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Update CompileFunction to use XlaBuilder only.
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Update CompileFunction to use XlaBuilder only.
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[Cleanup] Remove unused CreateCollectiveBroadcast function.
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[Cleanup] Remove unused CreateCollectiveBroadcast function.
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PR #11309: Run algebraic simplifier after layout normalization
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PR #11309: Run algebraic simplifier after layout normalization
Imported from GitHub PR https://github.com/openxla/xla/pull/11309
The newly introduced layout normalization pass (https://github.com/openxla/xla/pull/10811) leaves redundant bitcasts in the HLO graph. In particular, the bitcasts for the host buffer intializations confuse HostOffloadLegalize pass. The legalization pass tries to ensure that host-offloader’s alias analysis can treat dynamic-update-slice instructions as in-place by duplicating (for each use) the broadcast instructions that will be treated as buffer initializations. However, the layout normalization pass l introduces a single bitcast as a user of the broadcast instruction, so the broadcast does not get duplicated.
This patch runs algebraic simplifier after layout normalization to get rid of the redundant bitcasts.
Copybara import of the project:
--
aa918f1939b0c9f192034aad8f87d1d9358892e9 by Jaroslav Sevcik <[email protected]>:
Run algebraic simplifier after layout normalization
Merging this change closes #11309
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11309 from jaro-sevcik:introduce-algsimp-after-layoutnorm-gpu aa918f1939b0c9f192034aad8f87d1d9358892e9
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[oneDNN] Quantized MatMul with new API and features
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"Hi @penpornk Can you please review this PR ? Thank you!"
] | 2024-04-19T16:00:38 | 2024-05-16T14:43:22 | 2024-05-16T14:43:22 |
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_Reopened the closed PR https://github.com/tensorflow/tensorflow/pull/56792_
This PR introduces new API for quantized MatMul fusions. All fusions are implemented as single op _QuantizedMatMul with fused_ops attribute. This PR contains a number of features:
Weights can be quantized per-tensor or per-channel
MatMul + BiasAdd + Activation, where activation function can be any of {Relu, Relu6, LeakyRelu, Elu, Tanh, Sigmoid, GeluApproximate, GeluExact}
MatMul + BiasAdd + Add + Dequantize (when Add has same shape as the output of MatMul)
All fusions can have Dequantize as additional fusion.
All fusions can have Requantize as additional fusion, except the MatMul + BiasAdd + Add fusion.
When Dequantize is fused output can be float32
B matrix can be transposed.
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[TSL] Bump ml_dtypes for fp8 fix
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[TSL] Bump ml_dtypes for fp8 fix
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[oneDNN] QuantizeV2 with bfloat16 Input
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[
"Hi @penpornk Can you please review this PR ? Thank you!",
"@cantonios Thanks for reviewing this PR. I have addressed the comments. Please check.",
"`//tensorflow/core/kernels/mkl:mkl_quantize_op_test` is failing on Windows (Python 3.9) with the following message:\r\n```\r\n[ RUN ] All/MklQuantizeV2OpTest.small_minfirst_int/1\r\nunknown file: error: C++ exception with description \"could not create a primitive descriptor for a binary operation primitive\" thrown in the test body.\r\n[ FAILED ] All/MklQuantizeV2OpTest.small_minfirst_int/1, where GetParam() = 14 (0 ms)\r\n```\r\n",
"@penpornk Thanks for reporting the test failures. I think I have fixed it now. Please check.",
"@cantonios Is there any other issues with this PR?"
] | 2024-04-19T15:50:32 | 2024-06-05T08:12:02 | null |
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_Reopened the closed PR: https://github.com/tensorflow/tensorflow/pull/56613_
This PR depends on https://github.com/tensorflow/tensorflow/pull/66082
It extends QuantizeV2 op for converting bfloat16 tensor to quantized tensor. It helps quantizing a model with bfloat16 and 8-bit integer mixed precisions, for example using Intel Neural Compressor (INC) tool (https://github.com/intel/neural-compressor).
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Increasing minimum tiling for contracting dimension in Gemms with an 8-bit operand to 32 instead of 16. This is a workaround to avoid potential crashes that occur in Triton.
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Increasing minimum tiling for contracting dimension in Gemms with an 8-bit operand to 32 instead of 16. This is a workaround to avoid potential crashes that occur in Triton.
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PR #11611: Define GOOGLE_CUDA to turn on #11568
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PR #11611: Define GOOGLE_CUDA to turn on #11568
Imported from GitHub PR https://github.com/openxla/xla/pull/11611
The new NVTX annotations added in #11568 will not appear unless GOOGLE_CUDA is defined. The part of #9896 that was supposed to avoid this was rolled back in c4b031bf9522685f998ecbd12570f29b391495ec.
cc: @cheshire
Copybara import of the project:
--
80e8f28422aa1accfd4b972839b5f8ae1b7ff74b by Olli Lupton <[email protected]>:
Define GOOGLE_CUDA to turn on #11568
The new NVTX annotations added in #11568 will not appear unless
GOOGLE_CUDA is defined. The part of #9896 that was supposed to avoid
this was rolled back in c4b031bf9522685f998ecbd12570f29b391495ec.
Merging this change closes #11611
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11611 from olupton:nvtx-annotation-bazel-changes 80e8f28422aa1accfd4b972839b5f8ae1b7ff74b
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PR_kwDOArmXAs5tMK3Q
| 66,082 |
[oneDNN] Optimize MIN_FIRST mode quantization with oneDNN
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[
"@cantonios Thanks for the review again. I have addressed the comments.",
"Hi @cantonios Can you please review this PR ? Thank you!"
] | 2024-04-19T15:43:26 | 2024-05-01T12:52:39 | 2024-05-01T12:52:38 |
CONTRIBUTOR
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_Reopened the the closed PR https://github.com/tensorflow/tensorflow/pull/56776_
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I_kwDOArmXAs6GTl7a
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Wrong quantized_dimension (axis) when "per-channel" quantization
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[
"Hi @CarlosNacher ,\r\n\r\nCan you give me the full code to replicate this issue. The library imports and some other things are not clear . I will be able to debug quicker if you provide me the above details. ",
"Hey @sawantkumar ,\r\n\r\nFor sure! I have uploaded the code to a [Colab](https://colab.research.google.com/drive/17StLOhZJplMbZ2tew0UF85qXSUWd22vY?usp=sharing) file so that you can fully reproduce it. I have also written some comments in the notebook explaining the actual behaviour and the expected one.\r\n\r\nThe link to the data you will need to fully reproduce (the SavedModel and calibration data) is here: https://we.tl/t-MUNhBovDZr\r\n\r\nThank you so much for your response and for taking the time to help me!\r\n",
"Hi @CarlosNacher ,\r\n\r\nSorry for the late response, but the link for your savedModel seems to have expired. can you please provide another link to the model and data. ",
"Hi @sawantkumar ,\r\n\r\nFor sure, here: https://we.tl/t-iUsgchLLL7\r\n\r\nThank you.",
"Hi @sawantkumar , could you please give me some feedback aboout this? The links only last 7 days. If you need it, I can send it in a different format.\r\n\r\nThanks!",
"Hi @CarlosNacher ,\r\n\r\nI replicated your script and i got the tflite and i inspected it and confirmed that its quantizing the 0th dimension. \r\n\r\n ````\r\ndtype=float32),` 'zero_points': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), 'quantized_dimension': 0}\r\nTensor Name: tfl.pseudo_qconst5``\r\n```",
"Hi @CarlosNacher ,\r\n\r\nI will raise a PR to fix your issue and I will update you. Thank you",
"Hi @sawantkumar ,\r\n\r\nOkay, thank you so much! It's great that we can all contribute together. I look forward to your response.",
"Hi @CarlosNacher ,\r\n\r\nSure thing. However can you please confirm if this bug can be reproduced in tf 2.16.0 or tf-nightly . ",
"Hey @sawantkumar ! I confirm it, I have replicated using tf 2.16 and tf-nightly (2.17.0.dev20240528), the output trace of the code execution is in the Colab I attached. Thank you.",
"Hello @sawantkumar , \r\n\r\nAny update on this? If there is a PR, could I havee the link to keep updated? \r\n\r\nThank you in advance!",
"Hi @CarlosNacher, this issue may be resolved by following a different work flow. \r\n\r\nHave you tried using [AI-Edge-Torch](https://github.com/google-ai-edge/ai-edge-torch)?, you can find more information here: [googleblog](https://developers.googleblog.com/en/ai-edge-torch-high-performance-inference-of-pytorch-models-on-mobile-devices/).\r\n\r\nI have actually created a simple script for converting a CV model here:\r\n\r\n```py\r\nimport torch\r\nimport torchvision\r\nimport ai_edge_torch\r\n\r\nmobilenet_model = torchvision.models.mobilenet_v3_small()\r\nsample_inputs = (torch.randn(1, 3, 224, 224),)\r\n\r\nedge_model = ai_edge_torch.convert(mobilenet_model.eval(), sample_inputs)\r\nedge_model.export(\"mobilenet_v3_small.tflite\")\r\n```\r\n\r\nIf you want to, you can actually try visualizing the result in [model-explorer](https://github.com/google-ai-edge/model-explorer) as well.\r\n\r\nPlease try them out and let us know if this resolves your issue. If you still need further help, feel free to open a new issue at the respective repo.",
"Hi @pkgoogle \r\n\r\nI didn’t know that library, thank you so much for the information! I’ll check it out and let you know, however my final application of interest is to run the model in coral TPU USB accelerator, do you know if with the tool you comment it is achievable? \r\nOn the Readme, CPU and GPU support is mentioned but not TPU. As far I know, the only thing you need to run your .tflite on TPU is that it is FULLY INT8, so I suppose that it’ll work, but the no mention of TPU confuses me.\r\n\r\nCan you give me feedback about that? Anyway, again, it’ll try, and many thanks!"
] | 2024-04-19T15:26:37 | 2024-06-11T14:34:30 | null |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Microsoft Windows 11 Home 10.0.22631 version 22631 compilation
- TensorFlow installation (pip package or built from source): `pip install tensorflow==2.15.0`
- TensorFlow library (version, if pip package or github SHA, if built from source): 2.15.0
### 2. Code
```
# Create callib data
input_op_name = 'input'
numpy_file_path = 'calibration_data.npy'
calib_data = np.load(numpy_file_path)
data_count = calib_data.shape[0]
mean = [[[[0, 0, 0]]]]
std = [[[[1, 1, 1]]]]
calib_data_dict = {}
calib_data_dict[input_op_name] = \
[
calib_data.copy(),
mean,
std,
]
# representative_dataset_gen
def representative_dataset_gen():
for idx in range(data_count):
yield_data_dict = {}
for model_input_name in [input_op_name]:
calib_data, mean, std = calib_data_dict[model_input_name]
normalized_calib_data: np.ndarray = (calib_data[idx] - mean) / std
yield_data_dict[model_input_name] = tf.cast(tf.convert_to_tensor(normalized_calib_data), tf.float32)
yield yield_data_dict
###### CONVERT
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS_INT8,
tf.lite.OpsSet.SELECT_TF_OPS,
]
converter._experimental_disable_per_channel = False # to perform "per-channel" (it's the default value though)
converter.representative_dataset = representative_dataset_gen
converter.inference_input_type = "int8"
converter.inference_output_type = "int8"
TFLITE_FILEPATH = "model_full_int.tflite"
with open(TFLITE_FILEPATH , 'wb') as w:
w.write(tflite_model)
##### INFER
# Initialize the interpreter
interpreter = tf.lite.Interpreter(
model_path=str(TFLITE_FILEPATH"),
# experimental_preserve_all_tensors=True
)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()[0]
output_details = interpreter.get_output_details()[0]
test_image = pre_processed_input.copy() # One image I have loaded (shape 1, 1024, 608, 3)
# Check if the input type is quantized, then rescale input data to int8
if input_details['dtype'] in [np.int8, np.uint8, np.int16]:
input_scale, input_zero_point = input_details["quantization"]
test_image = test_image / input_scale + input_zero_point
test_image = np.expand_dims(test_image, axis=0).astype(input_details["dtype"])
interpreter.set_tensor(input_details["index"], test_image)
interpreter.invoke()
tflite_inference = interpreter.get_tensor(output_details["index"])[0]
if output_details["dtype"] in [np.int8, np.uint8, np.int16]:
output_scale, output_zero_point = output_details['quantization']
print("Output scale:", output_scale)
print("Output zero point:", output_zero_point)
print()
tflite_inference = output_scale * (tflite_inference.astype(np.float32) - output_zero_point)
```
### 3. Failure after conversion
The conversion is successful, but the generated model is wrong: **the inferred output (`tflite_inference`) is all the same value in the fully int8 quantized model and I think it could be because quantization per-channel is performing per-batchaxis instead**:
If I inspect the .tflite model, I found that in each operation (for example convolution) the number of `scale` and `zero_point` values are the same as number of axis=0 (batch i.e 256) not the number of channels (axis=-1 i.e 512)
...
**I miss one `quantized_dimension` parameter, as stated in docs:** https://www.tensorflow.org/lite/performance/quantization_spec#per-axis_vs_per-tensor
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[
"Hi @qaletka ,\r\n\r\nI have observed the issue with the page display in Polski Language. Switching to English looks fine.\r\n\r\nCC : @MarkDaoust , For a look into into this and comment if any.",
"Hi @SuryanarayanaY,\r\n\r\nYes, the English version of the website looks good. Other versions also have problems on the home page and individual subpages.\r\n\r\nExamples:\r\n\r\n- https://www.tensorflow.org/?hl=es\r\n- https://www.tensorflow.org/lite?hl=es\r\n- https://www.tensorflow.org/?hl=id\r\n- https://www.tensorflow.org/?hl=it\r\n\r\n\r\n\r\n",
"@joefernandez manages our relationship with the translation service.",
"@SuryanarayanaY @MarkDaoust @joefernandez so has anything changed?\r\n"
] | 2024-04-19T15:26:31 | 2024-05-14T20:13:06 | null |
NONE
| null | null | null |
**Description:**
I've noticed an issue with the homepage sections on the TensorFlow website. The sections seem to be displaying incorrectly, with some elements overlapping and others not loading properly.
**Steps to Reproduce:**
1. Go to the TensorFlow website homepage (https://www.tensorflow.org/?hl=pl).
2. Scroll through the sections on the homepage.
3. Observe the layout and display issues.
**Expected Behavior:**
The homepage sections should display correctly with all elements properly formatted and visible.
**Actual Behavior:**
Some sections are overlapping, and certain elements are not loading or displaying as expected. This makes it difficult to navigate and access the content effectively.
**Screenshots/Additional Information:**
**Environment:**
Browser: [Google Chrome (124.0.6367.60 )]
Operating System: [Windows 10]
Device: [Desktop]
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Allow for short-circuiting optimization in ShapeUtil::EqualStructure.
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Allow for short-circuiting optimization in ShapeUtil::EqualStructure.
Note that operator& doesn't do short-circuiting, unlike operator&&.
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PR_kwDOArmXAs5tLsrk
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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/ad7abc01b63e0349f1010df04dc134476ee6031d.
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Update the XNNPack version.
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Update the XNNPack version.
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[XLA:GPU] Remove unused function declaration.
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[XLA:GPU] Remove unused function declaration.
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Automated Code Change
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[Question] Is it possibile to use `jit_compile=True` (XLA) when input is a string?
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[
"@miticollo No, you cannot directly use jit_compile=True (XLA) with string inputs in TensorFlow. XLA is designed for numerical computations, and strings are not directly supported. Thank you!",
"Thank u for your clarification!",
"@miticollo Thank you for your response here. Could you please move this issue to closed status if it is resolved?\r\nThank you! ",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/66074\">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/66074\">No</a>\n"
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# TL; DR
See [this notebook](https://colab.research.google.com/drive/1vtxKx9O3NJPwET6NOgU0a2mFwBf64sCA?usp=sharing) on Colab.
# Long version
Let's consider the following function:
```python
def f(s):
return s
```
Then we convert it into graph:
```python
tf_f = tf.function(f)
tf_f_xla = tf.function(f, jit_compile=True)
```
Finally run them:
```python
tf_f(tf.constant("Hello World!"))
tf_f_xla(tf.constant("Hello World!"))
```
Unfortunately the last statement fails:
```text
InvalidArgumentError: Failed copying input tensor from /job:localhost/replica:0/task:0/device:CPU:0 to /job:localhost/replica:0/task:0/device:GPU:0 in order to run __inference_f_23: Can't copy Tensor with type string to device /job:localhost/replica:0/task:0/device:GPU:0. [Op:__inference_f_23]
```
# About question
I don't think this is an issue. But since I'm learning I would want to know why this happens.
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Update compatibility constraint in BUILD file.
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Update compatibility constraint in BUILD file.
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Integrate LLVM at llvm/llvm-project@7aad1ee70f2c
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Integrate LLVM at llvm/llvm-project@7aad1ee70f2c
Updates LLVM usage to match
[7aad1ee70f2c](https://github.com/llvm/llvm-project/commit/7aad1ee70f2c)
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Avoid overflows in check whether there is enough available input space
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Avoid overflows in check whether there is enough available input space
bytes_to_write can overflow int32 in a way that makes it look smaller than
AvailableInputSpace(). Instead do the comparison using size_t.
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Add nVidia-specific pass for rewriting sparse dots with RHS sparse operand
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Add nVidia-specific pass for rewriting sparse dots with RHS sparse operand
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PR #11654: [ROCm] Remove AllocateOwnedArray from rocm code.
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PR #11654: [ROCm] Remove AllocateOwnedArray from rocm code.
Imported from GitHub PR https://github.com/openxla/xla/pull/11654
Copybara import of the project:
--
30ce2b21d9e9ea7e8c474441f0b3745eef5ecd5b by Zoran Jovanovic <[email protected]>:
[ROCm] Remove AllocateOwnedArray from rocm code.
Merging this change closes #11654
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11654 from ROCm:ci_rocm-fix-AllocateOwnedArray 30ce2b21d9e9ea7e8c474441f0b3745eef5ecd5b
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Enable f8.
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Enable f8.
I decided to port the conversion function from ml_dtypes (with one fix
that's not yet submitted there) instead of the legacy XLA logic, since
the latter isn't generic and quite a bit more code.
Tested with convert_test_gpu, float8_test_gpu, JAX test suite.
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multihost_hlo_runner: support host parameter buffers
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multihost_hlo_runner: support host parameter buffers
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functional_hlo_runner: take PjRtExecutable in CopyArgumentsToDevice (NFC)
We will soon need the executable to obtain its CompilationOptions.
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Re-enable cublas fallback test (added result to test DB)
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Re-enable cublas fallback test (added result to test DB)
This change makes sure that autotuning is not triggered on A100.
Testing on other GPUs will require adding more autotuning DB entries, or re-writing the test entirely.
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[XLA:GPU] Fix a bug in ProvideParameters
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[XLA:GPU] Fix a bug in ProvideParameters
It did not handle the case when the subgraph has more than one root.
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Basic regression: Predict fuel efficiency Probelm
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"@illaoiiiii,\r\nAs you mentioned the document regression.ipynb is failing with the tensorflow v2.15. But when I tried to execute the same official document with the latest tensorflow stable version 2.16, the code was executed without any issue/error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/4729cef28ae6e88de02c82f0645e1b7f/regression.ipynb). Thank you!",
"@tilakrayal \r\ni just obey the tutorial and i use the colab , colab automatically instal the v2.15,I am learning the TensorFlow framework,maybe is not a very serious bug or problem",
"@illaoiiiii,\r\nYeah, It is not a bug and moreover the official docs will be upgraded to the latest stable version in upcoming days. As this issue is resolved in tensorflow v2.16, could you please feel free to move this issue to closed status. Thank you!",
"Hello :)",
"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/66057\">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/66057\">No</a>\n"
] | 2024-04-19T08:24:04 | 2024-05-09T01:48:44 | 2024-05-09T01:48:40 |
NONE
| null | null | null |
### Issue type
Documentation Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
google colab
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
In the tutorial beginner part Regression part ,the document have some problem shows like below
The code after this segment is not running. But i use ChatGPT to fix it .and the right code below
i think this is a easy problem and i hope official can fix it.
### Standalone code to reproduce the issue
```shell
the url is below
https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/keras/regression.ipynb
```
### Relevant log output
```shell
the bug log is below
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-16-87f1bea74834> in <cell line: 1>()
----> 1 normalizer.adapt(np.array(train_features))
11 frames
/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/constant_op.py in convert_to_eager_tensor(value, ctx, dtype)
101 dtype = dtypes.as_dtype(dtype).as_datatype_enum
102 ctx.ensure_initialized()
--> 103 return ops.EagerTensor(value, ctx.device_name, dtype)
104
105
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type int).
```
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Use a stream from GetDeviceToHostStream instead of BorrowStreamFromPool
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Support restoring tfhub modules under ParameterServerStrategy
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Support restoring tfhub modules under ParameterServerStrategy
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#tf-data #tf-data `index_flat_map` supports nested lists.
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NONE
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#tf-data #tf-data `index_flat_map` supports nested lists.
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PR_kwDOArmXAs5tHDEc
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[XLA] Use URem instead of SRem for sub-byte index calculation
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NONE
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[XLA] Use URem instead of SRem for sub-byte index calculation
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PR_kwDOArmXAs5tG8yU
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Stop using DeviceMemoryAllocator to create and cache streams in AutotuneConfig.
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NONE
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Stop using DeviceMemoryAllocator to create and cache streams in AutotuneConfig.
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NONE
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This is a no-op to github.
Reverts changelist 618184122
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NONE
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This is a no-op to github.
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PR_kwDOArmXAs5tGxHi
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Get the Stream from the AutotuneConfig rather than the allocator in GemmFusionAutotuner.
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NONE
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Get the Stream from the AutotuneConfig rather than the allocator in GemmFusionAutotuner.
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PR_kwDOArmXAs5tGuFn
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[PJRT] Change CreateErrorBuffer to take PjRtMemorySpace rather than PjRtDevice.
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NONE
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[PJRT] Change CreateErrorBuffer to take PjRtMemorySpace rather than PjRtDevice.
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Add vlog in wrapper for demo
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NONE
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Add vlog in wrapper for demo
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PR_kwDOArmXAs5tGqtJ
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Disable GPU load tracker.
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Disable GPU load tracker.
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Integrate LLVM at llvm/llvm-project@7aad1ee70f2c
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Integrate LLVM at llvm/llvm-project@7aad1ee70f2c
Updates LLVM usage to match
[7aad1ee70f2c](https://github.com/llvm/llvm-project/commit/7aad1ee70f2c)
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Add back TPU device check in MlirBridgePass::GetPassState()
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NONE
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Add back TPU device check in MlirBridgePass::GetPassState()
This unblocks some graphs that target TPU yet without replication.
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Add PrivacyInfo.xcprivacy for TFLite ios xcframeworks. Apple is enforcing apps and sdks to provide [Privacy Manifests files](https://developer.apple.com/documentation/bundleresources/privacy_manifest_files) and the enforcement will go into effect in three weeks.
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PR_kwDOArmXAs5tGSA8
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Integrate StableHLO at openxla/stablehlo@52172972
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Integrate StableHLO at openxla/stablehlo@52172972
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PR_kwDOArmXAs5tGMxP
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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/3703d27cfb6a542a84d1df2097bbe8166003f3f8.
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Automated Code Change
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Flatbuffer export memory optimization for large models.
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"Hi @jakubdolejs \r\n\r\nThere could be a bunch of reasons behind performance issues on pixel 4a compared to the iPhone 12. When you use Core ML delegate on the iPhone, it is using NPU which is much faster compared to the gpu on pixel 4a . Can you also benchmark your model on the pixel 4a using [tensorflow profiler](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark#parameters) which will give you detailed information regarding your model execution like how many partitions of the model are created before execution and how many layers are falling back to the cpu in case of gpu delegate. Also pixel 4a's GPU is not optimised for fp32 calculations , it is only optimised for fp16 operations , so that could be the culprit behind poor gpu performance while using fp32. Can you share the tensorflow lite profiler results once you benchmark your tflite model on the pixel using profiler.",
"Thank you @sawantkumar. I'll try the profiler and upload the results here.",
"Hello @sawantkumar,\r\n\r\nI ran the benchmark tool with different options on the float32 and float16 models. Please see the attached results. The file names ending with `gpu` are from runs that had the `--use_gpu` flag set to `true`. The ones ending with `nnapi` had the `--use_nnapi` flag set to `true`. The commands used to invoke the tests are included in the txt files.\r\n\r\nPlease let me know if you see anything unexpected in the results.\r\n\r\n[fp16_gpu.txt](https://github.com/tensorflow/tensorflow/files/15038893/fp16_gpu.txt)\r\n[fp16_nnapi.txt](https://github.com/tensorflow/tensorflow/files/15038894/fp16_nnapi.txt)\r\n[fp16.txt](https://github.com/tensorflow/tensorflow/files/15038895/fp16.txt)\r\n[fp32_gpu.txt](https://github.com/tensorflow/tensorflow/files/15038897/fp32_gpu.txt)\r\n[fp32_nnapi.txt](https://github.com/tensorflow/tensorflow/files/15038898/fp32_nnapi.txt)\r\n[fp32.txt](https://github.com/tensorflow/tensorflow/files/15038899/fp32.txt)\r\n",
"Hello @jakubdolejs,\r\n\r\nI've reviewed the log files, and everything appears as expected, except for the discrepancies noted in the files fp32_gpu.txt and fp16_gpu.txt. While the average latency GPU numbers from the TFLite profiler seem almost identical for both fp16 and fp32 models, the logs from your Android code indicate a clear difference between fp32 and fp16 GPU numbers. To facilitate a more accurate comparison, could you also profile your models on an iPhone 12 using TFLite Profiler for iOS?\r\n\r\nRegarding the inconsistency in inference numbers during the first few runs on the Pixel 4a, could you integrate a few warm-up loops in your Android code before benchmarking and let me know the results? Please feel free to reach out if you encounter any difficulties during this process.",
"Thank you @sawantkumar. I'll try the iOS app and report back. I really appreciate you helping me through this.",
"Hi @sawantkumar,\r\n\r\nHere are the benchmarks from iOS (iPhone 12 mini). It looks like the app runs the inference on the UI thread. For all the models I get this warning in the log: `This method should not be called on the main thread as it may lead to UI unresponsiveness.`. I redacted the above messages from the log output for brevity.\r\n\r\n### FP16 Model on CPU\r\n```\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Min num runs: [50]\r\nINFO: Inter-run delay (seconds): [-1]\r\nINFO: Num threads: [4]\r\nINFO: Benchmark name: [arc_psd_001_fp16_benchmark]\r\nINFO: Min warmup runs: [1]\r\nINFO: Graph: [/private/var/containers/Bundle/Application/1D088489-D83E-4A30-B2A8-26180514520A/TFLiteBenchmark.app/ARC_PSD-001_1.1.122_bst_yl80201_float16.tflite]\r\nINFO: Input layers: [in0]\r\nINFO: Input shapes: [1,640,640,3]\r\nINFO: #threads used for CPU inference: [4]\r\nINFO: Loaded model /private/var/containers/Bundle/Application/1D088489-D83E-4A30-B2A8-26180514520A/TFLiteBenchmark.app/ARC_PSD-001_1.1.122_bst_yl80201_float16.tflite\r\nInitialized TensorFlow Lite runtime.\r\nINFO: Initialized TensorFlow Lite runtime.\r\nWARN: Tensor # 0 is named inputs_0 but flags call it in0\r\nCreated TensorFlow Lite XNNPACK delegate for CPU.\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nINFO: The input model file size (MB): 22.4319\r\nINFO: Initialized session in 150.857ms.\r\nINFO: Running benchmark for at least 1 iterations and at least 0.5 seconds but terminate if exceeding 150 seconds.\r\nINFO: count=3 first=206791 curr=185620 min=185245 max=206791 avg=192552 std=10069\r\n\r\nINFO: Running benchmark for at least 50 iterations and at least 1 seconds but terminate if exceeding 150 seconds.\r\n\r\nINFO: count=50 first=189002 curr=222573 min=189002 max=222573 avg=208663 std=9484\r\n\r\nINFO: Inference timings in us: Init: 150857, First inference: 206791, Warmup (avg): 192552, Inference (avg): 208663\r\nINFO: Note: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.\r\nINFO: Memory footprint delta from the start of the tool (MB): init=87.2656 overall=159.392\r\n```\r\n\r\n### FP16 model with CoreML delegate:\r\n```\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Min num runs: [50]\r\nINFO: Inter-run delay (seconds): [-1]\r\nINFO: Num threads: [4]\r\nINFO: Benchmark name: [arc_psd_001_fp16_benchmark]\r\nINFO: Min warmup runs: [1]\r\nINFO: Graph: [/private/var/containers/Bundle/Application/50D22DE8-EDBA-4763-8298-7187B6D7FD12/TFLiteBenchmark.app/ARC_PSD-001_1.1.122_bst_yl80201_float16.tflite]\r\nINFO: Input layers: [in0]\r\nINFO: Input shapes: [1,640,640,3]\r\nINFO: Use CoreML: [1]\r\nINFO: #threads used for CPU inference: [4]\r\nINFO: Loaded model /private/var/containers/Bundle/Application/50D22DE8-EDBA-4763-8298-7187B6D7FD12/TFLiteBenchmark.app/ARC_PSD-001_1.1.122_bst_yl80201_float16.tflite\r\nInitialized TensorFlow Lite runtime.\r\nINFO: Initialized TensorFlow Lite runtime.\r\nWARN: Tensor # 0 is named inputs_0 but flags call it in0\r\ncoreml_version must be 2 or 3. Setting to 3.\r\nINFO: COREML delegate created.\r\nCoreML delegate: 215 nodes delegated out of 384 nodes, with 15 partitions.\r\nINFO: CoreML delegate: 215 nodes delegated out of 384 nodes, with 15 partitions.\r\nINFO: Explicitly applied COREML delegate, and the model graph will be partially executed by the delegate w/ 13 delegate kernels.\r\nCreated TensorFlow Lite XNNPACK delegate for CPU.\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nINFO: The input model file size (MB): 22.4319\r\nINFO: Initialized session in 2540.03ms.\r\nINFO: Running benchmark for at least 1 iterations and at least 0.5 seconds but terminate if exceeding 150 seconds.\r\n```\r\nNote that when running the FP16 model with CoreML delegate I got a `EXC_BAD_ACCESS` error here: https://github.com/tensorflow/tensorflow/blob/4615e171a2517812d32a3a4b66a838076d3e339b/tensorflow/lite/tools/benchmark/ios/TFLiteBenchmark/TFLiteBenchmark/BenchmarkViewController.mm#L133\r\n\r\nThat's why the log is truncated.\r\n\r\n### FP32 model on CPU:\r\n```\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Min num runs: [50]\r\nINFO: Inter-run delay (seconds): [-1]\r\nINFO: Num threads: [4]\r\nINFO: Benchmark name: [arc_psd_001_fp32_benchmark]\r\nINFO: Min warmup runs: [1]\r\nINFO: Graph: [/private/var/containers/Bundle/Application/6731B3C4-1818-4B08-977B-9D7C0C8DBD81/TFLiteBenchmark.app/ARC_PSD-001_1.1.122_bst_yl80201_float32.tflite]\r\nINFO: Input layers: [in0]\r\nINFO: Input shapes: [1,640,640,3]\r\nINFO: #threads used for CPU inference: [4]\r\nINFO: Loaded model /private/var/containers/Bundle/Application/6731B3C4-1818-4B08-977B-9D7C0C8DBD81/TFLiteBenchmark.app/ARC_PSD-001_1.1.122_bst_yl80201_float32.tflite\r\nInitialized TensorFlow Lite runtime.\r\nINFO: Initialized TensorFlow Lite runtime.\r\nWARN: Tensor # 0 is named inputs_0 but flags call it in0\r\nCreated TensorFlow Lite XNNPACK delegate for CPU.\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nINFO: The input model file size (MB): 44.7677\r\nINFO: Initialized session in 195.673ms.\r\nINFO: Running benchmark for at least 1 iterations and at least 0.5 seconds but terminate if exceeding 150 seconds.\r\nINFO: count=3 first=200505 curr=187763 min=185514 max=200505 avg=191261 std=6600\r\n\r\nINFO: Running benchmark for at least 50 iterations and at least 1 seconds but terminate if exceeding 150 seconds.\r\n\r\nINFO: count=50 first=190545 curr=222754 min=190545 max=223493 avg=210610 std=9386\r\n\r\nINFO: Inference timings in us: Init: 195673, First inference: 200505, Warmup (avg): 191261, Inference (avg): 210610\r\nINFO: Note: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.\r\nINFO: Memory footprint delta from the start of the tool (MB): init=44.5 overall=116.626\r\n```\r\n\r\n### FP32 model with CoreML delegate:\r\n```\r\nINFO: Log parameter values verbosely: [0]\r\nINFO: Min num runs: [50]\r\nINFO: Inter-run delay (seconds): [-1]\r\nINFO: Num threads: [4]\r\nINFO: Benchmark name: [arc_psd_001_fp32_benchmark]\r\nINFO: Min warmup runs: [1]\r\nINFO: Graph: [/private/var/containers/Bundle/Application/8336E5F7-0CCF-4333-9BD9-4CC385A1B930/TFLiteBenchmark.app/ARC_PSD-001_1.1.122_bst_yl80201_float32.tflite]\r\nINFO: Input layers: [in0]\r\nINFO: Input shapes: [1,640,640,3]\r\nINFO: Use CoreML: [1]\r\nINFO: #threads used for CPU inference: [4]\r\nINFO: Loaded model /private/var/containers/Bundle/Application/8336E5F7-0CCF-4333-9BD9-4CC385A1B930/TFLiteBenchmark.app/ARC_PSD-001_1.1.122_bst_yl80201_float32.tflite\r\nInitialized TensorFlow Lite runtime.\r\nINFO: Initialized TensorFlow Lite runtime.\r\nWARN: Tensor # 0 is named inputs_0 but flags call it in0\r\ncoreml_version must be 2 or 3. Setting to 3.\r\nINFO: COREML delegate created.\r\nCoreML delegate: 215 nodes delegated out of 253 nodes, with 15 partitions.\r\nINFO: CoreML delegate: 215 nodes delegated out of 253 nodes, with 15 partitions.\r\nINFO: Explicitly applied COREML delegate, and the model graph will be partially executed by the delegate w/ 13 delegate kernels.\r\nCreated TensorFlow Lite XNNPACK delegate for CPU.\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nINFO: The input model file size (MB): 44.7677\r\nINFO: Initialized session in 3285.41ms.\r\nINFO: Running benchmark for at least 1 iterations and at least 0.5 seconds but terminate if exceeding 150 seconds.\r\nINFO: count=12 first=66600 curr=41255 min=40902 max=66600 avg=43699.3 std=6953\r\n\r\nINFO: Running benchmark for at least 50 iterations and at least 1 seconds but terminate if exceeding 150 seconds.\r\n\r\nINFO: count=50 first=40886 curr=41157 min=40386 max=41937 avg=41173.8 std=325\r\n\r\nINFO: Inference timings in us: Init: 3285407, First inference: 66600, Warmup (avg): 43699.3, Inference (avg): 41173.8\r\nINFO: Note: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.\r\nINFO: Memory footprint delta from the start of the tool (MB): init=230.595 overall=303.407\r\n```",
"Hi @sawantkumar,\r\n\r\nI ran a test on the Pixel 4a with the different model combinations. I ran 50 iterations but this time I included a warmup of 10 inference runs. The first few runs are still very slow. Is this to be expected?\r\n\r\nHow do you recommend the warmup is handled in production? The app I'm building will need to run inference on a few images at a time but it shouldn't take 3 seconds per image.\r\n\r\nHere is the test function that produced the results in [this CSV file](https://github.com/tensorflow/tensorflow/files/15044923/inference_speed.csv):\r\n\r\n```kotlin\r\n@Test\r\nfun testInferenceSpeed() {\r\n val context = InstrumentationRegistry.getInstrumentation().context\r\n val assetManager = context.assets\r\n // Input serialized as a float array in JSON\r\n val jsonFile = \"face_on_iPad_001.jpg-flat.json\"\r\n assetManager.open(jsonFile).use { inputStream ->\r\n val json = inputStream.bufferedReader().use { it.readText() }\r\n val floatArray = Json.decodeFromString<FloatArray>(json)\r\n // Models – float32 and float16\r\n val models = mapOf(Pair(\"fp32\", \"ARC_PSD-001_1.1.122_bst_yl80201_float32.tflite\"), Pair(\"fp16\",\"ARC_PSD-001_1.1.122_bst_yl80201_float16.tflite\"))\r\n val options = arrayOf(\"gpu\", \"nnapi\", \"cpu\", \"xnnpack\")\r\n val table = mutableMapOf<String,Array<Long>>()\r\n val runCount = 50\r\n val warmupRunCount = 10\r\n for (model in models.entries) {\r\n assetManager.open(model.value).use { modelInputStream ->\r\n // Copy the model from assets to the cache directory\r\n val modelFile = File(context.cacheDir, model.value)\r\n modelFile.outputStream().use { outputStream ->\r\n modelInputStream.copyTo(outputStream)\r\n }\r\n for (option in options) {\r\n val interpreterOptions = InterpreterApi.Options()\r\n val compatibilityList = CompatibilityList()\r\n when (option) {\r\n \"gpu\" -> {\r\n compatibilityList.use {\r\n if (it.isDelegateSupportedOnThisDevice) {\r\n interpreterOptions.addDelegate(\r\n GpuDelegate(\r\n it.bestOptionsForThisDevice\r\n )\r\n )\r\n }\r\n }\r\n }\r\n \"nnapi\" -> {\r\n if (android.os.Build.VERSION.SDK_INT >= android.os.Build.VERSION_CODES.P) {\r\n interpreterOptions.addDelegate(NnApiDelegate())\r\n interpreterOptions.useNNAPI = true\r\n }\r\n }\r\n \"cpu\" -> {\r\n interpreterOptions.numThreads =\r\n Runtime.getRuntime().availableProcessors()\r\n interpreterOptions.useXNNPACK = false\r\n }\r\n\r\n \"xnnpack\" -> {\r\n interpreterOptions.numThreads =\r\n Runtime.getRuntime().availableProcessors()\r\n interpreterOptions.useXNNPACK = true\r\n }\r\n else -> throw IllegalArgumentException(\"Unknown option: $option\")\r\n }\r\n InterpreterApi.create(modelFile, interpreterOptions)\r\n .use { interpreterApi ->\r\n for (i in 0 until warmupRunCount) {\r\n interpreterApi.allocateTensors()\r\n val input = FloatBuffer.wrap(floatArray)\r\n val output =\r\n FloatBuffer.allocate(5 * 8400).also { it.rewind() }\r\n interpreterApi.run(input, output)\r\n }\r\n val times = mutableListOf<Long>()\r\n for (i in 0 until runCount) {\r\n interpreterApi.allocateTensors()\r\n val input = FloatBuffer.wrap(floatArray)\r\n val output =\r\n FloatBuffer.allocate(5 * 8400).also { it.rewind() }\r\n val time = measureTimeMillis {\r\n interpreterApi.run(input, output)\r\n }\r\n times.add(time)\r\n }\r\n table.getOrPut(\"${model.key}-${option}\") { times.toTypedArray() }\r\n }\r\n }\r\n }\r\n }\r\n var csv = table.keys.map { \"\\\"$it\\\"\" }.joinToString(\",\")\r\n val rowCount = table.values.map { it.size }.min()\r\n for (i in 0 until rowCount) {\r\n csv += \"\\n\"\r\n csv += table.keys.map { table[it]!![i].toString() }.joinToString(\",\")\r\n }\r\n File(context.cacheDir, \"inference_speed.csv\").outputStream().use { fileOutputStream ->\r\n OutputStreamWriter(fileOutputStream).use { outputStreamWriter ->\r\n outputStreamWriter.write(csv)\r\n }\r\n }\r\n }\r\n}\r\n```",
"Hi @jakubdolejs,\r\n\r\nApologies for the delay; I wasn't available over the weekend. After analyzing the iOS numbers, it's evident that the Core ML delegate on the iPhone 12 Mini outperforms the GPU delegate on the Pixel 4a by approximately 7x for fp32 models. Additionally, the iPhone 12 Mini's CPU executes models roughly 2x faster than the Pixel 4a's CPU. These results clearly indicate that the iPhone 12 Mini offers faster model execution both on CPU and GPU compared to the Pixel 4a.\r\n\r\nHowever, if you're aiming to maximize performance on your Pixel device, consider utilizing its DSP. Please ensure third-party access to the DSP is permitted on the Pixel phone, then optimize performance using [SNPE](https://developer.qualcomm.com/sites/default/files/docs/snpe/overview.html) provided by Qualcomm.",
"Also regarding handling GPU warm up runs in production , from my experience i have also seen that the first few inference runs on Android TFLite GPU can be slower because of initialization Overhead that is when you run inference for first few times, TensorFlow Lite needs to initialize various components, such as loading the model, allocating memory, and setting up the GPU context. This initialization process can take some time, causing the first few inferences to be slower. To handle such scenarios in production you can perform the GPU warm-up runs during the app's startup. This could be something like an inference loop of 50 or 100 iterations on dummy data on app's startup . Please let me know if you have any further issues or questions.",
"Hello @sawantkumar, \r\n\r\nI've done some more testing and profiling. I built an Android app that lets me change between the FP16 and FP32 models and toggle the different options. Here are my findings:\r\n\r\n- The initial slowdown only happens on some devices (e.g., Pixel 4a, Elo touch). On some devices (Pixel 6, Galaxy Tab S6 Lite) the inference runs at consistent speed from start to finish with any given options.\r\n- The initial slowdown happens regardless of which delegates are used.\r\n- I tried using the same model converted to NCNN and it runs at consistent speed on any device. The speed is comparable to the TfLite model after the \"warmup\".\r\n- The slower devices I mentioned like Pixel 4a run first 50 or so iterations in about 3500 ms, after which the speed increases to about 400–500 ms per inference. Even this is not consistent. Sometimes the inference keeps running slowly at over 3000 ms even after hundreds of iterations. For comparison, the faster devices run inference at roughly 300 ms from the get go.\r\n- On iOS, the story is slightly different. It takes about 2 seconds to load the model but afterwards the inference runs consistently at about 70 ms (with the CoreML delegate).\r\n\r\nFrom using NCNN I can see that even the underpowered devices don't require a warmup to run at acceptable speeds. I believe there may be a bug in TfLite. It shouldn't take 3 minutes to \"warm up\".\r\n\r\nWould you like me to file a separate issue with a bug report or can you escalate this one?",
"Hi @jakubdolejs ,\r\n\r\nWhen it comes to speed and performance, NCNN is generally considered to be faster than TFLite in many scenarios so your results are somewhat as expected. However i will replicate the issue on my available pixel phone using tflite and i will get back to you . I don't think there is a need to file a separate issue yet.",
"Hi @sawantkumar,\r\n\r\nJust checking whether you were able to repeat the issue on your device.\r\n\r\nI'm able to consistently reproduce the behaviour on Google Pixel 4a. I got a colleague to try it on a newer Pixel device (6 I believe). It runs slower than the same model converted to NCNN but fast enough for my needs. The newer Pixel also doesn't need any warmup. It runs at the same speed from the get go.\r\n\r\nCheers,\r\nJakub",
"Hi @jakubdolejs ,\r\n\r\nI couldn't get my hands on a pixel 4a so i was not able to fully replicate the issue. @pkgoogle can you please take a look at this.",
"Hi @jakubdolejs, I am also not able to use a real device to test this out. I did not see an uploaded model file of both types you are testing. If you can share that, that'll be great and help us zero in on what's going on here. Thanks.",
"Hi @pkgoogle,\r\n\r\nPlease see the attached zip archive with the model file. You can find the code to test the inference speed [in my post from Apr 19](https://github.com/tensorflow/tensorflow/issues/66025#issuecomment-2067175516).\r\n\r\n[ARC_PSD-001_1.1.122_bst_yl80201_float16.tflite.zip](https://github.com/tensorflow/tensorflow/files/15491204/ARC_PSD-001_1.1.122_bst_yl80201_float16.tflite.zip)\r\n\r\nPlease let me know if you have any questions.",
"@jakubdolejs Thanks for sharing! It always helps immensely.\r\n\r\n@qukhan, can you please take a look? Thanks."
] | 2024-04-18T20:40:06 | 2024-05-30T17:00:25 | null |
NONE
| null | null | null |
### Issue type
Performance
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
Google Pixel 4a running Android 13
### 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?
I'm running inference on Yolov8-based tflite model on Android using the Interpreter API. I noticed that the first 30 or so calls to the `Interpreter.run()` function take much longer than the subsequent calls. The difference is quite marked, starting at about 3500ms per run and ending at about 500ms.
I thought perhaps it was something about the input data so I tried a test with running the same call with the same input 100 times in a loop. Same behaviour, the first handful of inference runs take around 3 seconds, slowly speeding up to about 500–700ms by the 100th iteration.
I wanted to find out whether there is a specific combination of the interpreter options causing this behaviour so I wrote a test matrix initialising interpreters with different options:
- Using GPU delegate
- Using Google Play Services runtime
- Using model with precision reduced from float32 to float16
- Using bundled runtime
- Using model with precision reduced from float32 to float16
- Using NNAPI delegate
- Using Google Play Services runtime
- Using model with precision reduced from float32 to float16
- Using bundled runtime
- Using model with precision reduced from float32 to float16
- Using CPU with XNNPACK
- Using Google Play Services runtime
- Using model with precision reduced from float32 to float16
- Using bundled runtime
- Using model with precision reduced from float32 to float16
- Using CPU without XNNPACK
- Using Google Play Services runtime
- Using model with precision reduced from float32 to float16
- Using bundled runtime
- Using model with precision reduced from float32 to float16
There doesn't seem to be any difference whichever combination runs first takes suspicious amount of time for the first handful of inference runs. Sometimes the time never decreases and all the inference runs for the given configuration take a very long time (~3 seconds).
I'm including the code using the bundled runtime. The Play Services runtime times were in line with the bundled runtime.
The device (Google Pixel 4a) is used only for development. There are no other apps installed aside from the test app and whatever was pre-installed on the phone. The device wasn't connected to the internet while running the test.
## iOS comparison
In comparison, version 2.14.0 of TfLite for Swift (latest available on CocoaPods) using the CoreML delegate runs inference on the **same input** using the **same model** in 70ms on iPhone 12.
### Standalone code to reproduce the issue
```shell
fun testInferenceSpeed() {
val context = InstrumentationRegistry.getInstrumentation().context
val assetManager = context.assets
// Input serialized as a float array in JSON
val jsonFile = "face_on_iPad_001.jpg-flat.json"
assetManager.open(jsonFile).use { inputStream ->
val json = inputStream.bufferedReader().use { it.readText() }
val floatArray = Json.decodeFromString<FloatArray>(json)
// Models – float32 and float16
val models = arrayOf("ARC_PSD-001_1.1.122_bst_yl80201_float32.tflite", "ARC_PSD-001_1.1.122_bst_yl80201_float16.tflite")
val options = arrayOf("gpu", "nnapi", "cpu", "xnnpack")
for (model in models) {
assetManager.open(model).use { modelInputStream ->
// Copy the model from assets to the cache directory
val modelFile = File(context.cacheDir, model)
modelFile.outputStream().use { outputStream ->
modelInputStream.copyTo(outputStream)
}
for (option in options) {
val interpreterOptions = InterpreterApi.Options()
val compatibilityList = CompatibilityList()
when (option) {
"gpu" -> {
compatibilityList.use {
if (it.isDelegateSupportedOnThisDevice) {
interpreterOptions.addDelegate(
GpuDelegate(
it.bestOptionsForThisDevice
)
)
}
}
}
"nnapi" -> {
if (android.os.Build.VERSION.SDK_INT >= android.os.Build.VERSION_CODES.P) {
interpreterOptions.addDelegate(NnApiDelegate())
interpreterOptions.useNNAPI = true
}
}
"cpu" -> {
interpreterOptions.numThreads =
Runtime.getRuntime().availableProcessors()
interpreterOptions.useXNNPACK = false
}
"xnnpack" -> {
interpreterOptions.numThreads =
Runtime.getRuntime().availableProcessors()
interpreterOptions.useXNNPACK = true
}
else -> throw IllegalArgumentException("Unknown option: $option")
}
InterpreterApi.create(modelFile, interpreterOptions)
.use { interpreterApi ->
val times = mutableListOf<Long>()
for (i in 0 until 100) {
interpreterApi.allocateTensors()
val input = FloatBuffer.wrap(floatArray)
val output =
FloatBuffer.allocate(5 * 8400).also { it.rewind() }
val time = measureTimeMillis {
interpreterApi.run(input, output)
}
times.add(time)
}
Log.d(
TAG,
"Model: $model, Option: $option, Inference times (ms): [${times.map { it.toString()+"ms" }.joinToString()}], Average inference time: ${times.average()} ms"
)
}
}
}
}
}
}
```
### Relevant log output
```shell
Model: ARC_PSD-001_1.1.122_bst_yl80201_float32.tflite, Option: gpu, Inference times (ms): [2502ms, 3011ms, 2987ms, 2723ms, 3529ms, 4245ms, 3387ms, 4510ms, 4133ms, 4034ms, 4015ms, 3307ms, 3207ms, 3240ms, 2718ms, 2978ms, 2985ms, 3357ms, 2751ms, 2969ms, 2942ms, 3028ms, 2916ms, 3029ms, 4428ms, 2727ms, 4982ms, 4320ms, 3211ms, 2980ms, 4010ms, 3239ms, 2712ms, 3974ms, 3994ms, 3999ms, 3997ms, 3047ms, 3687ms, 3744ms, 2972ms, 2944ms, 3709ms, 3936ms, 3971ms, 3998ms, 3315ms, 4495ms, 3285ms, 4655ms, 2758ms, 3307ms, 4880ms, 4912ms, 3599ms, 2750ms, 2004ms, 2643ms, 3383ms, 3372ms, 1664ms, 3297ms, 2969ms, 1714ms, 2834ms, 3381ms, 1764ms, 2303ms, 1715ms, 3314ms, 3379ms, 1434ms, 3221ms, 2842ms, 1783ms, 1784ms, 1418ms, 1618ms, 1400ms, 1777ms, 1960ms, 1962ms, 1471ms, 2355ms, 2883ms, 1494ms, 2806ms, 2281ms, 2482ms, 2915ms, 1504ms, 2772ms, 3376ms, 1753ms, 3300ms, 1748ms, 2584ms, 3377ms, 3384ms, 1648ms], Average inference time: 3021.08 ms
Model: ARC_PSD-001_1.1.122_bst_yl80201_float32.tflite, Option: nnapi, Inference times (ms): [2288ms, 2105ms, 1637ms, 2280ms, 2085ms, 1695ms, 1634ms, 1759ms, 1637ms, 2006ms, 2210ms, 2018ms, 2050ms, 1979ms, 1698ms, 2201ms, 2105ms, 1989ms, 2040ms, 1966ms, 2034ms, 1970ms, 2031ms, 1970ms, 2033ms, 1968ms, 2034ms, 1966ms, 1763ms, 2160ms, 2077ms, 1987ms, 2040ms, 1966ms, 2033ms, 1859ms, 2106ms, 1993ms, 2041ms, 1965ms, 1826ms, 2117ms, 2073ms, 1979ms, 2041ms, 1969ms, 1632ms, 2109ms, 2212ms, 2024ms, 1362ms, 1284ms, 1970ms, 1806ms, 1212ms, 1800ms, 1231ms, 1452ms, 1465ms, 1128ms, 1185ms, 1519ms, 1246ms, 1824ms, 1224ms, 1719ms, 1234ms, 1964ms, 1133ms, 1973ms, 1689ms, 1241ms, 1890ms, 1194ms, 1187ms, 1108ms, 1089ms, 1091ms, 1086ms, 1084ms, 958ms, 1021ms, 1009ms, 999ms, 964ms, 1025ms, 1041ms, 980ms, 850ms, 1082ms, 1091ms, 976ms, 960ms, 1021ms, 1019ms, 991ms, 958ms, 850ms, 1008ms, 873ms], Average inference time: 1614.26 ms
Model: ARC_PSD-001_1.1.122_bst_yl80201_float32.tflite, Option: cpu, Inference times (ms): [1445ms, 1504ms, 1364ms, 1337ms, 1383ms, 1350ms, 1364ms, 1365ms, 1354ms, 1413ms, 1403ms, 1310ms, 1336ms, 1823ms, 1355ms, 1728ms, 1450ms, 1492ms, 1383ms, 1274ms, 1370ms, 1251ms, 1719ms, 1800ms, 1539ms, 1546ms, 1722ms, 1390ms, 1394ms, 1330ms, 1338ms, 1373ms, 1362ms, 1424ms, 1604ms, 1316ms, 1431ms, 1313ms, 1381ms, 1265ms, 1449ms, 1663ms, 1354ms, 1372ms, 1358ms, 1419ms, 1356ms, 1355ms, 1310ms, 1430ms, 1346ms, 1304ms, 1405ms, 1315ms, 1816ms, 1320ms, 1397ms, 1311ms, 1393ms, 1345ms, 1416ms, 1375ms, 1370ms, 1373ms, 1274ms, 1365ms, 1433ms, 1362ms, 1352ms, 1304ms, 1351ms, 1337ms, 1438ms, 1401ms, 1369ms, 1365ms, 1633ms, 1670ms, 1396ms, 1657ms, 1367ms, 1404ms, 1373ms, 1439ms, 1387ms, 1371ms, 1339ms, 1411ms, 1416ms, 1370ms, 1483ms, 1389ms, 1341ms, 1402ms, 1320ms, 1370ms, 1424ms, 1479ms, 1520ms, 1308ms], Average inference time: 1414.73 ms
Model: ARC_PSD-001_1.1.122_bst_yl80201_float32.tflite, Option: xnnpack, Inference times (ms): [1159ms, 1131ms, 1130ms, 1130ms, 1130ms, 1131ms, 1130ms, 1122ms, 1130ms, 1130ms, 1130ms, 1131ms, 1130ms, 1130ms, 1130ms, 1130ms, 1130ms, 1129ms, 1131ms, 1130ms, 1130ms, 1131ms, 1131ms, 1130ms, 1130ms, 1130ms, 1130ms, 1132ms, 1130ms, 1130ms, 1130ms, 1130ms, 1131ms, 1130ms, 1130ms, 1130ms, 1130ms, 1131ms, 1130ms, 1130ms, 1130ms, 1130ms, 1130ms, 1130ms, 1130ms, 1131ms, 1129ms, 1130ms, 1131ms, 1130ms, 1129ms, 1129ms, 1131ms, 1130ms, 1130ms, 1129ms, 1131ms, 1130ms, 1130ms, 1129ms, 1130ms, 1130ms, 1131ms, 1130ms, 1129ms, 1129ms, 1130ms, 1130ms, 1130ms, 1129ms, 1130ms, 1134ms, 1129ms, 1131ms, 1130ms, 1129ms, 1130ms, 1130ms, 1130ms, 1131ms, 1129ms, 1131ms, 1130ms, 1129ms, 1130ms, 1130ms, 1130ms, 1130ms, 1130ms, 1131ms, 1129ms, 1130ms, 1130ms, 1130ms, 1130ms, 1130ms, 1131ms, 1129ms, 1131ms, 1129ms], Average inference time: 1130.3 ms
Model: ARC_PSD-001_1.1.122_bst_yl80201_float16.tflite, Option: gpu, Inference times (ms): [418ms, 714ms, 771ms, 622ms, 817ms, 814ms, 785ms, 813ms, 810ms, 812ms, 591ms, 812ms, 812ms, 812ms, 815ms, 662ms, 811ms, 812ms, 815ms, 624ms, 810ms, 807ms, 809ms, 811ms, 813ms, 814ms, 810ms, 813ms, 809ms, 809ms, 784ms, 810ms, 810ms, 809ms, 809ms, 770ms, 775ms, 812ms, 811ms, 804ms, 787ms, 809ms, 811ms, 810ms, 663ms, 816ms, 809ms, 812ms, 601ms, 809ms, 811ms, 808ms, 810ms, 809ms, 810ms, 816ms, 811ms, 810ms, 675ms, 809ms, 811ms, 810ms, 624ms, 808ms, 808ms, 813ms, 812ms, 811ms, 810ms, 816ms, 810ms, 809ms, 810ms, 812ms, 809ms, 660ms, 811ms, 806ms, 810ms, 808ms, 808ms, 812ms, 811ms, 820ms, 809ms, 809ms, 814ms, 813ms, 812ms, 811ms, 812ms, 817ms, 809ms, 810ms, 809ms, 811ms, 810ms, 589ms, 812ms, 812ms], Average inference time: 786.15 ms
Model: ARC_PSD-001_1.1.122_bst_yl80201_float16.tflite, Option: nnapi, Inference times (ms): [1156ms, 1128ms, 1127ms, 1127ms, 1127ms, 1127ms, 1127ms, 1128ms, 1127ms, 1127ms, 1127ms, 1127ms, 1128ms, 1127ms, 1126ms, 1127ms, 1129ms, 1128ms, 1128ms, 1128ms, 1128ms, 1129ms, 1128ms, 1127ms, 1128ms, 1127ms, 1128ms, 1127ms, 1127ms, 1128ms, 1127ms, 1127ms, 1128ms, 1127ms, 1128ms, 1128ms, 1127ms, 1128ms, 1128ms, 1127ms, 1127ms, 1128ms, 1128ms, 1128ms, 1127ms, 1129ms, 1128ms, 1127ms, 1129ms, 1127ms, 1128ms, 1127ms, 1127ms, 1128ms, 1130ms, 1126ms, 1127ms, 1127ms, 1127ms, 1127ms, 1128ms, 1127ms, 1127ms, 1127ms, 1127ms, 1130ms, 1128ms, 1127ms, 1127ms, 1129ms, 1127ms, 1127ms, 1128ms, 1127ms, 1127ms, 1127ms, 1127ms, 1128ms, 1128ms, 1127ms, 1128ms, 1127ms, 1127ms, 1127ms, 1127ms, 1127ms, 1127ms, 1129ms, 1127ms, 1127ms, 1127ms, 1123ms, 1127ms, 1128ms, 1127ms, 1127ms, 1127ms, 1128ms, 1126ms, 1128ms], Average inference time: 1127.71 ms
Model: ARC_PSD-001_1.1.122_bst_yl80201_float16.tflite, Option: cpu, Inference times (ms): [1293ms, 1412ms, 1377ms, 1389ms, 1452ms, 1516ms, 1465ms, 1520ms, 1476ms, 1383ms, 1373ms, 1440ms, 1557ms, 1592ms, 1405ms, 1328ms, 1385ms, 1342ms, 1356ms, 1348ms, 1743ms, 1693ms, 1603ms, 1329ms, 1391ms, 1356ms, 1441ms, 1439ms, 1316ms, 1309ms, 1305ms, 1556ms, 1467ms, 1641ms, 1385ms, 1420ms, 1352ms, 1342ms, 1584ms, 1272ms, 1332ms, 1388ms, 1327ms, 1311ms, 1446ms, 1699ms, 1380ms, 1692ms, 1779ms, 1335ms, 1389ms, 1598ms, 1441ms, 1441ms, 1340ms, 1363ms, 1435ms, 1360ms, 1407ms, 1321ms, 1447ms, 1422ms, 1362ms, 1474ms, 1366ms, 1390ms, 1622ms, 1723ms, 1386ms, 1438ms, 1412ms, 1352ms, 1650ms, 1679ms, 1432ms, 1742ms, 1469ms, 1291ms, 1403ms, 1446ms, 1419ms, 1416ms, 1395ms, 1280ms, 1491ms, 1644ms, 1297ms, 1314ms, 1391ms, 1429ms, 1379ms, 1755ms, 1505ms, 1551ms, 1662ms, 1396ms, 1317ms, 1409ms, 1366ms, 1360ms], Average inference time: 1444.19 ms
Model: ARC_PSD-001_1.1.122_bst_yl80201_float16.tflite, Option: xnnpack, Inference times (ms): [1158ms, 1127ms, 1128ms, 1127ms, 1128ms, 1128ms, 1128ms, 1127ms, 1127ms, 1127ms, 1128ms, 1127ms, 1131ms, 1127ms, 1129ms, 1128ms, 1126ms, 1127ms, 1127ms, 1126ms, 1127ms, 1128ms, 1128ms, 1127ms, 1127ms, 1130ms, 1128ms, 1128ms, 1128ms, 1127ms, 1127ms, 1127ms, 1127ms, 1127ms, 1128ms, 1127ms, 1129ms, 1126ms, 1128ms, 1129ms, 1127ms, 1128ms, 1128ms, 1128ms, 1129ms, 1127ms, 1128ms, 1128ms, 1129ms, 1128ms, 1127ms, 1128ms, 1127ms, 1128ms, 1127ms, 1127ms, 1127ms, 1127ms, 1128ms, 1127ms, 1127ms, 1128ms, 1127ms, 1127ms, 1128ms, 1127ms, 1128ms, 1127ms, 1128ms, 1128ms, 1127ms, 1128ms, 1127ms, 1128ms, 1126ms, 1127ms, 1128ms, 1127ms, 1127ms, 1127ms, 1128ms, 1130ms, 1127ms, 1127ms, 1128ms, 1128ms, 1127ms, 1128ms, 1127ms, 1128ms, 1127ms, 1127ms, 1127ms, 1128ms, 1127ms, 1125ms, 1128ms, 1128ms, 1127ms, 1128ms], Average inference time: 1127.84 ms
```
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PR_kwDOArmXAs5tF7Bq
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#sdy support some round tripping of Shardonnay shardings and meshes b/w MHLO+SDY and HLO.
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#sdy support some round tripping of Shardonnay shardings and meshes b/w MHLO+SDY and HLO.
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PR_kwDOArmXAs5tFwng
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[oneDNN] Add accidentally removed ops back to MklList for auto_mixed_precision
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[
"Hi @ezhulenev Can you please review this PR ? Thank you!",
"Hi @ezhulenev Can you please review this PR ? Thank you!"
] | 2024-04-18T19:43:39 | 2024-05-22T17:30:08 | 2024-05-22T17:30:08 |
CONTRIBUTOR
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[PR-62817](https://github.com/tensorflow/tensorflow/pull/62817) accidentally removed these ops, so adding them back.
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PR_kwDOArmXAs5tFwB8
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[xla_compile][NFC] Allow loading autotune results from a SymbolRepository.
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[xla_compile][NFC] Allow loading autotune results from a SymbolRepository.
Doing so allows recompilation of uploaded modules with autotune results to
mimic more cases...and allows full recompilation without GPU hardware.
We now have enough arguments in XlaCompileMain that it's time to extract some
options structs, which significantly improved usability at the cost of verbose
code in places.
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PR_kwDOArmXAs5tFq4D
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[Cleanup] Add comments for fields.
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[Cleanup] Add comments for fields.
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Integrate TFRT+IFRT with tensorflow serving
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Integrate TFRT+IFRT with tensorflow serving
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Add containers with CUDA 12.3, CUDNN 8.9.
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Fix an error when setting bfc_allocator memory based on gpu_system_memory_size flag.
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#tf-data Clean up a TODO.
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benchmark_model no longer cross-compiles for Android from macOS
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[
"@brtal You are right! As you mentioned, downgrading to TensorFlow 2.15.0 (or a version before the Abseil upgrade) would likely resolve the issue. This is a straightforward approach if you don't rely on the latest features in TensorFlow 2.16.1. Otherwise switching to other operation systems could be another possible option to avoid such issues. Meanwhile we will look for the permanent fix for the same.\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/66015\">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/66015\">No</a>\n",
"I realize this was automatically closed, but I'd love to see it fixed!"
] | 2024-04-18T17:58:47 | 2024-05-10T18:20:28 | 2024-05-09T01:48:41 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
macOS 14.2.1
### Mobile device
Android
### Python version
3.10
### Bazel version
6.5.0
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
.tf_configure.bazelrc:
```
build --action_env ANDROID_NDK_HOME="/opt/homebrew/share/android-commandlinetools/ndk/25.1.8937393"
build --action_env ANDROID_NDK_VERSION="25"
build --action_env ANDROID_NDK_API_LEVEL="29"
build --action_env ANDROID_BUILD_TOOLS_VERSION="34.0.0"
build --action_env ANDROID_SDK_API_LEVEL="34"
build --action_env ANDROID_SDK_HOME="/opt/homebrew/share/android-commandlinetools"
```
Build command:
```
bazel build --verbose_failures --subcommands=true --config=android_arm64 --action_env PYTHON_BIN_PATH=bazel-build-env/bin/python --copt=-fPIC --copt=-g0 //tensorflow/lite/tools/benchmark:benchmark_model
```
Error:
```
SUBCOMMAND: # //tensorflow/lite/tools/benchmark:benchmark_model [action 'Linking tensorflow/lite/tools/benchmark/benchmark_model', configuration: e459edaf8e95924d717b7494ab8b4d569c035fb75fba832bc6414ced642b01b5, execution platform: @local_execution_config_platform//:platform]
(cd /private/var/tmp/_bazel_brtal/f4cc29144dbf6a9c1f1f53a3615b64b4/execroot/org_tensorflow && \
exec env - \
ANDROID_BUILD_TOOLS_VERSION=34.0.0 \
ANDROID_NDK_API_LEVEL=29 \
ANDROID_NDK_HOME=/opt/homebrew/share/android-commandlinetools/ndk/25.1.8937393 \
ANDROID_NDK_VERSION=25 \
ANDROID_SDK_API_LEVEL=34 \
ANDROID_SDK_HOME=/opt/homebrew/share/android-commandlinetools \
PATH='/opt/homebrew/opt/ruby/bin:/opt/homebrew/bin:/opt/homebrew/sbin:/usr/local/bin:/System/Cryptexes/App/usr/bin:/usr/bin:/bin:/usr/sbin:/sbin:/var/run/com.apple.security.cryptexd/codex.system/bootstrap/usr/local/bin:/var/run/com.apple.security.cryptexd/codex.system/bootstrap/usr/bin:/var/run/com.apple.security.cryptexd/codex.system/bootstrap/usr/appleinternal/bin:/Library/Apple/usr/bin:/usr/local/sbin:/var/lib/qcom/bin:/Applications/Sublime Text.app/Contents/SharedSupport/bin' \
PWD=/proc/self/cwd \
PYTHON_BIN_PATH=/Users/brtal/dev/tetracode/build/third_party.build/tflite/src/bazel-build-env/bin/python \
PYTHON_LIB_PATH=/Users/brtal/dev/tetracode/build/third_party.build/tflite/src/bazel-build-env/lib/python3.12/site-packages \
TF2_BEHAVIOR=1 \
external/androidndk/toolchains/llvm/prebuilt/darwin-x86_64/bin/clang @bazel-out/arm64-v8a-opt/bin/tensorflow/lite/tools/benchmark/benchmark_model-2.params)
# Configuration: e459edaf8e95924d717b7494ab8b4d569c035fb75fba832bc6414ced642b01b5
# Execution platform: @local_execution_config_platform//:platform
ERROR: /Users/brtal/dev/tensorflow/tensorflow/lite/tools/benchmark/BUILD:31:10: Linking tensorflow/lite/tools/benchmark/benchmark_model failed: (Exit 1): clang failed: error executing command (from target //tensorflow/lite/tools/benchmark:benchmark_model)
(cd /private/var/tmp/_bazel_brtal/f4cc29144dbf6a9c1f1f53a3615b64b4/execroot/org_tensorflow && \
exec env - \
ANDROID_BUILD_TOOLS_VERSION=34.0.0 \
ANDROID_NDK_API_LEVEL=29 \
ANDROID_NDK_HOME=/opt/homebrew/share/android-commandlinetools/ndk/25.1.8937393 \
ANDROID_NDK_VERSION=25 \
ANDROID_SDK_API_LEVEL=34 \
ANDROID_SDK_HOME=/opt/homebrew/share/android-commandlinetools \
PATH='/opt/homebrew/opt/ruby/bin:/opt/homebrew/bin:/opt/homebrew/sbin:/usr/local/bin:/System/Cryptexes/App/usr/bin:/usr/bin:/bin:/usr/sbin:/sbin:/var/run/com.apple.security.cryptexd/codex.system/bootstrap/usr/local/bin:/var/run/com.apple.security.cryptexd/codex.system/bootstrap/usr/bin:/var/run/com.apple.security.cryptexd/codex.system/bootstrap/usr/appleinternal/bin:/Library/Apple/usr/bin:/usr/local/sbin:/var/lib/qcom/bin:/Applications/Sublime Text.app/Contents/SharedSupport/bin' \
PWD=/proc/self/cwd \
PYTHON_BIN_PATH=/Users/brtal/dev/tetracode/build/third_party.build/tflite/src/bazel-build-env/bin/python \
PYTHON_LIB_PATH=/Users/brtal/dev/tetracode/build/third_party.build/tflite/src/bazel-build-env/lib/python3.12/site-packages \
TF2_BEHAVIOR=1 \
external/androidndk/toolchains/llvm/prebuilt/darwin-x86_64/bin/clang @bazel-out/arm64-v8a-opt/bin/tensorflow/lite/tools/benchmark/benchmark_model-2.params)
# Configuration: e459edaf8e95924d717b7494ab8b4d569c035fb75fba832bc6414ced642b01b5
# Execution platform: @local_execution_config_platform//:platform
ld.lld: error: unknown argument '-framework'
ld.lld: error: cannot open CoreFoundation: No such file or directory
clang: error: linker command failed with exit code 1 (use -v to see invocation)
Target //tensorflow/lite/tools/benchmark:benchmark_model failed to build
INFO: Elapsed time: 167.295s, Critical Path: 12.29s
INFO: 987 processes: 3 internal, 984 local.
FAILED: Build did NOT complete successfully
```
This is because `benchmark_model` is linked with `-Wl,-framework,CoreFoundation`, which isn't a valid flag for an android target.
This worked fine in TF Lite 2.15.
With a git bisect, I isolated the change to the Abseil upgrade: https://github.com/tensorflow/tensorflow/commit/6ed0fd871fba4786d7dd3b3b31886b2a1a2beaf3
### Standalone code to reproduce the issue
```shell
See above.
```
### Relevant log output
_No response_
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Separate backend specific code in `:gpu_timer`
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Separate backend specific code in `:gpu_timer`
1. Moves the `GpuSemaphore` type out of `GpuTimer` into its own file and target
2. Moves backend specific code from `:gpu_timer` into its own files.
3. Splits `:gpu_timer_kernel` into the backend specific targets `:gpu_timer_kernel_{cuda|rocm}`.
4. Removes the `:gpu_timer_header` target
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Only for testing purpose
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Integrate LLVM at llvm/llvm-project@63d8058ef50a
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Integrate LLVM at llvm/llvm-project@63d8058ef50a
Updates LLVM usage to match
[63d8058ef50a](https://github.com/llvm/llvm-project/commit/63d8058ef50a)
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| 66,010 |
Update flatbuffers used in tflite.
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Update flatbuffers used in tflite.
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Disable flaky test caused by the GEMM autotuner refactoring
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Disable flaky test caused by the GEMM autotuner refactoring
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PR_kwDOArmXAs5tEXar
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Implement ConditionallySpeculatable for DynamicReshape
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PR_kwDOArmXAs5tETVq
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Add convenience functions for writing and reading data from files.
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Add convenience functions for writing and reading data from files.
Defines and implements `ReadFileToString` and `WriteStringToFile` to avoid using explicit `tsl::Env`.
Reverts c712957000de89fa7a1f5fee890946b8cd8c1220
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PR_kwDOArmXAs5tERpf
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Modify IfrtIRCompileOptions to use shared_ptr for LoadedExecutable instead of a raw pointer.
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PR_kwDOArmXAs5tEItq
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Separate backend specific code in redzone_allocator
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NONE
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Separate backend specific code in redzone_allocator
- Moves `LoadKernelOrGetPtr` from `asm_compiler.h` into `redzone_allocator_kernel_cuda.cc` since it's the only user of this function.
- Separate CUDA / ROCm code into separate files: `redzone_allocator_kernel_{cuda|rocm}.cc`.
- Removes all preprocessor branches from `redzone_allocator.cc`.
- Removes unnecessary build macros from `redzone_allocator_test` target definition.
- Reenables ASAN for the `redzone_allocator_test`.
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PR_kwDOArmXAs5tD2t4
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Stop using `usePropertiesForAttributes` in TFRT MLIR dialects
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Stop using `usePropertiesForAttributes` in TFRT MLIR dialects
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| 2,250,860,163 |
PR_kwDOArmXAs5tDzpg
| 66,003 |
Extend gpu_only_cc_library to handle backend specific targets
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Extend gpu_only_cc_library to handle backend specific targets
Example:
```
gpu_only_cc_library(
name = "target",
cuda_srcs = [ "target_cuda.cc" ],
rocm_srcs = [ "target_rocm.cc" ],
cuda_deps = [ ":cuda_driver" ],
rocm_deps = [ ":rocm_driver" ],
hdrs = [ "target.h" ],
)
```
The macro simplifies and encourages a strict separation of CUDA/ROCm specific code. It generates separate targets for each backend that have to be compileable on its own.
It fulfills the following requirements:
- Separate independently buildable targets for each backend
- build_cleaner compatible
- No preprocessor branches needed
Note that this is only the first step towards better backend code separation. It forces the user to put some kind of abstraction between backend-agnostic and backend-specific code. For now this can be as simple as a free function interface. In later steps we can move towards abstract base classes and factory functions.
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PR_kwDOArmXAs5tDq9p
| 66,002 |
Skip `test_spmd_preserves_input_sharding_vmap_grad` unless `xla_extension_version >= 258`
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Skip `test_spmd_preserves_input_sharding_vmap_grad` unless `xla_extension_version >= 258`
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PR_kwDOArmXAs5tDb14
| 66,001 |
Eliminate StreamExecutor::AllocatedOwnedScalar to enable eliminate circular dependency between device_memory_allocator and stream_executor_pimpl.h.
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Eliminate StreamExecutor::AllocatedOwnedScalar to enable eliminate circular dependency between device_memory_allocator and stream_executor_pimpl.h.
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I_kwDOArmXAs6GJkah
| 66,000 |
Configurable attribute "deps" in @XNNPACK//:prod_microkernels doesn't match this configuration
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[
"@Venkat6871 Any updates?",
"Hi @Nayana-ibm ,\r\n\r\nI will replicate the issue and will get back to you regarding this. \r\n",
"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/66000\">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/66000\">No</a>\n",
"Applied below patch and issue resolved. \r\n\r\n```\r\ndiff --git a/tensorflow/workspace2.bzl b/tensorflow/workspace2.bzl\r\nindex a09ac522b0a..373275557f9 100644\r\n--- a/tensorflow/workspace2.bzl\r\n+++ b/tensorflow/workspace2.bzl\r\n@@ -153,6 +153,7 @@ def _tf_repositories():\r\n sha256 = \"256ab4034e93bdeea2e1216cd8361a85bd4ee4884a89bf5e4e142abcdc796805\",\r\n strip_prefix = \"XNNPACK-50037f8072731a2cc30a961b96e199ad691887e4\",\r\n urls = tf_mirror_urls(\"https://github.com/google/XNNPACK/archive/50037f8072731a2cc30a961b96e199ad691887e4.zip\"),\r\n+ patch_file = [\"//third_party:xnn.patch\"],\r\n )\r\n # LINT.ThenChange(//tensorflow/lite/tools/cmake/modules/xnnpack.cmake)\r\n\r\ndiff --git a/third_party/xnn.patch b/third_party/xnn.patch\r\nnew file mode 100644\r\nindex 00000000000..00b9f4e2b12\r\n--- /dev/null\r\n+++ b/third_party/xnn.patch\r\n@@ -0,0 +1,12 @@\r\n+diff --git a/build_defs.bzl b/build_defs.bzl\r\n+index 32814271..42f3a7e9 100644\r\n+--- a/build_defs.bzl\r\n++++ b/build_defs.bzl\r\n+@@ -260,6 +260,7 @@\r\n+ \"//build_config:emscripten_wasmsimd\": wasmsimd_deps,\r\n+ \"//build_config:emscripten_wasmrelaxedsimd\": wasmrelaxedsimd_deps,\r\n+ \"//build_config:riscv\": riscv_deps,\r\n++ \"//conditions:default\": [],\r\n+ }),\r\n+ defines = defines,\r\n+ compatible_with = compatible_with,\r\n```\r\n\r\nNot sure why default condition is not added xnnpack/build.bzl .\r\n\r\n",
"Resolved through https://github.com/google/XNNPACK/issues/6359",
"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/66000\">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/66000\">No</a>\n"
] | 2024-04-18T13:07:08 | 2024-05-14T09:58:24 | 2024-05-14T09:58:20 |
CONTRIBUTOR
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
master
### Custom code
No
### OS platform and distribution
Linux ubuntu 20.04 (s390x)
### Mobile device
NA
### Python version
3.8.10
### Bazel version
6.1.0
### GCC/compiler version
9.4.0
### CUDA/cuDNN version
TF_NEED_CUDA=0
### GPU model and memory
_No response_
### Current behavior?
I am building Tensorflow on s390x platform using command: bazel build --define=tensorflow_mkldnn_contraction_kernel=0 --define tflite_with_xnnpack=false //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_cpu
Build fails with an error:
```
ERROR: /root/.cache/bazel/_bazel_root/efb88f6336d9c4a18216fb94287b8d97/external/XNNPACK/BUILD.bazel:2998:26: configurable attribute "deps" in @XNNPACK//:prod_microkernels doesn't match this configuration. Would a default condition help?
Conditions checked:
@XNNPACK//build_config:aarch32
@XNNPACK//build_config:aarch64
@XNNPACK//build_config:x86
@XNNPACK//build_config:emscripten_wasm
@XNNPACK//build_config:emscripten_wasmsimd
@XNNPACK//build_config:emscripten_wasmrelaxedsimd
@XNNPACK//build_config:riscv
```
Even though setting flag tflite_with_xnnpack=false , build fails. Is there any more flags or changes are required to build Tensorflow successfully? Tried to check the code which populates build_config however didn't get much information.
### Standalone code to reproduce the issue
```shell
git clone https://github.com/tensorflow/tensorflow
cd tensorflow/
TF_SYSTEM_LIBS="boringssl" bazel --host_jvm_args="-Xms1024m" --host_jvm_args="-Xmx2048m" build --define=tensorflow_mkldnn_contraction_kernel=0 --define tflite_with_xnnpack=false --define=use_xnnpack=true //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_cpu
```
### Relevant log output
```shell
ERROR: /root/.cache/bazel/_bazel_root/efb88f6336d9c4a18216fb94287b8d97/external/XNNPACK/BUILD.bazel:2998:26: configurable attribute "deps" in @XNNPACK//:prod_microkernels doesn't match this configuration. Would a default condition help?
Conditions checked:
@XNNPACK//build_config:aarch32
@XNNPACK//build_config:aarch64
@XNNPACK//build_config:x86
@XNNPACK//build_config:emscripten_wasm
@XNNPACK//build_config:emscripten_wasmsimd
@XNNPACK//build_config:emscripten_wasmrelaxedsimd
@XNNPACK//build_config:riscv
```
```
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Could not find any nvml.h during building from source in docker
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"@YuMJie,\r\nThe latest tensorflow stable v2.16.1 will be compatible with Clang 17.0.6, Bazel 6.5.0, cuDNN8.9 and CUDA 12.3. COuld you please try to follow the same while installing. The nvml.h header file is part of the NVIDIA Management Library (NVML) which allows programmatic interaction with NVIDIA GPUs. NVML typically comes bundled with the NVIDIA display driver. Check if you have the latest drivers installed from the official NVIDIA website.\r\n\r\nhttps://github.com/openxla/xla/issues/6923\r\n\r\nThank you!",
"I found why did not find the nvml.h: If I run `git pull` in the dir tensorflow_src and run building ,it will occur this error. However , directly running the build will not have this error .",
"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/65999\">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/65999\">No</a>\n",
"If you don't run `git pull` then it is building a version from 2022 and not a more recent version. The part of the NVIDIA driver does not seem to be installed but that could be because the container assumes you have the driver on the host system, not in the container?"
] | 2024-04-18T12:18:12 | 2024-05-30T14:58:34 | 2024-04-23T16:10:35 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
linux ubutun 20.04
### Mobile device
linux ubutun 20.04
### Python version
3.11
### Bazel version
7.1.1
### GCC/compiler version
11.4.0
### CUDA/cuDNN version
12.3
### GPU model and memory
3090 24G
### Current behavior?
I try to build the tensorflow from source code. So I download the docker image and build. However it error during the `run ./configure`
### Standalone code to reproduce the issue
```shell
$ git clone https://github.com/tensorflow/tensorflow.git
$ docker pull tensorflow/tensorflow:lastest-gpu
$ docker run --gpus all -it -w /tensorflow -v $PWD:/mnt -e HOST_PERMS="$(id -u):$(id -g)" \
tensorflow/tensorflow:lastest-gpu bash
# within the container
$ cd mnt/tensorflow
$ ./configure
```
### Relevant log output
```shell
root@27cd49d821ca:/mnt/tensorflow# nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Wed_Nov_22_10:17:15_PST_2023
Cuda compilation tools, release 12.3, V12.3.107
Build cuda_12.3.r12.3/compiler.33567101_0
root@27cd49d821ca:/mnt/tensorflow# ./configure
WARNING: current bazel installation is not a release version.
Please specify the location of python. [Default is /usr/bin/python3]:
Found possible Python library paths:
/usr/lib/python3/dist-packages
/usr/local/lib/python3.11/dist-packages
Please input the desired Python library path to use. Default is [/usr/lib/python3/dist-packages]
/usr/local/lib/python3.11/dist-packages
Do you wish to build TensorFlow with ROCm support? [y/N]: N
No ROCm support will be enabled for TensorFlow.
Do you wish to build TensorFlow with CUDA support? [y/N]: Y
CUDA support will be enabled for TensorFlow.
Do you wish to build TensorFlow with TensorRT support? [y/N]: N
No TensorRT support will be enabled for TensorFlow.
Could not find any nvml.h in any subdirectory:
''
'include'
'include/cuda'
'include/*-linux-gnu'
'extras/CUPTI/include'
'include/cuda/CUPTI'
'local/cuda/extras/CUPTI/include'
'targets/x86_64-linux/include'
of:
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'/usr'
'/usr/local/cuda'
'/usr/local/cuda/lib64/stubs'
'/usr/local/cuda/targets/x86_64-linux/lib'
Asking for detailed CUDA configuration...
Please specify the CUDA SDK version you want to use. [Leave empty to default to CUDA 11]: 12.3
Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 2]:
Please specify the locally installed NCCL version you want to use. [Leave empty to use http://github.com/nvidia/nccl]:
Please specify the comma-separated list of base paths to look for CUDA libraries and headers. [Leave empty to use the default]:
Could not find any nvml.h in any subdirectory:
''
'include'
'include/cuda'
'include/*-linux-gnu'
'extras/CUPTI/include'
'include/cuda/CUPTI'
'local/cuda/extras/CUPTI/include'
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'/usr/local/cuda-12.3'
'/usr/local/cuda/lib64/stubs'
'/usr/local/cuda/targets/x86_64-linux/lib'
Asking for detailed CUDA configuration...
Please specify the CUDA SDK version you want to use. [Leave empty to default to CUDA 11]:
```
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PR_kwDOArmXAs5tCp9g
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Set the `Method` used to quantize the `CallOp` to `QuantizationResult` in the report.
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[] | 2024-04-18T12:05:42 | 2024-04-22T06:03:14 | 2024-04-22T06:03:13 |
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Set the `Method` used to quantize the `CallOp` to `QuantizationResult` in the report.
`QuantizationResults` will be shown to the users along with the quantization method applied to them.
This provides better observability to the users and a way to inspect the results systematically.
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Fix fusion parameter resolving in indexing analysis
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Fix fusion parameter resolving in indexing analysis
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[xla:cpu] Make `HandleCustomCall` support typed FFI
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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/65996/checks?check_run_id=23973806800) 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."
] | 2024-04-18T11:41:05 | 2024-05-03T10:49:53 | 2024-05-03T10:49:51 |
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[xla:cpu] Make `HandleCustomCall` support typed FFI
Fixes #10056
Co-authored-by: pparuzel <[email protected]>
Co-authored-by: Adam-Banas <[email protected]>
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Sync `ml_dtype` version with the one used in XLA.
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Sync `ml_dtype` version with the one used in XLA.
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[XLA:GPU] Preserve metadata when removing degenerate reduce dims.
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[] | 2024-04-18T11:12:27 | 2024-04-18T12:42:45 | 2024-04-18T12:42:44 |
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[XLA:GPU] Preserve metadata when removing degenerate reduce dims.
When removing degenerate (equals to 1) dimensions, bitcast op inherits the metadata (through ReplaceWithNewInstruction()), but not the new reduce instruction. Explicitly copy the metadata to the new reduce instruction.
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Refactor GEMM autotuner
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[] | 2024-04-18T10:42:59 | 2024-04-18T12:26:06 | 2024-04-18T12:26:04 |
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Refactor GEMM autotuner
The refactoring is trying to fix two issues:
1) Expose API for compiling and autotuning dot fusions - previously the implementation was hidden in an anonymous namespace and used a global object for storing the results (autotuning cache). This might be useful for modelling matmul performance.
2) Reduce code duplication - previously the filtering of the triton configs was done separately for default and exhaustive search. Some configs need to be adjusted to be correctly compiled by Triton, for example:
- when there are less elements in a tensor than the number of threads (happens for sparse dot metadata with small tiles);
- when compiling for Hopper with `wgmma` enabled (block_m or num_warps may need to be adjusted).
Notably, `kDefaultGemmTiling` could need such adjustments, so returning it as-is may result in fatal compilation errors. With this patch, the same config generator is used for all code paths.
There are a few more changes:
- do not handle reference implementation (cuBLAS) separately; instead, use it in the `Config` variant along with the other options (cuDNN, triton).
- do not repeat cuDNN conditions - put them in one place.
- use actual tile sizes and SM count for calculating the split-K upper bound (more accurate heuristic than a global limit).
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