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} | In PjRtStreamExecutorBuffer::Delete, fix a bug causing memory corruption.
The bug is that we don't wait for events using the buffer on the Compute stream, and also the definition events. This can cause a race condition where the buffer is deleted before it is read/written, which can lead to memory corruptions.
For events on the compute stream, the fix is to schedule the deallocation on the compute stream.
For the definition events, the fix is to wait for them on a stream in a stream pool if they are not defined on the compute stream.
Added a fixed-size stream pool to avoid the overhead and deadlock that could be introduced by BorrowStreamFromPool, since we previously saw deadlocks between the cuStreamCreate and other cuda calls like cuMemHostAlloc.
This change probably wouldn't be needed if all writes are scheduled on the compute stream, but that is not the case today. We have a lot of usages of the h2d and d2d stream.
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} | Replace numpy string aliases for numpy 2.0 compatibility
NumPy 2.0 removes the aliases np.string_ (for np.bytes_) and np.unicode_ (for np.str_) to enforce a single way to access string data types.
All instances where np.string_ and np.unicode_ are used in TF have been updated.
Note that, internally, tf.string is now mapped to np.bytes_ to align with NumPy 2.0's changes. However, the public-facing TensorFlow types tf.string remains unchanged as it does not have duplicate naming issues, so it is probably not worth updating its name.
See https://numpy.org/devdocs/numpy_2_0_migration_guide.html#changes-to-namespaces and https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.string_.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12896 from openxla:cleanup 32ded36419a4a1b2177a6374846497a8bb458ac9
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} | Remove reference to `mlir::Operations` in `Thunks`.
The `mlir::Operation` in `Thunk` (`Thunk::op_`) is never set to a value different from `nullptr`.
Conveniently, this also removes the need for `Thunk::ClearCompileTimeInfo`, which in turn leaves `ForAllThunks` (in `compile_module_to_llvm_ir.cc`) unused.
I'm doing this first so that I can then (a) move `ForAllThunks` to its own file, and (b) change its behavior to actually iterate over all `Thunks`: for example, `ForAllThunks` currently does not iterate over the `embedded_thunk` in `AddressComputationThunk`. This behavior will be safer to change after there's no remaining users of the function.
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"Hi @jpienaar Any update on this PR? Please. Thank you!"
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Note that llvm::StringRef::equals has been deprecated upstream.
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} | Adapt to NumPy 2.0 namespace changes: np.inf and np.nan
NumPy 2.0 introduces namespace changes that deprecate certain ways of accessing special values like np.inf and np.nan.
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See https://numpy.org/devdocs/numpy_2_0_migration_guide.html#changes-to-namespaces.
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} | Align DRQ TransposeConv XNNPack test with reference TFLite implementation
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} | PR #12637: [ROCm] Fix build break due to 63c33b and f5ab2a4
Imported from GitHub PR https://github.com/openxla/xla/pull/12637
Copybara import of the project:
--
9a090b699df98d7c2a27311fbf353d4a0390e87f by Harsha HS <[email protected]>:
[ROCm] Fix build break due to 63c33b and f5ab2a4
Merging this change closes #12637
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12637 from ROCm:ci_fix_build_break_20240517 9a090b699df98d7c2a27311fbf353d4a0390e87f
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} | [Gradients] Optimize `_GatherGrad` when shape information is available.
This change avoids using `tf.shape()` or `tf.size()` and creating a data dependency on `params` or `indices` when we have a enough static shape information to determine `values_shape`. In this case, it also reduces graph size for `_GatherGrad` by eliminating `tf.expand_dims()` and `tf.concat()` nodes for building the `tf.reshape()` argument.
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} | Remove duplicate test macros from TSL
`tsl/platform/test.h` used to define a macro `DISABLED_ON_GPU_ROCM` which also exists in
`xla/tests/test_macros.h`. This led to build failures if both headers were included.
Since `test_macros.h` contains a huge set of similar macros let's remove the one
from `platform/test.h` and use `test_macros.h` everywhere.
I'm also fixing a CUDA include issue along the way.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12637 from ROCm:ci_fix_build_break_20240517 9a090b699df98d7c2a27311fbf353d4a0390e87f
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In such case, the variable
tensor is obtained at runtime through the returned name instead
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} | IsSimplifiedScatter should also return false if update_window_dims is unsorted.
We started relying on that with LayoutNormalization creating Scatters that
don't have sorted update_window_dims, and want ScatterSimplifier to clean this
up.
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} | PR #12520: [GPU] Add new flag xla_gpu_exclude_nondeterministic_ops.
Imported from GitHub PR https://github.com/openxla/xla/pull/12520
It's more granular than the existing --xla_gpu_deterministic_ops because it allows doing an autotuning compilation with non-deterministic ops disabled.
--xla_gpu_deterministic_ops is a superset of --xla_gpu_exclude_nondeterministic_ops, so --xla_gpu_deterministic_ops=true will be setting --xla_gpu_exclude_nondeterministic_ops=true too.
Copybara import of the project:
--
4e2837457dc426154bf80f321c001c916a7d3677 by Ilia Sergachev <[email protected]>:
[GPU] Add new flag xla_gpu_exclude_nondeterministic_ops.
Merging this change closes #12520
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12520 from openxla:new_determinism_flag 4e2837457dc426154bf80f321c001c916a7d3677
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} | PR #12515: [ROCm] Fix build break introduced in ffa7bb5 and df736d7
Imported from GitHub PR https://github.com/openxla/xla/pull/12515
Fix build break introduced in ffa7bb5 and df736d7
Copybara import of the project:
--
499687ea87e38be681cb978d15ebd314377623c4 by Harsha HS <[email protected]>:
Fix build break introduced in ffa7bb5 and df736d7
Merging this change closes #12515
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12515 from ROCm:ci_fix_build_break_20240515 499687ea87e38be681cb978d15ebd314377623c4
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"Data from /Users/pinji/Desktop/MoCoDiff/tf-0513/tensorflow-LeNet/LeNet-12-654/case/tensorflow_cpu/output.npz:\r\nArray name: output\r\n[[-0.19694163, -0.09662387, -0.2447289 , ..., -0.03054091, -0.01255638,\r\n 0.11781679],\r\n [-0.09944421, -0.1292284 , -0.20278051, ..., -0.01450334, 0.1526829 ,\r\n 0.2054872 ],\r\n [ 0.07628068, -0.14517091, -0.16096437, ..., -0.21423727, 0.14428714,\r\n 0.09608107],\r\n ...,\r\n [-0.20587711, -0.10656235, -0.33036825, ..., -0.13541238, 0.3215404 ,\r\n 0.2165656 ],\r\n [-0.11513966, -0.1102111 , -0.3434586 , ..., -0.19525276, 0.08722814,\r\n -0.05503507],\r\n [-0.09822497, -0.10671525, -0.2368006 , ..., -0.16467465, 0.15050802,\r\n 0.10809175]]\r\n\r\n========================================\r\nData from /Users/pinji/Desktop/MoCoDiff/tf-0513/tensorflow-LeNet/LeNet-12-654/case/tensorflow_gpu/output.npz:\r\nArray name: output\r\n[[-0.19693479, -0.09660047, -0.24478191, ..., -0.03068957, -0.01259471,\r\n 0.1177731 ],\r\n [-0.09948827, -0.12920822, -0.20286846, ..., -0.01475166, 0.15262538,\r\n 0.20549278],\r\n [ 0.07635015, -0.14510253, -0.16103148, ..., -0.21449052, 0.14431402,\r\n 0.09607705],\r\n ...,\r\n [-0.20597562, -0.1065662 , -0.33040133, ..., -0.13553414, 0.32143065,\r\n 0.2166584 ],\r\n [-0.1152329 , -0.11016633, -0.34351912, ..., -0.19536608, 0.08703801,\r\n -0.05509032],\r\n [-0.09832728, -0.10680126, -0.2368007 , ..., -0.1649086 , 0.1503351 ,\r\n 0.10806311]]\r\n\r\n========================================\r\n543 diff: 0.055949583649635315\r\ncpu-543: [-0.1139553 -0.04668004 -0.4390249 0.20775127 0.0899936 0.22854462\r\n -0.46038878 -0.15526699 0.23111053 -0.03327706]\r\ngpu-543: [-0.12328502 -0.0404007 -0.4342668 0.21254592 0.05579592 0.24491714\r\n -0.479826 -0.15429592 0.17516094 -0.05355967]\r\n",
"@PhyllisJi,\r\nI tried to execute the mentioned code. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/72178a625ffc1c316d3f59475794c9e9/untitled1907.ipynb). In the given code snippet you have defined the class and its methods but are not calling them anywhere. Also try to execute the code with the keras3.0 which is default for the TensorFlow 2.16 and let us know if you are facing the same issue. Thank you!",
"> @PhyllisJi, I tried to execute the mentioned code. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/72178a625ffc1c316d3f59475794c9e9/untitled1907.ipynb). In the given code snippet you have defined the class and its methods but are not calling them anywhere. Also try to execute the code with the keras3.0 which is default for the TensorFlow 2.16 and let us know if you are facing the same issue. Thank you!\r\n\r\nDue to the need for data support, I have put the reproduction code, data and steps in the repository, which you can reproduce by clone. https://github.com/PhyllisJi/MoCoDiff_Bug/tree/tf-issue%2367829",
"@PhyllisJi,\r\nAFAIK generally there shouldn't be much difference between CPU and GPU runs. And also the API **tf.keras.layers.dense** might not be the reason for the difference. The reason could be due to the optimized way of calculating numbers on Nvidia which is different compared to others, there will be a small amount of precision errors. Could you please try to check the same code with the keras3.0 and tensorflowv2.16 and let us know whether you are facing the same issue. Thank you!",
"Ok，I will try today\n\n---- Replied Message ----\n| From | ***@***.***> |\n| Date | 06/05/2024 22:21 |\n| To | tensorflow/tensorflow ***@***.***> |\n| Cc | PinJi_NJU ***@***.***>,\nMention ***@***.***> |\n| Subject | Re: [tensorflow/tensorflow] tf.keras.layers.Dense leads to significant differences between CPU and GPU runs of the model implementation code (Issue #67829) |\n\n@PhyllisJi,\nAFAIK generally there shouldn't be much difference between CPU and GPU runs. And also the API tf.keras.layers.dense might not be the reason for the difference. The reason could be due to the optimized way of calculating numbers on Nvidia which is different compared to others, there will be a small amount of precision errors. Could you please try to check the same code with the keras3.0 and let us know whether you are facing the same issue. Thank you!\n\n—\nReply to this email directly, view it on GitHub, or unsubscribe.\nYou are receiving this because you were mentioned.Message ID: ***@***.***>"
] | 2024-05-17T10:19:55 | 2024-06-06T16:04:26 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.14.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.2
### GPU model and memory
_No response_
### Current behavior?
The difference in the output values of the entire neural network for forward propagation exceeds 0.05 when trained with CPU and GPU respectively. But the outputs are consistent before adding the line f**c3_output = tf.keras.layers.Dense(units=10, use_bias=True, name="linear3_mutated") ( relu4_output)**.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ''
def Model_VlysjQxB81qtaIXsA_VkCXmPGmE7aDNP(input):
input = tf.keras.Input(shape=input)
_zeropadding_input = tf.keras.layers.ZeroPadding2D(padding=((0, 0), (0, 0)))(input)
conv1_output = tf.keras.layers.Conv2DTranspose(filters=6, kernel_size=(5, 5), strides=(1, 1), padding="valid", output_padding=(0, 0), data_format="channels_last", dilation_rate=(1, 1), use_bias=True, name="conv1_mutated")(input)
relu1_output = tf.nn.relu(conv1_output)
_zeropadding_relu1_output = tf.keras.layers.ZeroPadding2D(padding=((0, 0), (0, 0)))(relu1_output)
maxpool1_output = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding="valid", data_format="channels_last", name="pool1")(_zeropadding_relu1_output)
_zeropadding_maxpool1_output = tf.keras.layers.ZeroPadding2D(padding=((0, 0), (0, 0)))(maxpool1_output)
conv2_output = tf.keras.layers.Conv2D(filters=16, kernel_size=(6, 8), strides=(1, 1), padding="valid", data_format="channels_last", dilation_rate=(1, 1), groups=1, use_bias=True, name="conv2_mutated")(_zeropadding_maxpool1_output)
relu2_output = tf.math.softsign(conv2_output)
_zeropadding_relu2_output = tf.keras.layers.ZeroPadding2D(padding=((0, 0), (0, 0)))(relu2_output)
maxpool2_output = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding="valid", data_format="channels_last", name="pool2")(_zeropadding_relu2_output)
output_transpose = [(0), (0, 1), (0, 2, 1), (0, 3, 1, 2), (0, 4, 1, 2, 3)]
maxpool2_output = tf.transpose(maxpool2_output, list(output_transpose[(len(maxpool2_output.shape) - 1)]))
flatten_output = tf.keras.layers.Flatten(data_format="channels_last", name="flatten")(maxpool2_output)
fc1_output = tf.keras.layers.Dense(units=120, use_bias=True, name="linear1")(flatten_output)
relu3_output = tf.keras.layers.ThresholdedReLU(theta=0.1, name="relu3_mutated")(fc1_output)
fc2_output = tf.keras.layers.Dense(units=84, use_bias=True, name="linear2_mutated")(relu3_output)
relu4_output = tf.math.erf(fc2_output)
fc3_output = tf.keras.layers.Dense(units=10, use_bias=True, name="linear3_mutated")(relu4_output)
output_transpose = [(0), (0, 1), (0, 2, 1), (0, 3, 1, 2), (0, 4, 1, 2, 3)]
fc3_output = tf.transpose(fc3_output, list(output_transpose[(len(fc3_output.shape) - 1)]))
tail_flatten_output = tf.keras.layers.Flatten(data_format="channels_last", name="tail_flatten")(fc3_output)
tail_fc_output = tf.keras.layers.Dense(units=10, use_bias=True, name="tail_fc")(tail_flatten_output)
tail_fc_output = tail_fc_output
model = tf.keras.models.Model(inputs=input, outputs=tail_fc_output)
return model
def go():
with tf.device('/CPU:0'):
try:
shape = [1, 1, 28, 28]
_numpy = np.random.random(shape).astype(np.float32)
tf_input = tf.convert_to_tensor(_numpy.transpose(0, 2, 3, 1), dtype=tf.float32)
tf_model = Model_VlysjQxB81qtaIXsA_VkCXmPGmE7aDNP(tf_input.shape[1:])
tf_output = tf_model(tf_input)
flag = True
except Exception:
flag = False
return flag
def initialize(model):
module_dir = os.path.dirname(__file__)
gradient_transpose = [(0,), (1, 0), (2, 1, 0), (2, 3, 1, 0), (2, 3, 4, 1, 0)]
for layer in model.layers:
matrix_path = module_dir + '/../initializer/' + layer.name
if hasattr(layer, 'kernel_initializer'):
weight_init_path = matrix_path + '/weight.npz'
weight_init = np.load(weight_init_path)
weight_init = weight_init['matrix']
tf_weight = tf.convert_to_tensor(weight_init, dtype=tf.float32)
tf_weight = tf.transpose(tf_weight, gradient_transpose[len(tf_weight.shape) - 1])
layer.kernel.assign(tf.keras.initializers.Constant(tf_weight)(layer.kernel.shape))
if hasattr(layer, 'bias_initializer') and layer.use_bias:
bias_init_path = matrix_path + '/bias.npz'
bias_init = np.load(bias_init_path)
bias_init = bias_init['matrix']
tf_bias = tf.convert_to_tensor(bias_init, dtype=tf.float32)
tf_bias = tf.transpose(tf_bias, gradient_transpose[len(tf_bias.shape) - 1])
layer.bias.assign(tf.keras.initializers.Constant(tf_bias)(layer.bias.shape))
def train(inp, label):
with tf.device('/CPU:0'):
shape = inp.shape
tf_input = tf.convert_to_tensor(inp.transpose(0, 2, 3, 1), dtype=tf.float32)
tf_model = Model_VlysjQxB81qtaIXsA_VkCXmPGmE7aDNP(tf_input.shape[1:])
initialize(tf_model)
tf_output = tf_model(tf_input)
output_transpose = [(0), (0, 1), (0, 2, 1), (0, 3, 1, 2), (0, 4, 1, 2, 3)]
tf_output_trans = tf.transpose(tf_output, list(output_transpose[(len(tf_output.shape) - 1)])).numpy()
tf_targets = tf.convert_to_tensor(label)
with tf.GradientTape() as tape:
tf_predictions = tf_model(tf_input)
tf_loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)(tf_targets, tf_predictions)
tf_gradients = tape.gradient(tf_loss, tf_model.trainable_variables)
tf_gradients_dic = {}
for var, gradient in zip(tf_model.trainable_variables, tf_gradients):
gradient_transpose = [(0, ), (1, 0), (2, 0, 1), (3, 2, 0, 1), (4, 3, 0, 1, 2)]
tf_gradient = tf.transpose(gradient, list(gradient_transpose[len(gradient.shape) - 1])).numpy()
tf_gradients_dic.setdefault(var.name.replace('/', '.')[:-2], tf_gradient)
return tf_gradients_dic, float(tf_loss.numpy()), tf_output_trans
```
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} | PR #12515: [ROCm] Fix build break introduced in ffa7bb5 and df736d7
Imported from GitHub PR https://github.com/openxla/xla/pull/12515
Fix build break introduced in ffa7bb5 and df736d7
Copybara import of the project:
--
499687ea87e38be681cb978d15ebd314377623c4 by Harsha HS <[email protected]>:
Fix build break introduced in ffa7bb5 and df736d7
Merging this change closes #12515
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12515 from ROCm:ci_fix_build_break_20240515 499687ea87e38be681cb978d15ebd314377623c4
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Currently we crash on unknown flags left after XLA_FLAGS parsing, but all the
errors are just silently ignored. This leaves a large footgun which ignores
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Special thanks to github user lingzhi98, who proposed and benchmarked
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It turned out that the Tiling abstraction made this harder than it needs to be.
This is an independent reimplementation that removes the Tiling abstraction, but
reuses lingzhi98's tests and most of their heuristic for enabling vectorization.
The differences in the heuristic are:
- respect the shared memory budget
- enable vectorization if the dimensions are divisible by 2, not just when
they are divisible by 64.
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"Seems like a reasonable request, @miaout17, can you please take a look? Thanks. "
] | 2024-05-17T09:28:12 | 2024-05-20T21:13:22 | null | NONE | null | null | null | Add new data types in the TFLite format schema that could be leveraged by custom accelerators, such as:
- Custom floating point format, having configurable number of bits for exponent and mantissa (something like **TensorType_FLOAT8** using custom format options)
This is relevant only for the TFLite format (schema) used for model representation and not necessary for the TFLite tools or runtime.
This is relevant when industry has proprietary tools that rely on the TFLite format but not on the TFLite tools or runtime (interpreter), | {
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} | Followup to Scatter Layout normalization
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check for that and adjust a test case that would in fact be incorrectly
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} | Fix various code style and build issues in stream_executor/rocm
- Adding namespace to `string` -> `std::string` (Not sure why this compiled in the first place)
- Remove unused local variables
- Avoid unintended fallthrough in switch statement
- Fix member variable initialization order in a constructor
- Avoid read of unintialized variable
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11767 from Intel-tensorflow:yang/ci 0c2500b703b73bc340ccc9e3817c7ce54cc8d718
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} | Fix ROCm XLA profiling code
- Add missing dependencies to make the layering check happy
- Add missing cast to function call to make latest clang happy
- Add missing mutex_lock to fix thread safety analysis error
- Replace flat_hash_map by a node_hash_map since the ValueType is not moveable or copyable.
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"@Blackoowhite Some TensorFlow operations might not be directly supported by TFLite. During conversion, the converter checks for compatibility and throws errors if it encounters unsupported operations. \r\nIn order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here. Also find resources on exporting models to SavedModel format in the TensorFlow documentation [here]( https://www.tensorflow.org/guide/saved_model.).\r\n\r\nThank you!",
"Thanks everyone, I havd found the issue. I must import keras like tf.python.keras..... instead of tf.keras......",
"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/67811\">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/67811\">No</a>\n"
] | 2024-05-17T08:21:35 | 2024-05-18T03:19:38 | 2024-05-18T03:19:36 | NONE | null | null | null | 1. System information
- My code compile under windows
- Python version is : 3.10.8
- TF version is : 2.16.1
2. Issue
I generated a sequential model, and I want to transfer it into tflite, but I got the error.
3. Code
`
folder_path = r"D:\VisualStudioCode\Project\FL_For_Android\android\tflite_convertor\tflite_file"
print(os.path.exists(folder_path))
h5_path = os.path.join(folder_path, "model.h5")
tflite_path = os.path.join(folder_path, "model.tflite")
# Define the head model (which is actually the full model in this case).
model: Sequential = tf.keras.Sequential(
[
tf.keras.Input(shape=(32, 32, 3)),
tf.keras.layers.Conv2D(6, 5, activation="relu"),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
tf.keras.layers.Conv2D(16, 5, activation="relu"),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(units=120, activation="relu"),
tf.keras.layers.Dense(units=84, activation="relu"),
tf.keras.layers.Dense(units=10, activation="softmax"),
]
)
model.compile(loss="categorical_crossentropy", optimizer="sgd")
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
with open(tflite_path, 'wb') as f:
f.write(tflite_model)
`
4. Info & logs
`2024-05-17 16:21:00.770822: 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`.
2024-05-17 16:21:01.229299: 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`.
True
2024-05-17 16:21:02.362680: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`.
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
W0000 00:00:1715934062.706921 22212 tf_tfl_flatbuffer_helpers.cc:390] Ignored output_format.
W0000 00:00:1715934062.707266 22212 tf_tfl_flatbuffer_helpers.cc:393] Ignored drop_control_dependency.
2024-05-17 16:21:02.708010: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: C:\Users\BLACK0~1\AppData\Local\Temp\tmpxvz0370l
2024-05-17 16:21:02.708775: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve }
2024-05-17 16:21:02.708865: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: C:\Users\BLACK0~1\AppData\Local\Temp\tmpxvz0370l
2024-05-17 16:21:02.713857: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled
2024-05-17 16:21:02.715152: I tensorflow/cc/saved_model/loader.cc:234] Restoring SavedModel bundle.
2024-05-17 16:21:02.736379: I tensorflow/cc/saved_model/loader.cc:218] Running initialization op on SavedModel bundle at path: C:\Users\BLACK0~1\AppData\Local\Temp\tmpxvz0370l
2024-05-17 16:21:02.741926: I tensorflow/cc/saved_model/loader.cc:317] SavedModel load for tags { serve }; Status: success: OK. Took 33913 microseconds.
2024-05-17 16:21:02.751512: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
loc(fused["ReadVariableOp:", callsite("sequential_1/conv2d_1/Reshape/ReadVariableOp@__inference_serving_default_183"("d:\\VisualStudioCode\\Project\\FL_For_Android\\android\\tflite_convertor\\test_convert_to_tflite.py":37:1) at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\tensorflow\\lite\\python\\lite.py":1175:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\tensorflow\\lite\\python\\lite.py":1129:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\tensorflow\\lite\\python\\lite.py":1636:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\tensorflow\\lite\\python\\lite.py":1614:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\tensorflow\\lite\\python\\convert_phase.py":205:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\tensorflow\\lite\\python\\lite.py":1537:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\backend\\tensorflow\\layer.py":58:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\backend\\tensorflow\\layer.py":120:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py":117:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\layers\\layer.py":826:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py":117:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\ops\\operation.py":48:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py":156:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\models\\sequential.py":206:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\models\\functional.py":199:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\ops\\function.py":151:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\models\\functional.py":583:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py":117:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\layers\\layer.py":826:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py":117:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\ops\\operation.py":48:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py":156:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py":233:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\ops\\numpy.py":4527:1 at callsite("D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py":1618:1 at "D:\\Python\\python3.10.8\\lib\\site-packages\\keras\\src\\backend\\tensorflow\\core.py":64:1))))))))))))))))))))))))))]): error: missing attribute 'value'
LLVM ERROR: Failed to infer result type(s).` | {
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`:convolution_thunk` includes `rocm_config.h`, hence we need to depend
on `rocm_headers` to make the layering check happy.
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} | PR #11767: Add SYCL build script
Imported from GitHub PR https://github.com/openxla/xla/pull/11767
This is a sub PR of https://github.com/openxla/xla/pull/9042, it adds a script to build xla with `--config=sycl`.
Here is how to use:
```bash
workspace=$1
xla_path=$workspace/xla
cd $workspace
git clone -b yang/ci https://github.com/Intel-tensorflow/xla xla
bash $xla_path/build_tools/sycl/build.sh $workspace
```
Copybara import of the project:
--
d811bba1548f0c983da473a0940b1ea3d8b2c436 by Sheng, Yang <[email protected]>:
Add SYCL build script
--
0c2500b703b73bc340ccc9e3817c7ce54cc8d718 by Sheng, Yang <[email protected]>:
change path
Merging this change closes #11767
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11767 from Intel-tensorflow:yang/ci 0c2500b703b73bc340ccc9e3817c7ce54cc8d718
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We don't support nested fusions. This creates one because the
map gets wrapped in a fusion, and the computation already
contains a fusion.
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Adding some more direct dependencies to make the layering check happy.
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} | Move unique_indices from Gather to Scatter where it belongs.
Somehow the attribute unique_indices ended up in the documentation for Gather,
but it is an attribute for Scatter.
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} | [pt composite lowerings]
Add lowering from composite for average pool when ceil_mode=true and count_pad=true
Additionally, the following refactors:
* Simplify padding string determination
* Use intermediate struct to parse attrs into c types
* Rename some functions to be more cleared where they are used
* Constrain enum usage to within one function, so remove enum
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} | Add missing ROCm dependency in gpu/runtime
`:convolution_thunk` includes `rocm_config.h`, hence we need to depend
on `rocm_headers` to make the layering check happy.
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} | [XLA:GPU] Add support for destructuring collapsing reshapes in symbolic tiles.
Take a reshape `[6,8] reshape([48])`. The indexing map going through this\
reshape from output to input would be\
`(d0, d1) -> (d0 * 8 + d1)`.\
Previously to this change, there was no support for extracting sizes and\
strides from a multivariate expression.
Deriving a `size_map` for the indexing map above is as simple as creating a map\
`(s0, s1) -> (s0 * s1)`,\
which is easy enough. However, deriving a `stride_map` for the expression is\
complicated. Conceptually, the stride of the composite expression should\
correspond to the stride of the minormost dimension involved in the reshape\
along which we capture more than a single element.
This holds because there are restrictions on what a valid tiling can be when\
going through a collapse. Let `s` be an `n`-dimensional shape that is fully\
collapsed. In order to be propagated successfully through the collapse, the\
pattern of the tiling of `s` has to look like\
`(1*, partial_dim?, full_dims*, 1*)`\
where `full_dims` are dimensions that are captured completely, and\
`partial_dim` is a dimension that can be captured with an arbitrary tile.\
This restriction is necessary to ensure that the gap between two elements\
captured in the expression is always the same (i.e., the set of elements can\
be described using a single `stride` and is thus a tile).
Based on the above, algorithm for extracting the `stride` could therefore be\
represented as a series of nested `if` statements\
`if size0 != 1 then stride0 else (if size1 != 1 then stride1 else ...)`\
where `{size,stride}i` corresponds to the `i-th` major {size,stride}.
We implement a utility function that allows us to generate `if` statements as\
affine expressions.
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} | Disable NCCL persistent plan allocator on ROCm
It had already been disabled for OSS builds, this is only making
sure it's also disabled when `PLATFORM_GOOGLE` is set to true.
It's a non-functional change - just some ugly macro hackery.
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FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13018 from ROCm:ci_amdtags_20240523 0bf9ab80d06284883df19f17f421eb6a691c0dd8
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} | Revive the PLATFORM_GOOGLE code in the ROCm wrappers
The ROCm wrappers is basically a hand-written implementation layer library.
We bypass it when `PLATFORM_GOOGLE` is set and just statically link the ROCm
and HIP libraries instead. The bypass code was broken since we didn't use it
for a long time.
So this CL is fixing a bunch of issues with the code and makes it work with
the latest ROCm version.
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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/67798/checks?check_run_id=25087278619) 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 @xujuntwt95329 Can you please sign CLA. Thank you!",
"Hi @xujuntwt95329 Can you please sign CLA. Thank you!"
] | 2024-05-17T06:29:25 | 2024-06-07T17:24:51 | null | NONE | null | false | {
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> tensorflow\lite\core\c\operator.cc(28,29): error C7
> 555: use of designated initializers requires at least '/std:c++20' | {
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} | Fix TF Grappler ROCm build issue
The `gcnArch` property had been deprecated for a while (at least for 4 years).
`gcnArchName` is the replacement and since it's already available for so
long it shouldn't break anyone.
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This was introduced by the `xla::Status`/`absl::Status` cleanup by accident.
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"Hi **@akote123** ,\r\n- Looks like this is duplicate of issue #[61322](https://github.com/tensorflow/tensorflow/issues/61322). Can you please confirm once?\r\n\r\nThank you!",
"This is not a duplicate. This (https://github.com/tensorflow/tensorflow/issues/61322) issue is about oneDNN primitives. The current issue about enabling oneDNN graph API in TF. In pytorch there is API in torch script to enable the same (https://pytorch.org/blog/celebrate-pytorch-2.0/). So I was curious to know is similar feature like this is available in Tensorflow."
] | 2024-05-17T05:17:16 | 2024-05-21T08:19:48 | null | NONE | null | null | null | How we can enable oneDNN graph API in tensorflow? | {
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