530ef061ba4201/execroot/org_tensorflow/bazel-out/aarch64-opt/testlogs/tensorflow/compiler/tests/stochastic_cast_op_test_cpu/shard_13_of_20/test.log)
INFO: From Testing //tensorflow/compiler/tests:stochastic_cast_op_test_cpu (shard 13 of 20):
==================== Test output for //tensorflow/compiler/tests:stochastic_cast_op_test_cpu (shard 13 of 20):
2024-04-15 10:17:41.267151: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
Running tests under Python 3.11.6: /root/.cache/bazel/_bazel_root/574657b8af23672198530ef061ba4201/execroot/org_tensorflow/bazel-out/aarch64-opt/bin/tensorflow/compiler/tests/stochastic_cast_op_test_cpu.runfiles/python_aarch64-unknown-linux-gnu/bin/python3
WARNING:tensorflow:From /root/.cache/bazel/_bazel_root/574657b8af23672198530ef061ba4201/execroot/org_tensorflow/bazel-out/aarch64-opt/bin/tensorflow/compiler/tests/stochastic_cast_op_test_cpu.runfiles/org_tensorflow/tensorflow/compiler/tests/xla_test.py:106: Context.enable_xla_devices (from tensorflow.python.eager.context) is deprecated and will be removed in a future version.
Instructions for updating:
XLA:CPU and XLA:GPU devices are deprecated
W0415 10:17:44.369471 247748062629904 deprecation.py:50] From /root/.cache/bazel/_bazel_root/574657b8af23672198530ef061ba4201/execroot/org_tensorflow/bazel-out/aarch64-opt/bin/tensorflow/compiler/tests/stochastic_cast_op_test_cpu.runfiles/org_tensorflow/tensorflow/compiler/tests/xla_test.py:106: Context.enable_xla_devices (from tensorflow.python.eager.context) is deprecated and will be removed in a future version.
Instructions for updating:
XLA:CPU and XLA:GPU devices are deprecated
[ RUN ] StochasticCastOpTest.testStochasticCastOpResultProbability_0.125_from_bfloat16_to_int16
INFO:tensorflow:Start test case: StochasticCastOpTest.testStochasticCastOpResultProbability_0.125_from_bfloat16_to_int16
I0415 10:17:44.370704 247748062629904 xla_test.py:231] Start test case: StochasticCastOpTest.testStochasticCastOpResultProbability_0.125_from_bfloat16_to_int16
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1713176264.392647 68553 service.cc:145] XLA service 0xbd1261c1dc00 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1713176264.392683 68553 service.cc:153] StreamExecutor device (0): Host, Default Version
2024-04-15 10:17:44.398999: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled
I0000 00:00:1713176264.411515 73786 xla_device.cc:462] XLA_GPU and XLA_CPU devices are deprecated and will be removed in subsequent releases. Instead, use either @tf.function(jit_compile=True) for must-compile semantics, or run with TF_XLA_FLAGS=--tf_xla_auto_jit=2 for auto-clustering best-effort compilation.
I0000 00:00:1713176264.457087 73787 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
LLVM ERROR: Cannot select: 0xe14f2452b580: v8bf16,ch = masked_load<(load unknown-size from %ir.lsr.iv217, align 2, !alias.scope !4, !noalias !7)> 0xe14f241a0ea0, 0xe14f2452bd60, undef:i64, 0xe14f244faa10, undef:v8bf16
0xe14f2452bd60: i64,ch = CopyFromReg 0xe14f241a0ea0, Register:i64 %65
0xe14f2452d220: i64 = Register %65
0xe14f2452b9e0: i64 = undef
0xe14f244faa10: v8i16 = AArch64ISD::VASHR 0xe14f24529720, Constant:i32<15>
0xe14f24529720: v8i16 = AArch64ISD::VSHL 0xe14f2454cb00, Constant:i32<15>
0xe14f2454cb00: v8i16 = any_extend 0xe14f2452b970
0xe14f2452b970: v8i8,ch = CopyFromReg 0xe14f241a0ea0, Register:v8i8 %66
0xe14f2452bdd0: v8i8 = Register %66
0xe14f245602e0: i32 = Constant<15>
0xe14f245602e0: i32 = Constant<15>
0xe14f2452be40: v8bf16 = undef
In function: parallel_fusion
Fatal Python error: Aborted
```
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[XLA:GPU] Minor refactoring to cleanup argument passing in `conv_algorithm_picker`.
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[XLA:GPU] Minor refactoring to cleanup argument passing in `conv_algorithm_picker`.
The `PickBest...` methods have more arguments that necessary. This change removes the unnecessary arguments, pushing some logic to leaf methods. This makes the top level `PickBestAlgorithmNoCache` simpler and symmetrical when it comes to the different platforms.
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PR #11514: Move cublasLT workspace allocation to buffer assignment
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PR #11514: Move cublasLT workspace allocation to buffer assignment
Imported from GitHub PR https://github.com/openxla/xla/pull/11514
Move cublasLT workspace allocation to buffer assignment to support command buffer lowering
Copybara import of the project:
--
18534f888a871b99f3b7819f72118931117100f9 by Shawn Wang <[email protected]>:
Move cublasLT workspace allocation to buffer assignment to support command buffer lowering
remove duplicated header
--
39434346a84f1b9e2fe1212d95b37ce80dfd5ccf by Shawn Wang <[email protected]>:
fix test failures
Merging this change closes #11514
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11514 from shawnwang18:shawnw/cublaslt_command_buffer 39434346a84f1b9e2fe1212d95b37ce80dfd5ccf
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Integrate LLVM at llvm/llvm-project@676d3bafc09d
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Integrate LLVM at llvm/llvm-project@676d3bafc09d
Updates LLVM usage to match
[676d3bafc09d](https://github.com/llvm/llvm-project/commit/676d3bafc09d)
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Move fMHA test temporarily to H100
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Move fMHA test temporarily to H100
fMHA is broken on A100 when used with cuDNN 9. It seems the
tested pattern is not supported on A100 in cuDNN 9.
Moving the test temporarily to H100 until we had time to investigate
and fix it properly.
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NFC: Use the free function variants for `dyn_cast/cast/isa/...`.
The member functions in `Type/Attribute/Value/Location/AffineExpr` are [deprecated](https://mlir.llvm.org/deprecation) and will go away.
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[] | 2024-04-18T04:51:24 | 2024-04-20T10:23:32 | 2024-04-20T10:23:31 |
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NFC: Use the free function variants for `dyn_cast/cast/isa/...`.
The member functions in `Type/Attribute/Value/Location/AffineExpr` are [deprecated](https://mlir.llvm.org/deprecation) and will go away.
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I_kwDOArmXAs6GF4BJ
| 65,976 |
XNNPACK delegate doesn't support to broadcast last dimension for Prelu operator
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[
"This is indeed a bug and a PR is open in XNNPack to address it. I will close this once the XNNPack version in OS tflite has been updated. https://github.com/google/XNNPACK/pull/6304"
] | 2024-04-18T04:01:14 | 2024-04-22T07:37:32 | 2024-04-22T07:37:32 |
NONE
| null | null | null |
Below test can reproduce this issue that is modified from [TEST_P(PReluOpTest, PReluFloat32) ](https://github.com/tensorflow/tensorflow/blob/c9a3144cda64eabb553967ff73e37f9836a5d0c2/tensorflow/lite/kernels/activations_test.cc#L2467):
```
TEST_P(PReluOpTest, PReluFloat32) {
FloatPReluOpModel m({TensorType_FLOAT32, {1, 2, 2, 3}},
{TensorType_FLOAT32, {1, 1, 1, 1}});
m.SetInput({
0.0f, 0.0f, 0.0f, // Row 1, Column 1
1.0f, 1.0f, 1.0f, // Row 1, Column 2
-1.0f, -1.0f, -1.0f, // Row 2, Column 1
-2.0f, -2.0f, -2.0f, // Row 2, Column 2
});
m.SetAlpha({1.0f});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.GetOutput(), ElementsAreArray({
0.0f, 0.0f, 0.0f, // Row 1, Column 1
1.0f, 1.0f, 1.0f, // Row 1, Column 2
0.0f, -1.0f, -2.0f, // Row 2, Column 1
0.0f, -2.0f, -4.0f, // Row 2, Column 2
}));
}
```
A solution is to use [TFLite kernel of prelu](https://source.chromium.org/chromium/chromium/src/+/main:third_party/tflite/src/tensorflow/lite/kernels/activations.cc;l=1454;drc=11c6fee8868a670b11ae04cf5649f7805f6cc16a?q=third_party%2Ftflite%2Fsrc%2Ftensorflow%2Flite%2Fkernels%2Factivations.cc&ss=chromium%2Fchromium%2Fsrc) for broadcasting the last dimension, so the [VisitPreluNode](https://source.chromium.org/chromium/chromium/src/+/main:third_party/tflite/src/tensorflow/lite/delegates/xnnpack/xnnpack_delegate.cc?q=TfLiteStatus%20VisitPreluNode&ss=chromium%2Fchromium%2Fsrc:third_party%2F) in XNNPACK Delegate need to validate the broadcastable last dimension and return not support error to fallback TFLite kernel.
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PR_kwDOArmXAs5s_hKQ
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Support shape transpose in `hlo_sharding_util::ReshapeSharding`.
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[] | 2024-04-18T02:19:50 | 2024-04-24T03:55:04 | 2024-04-24T03:55:03 |
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Support shape transpose in `hlo_sharding_util::ReshapeSharding`.
Before this cl, `hlo_sharding_util::ReshapeSharding` can handle the cases where source and target shapes can be transformed to each other by merging and splitting dimension sizes. It returns `std::nullopt` if transpose is needed between source and target shapes.
This cl extracts the `gcd(source_sharding_tile_size, target_shape)` when `source_shape % source_sharding_tile_size == 0` in the major dimensions. We also skip the source_dim if `source_sharding_tile_size` is 1. An example is shown below.
```
input_shape: [6, 2, 5]
output_shape: [4, 3, 5]
input_sharding: {devices=[2, 1, 5]<=[10]}
output_sharding: {devices=[2, 1, 5]<=[10]}
```
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Remove duplicate dependency in TSL
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Remove duplicate dependency in TSL
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PR_kwDOArmXAs5s_Ufn
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Move PJRT integration doc to XLA docs.
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Move PJRT integration doc to XLA docs.
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PR_kwDOArmXAs5s_Smg
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bugfix hlo_parser output missing layout for ReplicaId and PartitionId
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Loosen unit tests that check strings derived from Protobuf DebugString
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Loosen unit tests that check strings derived from Protobuf DebugString
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http://github.com/tensorflow/runtime/commit/fc9cb9f1253679cb2b645c26e0198cd0213d4378.
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[XLA:GPU] Export autotune results to uploader registries.
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Doing so allows debugging the results at scale and re-using them for later
recompilation.
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improve some logging in heap simulator
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[TSL] Move away from deprecated typedef
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NONE
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[TSL] Move away from deprecated typedef
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[PJRT C API] Add stream extension to support DLPack and implement this extension in CUDA plugin.
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[PJRT C API] Add stream extension to support DLPack and implement this extension in CUDA plugin.
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PR_kwDOArmXAs5s-cfo
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NFC: Use the free function variants for `dyn_cast/cast/isa/...`.
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[] | 2024-04-17T21:08:45 | 2024-05-28T10:58:03 | 2024-05-28T10:57:59 |
NONE
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NFC: Use the free function variants for `dyn_cast/cast/isa/...`.
The member functions in `Type/Attribute/Value/Location/AffineExpr` are [deprecated](https://mlir.llvm.org/deprecation) and will go away.
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PR_kwDOArmXAs5s-bot
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[IFRT] Add `xla::ifrt::IoCallableProgram` type
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NONE
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[IFRT] Add `xla::ifrt::IoCallableProgram` type
`xla::ifrt::IoCallableProgram` represents a user program (tentatively named an
"I/O callable") that can be compiled into and executed as an
`xla::ifrt::LoadedExecutable`.
The main role of `IoCallableProgram` is to define a standard representation for
a range of programs that satisfy:
* Can be efficiently serialized into a string.
* Have a fixed list of devices used for executions. (**)
* All inputs and outputs are IFRT `Array`s.
* Have input and output array types statically known in advance to compilation
or execution. (**)
* Do not expect the IFRT user to help manage their internal state. The IFRT
user can destroy `xla::ifrt::LoadedExecutable` any time.
(**) indicates differences from more flexible `xla::ifrt::PluginProgram`.
Similar to how IFRT implementations decide whether to support specific IFRT
program types, the implementations can decide what the types of I/O callables
to support.
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PR_kwDOArmXAs5s-Zff
| 65,962 |
[XLA:CollectiveMatmul] Use persistent einsum labels for collective matmuls created by SPMD and do not simplify those while loops.
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[XLA:CollectiveMatmul] Use persistent einsum labels for collective matmuls created by SPMD and do not simplify those while loops.
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PR_kwDOArmXAs5s-WlM
| 65,961 |
Eliminate StreamExecutor::AllocateOwnedArray interface to help eliminate circular includes between ScopedDeviceMemory and StreamExecutor.
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NONE
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Eliminate StreamExecutor::AllocateOwnedArray interface to help eliminate circular includes between ScopedDeviceMemory and StreamExecutor.
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| 2,249,233,596 |
PR_kwDOArmXAs5s-UBI
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#sdy support some round tripping of Shardonnay shardings and meshes b/w MHLO and HLO.
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#sdy support some round tripping of Shardonnay shardings and meshes b/w MHLO and HLO.
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[
"@Darknight1500,\r\nWithout the reproducible code, it would be difficult for us to debug the issue. In order to expedite the trouble-shooting process, could you please provide a minimal code snippet and the TensorFlow version you are using.\r\n\r\nAlso if you are trying to get the information on the tensorflow lite, I request to have a look at this official document for reference.\r\nhttps://www.tensorflow.org/lite/guide\r\n\r\nTensorFlow Lite is a set of tools that enables on-device machine learning by helping developers run their models on mobile, embedded, and edge devices.\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/65959\">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/65959\">No</a>\n"
] | 2024-04-17T20:35:50 | 2024-05-03T01:48:41 | 2024-05-03T01:48:37 |
NONE
| null | null | null |
**System information**
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installed from (source or binary):
- TensorFlow version (or github SHA if from source):
**Provide the text output from tflite_convert**
```
# Copy and paste here
```
**Standalone code to reproduce the issue**
Provide a reproducible test case that is the bare minimum necessary to generate
the problem. If possible, please share a link to Colab/Jupyter/any notebook.
Also, please include a link to a GraphDef or the model if possible.
**Any other info / logs**
Include any logs or source code that would be helpful to diagnose the problem.
If including tracebacks, please include the full traceback. Large logs and files
should be attached.
|
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PR_kwDOArmXAs5s-POR
| 65,958 |
Skip custom_gradient_memory_test.testRecomputeGradXla.
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Skip custom_gradient_memory_test.testRecomputeGradXla.
Has been failing for a few days now.
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PR_kwDOArmXAs5s-Dwu
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Update TFRT dependency to use revision
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Update TFRT dependency to use revision
http://github.com/tensorflow/runtime/commit/87b56f63a6be054b4a9f59c7b2def04eff1966d5.
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PR_kwDOArmXAs5s-AXi
| 65,956 |
Integrate StableHLO at openxla/stablehlo@52172972
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Integrate StableHLO at openxla/stablehlo@52172972
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PR_kwDOArmXAs5s9_LT
| 65,955 |
Do not compile with backend compiler if there is no restore op and no return value
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Do not compile with backend compiler if there is no restore op and no return value
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#tf-data Add `output_cardinality` to `index_flat_map`.
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I_kwDOArmXAs6GDmj_
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Action of the matrix exponential on vectors
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[
"Hi @ivanZanardi ,\r\n\r\nThanks for the request. Could you please confirm whether this feature available in any other libraries like numpy,scipy,Jax etc?",
"Hi @SuryanarayanaY,\r\n\r\nThanks for getting back to me. Yes, the implementation of the referenced algorithm can be found in SciPy's [`scipy.sparse.linalg.expm_multiply`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.expm_multiply.html) function.\r\n\r\nThanks,\r\nIvan",
"Any updates on this?\r\n\r\nThanks,\r\nIvan"
] | 2024-04-17T19:25:46 | 2024-04-29T06:45:09 | null |
NONE
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.10.0
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Hello everyone,
I was wondering if there are any plans to implement the **action of the matrix exponential on vectors** in the codebase. This targeted computation, rather than the full matrix exponential (`tf.linalg.expm`), would greatly benefit users, especially those using exponential-based integrators.
References:
- SIAM Paper: [Link](https://doi.org/10.1137/100788860)
- MATLAB Code: [GitHub](https://github.com/higham/expmv)
Thanks for considering!
Ivan
### Standalone code to reproduce the issue
```shell
n/a
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5s9wep
| 65,952 |
Wait for compilation to complete in PyClient::CompileIfrtProgram
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Wait for compilation to complete in PyClient::CompileIfrtProgram
Users expect jax compile() API to raise an exception when compilation fails.
Some backends return a future of an executable rather than the compilation result.
This does not raise exceptions in case of failures.
jax.stages.Compiled object in python is not a Future. To get an exception, users must wait on the compilation to complete. Only way to wait for the compilation to complete is to wait for compilation using APIs like get_compiled_memory_stats on the executable that internally materialize the executable.
Users already can run compilation asynchonously in a threadpool to have it generally be non-blocking across backends.
To simplify semantics, this CL makes JAX compilation always blocking regardless of whether IFRT implementations support async compilation so that it raises an exception in case of a failure to compile.
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PR_kwDOArmXAs5s9mhS
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Integrate LLVM at llvm/llvm-project@676d3bafc09d
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Integrate LLVM at llvm/llvm-project@676d3bafc09d
Updates LLVM usage to match
[676d3bafc09d](https://github.com/llvm/llvm-project/commit/676d3bafc09d)
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Reverts 603690b3ea5c656dcf228543d91e3f594f0807d0
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Reverts 603690b3ea5c656dcf228543d91e3f594f0807d0
|
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PR_kwDOArmXAs5s9QRD
| 65,949 |
Create a dependency on pywrap_calibration.so for the tensorflow wheel
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Create a dependency on pywrap_calibration.so for the tensorflow wheel
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PR_kwDOArmXAs5s9IIT
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Support of C++20 for OSS TF for Linux x86_64 Ubuntu 22.04
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Support of C++20 for OSS TF for Linux x86_64 Ubuntu 22.04
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PR_kwDOArmXAs5s82Fu
| 65,947 |
Reverts 4cc17617fe8fb0bcd2400429d3be08acd87972c4
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Reverts 4cc17617fe8fb0bcd2400429d3be08acd87972c4
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PR_kwDOArmXAs5s8yZ9
| 65,946 |
Integrate LLVM at llvm/llvm-project@dd84d23adc84
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Integrate LLVM at llvm/llvm-project@dd84d23adc84
Updates LLVM usage to match
[dd84d23adc84](https://github.com/llvm/llvm-project/commit/dd84d23adc84)
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I_kwDOArmXAs6GCTx_
| 65,945 |
Incapable of loading a tf v1 model
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[
"@Afganitia Could you please consider upgrading your TF v1 model to TF2 format. Please refer to [this](https://www.tensorflow.org/guide/migrate/upgrade) guide for migration. \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/65945\">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/65945\">No</a>\n"
] | 2024-04-17T16:49:19 | 2024-05-09T01:48:50 | 2024-05-09T01:48:42 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
Windows 10
### Mobile device
_No response_
### Python version
3.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I expected tf to load the model (it is a v1 model, but in theory this is houw you should load them?).
Model in question: https://drive.google.com/file/d/1TnN1j34owIPMyi7gRZJO3UAwZhNtXQ9i/view?usp=drive_link
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
# Define the path to the directory
directory_path = r"D:\Admin\Documents\Universitat\UdG\TFM\Modelos\trn-_cnn_random__random_sp_gpu-cnn_for_random_pfam-5356760"
# Print the contents of the directory
print("Contents of the directory:")
for item in os.listdir(directory_path):
print(item)
# Define the correct path to the SavedModel directory
saved_model_path = r"D:\Admin\Documents\Universitat\UdG\TFM\Modelos\trn-_cnn_random__random_sp_gpu-cnn_for_random_pfam-5356760\saved_model.pb"
# Load the SavedModel
loaded_model = tf.saved_model.load(saved_model_path)
```
### Relevant log output
```shell
C:\Python312\python.exe C:\Users\Admin\AppData\Roaming\JetBrains\PyCharmCE2024.1\scratches\scratch.py
2024-04-17 18:44:10.129957: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-04-17 18:44:11.239742: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
Contents of the directory:
saved_model.pb
variables
Traceback (most recent call last):
File "C:\Users\Admin\AppData\Roaming\JetBrains\PyCharmCE2024.1\scratches\scratch.py", line 16, in <module>
loaded_model = tf.saved_model.load(saved_model_path)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Python312\Lib\site-packages\tensorflow\python\saved_model\load.py", line 912, in load
result = load_partial(export_dir, None, tags, options)["root"]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Python312\Lib\site-packages\tensorflow\python\saved_model\load.py", line 1016, in load_partial
loader_impl.parse_saved_model_with_debug_info(export_dir))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Python312\Lib\site-packages\tensorflow\python\saved_model\loader_impl.py", line 59, in parse_saved_model_with_debug_info
saved_model = parse_saved_model(export_dir)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Python312\Lib\site-packages\tensorflow\python\saved_model\loader_impl.py", line 119, in parse_saved_model
raise IOError(
OSError: SavedModel file does not exist at: D:\Admin\Documents\Universitat\UdG\TFM\Modelos\trn-_cnn_random__random_sp_gpu-cnn_for_random_pfam-5356760\saved_model.pb\{saved_model.pbtxt|saved_model.pb}
Process finished with exit code 1
```
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Reverts 603690b3ea5c656dcf228543d91e3f594f0807d0
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I_kwDOArmXAs6GCA9Y
| 65,943 |
Cannot import tflite_support.task when tensorflow is installed
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[
"I found this problem mentioned already in tflite-support repo.\r\nhttps://github.com/tensorflow/tflite-support/issues/954",
"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/65943\">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/65943\">No</a>\n"
] | 2024-04-17T16:06:52 | 2024-04-18T09:36:52 | 2024-04-18T09:36:49 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
Ubuntu 22.04
### 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
_No response_
### Current behavior?
When I have installed both tflite_support (0.4.4) and tensorflow and try to import:
`from tflite_support.task import text as tflite_text`
I get following error:
```
File "/home/xxx/miniconda3/envs/mf/lib/python3.10/site-packages/tflite_support/__init__.py", line 53, in <module>
from tflite_support import task
File "/home/xxx/miniconda3/envs/mf/lib/python3.10/site-packages/tflite_support/task/__init__.py", line 32, in <module>
from . import vision
File "/home/xxx/miniconda3/envs/mf/lib/python3.10/site-packages/tflite_support/task/vision/__init__.py", line 20, in <module>
from tensorflow_lite_support.python.task.vision import image_classifier
File "/home/xxx/miniconda3/envs/mf/lib/python3.10/site-packages/tensorflow_lite_support/python/task/vision/image_classifier.py", line 23, in <module>
from tensorflow_lite_support.python.task.vision.core import tensor_image
File "/home/xxx/miniconda3/envs/mf/lib/python3.10/site-packages/tensorflow_lite_support/python/task/vision/core/tensor_image.py", line 19, in <module>
from tensorflow_lite_support.python.task.vision.core.pybinds import image_utils
ImportError: generic_type: cannot initialize type "StatusCode": an object with that name is already defined
```
### Standalone code to reproduce the issue
```shell
`from tflite_support.task import text as tflite_text`
```
### Relevant log output
_No response_
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PR_kwDOArmXAs5s8VVf
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[XLA] Fix hlo_constant_splitter bug where constants are split per users instead of per use.
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[XLA] Fix hlo_constant_splitter bug where constants are split per users instead of per use.
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PR_kwDOArmXAs5s8U3C
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Create repo_mapping so that tf_runtime can refer to XLA and TSL differently from TF
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Create repo_mapping so that tf_runtime can refer to XLA and TSL differently from TF
Right now this is a noop, but this enables a future change to move tf_runtime to refer to XLA as `@xla` rather than `@local_xla`, and similar for TSL.
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PR_kwDOArmXAs5s8CfC
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#shlo_ref Add `SI64` data type.
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#shlo_ref Add `SI64` data type.
That type is not planned for support for the operations but some of them hard specify it for their attributes (eg. `dot_general`).
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PR_kwDOArmXAs5s7rLr
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shlo_ref: Add dot_general op and unit tests
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[
"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/65939/checks?check_run_id=23933119233) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.",
"Hi @nishantsarda-mcw This PR is in draft, any update on this? Please. Thank you!"
] | 2024-04-17T14:38:28 | 2024-05-14T17:23:06 | null |
NONE
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- Adds initial version of dot_general op
- Adds unit tests for dot_general op (quantized tests are yet to be added)
CC: @rascani @qukhan
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PR_kwDOArmXAs5s7qCs
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[XLA:GPU] Unify the Redzone buffer creation for autotuning.
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[] | 2024-04-17T14:36:11 | 2024-04-18T20:12:21 | 2024-04-18T20:12:20 |
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[XLA:GPU] Unify the Redzone buffer creation for autotuning.
The three different autotuning passes (cublas, cudnn, triton) have similar code for creating redzone allocators and initializing the buffers. This changes unifies the logic in order to ensure consistency and to make it easier to use this logic in future code.
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I_kwDOArmXAs6GBCMz
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Math problem
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[
"@iamvitalyo,\r\nLooks like this issue is not related tensorflow. Kindly open a tensorflow discussion [forum](https://discuss.tensorflow.org/) issue for this as it is not a bug or feature request. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/65937\">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/65937\">No</a>\n"
] | 2024-04-17T14:17:42 | 2024-05-03T01:48:43 | 2024-05-03T01:48:40 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
all
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
if 0+1=1 then 0*1=1 is true, explaine i need * on 2 one person with samurai sword, if i have sword is exist is not 0, if i dont have sword *0 then person will be still 1, this error and -a=b, x=a+b ==== -1=1, 0 = 1+1, and maybe another our math is 50% what circle fforgiive but math antyuthopiia, mathh of evil, math of evil drugs and food, there also our current AI on evil side, to fix
make better our ai please look on math and circle and use self mind and logic.
https://github.com/iamvitalyo/Aphrodite
### Standalone code to reproduce the issue
```shell
if 0+1=1 then 0*1=1 is true, explaine i need * on 2 one person with samurai sword, if i have sword is exist is not 0, if i dont have sword *0 then person will be still 1, this error and -a=b, x=a+b ==== -1=1, 0 = 1+1, and maybe another our math is 50% what circle fforgiive but math antyuthopiia, mathh of evil, math of evil drugs and food, there also our current AI on evil side, to fix
make better our ai please look on math and circle and use self mind and logic.
https://github.com/iamvitalyo/Aphrodite
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5s7IZ1
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Fix checkfail in GatherV2
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The Op `GatherV2` leads to check fail particularly when `axis=kint64max`.
This is due to the below piece of code.
https://github.com/tensorflow/tensorflow/blob/1592da0d245b8123f95e8b72c2f4974343b8a0c7/tensorflow/core/kernels/gather_op.cc#L81-L84
From the above code `min_params_dim` will become `axis+1` and this leads to `overflow` to -ve value as axis is already `kint64max` and the `OP_REQUIRES` check `params.dims() >= min_params_dim` will not return error .
Anything less than or greater than that leads to valid exception.
Might fix #65869
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Not getting the same result when using .tflite in C and Python.
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[
"Hi @saad-koukous \r\n\r\nCan you provide more details like the code for handling input and output in both cases ( in python and c) and also the model execution code?",
"Hi @sawantkumar, Thank you for your response. However, I can't share the .tflite model and data.\r\nbut i can share with you how i'm allocating input tensor and output tensor.\r\n**interprater method:**\r\n```\r\n// Allocate tensors\r\n if (TfLiteInterpreterAllocateTensors(interpreter) != kTfLiteOk) {\r\n fprintf(stderr, \"Failed to allocate tensors\\n\");\r\n return 1;\r\n }\r\nconst TfLiteTensor* input_tensor = TfLiteInterpreterGetInputTensor(interpreter, 0);\r\n//and here i'm looping on a file to load data in the input_tensor\r\n```\r\n**Signature Runner method** \r\n\r\n```\r\n// Allocate tensors\r\n if (TfLiteSignatureRunnerAllocateTensors(signature_runner) != kTfLiteOk) {\r\n fprintf(stderr, \"Failed to allocate tensors\\n\");\r\n return 1;\r\n const char *signature_inputName = TfLiteSignatureRunnerGetInputName(signature_runner, 0);\r\n printf(\"Signature input name: %s\\n\", signature_inputName);\r\n }\r\n TfLiteTensor *input_tensor = TfLiteSignatureRunnerGetInputTensor(signature_runner, signature_inputName);\r\n //and here i'm looping on a file to load data in the input_tensor\r\n```\r\n**python**\r\n\r\n```\r\ninterpreter.allocate_tensors()\r\nsignature = interpreter.get_signature_runner('signature_name')\r\ninfe = serving_default(input=input) #same input of the c program\r\noutput = infe['output']\r\n```",
"Hi @saad-koukous ,\r\n\r\nCan you provide me with some more details like what kind of model this is, an object detection or some nlp model, what do you mean when you say you don't get the same output? Also if you can let me what specific layers are being used and what activation functions are being used in your tflite model, it will be easier for me to debug the issue.",
"Hi @sawantkumar,\r\nThe model is designed for image segmentation, and when I input the same data into the TensorFlow Lite model using both Python and the C API, I'm not getting the same results at all; in fact, the results are significantly different.\r\nthe input is a matrix where elements are float32.",
"Hi @saad-koukous , \r\n\r\nThanks for the information, let me replicate the issue and i will get back to you."
] | 2024-04-17T13:20:33 | 2024-06-11T14:22:13 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.17.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04.4 LTS
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
bazel 6.5.0
### GCC/compiler version
gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When I run inferences using the TFLite model in Python, I get the correct output. However, when using the C API, I'm not getting the same output, whether using the interpreter or signature runner.
### Standalone code to reproduce the issue
```shell
information about the model in with the c_api:
Signature count: 1
Signature key: serving_default
Input count: 1
Output count: 2
Signature input name: input
Signature output name: 64
Input tensor:
Number of dimensions: 4
Dimensions: 1 x 3 x 40 x 180
```
### Relevant log output
_No response_
|
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Support non-scalar constants in mlir fusion emitters.
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Support non-scalar constants in mlir fusion emitters.
We can reuse some code from RewriteAllocateShared rewrite pattern to create a
GlobalOp.
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Automated Code Change
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Adjust rtol value for confirming quantization.
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Adjust rtol value for confirming quantization.
Fixed for better readability.
This constraint should be enough to test whether a f32 value dequantized from i8 was changed.
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Fix DUS emitter when the output is bitcast.
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TensorFlow lite Whisper model get worse inference result.
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[
"Please take a look.",
"Model working well with below converting script:\r\n```\r\nclass ModelClass(tf.Module):\r\n def __init__(self, whisper_model, *args, **kwargs):\r\n super().__init__(*args, **kwargs)\r\n\r\n self.model = whisper_model\r\n \r\n @tf.function(\r\n input_signature=(\r\n tf.TensorSpec((1, None, None), dtype=tf.float32, name=\"input_features\"),\r\n )\r\n )\r\n def call(self, intput_features):\r\n return {\"output_ids\": self.model.generate(input_features=intput_features)}\r\n \r\n def get_config(self):\r\n return dict()\r\n \r\nwrapper = ModelClass(tf_model)\r\n\r\n# Convert mode lo tflite with dynamic quantization\r\nconverter = tf.lite.TFLiteConverter.from_keras_model(wrapper)\r\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\r\nconverter.target_spec.supported_ops = [\r\n tf.lite.OpsSet.TFLITE_BUILTINS,\r\n tf.lite.OpsSet.SELECT_TF_OPS,\r\n]\r\ntflite_quant_model = converter.convert()\r\nopen(tf_model_path , \"wb\").write(tflite_quant_model)\r\n```"
] | 2024-04-17T09:05:37 | 2024-04-23T07:36:17 | 2024-04-23T07:36:17 |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution: Windows 10
- TensorFlow installation: via Pypi
- TensorFlow library: 2.16.1
### 2. Conversion success
- Tflite model have been save to .tflite file, with size x4 smaller
Code for model conversion:
```
model_checkpoint = "openai/whisper-tiny"
dataset_name = "libspeech_asr"
wrapper_path = r"tf_model/whisper_wrapper"
tf_model_path = r"tf_model/whisper.tflite"
processor = AutoProcessor.from_pretrained(model_checkpoint)
tf_model = TFWhisperForConditionalGeneration.from_pretrained(model_checkpoint)
dataset = load_dataset(dataset_name)
# Wrap model for conversion
class ModelClass(tf.keras.Model):
def __init__(self, whisper_model, *args, **kwargs):
super().__init__(*args, **kwargs)
self.model = whisper_model
@tf.function(
input_signature=(
tf.TensorSpec((1, None, None), dtype=tf.float32, name="input_features"),
)
)
def call(self, intput_features):
return {"output_ids": self.model.generate(input_features=intput_features)}
def get_config(self):
return dict()
wrapper = ModelClass(tf_model)
tf.saved_model.save(
wrapper,
wrapper_path,
signatures={"serving_default": wrapper.call}
)
# Convert mode lo tflite with dynamic quantization
converter = tf.lite.TFLiteConverter.from_saved_model(wrapper_path)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS,
tf.lite.OpsSet.SELECT_TF_OPS,
]
tflite_quant_model = converter.convert()
open(tf_model_path , "wb").write(tflite_quant_model)
```
### 3. Inference results gets worse compared to TensorFlow model.
- After conversion, inference Tflite model and TensorFlow model with first row of the dataset:
```
def tflite_inference(inter, processor, tokenizer, data):
input_details = inter.get_input_details()
output_details = inter.get_output_details()
tokenized_output = processor(data["array"], sampling_rate=data["sampling_rate"], return_tensors="tf")
input_features = tokenized_output.input_features
my_signature = interpreter.get_signature_runner()
output = my_signature(input_features=input_features)["output_ids"]
return tokenizer.decode(output[0], skip_special_tokens=True)
def tensorflow_inference(model, processor, tokenizer, data):
tokenized_output = processor(data["array"], sampling_rate=data["sampling_rate"], return_tensors="tf")
input_features = tokenized_output.input_features
output = model(input_features=input_features)["output_ids"]
return tokenizer.decode(output[0], skip_special_tokens=True)
first_row = datasets[0]
true_label = first_row["text"]
tensorflow_inference_result = tensorflow_inference(interpreter, processor, tokenizer, first_row).upper()
tflite_inference_result = tflite_inference(wrapper, processor, tokenizer, first_row).upper()
```
- **Results**:
+ True label: "CONCORD RETURNED TO ITS PLACE AMIDST THE TENTS"
+ TensorFlow model result: " CONQUERED RETURNED TO ITS PLACE AMIDST THE TENTS."
+ Tflite model result: "!!,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,..."
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Tested with JAX test suite.
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Integrate LLVM at llvm/llvm-project@dd84d23adc84
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Integrate LLVM at llvm/llvm-project@dd84d23adc84
Updates LLVM usage to match
[dd84d23adc84](https://github.com/llvm/llvm-project/commit/dd84d23adc84)
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Fix allocate_shared with complex tensors.
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Fix allocate_shared with complex tensors.
complex<...> is not a valid element type for LLVM arrays.
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[xla][gpu] Move sparse tests from Triton repo to XLA repo.
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[XLA:GPU] Take the workspace size from the result shape when autotuning CuBlas.
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[XLA:GPU] Take the workspace size from the result shape when autotuning CuBlas.
The last part of the result tuple represents the workspace. We should use this to make the code simpler and also to allocate a more precise amount of memory.
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Allow per-channel quantization without specifying quantization dimension
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Allow per-channel quantization without specifying quantization dimension
Current QuantizedType in QuantizationConfig can only specify per-channel quantization with specified quantization dimension. But quantization dimension may vary depending on the dimension layout within a model, but at the same time, it is straight forward to decide quantization dimension for the most of the times(output feature dimension for convolution, and non-batching and non-contracting dimension for dot_general - usually it is unique for ODML cases). Therefore, allowing option to do per-channel quantization without specifying quantization dimension will make it more accessible.
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Fix xla_gpu.shuffle_reduce with complex arguments.
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Fix xla_gpu.shuffle_reduce with complex arguments.
complex can't be bitcast to vector directly.
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cl 命令行 无法打开 calibration_statistics_collector_average_min_max.obj.params
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"same issue cl : Command line error D8022 : cannot open 'bazel-out/x64_windows-opt/bin/tensorflow/compiler/mlir/quantization/tensorflow/calibrator/_objs/calibration_statistics_collector_average_min_max/calibration_statistics_collector_average_min_max.obj.params' \r\nTF Version 2.15.1",
"Seems that the latest version of TF doesn't support MSVC 2019/MSVC2022 compiler any more, please use CLANG to Build. ",
"@Venkat6871 do you have any update on this issue? I had turned on Developer mode, it is not access right problem. I checked the BUILD file under calibrator folder, no problem of this file. and calibration_statistics_collector_average_min_max.obj.params file had existed, why cl.exe cannot open this file. it is format issue or not?\r\nBR ",
"Hi **@ShifengJin** ,\r\nSorry for the delay, Could you verify that the version of the `C` compiler used by Bazel are compatible with TensorFlow. Sometimes, mismatched compiler versions can lead to build errors. And if you are using an older version of the C++ compiler, consider upgrading to a newer version that may have better memory management. Please delete any existing build files and directories to ensure a fresh start.\r\n\r\nThank you!\r\n\r\n",
"Microsoft (R) C/C++ Optimizing Compiler Version 19.39.33523 for x64\r\nCopyright (C) Microsoft Corporation. All rights reserved.\r\nit should be latest version of MSVC",
"MSVC\\14.39.33519",
"I'm also facing exact error and it is not actually bind to this particular `calibration_statistics_collector_average_min_max.obj.params` file, but it occurs for some other *.params file as well.\r\n\r\nFaced this while trying to build the following target,\r\n`bazel build --config=opt --cxxopts=\"-std=c++17\" //tensorflow:tensorflow_cc.dll`",
"that's a problem why we cannot use c++17 to build, i used win-clang, it can be build successfully. actually, this file is existed on the bazel_out folder already, if you run cl.exe manually, this file can be opened. I suspected it related with build setup. but we had followed the instruction in release note. I also met another problem of smallvector like below: \r\ntensorflow/dtensor/mlir/expansions/save_restore_spmd_expander.cc(812): error C2976: 'llvm::SmallVector': too few template arguments. I checked the code, find that for TFV2.15.0 they use llvm17.0.6. (I want to use the latest llvm with --override_repository=llvm-raw=<path> failed also) and in save_restore_spmd_expander.cc:812\r\nStatusOr<llvm::SmallVector<Layout>> GetLayoutsFromAssignVariableOps\r\nI am mad with TF c++ windows build, I gave up windows build , and works on linux environment now. ",
"@Venkat6871 please don't wait the his response, we met the same issue, I can provided the info you wanted",
"As @weijianghn said, I do not get any errors when compiling in LLVM/clang 17.0.6. Getting it only in MSVC. But I'm facing other error because of missing symbols while trying to link the tensorflow.dll. The problem is I could solve missing symbols by simply adding `TF_EXPORT` in the respective source and recompile, but the missing symbol is from a dynamically generated proto file C++ header stub."
] | 2024-04-17T07:26:46 | 2024-04-29T05:44:50 | 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
Windows10
### Mobile device
_No response_
### Python version
3.11
### Bazel version
6.5
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
cl: 命令行 error D8022 :无法打开 calibration_statistics_collector_average_min_max.obj.params
### Standalone code to reproduce the issue
```shell
cl: 命令行 error D8022 :无法打开“bazel-out/x64_windows-opt/bin/tensorflow/compiler/mlir/quantization/tensorflow/calibrator/_objs/calibration_statistics_collector_average_min_max/calibration_statistics_collector_average_min_max.obj.params”
```
### Relevant log output
```shell
(python311) K:\tensorflow\tensorflow-2.16.1>bazel --output_user_root="../output_user_root" build --noincompatible_do_not_split_linking_cmdline --verbose_failures --config=opt --config=windows //tensorflow:tensorflow_cc.dll
WARNING: The following configs were expanded more than once: [monolithic, no_tfrt]. For repeatable flags, repeats are counted twice and may lead to unexpected behavior.
INFO: Reading 'startup' options from k:\tensorflow\tensorflow-2.16.1\.bazelrc: --windows_enable_symlinks
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=306
INFO: Reading rc options for 'build' from k:\tensorflow\tensorflow-2.16.1\.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Options provided by the client:
'build' options: --python_path=D:/Anaconda3/envs/python311/python.exe
INFO: Reading rc options for 'build' from k:\tensorflow\tensorflow-2.16.1\.bazelrc:
'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --features=-force_no_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility
INFO: Reading rc options for 'build' from k:\tensorflow\tensorflow-2.16.1\.tf_configure.bazelrc:
'build' options: --action_env PYTHON_BIN_PATH=D:/Anaconda3/envs/python311/python.exe --action_env PYTHON_LIB_PATH=D:/Anaconda3/envs/python311/Lib/site-packages --python_path=D:/Anaconda3/envs/python311/python.exe --copt=/d2ReducedOptimizeHugeFunctions --host_copt=/d2ReducedOptimizeHugeFunctions --define=override_eigen_strong_inline=true
INFO: Found applicable config definition build:short_logs in file k:\tensorflow\tensorflow-2.16.1\.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file k:\tensorflow\tensorflow-2.16.1\.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:opt in file k:\tensorflow\tensorflow-2.16.1\.tf_configure.bazelrc: --copt=/arch:AVX --host_copt=/arch:AVX
INFO: Found applicable config definition build:windows in file k:\tensorflow\tensorflow-2.16.1\.bazelrc: --copt=/W0 --host_copt=/W0 --copt=/Zc:__cplusplus --host_copt=/Zc:__cplusplus --copt=/D_USE_MATH_DEFINES --host_copt=/D_USE_MATH_DEFINES --features=compiler_param_file --features=archive_param_file --copt=/d2ReducedOptimizeHugeFunctions --host_copt=/d2ReducedOptimizeHugeFunctions --enable_runfiles --cxxopt=/std:c++17 --host_cxxopt=/std:c++17 --config=monolithic --copt=-DWIN32_LEAN_AND_MEAN --host_copt=-DWIN32_LEAN_AND_MEAN --copt=-DNOGDI --host_copt=-DNOGDI --copt=/Zc:preprocessor --host_copt=/Zc:preprocessor --linkopt=/DEBUG --host_linkopt=/DEBUG --linkopt=/OPT:REF --host_linkopt=/OPT:REF --linkopt=/OPT:ICF --host_linkopt=/OPT:ICF --verbose_failures --features=compiler_param_file --config=no_tfrt
INFO: Found applicable config definition build:monolithic in file k:\tensorflow\tensorflow-2.16.1\.bazelrc: --define framework_shared_object=false --define tsl_protobuf_header_only=false --experimental_link_static_libraries_once=false
INFO: Found applicable config definition build:no_tfrt in file k:\tensorflow\tensorflow-2.16.1\.bazelrc: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/ir,tensorflow/compiler/mlir/tfrt/ir/mlrt,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ifrt,tensorflow/compiler/mlir/tfrt/tests/mlrt,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/compiler/mlir/tfrt/transforms/mlrt,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/runtime_fallback/test,tensorflow/core/runtime_fallback/test/gpu,tensorflow/core/runtime_fallback/test/saved_model,tensorflow/core/runtime_fallback/test/testdata,tensorflow/core/tfrt/stubs,tensorflow/core/tfrt/tfrt_session,tensorflow/core/tfrt/mlrt,tensorflow/core/tfrt/mlrt/attribute,tensorflow/core/tfrt/mlrt/kernel,tensorflow/core/tfrt/mlrt/bytecode,tensorflow/core/tfrt/mlrt/interpreter,tensorflow/compiler/mlir/tfrt/translate/mlrt,tensorflow/compiler/mlir/tfrt/translate/mlrt/testdata,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils,tensorflow/core/tfrt/utils/debug,tensorflow/core/tfrt/saved_model/python,tensorflow/core/tfrt/graph_executor/python,tensorflow/core/tfrt/saved_model/utils
INFO: Found applicable config definition build:windows in file k:\tensorflow\tensorflow-2.16.1\.bazelrc: --copt=/W0 --host_copt=/W0 --copt=/Zc:__cplusplus --host_copt=/Zc:__cplusplus --copt=/D_USE_MATH_DEFINES --host_copt=/D_USE_MATH_DEFINES --features=compiler_param_file --features=archive_param_file --copt=/d2ReducedOptimizeHugeFunctions --host_copt=/d2ReducedOptimizeHugeFunctions --enable_runfiles --cxxopt=/std:c++17 --host_cxxopt=/std:c++17 --config=monolithic --copt=-DWIN32_LEAN_AND_MEAN --host_copt=-DWIN32_LEAN_AND_MEAN --copt=-DNOGDI --host_copt=-DNOGDI --copt=/Zc:preprocessor --host_copt=/Zc:preprocessor --linkopt=/DEBUG --host_linkopt=/DEBUG --linkopt=/OPT:REF --host_linkopt=/OPT:REF --linkopt=/OPT:ICF --host_linkopt=/OPT:ICF --verbose_failures --features=compiler_param_file --config=no_tfrt
INFO: Found applicable config definition build:monolithic in file k:\tensorflow\tensorflow-2.16.1\.bazelrc: --define framework_shared_object=false --define tsl_protobuf_header_only=false --experimental_link_static_libraries_once=false
INFO: Found applicable config definition build:no_tfrt in file k:\tensorflow\tensorflow-2.16.1\.bazelrc: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/ir,tensorflow/compiler/mlir/tfrt/ir/mlrt,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ifrt,tensorflow/compiler/mlir/tfrt/tests/mlrt,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/compiler/mlir/tfrt/transforms/mlrt,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/runtime_fallback/test,tensorflow/core/runtime_fallback/test/gpu,tensorflow/core/runtime_fallback/test/saved_model,tensorflow/core/runtime_fallback/test/testdata,tensorflow/core/tfrt/stubs,tensorflow/core/tfrt/tfrt_session,tensorflow/core/tfrt/mlrt,tensorflow/core/tfrt/mlrt/attribute,tensorflow/core/tfrt/mlrt/kernel,tensorflow/core/tfrt/mlrt/bytecode,tensorflow/core/tfrt/mlrt/interpreter,tensorflow/compiler/mlir/tfrt/translate/mlrt,tensorflow/compiler/mlir/tfrt/translate/mlrt/testdata,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils,tensorflow/core/tfrt/utils/debug,tensorflow/core/tfrt/saved_model/python,tensorflow/core/tfrt/graph_executor/python,tensorflow/core/tfrt/saved_model/utils
WARNING: The following configs were expanded more than once: [monolithic, no_tfrt]. For repeatable flags, repeats are counted twice and may lead to unexpected behavior.
WARNING: Download from https://golang.org/dl/?mode=json&include=all failed: class java.io.IOException connect timed out
INFO: Analyzed target //tensorflow:tensorflow_cc.dll (0 packages loaded, 0 targets configured).
INFO: Found 1 target...
ERROR: K:/tensorflow/tensorflow-2.16.1/tensorflow/compiler/mlir/quantization/tensorflow/calibrator/BUILD:97:11: Compiling tensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_statistics_collector_average_min_max.cc failed: (Exit 2): cl.exe failed: error executing command (from target //tensorflow/compiler/mlir/quantization/tensorflow/calibrator:calibration_statistics_collector_average_min_max)
cd /d K:/tensorflow/output_user_root/nxvry2vz/execroot/org_tensorflow
SET INCLUDE=C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.29.30133\ATLMFC\include;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.29.30133\include;C:\Program Files (x86)\Windows Kits\NETFXSDK\4.8\include\um;C:\Program Files (x86)\Windows Kits\10\include\10.0.22621.0\ucrt;C:\Program Files (x86)\Windows Kits\10\include\10.0.22621.0\shared;C:\Program Files (x86)\Windows Kits\10\include\10.0.22621.0\um;C:\Program Files (x86)\Windows Kits\10\include\10.0.22621.0\winrt;C:\Program Files (x86)\Windows Kits\10\include\10.0.22621.0\cppwinrt
SET PATH=C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\\Extensions\Microsoft\IntelliCode\CLI;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.29.30133\bin\HostX64\x64;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\VC\VCPackages;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\CommonExtensions\Microsoft\TestWindow;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\CommonExtensions\Microsoft\TeamFoundation\Team Explorer;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\MSBuild\Current\bin\Roslyn;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Team Tools\Performance Tools\x64;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Team Tools\Performance Tools;C:\Program Files (x86)\Microsoft Visual Studio\Shared\Common\VSPerfCollectionTools\vs2019\\x64;C:\Program Files (x86)\Microsoft Visual Studio\Shared\Common\VSPerfCollectionTools\vs2019\;C:\Program Files (x86)\Microsoft SDKs\Windows\v10.0A\bin\NETFX 4.8 Tools\x64\;C:\Program Files (x86)\HTML Help Workshop;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\Tools\devinit;C:\Program Files (x86)\Windows Kits\10\bin\10.0.22621.0\x64;C:\Program Files (x86)\Windows Kits\10\bin\x64;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\\MSBuild\Current\Bin;C:\Windows\Microsoft.NET\Framework64\v4.0.30319;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\Tools\;;C:\Windows\system32;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\CommonExtensions\Microsoft\CMake\CMake\bin;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\CommonExtensions\Microsoft\CMake\Ninja;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\VC\Linux\bin\ConnectionManagerExe
SET PWD=/proc/self/cwd
SET PYTHON_BIN_PATH=D:/Anaconda3/envs/python311/python.exe
SET PYTHON_LIB_PATH=D:/Anaconda3/envs/python311/Lib/site-packages
SET TEMP=C:\Users\ALVA\AppData\Local\Temp
SET TF2_BEHAVIOR=1
SET TMP=C:\Users\ALVA\AppData\Local\Temp
C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.29.30133\bin\HostX64\x64\cl.exe @bazel-out/x64_windows-opt/bin/tensorflow/compiler/mlir/quantization/tensorflow/calibrator/_objs/calibration_statistics_collector_average_min_max/calibration_statistics_collector_average_min_max.obj.params
# Configuration: a55a642a68a20afa079b952d4b022b7bc800f40fe5a8b93a3ee5697ec31a8ff5
# Execution platform: @local_execution_config_platform//:platform
用于 x64 的 Microsoft (R) C/C++ 优化编译器 19.29.30153 版
版权所有(C) Microsoft Corporation。保留所有权利。
cl: 命令行 error D8022 :无法打开“bazel-out/x64_windows-opt/bin/tensorflow/compiler/mlir/quantization/tensorflow/calibrator/_objs/calibration_statistics_collector_average_min_max/calibration_statistics_collector_average_min_max.obj.params”
Target //tensorflow:tensorflow_cc.dll failed to build
INFO: Elapsed time: 0.783s, Critical Path: 0.08s
INFO: 13 processes: 13 internal.
FAILED: Build did NOT complete successfully
```
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| 2,247,559,905 |
PR_kwDOArmXAs5s4lk1
| 65,904 |
Use data layout attributes for index bit widths.
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Use data layout attributes for index bit widths.
This fixes an issue in our code where we use too wide indices, and also works
around a bug in LLVM's separate-const-offset-from-gep pass, which produces
incorrect results in the presence of truncations. After this change, we will
use the same bit width for GEPs as for other indices, so there will never be
any truncs.
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PR #11570: [ROCm] FileCheck fixes
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PR #11570: [ROCm] FileCheck fixes
Imported from GitHub PR https://github.com/openxla/xla/pull/11570
This PR patches several FileCheck pattern tests so that they pass on ROCm.
Copybara import of the project:
--
235021f497b6d5bc1a84ebeaa1e9686acb9a2708 by Eugene Kuznetsov <[email protected]>:
ROCm FileCheck pattern fixes
Merging this change closes #11570
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11570 from ROCm:rocm_filecheck_fixes_240415 235021f497b6d5bc1a84ebeaa1e9686acb9a2708
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[XLA:GPU] Remove mentions of deprecated `--xla_gpu_simplify_all_fp_conversions` flag.
This flag was replaced with `--xla_allow_excess_precision`.
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PR_kwDOArmXAs5s3H0d
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PR_kwDOArmXAs5s3Gaa
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Create a dependency on pywrap_calibration.so for the tensorflow wheel
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Create a dependency on pywrap_calibration.so for the tensorflow wheel
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[xla:gpu] Define what execution stream id means and how it should work with async operations
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NONE
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[xla:gpu] Define what execution stream id means and how it should work with async operations
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PR_kwDOArmXAs5s251Q
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Remove the CalibrationSingleton
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NONE
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Remove the CalibrationSingleton
The Singleton is a legacy design that only works on CPU.
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PR_kwDOArmXAs5s23qF
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#tf-data Add a repetition test for the `index_flat_map` dataset.
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"subscriptions_url": "https://api.github.com/users/yangustc07/subscriptions",
"organizations_url": "https://api.github.com/users/yangustc07/orgs",
"repos_url": "https://api.github.com/users/yangustc07/repos",
"events_url": "https://api.github.com/users/yangustc07/events{/privacy}",
"received_events_url": "https://api.github.com/users/yangustc07/received_events",
"type": "User",
"site_admin": false
}
] | null |
[] | 2024-04-16T23:54:21 | 2024-04-17T00:29:40 | 2024-04-17T00:29:39 |
NONE
| null | true |
{
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|
#tf-data Add a repetition test for the `index_flat_map` dataset.
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https://api.github.com/repos/tensorflow/tensorflow/issues/65892/timeline
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