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A `TFShapeInferencePass` is also required before running the legalization passes to resolve unknown shapes after variable freezing.
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Fixes strict subclass check for checkpoint callback field.
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Update the sharding in `ShardonnayXLATest.CostantSplitter`.
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In `PrepareForExportPass`, the added broadcast inherits the `kShardingAttr` from the original constant op.
Before this cl, `kShardingAttr` of the splat constant is dropped in the `PrepareForExportPass`. This cl fixes this issue and adds a test case, which fails without this cl and succeeds with this cl.
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[xla:tpu] Allow host offloading ops to pass hlo verification when changing layout.
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[xla:tpu] Allow host offloading ops to pass hlo verification when changing layout.
Host offloading ops (dynamic slice, copy, and dynamic update slice) move tensors between S(0) and S(5) memory space. This can cause the hlo verifier to fail as it interprets this layout to be unequal. This change allows for memory space to be ignored when comparing layouts for these ops.
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[xla:gpu] Add a utility for constructing a collective-pipeliner to pipeline
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[xla:gpu] Add a utility for constructing a collective-pipeliner to pipeline
Send-Recv chains for GPU.
The pipeliner retrieves pipelining decision from a frontend attribute annotated
by a previous pass. It also modifies a frontend attribute about loop iterations
used for runtime optimization.
Add a test.
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Ensure reported costs are consistent.
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Ensure reported costs are consistent.
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PR_kwDOArmXAs5pUqw0
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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/a1292bbecd65ad194d9b14681d296d6a286fe997.
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PR_kwDOArmXAs5pUkzG
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PR #10390: Split FP8 And Non-FP8 GEMM Rewriters
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PR #10390: Split FP8 And Non-FP8 GEMM Rewriters
Imported from GitHub PR https://github.com/openxla/xla/pull/10390
Separates the rewriters for FP8 and non-FP8 GEMMs and moves the rewrite of FP8 GEMMs ahead of the Triton GEMM rewriter.
Copybara import of the project:
--
ee321f056e816191e960ec9aab882b077aad6f55 by Philipp Hack <[email protected]>:
Separates the rewriters for FP8 and non-FP8 GEMMs.
--
488e7cde2b8fd6351c0cfa4a9359d7b47aca596b by Philipp Hack <[email protected]>:
Separates the rewriters for FP8 and non-FP8 GEMMs.
Merging this change closes #10390
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10390 from philipphack:u_fp8_rewriter_split_xla 488e7cde2b8fd6351c0cfa4a9359d7b47aca596b
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[XLA:Python] Port pprof_profile_builder to nanobind.
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[XLA:Python] Port pprof_profile_builder to nanobind.
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PR_kwDOArmXAs5pUgsX
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[TSL] Fix some erroneous IWYU comments in tsl/platform/protobuf.h, which were missing a space making them ineffective.
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[TSL] Fix some erroneous IWYU comments in tsl/platform/protobuf.h, which were missing a space making them ineffective.
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[xla] Extend collective-pipeliner to support mutation of backward cloned
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[xla] Extend collective-pipeliner to support mutation of backward cloned
collective instruction.
This is needed to modify loop iteration related frontend attributes to reflect
the peeling of the first loop iteration for backward pipelining for the GPU
backend.
Modify an existing test to test the implementation.
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PR_kwDOArmXAs5pUV8V
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[XLA:Runtime] Moved the nccl_p2p_thunk_common, nccl_recv_thunk and nccl_send_thunk to the new Runtime folder and removed unused dependencies. Updated the necessary directories pointing to these thunks and removed it from the nccl_collectives_thunks build. #5758
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[XLA:Runtime] Moved the nccl_p2p_thunk_common, nccl_recv_thunk and nccl_send_thunk to the new Runtime folder and removed unused dependencies. Updated the necessary directories pointing to these thunks and removed it from the nccl_collectives_thunks build. #5758
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PR_kwDOArmXAs5pUTPa
| 63,466 |
Fix gemm_rewrite_test failures on Hopper.
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Fix gemm_rewrite_test failures on Hopper.
Some FP8 tests were failing since we didn't properly set the shape of the matrix or vector bias in the GemmConfig.
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PR_kwDOArmXAs5pURJ3
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PR #10011: [ROCM] cuSolver API fixes 27/02/24
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PR #10011: [ROCM] cuSolver API fixes 27/02/24
Imported from GitHub PR https://github.com/openxla/xla/pull/10011
In this PR, we fix ROCM build after the recent cuSolver API changes from https://github.com/openxla/xla/commit/dad57bd0d943e08795b3cd55dd27fb4abe0cd9c4
Also a small rocblas fix after the recent refactoring.
@xla-rotation: would you please have a look ?
Copybara import of the project:
--
6d7265ba17be640da97c87d256919dadb4fe4d71 by Chao Chen <[email protected]>:
use potrfDSCZ
--
d965fd3a817838c4b7440868afb6884ea011b078 by Pavel Emeliyanenko <[email protected]>:
fixing use_hgemm_alt_impl_ flag
Merging this change closes #10011
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10011 from ROCm:ci_cusolver_fix_240227 d965fd3a817838c4b7440868afb6884ea011b078
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PR_kwDOArmXAs5pUPIq
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Remove DeviceOptions from StreamExecutorConfig.
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Remove DeviceOptions from StreamExecutorConfig.
It's only used by CudaPlatform, so do the equivalent work there.
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PR_kwDOArmXAs5pUI8y
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hlo_reachability: make operator|= friendly to auto-vectorization
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hlo_reachability: make operator|= friendly to auto-vectorization
This vectorizes the OR loop, at least with clang trunk on
AVX-capable x86 CPUs.
name old time/op new time/op delta
BM_HloReachabilityBitSetUnion/1 1.50ns ± 0% 2.99ns ± 0% +99.47% (p=0.008 n=5+5)
BM_HloReachabilityBitSetUnion/64 1.50ns ± 1% 2.99ns ± 0% +99.51% (p=0.008 n=5+5)
BM_HloReachabilityBitSetUnion/128 2.62ns ± 0% 3.74ns ± 0% +42.96% (p=0.008 n=5+5)
BM_HloReachabilityBitSetUnion/256 4.86ns ± 0% 5.23ns ± 0% +7.54% (p=0.008 n=5+5)
BM_HloReachabilityBitSetUnion/512 9.35ns ± 0% 4.31ns ± 9% -53.92% (p=0.008 n=5+5)
BM_HloReachabilityBitSetUnion/4096 72.2ns ± 0% 19.8ns ± 0% -72.52% (p=0.008 n=5+5)
BM_HloReachabilityBitSetUnion/32768 589ns ± 0% 152ns ± 0% -74.22% (p=0.016 n=4+5)
BM_HloReachabilityBitSetUnion/262144 4.71µs ± 1% 1.84µs ± 0% -61.01% (p=0.016 n=4+5)
BM_HloReachabilityBuild/1 165ns ± 1% 164ns ± 0% ~ (p=0.343 n=4+4)
BM_HloReachabilityBuild/64 6.28µs ± 3% 6.37µs ± 4% ~ (p=0.548 n=5+5)
BM_HloReachabilityBuild/128 13.1µs ± 5% 13.0µs ± 1% ~ (p=0.690 n=5+5)
BM_HloReachabilityBuild/256 28.4µs ± 4% 27.6µs ± 2% -2.65% (p=0.032 n=5+5)
BM_HloReachabilityBuild/512 62.1µs ± 2% 59.8µs ± 1% -3.68% (p=0.008 n=5+5)
BM_HloReachabilityBuild/4096 1.17ms ± 1% 0.98ms ± 2% -16.49% (p=0.008 n=5+5)
BM_HloReachabilityBuild/32768 52.4ms ± 1% 41.7ms ± 2% -20.45% (p=0.008 n=5+5)
BM_HloReachabilityBuild/262144 4.77s ± 7% 4.23s ± 6% -11.43% (p=0.008 n=5+5)
Some notes:
- For small inputs (e.g. n <= 64), vectorization is overkill for the "Union"
benchmark and therefore slower than the scalar baseline. However, this
slowdown is not measurable in the "Build" benchmark, which is where BitSet's
"or" (a.k.a. "Union") op is used.
- The benefits of vectorization might fizzle out given large enough inputs--see
the reduced speedup from 32K to 256K inputs--, which is probably due to the
reduced cache locality.
Assembly snippets (via `perf annotate`):
0000000034fa1640 <xla::(anonymous namespace)::BM_HloReachabilityBitSetUnion(benchmark::State&)>:
xla::(anonymous namespace)::BM_HloReachabilityBitSetUnion(benchmark::State&):
- Before:
138: xor %esi,%esi
0.00 nop
12.61 140: mov -0x30(%rbp),%rdx
2.61 mov (%rdx,%rsi,8),%rdx
13.57 or %rdx,(%rax,%rsi,8)
2.17 inc %rsi
0.15 mov -0x50(%rbp),%rax
0.05 mov -0x48(%rbp),%rdx
12.38 mov %rdx,%rdi
2.40 sub %rax,%rdi
0.03 sar $0x3,%rdi
0.02 cmp %rdi,%rsi
0.10 ↑ jb 140
3.59 ↑ jmp e3
- After:
0.04 xor %r8d,%r8d
0.51 cs nopw 0x0(%rax,%rax,1)
2.27 180: vmovups (%rdx,%r8,8),%xmm0
1.84 vmovups 0x10(%rdx,%r8,8),%xmm1
1.39 vmovups 0x20(%rdx,%r8,8),%xmm2
1.18 vmovups 0x30(%rdx,%r8,8),%xmm3
8.69 vorps (%rax,%r8,8),%xmm0,%xmm0
4.81 vorps 0x10(%rax,%r8,8),%xmm1,%xmm1
6.02 vorps 0x20(%rax,%r8,8),%xmm2,%xmm2
4.38 vorps 0x30(%rax,%r8,8),%xmm3,%xmm3
2.57 vmovups %xmm0,(%rax,%r8,8)
0.89 vmovups %xmm1,0x10(%rax,%r8,8)
2.39 vmovups %xmm2,0x20(%rax,%r8,8)
1.10 vmovups %xmm3,0x30(%rax,%r8,8)
1.70 add $0x8,%r8
0.30 cmp %r8,%rdi
0.40 ↑ jne 180
cmp %rdi,%rsi
0.62 ↑ je 90
↑ jmp d7
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/9319 from jaro-sevcik:no-cublas-or-triton-for-small-matmuls 690bb0e1d8b5894f64134d68a031d387399c638f
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| 2,180,436,048 |
PR_kwDOArmXAs5pUIRH
| 63,462 |
hlo_reachability_test: add benchmarks for HloReachabilityMap
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hlo_reachability_test: add benchmarks for HloReachabilityMap
Add two microbenchmarks: one for BitSet's operator|= (a.k.a. Union)
and another for its main user, HloReachabilityMap::Build.
Having these two will allow us to evaluate changes to operator|='s
implementation.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/62647 from Intel-tensorflow:othakkar/pattern_matcher_control_inouts 4a7cea22083f22a4e4fb26e05707b5961ae3c67f
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| 2,180,415,070 |
PR_kwDOArmXAs5pUDqE
| 63,461 |
Add CompileFunction streamz.
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Add CompileFunction streamz.
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| 2,180,412,025 |
PR_kwDOArmXAs5pUC_W
| 63,460 |
PR #10389: [NVIDIA] Enable autotune of Cublaslt Fp8 Gemm
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NONE
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PR #10389: [NVIDIA] Enable autotune of Cublaslt Fp8 Gemm
Imported from GitHub PR https://github.com/openxla/xla/pull/10389
This PR enables the autotune of cublaslt fp8 gemm by default.
cc. @reedwm @philipphack @wenscarl @nluehr
Copybara import of the project:
--
6a379d5a9bb58c909b59eae24d369f9dee0c9039 by kaixih <[email protected]>:
Enable fp8 matmul autotuning
Merging this change closes #10389
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10389 from kaixih:autotune_fp8 6a379d5a9bb58c909b59eae24d369f9dee0c9039
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| 2,180,380,788 |
PR_kwDOArmXAs5pT8EG
| 63,459 |
Don't require a WhileOp for the embedding sequencing MLIR rewrite pass
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NONE
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Don't require a WhileOp for the embedding sequencing MLIR rewrite pass
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| 2,180,374,915 |
PR_kwDOArmXAs5pT6xg
| 63,458 |
Add IsInferenceGraph util method in mlir_bridge_pass_util_test.cc
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Add IsInferenceGraph util method in mlir_bridge_pass_util_test.cc
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| 2,180,374,803 |
PR_kwDOArmXAs5pT6v5
| 63,457 |
Integrate LLVM at llvm/llvm-project@6cd68c2f8783
|
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NONE
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Integrate LLVM at llvm/llvm-project@6cd68c2f8783
Updates LLVM usage to match
[6cd68c2f8783](https://github.com/llvm/llvm-project/commit/6cd68c2f8783)
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| 2,180,359,727 |
PR_kwDOArmXAs5pT3fB
| 63,456 |
[XLA:MSA] Refactor MemoryBoundLoopOptimizer out of memory_space_assignment file.
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[XLA:MSA] Refactor MemoryBoundLoopOptimizer out of memory_space_assignment file.
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Could not load library cudnn_cnn_infer64_8.dll. Error code 126
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"Hi **@Tanishq-JM** ,\r\n\r\nUpdate GPU drivers: \r\nEnsure that you have the latest NVIDIA drivers installed for your gpu(Nvidia Quadro T2000). You can download the latest drivers from the NVIDIA website and follow the installation instructions.\r\n\r\nCheck CUDA and cuDNN installation:\r\nMake sure that CUDA and cuDNN are installed correctly and that the paths to their binaries are included in your system's PATH environment variable.\r\n\r\nVerify TensorFlow installation: \r\nReinstall TensorFlow using pip to ensure that it's correctly installed and compatible with your system configuration. You can do this by running:\r\n```\r\npip install --upgrade tensorflow\r\n```\r\nHere i am providing [gist](https://colab.sandbox.google.com/gist/Venkat6871/64bb3ffc5bf441523b308f8e5444cd0c/63455_2-15-v.ipynb) for your reference, Once go through it.\r\n\r\nThank you!\r\n\r\n",
"# Thank You @Venkat6871 for your guidance i installed VS and Nvidia graphic drivers .\r\nBut The problem is still there when i am using another version of tensorflow it uses CPU instead but TF 2.1 needs some .dll file and its unable to access it .\r\n\r\n#code \r\n`import tensorflow as tf\r\nimport subprocess\r\nfrom tensorflow.python.client import device_lib\r\nprint(device_lib.list_local_devices())\r\n\r\ndef get_gpu_info():\r\n try:\r\n result = subprocess.run(['nvidia-smi'], stdout=subprocess.PIPE)\r\n return result.stdout.decode('utf-8')\r\n except FileNotFoundError:\r\n return \"nvidia-smi not found. Make sure NVIDIA drivers are installed.\"\r\n\r\nfrom keras import layers, models, datasets\r\nimport time\r\n\r\n(train_images, train_labels), (_, _) = datasets.mnist.load_data()\r\ntrain_images = train_images.reshape(-1, 28, 28, 1).astype('float32') / 255.0\r\nmodel = models.Sequential([\r\n layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),\r\n layers.MaxPooling2D((2, 2)),\r\n layers.Conv2D(64, (3, 3), activation='relu'),\r\n layers.MaxPooling2D((2, 2)),\r\n layers.Conv2D(64, (3, 3), activation='relu'),\r\n layers.Flatten(),\r\n layers.Dense(64, activation='relu'),\r\n layers.Dense(10, activation='softmax')\r\n])\r\n\r\nmodel.compile(optimizer='adam',\r\n loss='sparse_categorical_crossentropy',\r\n metrics=['accuracy'])\r\n\r\nstart_time = time.time()\r\nend_time = start_time + 30\r\n\r\nwhile time.time() < end_time:\r\n gpu_info = get_gpu_info()\r\n print(gpu_info)\r\n\r\nmodel.fit(train_images, train_labels, epochs=1, batch_size=64, verbose=0)\r\nloss, accuracy = model.evaluate(train_images, train_labels, verbose=0)\r\nprint(f\"Loss: {loss}, Accuracy: {accuracy}\")\r\n`\r\n\r\n# Output : \r\n\r\n\r\n(ML) PS D:\\python> & d:/python/ML/Scripts/python.exe d:/python/test.py\r\n2024-03-14 11:50:53.845372: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-03-14 11:50:55.378833: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /device:GPU:0 with 2128 MB memory: -> device: 0, name: Quadro T2000, pci bus id: 0000:01:00.0, compute capability: 7.5\r\n[name: \"/device:CPU:0\"\r\ndevice_type: \"CPU\"\r\nmemory_limit: 268435456\r\nlocality {\r\n}\r\nincarnation: 12036372220610172862\r\nxla_global_id: -1\r\n, name: \"/device:GPU:0\"\r\ndevice_type: \"GPU\"\r\nmemory_limit: 2232156160\r\nlocality {\r\n bus_id: 1\r\n links {\r\n }\r\n}\r\nincarnation: 1063671899388078998\r\nphysical_device_desc: \"device: 0, name: Quadro T2000, pci bus id: 0000:01:00.0, compute capability: 7.5\"\r\nxla_global_id: 416903419\r\n]\r\n2024-03-14 11:50:55.686954: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2128 MB memory: -> device: 0, name: Quadro T2000, \r\npci bus id: 0000:01:00.0, compute capability: 7.5\r\nThu Mar 14 11:50:55 2024 \r\n\r\n\r\n\r\n\r\n\r\n2024-03-14 11:50:57.159136: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8600\r\nCould not load library cudnn_cnn_infer64_8.dll. Error code 126\r\nPlease make sure cudnn_cnn_infer64_8.dll is in your library path!\r\n(ML) PS D:\\python>\r\n\r\nHere in the Photo is showing a process is present and there is some GPU getting used at start .\r\n\r\n# where i am doing mistake ?\r\n\r\n> ` Environment variables `\r\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.2\\bin\r\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.2\\extras\\CUPTI\\lib64\r\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.2\\extras\\CUPTI\\include\r\nD:\\Tanishq games\\google install\\New folder\\cuda\\bin\r\nD:\\Tanishq games\\google install\\New folder\\cuda\\include\r\n\r\n>` Cuda == 11.2 and cudnn == 8.1 (11.2)`\r\n",
"Hi **@Tanishq-JM** ,\r\n-Could you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved.\r\n\r\nThank ypu!"
] | 2024-03-11T22:08:44 | 2024-06-11T14:41:30 | null |
NONE
| null | null | null |
### Issue type
bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.10.0
### Custom code
Yes
### OS platform and distribution
windows 10
### Mobile device
Thinkpad p53
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.2 / 11.2(8.1 I think)
### GPU model and memory
Nvidia T2000 4GB
### Current behavior?
# Problem :
I expect this problem to be resolved soon. I am unable to use my GPU for training, as it remains at 0% usage. When I try another version, it only uses my CPU, which takes days and causes my computer (laptop) to overheat.
`python code `
import tensorflow as tf
import subprocess
physical_devices = tf.config.list_physical_devices('GPU')
print("Num GPUs Available:", len(physical_devices),physical_devices)
print(f"tensor version : {tf.__version__}")
def get_gpu_info():
try:
result = subprocess.run(['nvidia-smi'], stdout=subprocess.PIPE)
return result.stdout.decode('utf-8')
except FileNotFoundError:
return "nvidia-smi not found. Make sure NVIDIA drivers are installed."
gpu_info = get_gpu_info()
print(gpu_info)
if physical_devices:
for i, gpu in enumerate(physical_devices):
print("GPU", i, physical_devices,":", gpu)
from keras import layers, models, datasets
import time
import os
(train_images, train_labels), (_, _) = datasets.mnist.load_data()
train_images = train_images.reshape(-1, 28, 28, 1).astype('float32') / 255.0
model = models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
start_time = time.time()
end_time = start_time + 30
while time.time() < end_time:
model.fit(train_images, train_labels, epochs=1, batch_size=64, verbose=0)
loss, accuracy = model.evaluate(train_images, train_labels, verbose=0)
print(f"Loss: {loss}, Accuracy: {accuracy}")
# environment variables

all the libs are copy and pasted already in the bin and lib folder
### Standalone code to reproduce the issue
```shell
# Error
I am unable to utilize my NVIDIA Quadro T2000 GPU for training, as it remains at 0% usage. Additionally, when I attempt to use a different version, I encounter the following error: "Could not load library cudnn_cnn_infer64_8.dll. Error code 126. Please make sure cudnn_cnn_infer64_8.dll is in your library path." This forces the training to use only my CPU, which is inefficient and causes my laptop to overheat. Despite having a good GPU (NVIDIA Quadro T2000), Intel Core i7 9th Gen processor, and 32GB of RAM, I am unable to leverage my GPU for training. I have already tried setting up the environment variables and other dependencies, as well as using CUDA 11.2 with cuDNN 8.1.
```
### Relevant log output
```shell
d:/python/test.py
Num GPUs Available: 1 [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
tensor version : 2.10.0
Tue Mar 12 03:11:23 2024
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 529.08 Driver Version: 529.08 CUDA Version: 12.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Quadro T2000 WDDM | 00000000:01:00.0 Off | N/A |
| N/A 39C P8 3W / 30W | 0MiB / 4096MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
GPU 0 [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')] : PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')
Could not load library cudnn_cnn_infer64_8.dll. Error code 126
Please make sure cudnn_cnn_infer64_8.dll is in your library path!
```
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PR_kwDOArmXAs5pTvM6
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[XLA:Python] Fix build failure on Mac OS.
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[XLA:Python] Fix build failure on Mac OS.
ssize_t and int64_t aren't the same thing on Mac. But we always want to use int64_t, so just change the type to that and convert around the NumPy call.
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PR_kwDOArmXAs5pTngY
| 63,453 |
Modify the matrix class to keep track of both memory and communication resharding costs for a given edge as part of one matrix object.
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Modify the matrix class to keep track of both memory and communication resharding costs for a given edge as part of one matrix object.
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internal changes to unroller
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internal changes to unroller
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PR_kwDOArmXAs5pTjxX
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#tf-data Replace `std::function` with `IndexMapperFn`.
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#tf-data Replace `std::function` with `IndexMapperFn`.
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PR_kwDOArmXAs5pTgWR
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Improved readability based on the article
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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/63450/checks?check_run_id=22533997144) 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."
] | 2024-03-11T21:18:16 | 2024-03-29T15:10:26 | 2024-03-29T15:10:23 |
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Improved readability based on the article
Changes based on the Hurwitz Zeta algorithm from the article linked in the comments.
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PR #10182: [ROCM] fixing hipblas-lt large startup overhead
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[] | 2024-03-11T21:09:20 | 2024-03-15T18:37:45 | 2024-03-15T18:37:44 |
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PR #10182: [ROCM] fixing hipblas-lt large startup overhead
Imported from GitHub PR https://github.com/openxla/xla/pull/10182
This PR https://github.com/openxla/xla/pull/6595 introduced **matmul_algorithm_cache_** which calls MatmulPlan::GetAlgorithms while holding a mutex. This is not bad on CUDA side but on ROCM, hipblas-lt has a large startup overhead, and when running this code with multiple GPUs, this startup overhead increases linearly with the number of GPUs.
However, **MatmulPlan::GetAlgorithms** can be run in parallel across different StreamExecutorsL anyway library functions are called under [per-instance mutex](https://github.com/ROCm/xla/blob/43c67a4d135b5e1e023c3e67b9062b9aa88d6ab8/xla/stream_executor/cuda/cuda_blas_lt.cc#L244).
I also did the same for **matmul_plans_cache_**, besides I added a multi-GPU matmul test which runs with cublas and cublas-lt libraries.
@xla-rotation: could you please have a look ?
Copybara import of the project:
--
43c67a4d135b5e1e023c3e67b9062b9aa88d6ab8 by Pavel Emeliyanenko <[email protected]>:
fixing hipblas-lt large startup overhead
Merging this change closes #10182
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10182 from ROCm:ci_hipblas_lt_startup_overhead 43c67a4d135b5e1e023c3e67b9062b9aa88d6ab8
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Add logs for coordination service initialization.
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Add logs for coordination service initialization.
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PR_kwDOArmXAs5pTZmU
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HloSchedule: Add insert_instruction() to HloInstructionSequence.
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HloSchedule: Add insert_instruction() to HloInstructionSequence.
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Add logs for removing cache entry
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Extract passes required for unrolling to the while_loop_unroller interface.
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Extract passes required for unrolling to the while_loop_unroller interface.
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Update XLA visibility for Enzyme
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Update XLA visibility for Enzyme
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PR_kwDOArmXAs5pTVJt
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PR #10066: [ROCM] rocm tracer fixes for jax profiler
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PR #10066: [ROCM] rocm tracer fixes for jax profiler
Imported from GitHub PR https://github.com/openxla/xla/pull/10066
1. Don't subscribe to HIP_API activity events
2. Use array for per_device_collector
3. Fix device_id indexing
Copybara import of the project:
--
50d23258e024e75d640a241775a4a6dbfbfb5d51 by Rahul Batra <[email protected]>:
[ROCM] rocm tracer fixes for jax profiler
Merging this change closes #10066
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10066 from ROCm:ci_profiler_fixes 50d23258e024e75d640a241775a4a6dbfbfb5d51
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PR #10097: [ROCm] Allow host buffer allocation in GpuExecutor::Allocate
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PR #10097: [ROCm] Allow host buffer allocation in GpuExecutor::Allocate
Imported from GitHub PR https://github.com/openxla/xla/pull/10097
Copybara import of the project:
--
79a89bec3a27e37fdd483315c1ea7e855b988adb by Dragan Mladjenovic <[email protected]>:
[ROCm] Allow host buffer allocation in GpuExecutor::Allocate
Merging this change closes #10097
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10097 from ROCm:host_alloc 79a89bec3a27e37fdd483315c1ea7e855b988adb
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Internal package group change
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NONE
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Internal package group change
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PR_kwDOArmXAs5pS9X7
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Remove internal eigen thread interface in ifrt-tfrt sharding utils
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Remove internal eigen thread interface in ifrt-tfrt sharding utils
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PR_kwDOArmXAs5pS1QI
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Support reading from filepath and byte data for Vertex Tensorboard profiler.
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Support reading from filepath and byte data for Vertex Tensorboard profiler.
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PR_kwDOArmXAs5pS1CK
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Permit `i4` arguments in XLA `while` ops.
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Permit `i4` arguments in XLA `while` ops.
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PR_kwDOArmXAs5pSruR
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Duplicate stablehlo.quantization.CalibrationOptions from tensorflow.quantization.CalibrationOptions. And migrate all usage for TF and StableHLO quantizer.
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Duplicate stablehlo.quantization.CalibrationOptions from tensorflow.quantization.CalibrationOptions. And migrate all usage for TF and StableHLO quantizer.
Populate default CalibrationOption during Run.
Add CalibrationOptions to QuantiazationConfig.
Add Calibration option integration tests for StableHLO Quantizer.
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PR_kwDOArmXAs5pSoE7
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[XLA:Python] Fix bug introduced by recent change that mapped int4 to int8 in a type conversion.
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[XLA:Python] Fix bug introduced by recent change that mapped int4 to int8 in a type conversion.
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PR_kwDOArmXAs5pShPY
| 63,435 |
#tf-data-service Fix a race condition when loading element_spec file.
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#tf-data-service Fix a race condition when loading element_spec file.
The element_spec file can be empty when it is being written. If it is
read while being written, race conditions can occur:
```
No encoder for object of type <class 'google3.third_party.tensorflow.core.protobuf.struct_pb2.StructuredValue'>.
```
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PR_kwDOArmXAs5pSgJ_
| 63,434 |
When converting a graph to a module that came from MlirXlaOpKernel force the use of _output_shapes.
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When converting a graph to a module that came from MlirXlaOpKernel force the use of _output_shapes.
When an Op is to be lowered by MlirXlaOpKernel all the output tensor shapes have been thrown away (except in the attribute) - this hasn't been a problem for most kernels because they generate the output shape based on inputs or other features; however, some Ops (e.g. _XlaHostComputeMlir') do not and are broken by the go/tuba bridge changes. This change forces the graph to module conversion to use the _output_shapes attribute, if present, to reintroduce the Op's shape.
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PR_kwDOArmXAs5pSXhL
| 63,433 |
Integrate LLVM at llvm/llvm-project@6cd68c2f8783
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Integrate LLVM at llvm/llvm-project@6cd68c2f8783
Updates LLVM usage to match
[6cd68c2f8783](https://github.com/llvm/llvm-project/commit/6cd68c2f8783)
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PR_kwDOArmXAs5pSII9
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[xla] Extend collective-pipeliner to support loop variant parameters in
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[xla] Extend collective-pipeliner to support loop variant parameters in
backward chains.
Add a predicate to the collective-pipeliner config for a backend to specify
whether an individual loop variant parameter is allowed in a pipelining chain.
This predicate is currently only used to support kBackward pipelining.
Enhance TransformLoopBackward to handle loop variant parameter in backward
chains.
Add a test.
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PR_kwDOArmXAs5pRuwJ
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Add CompileSingleOp streamz metrics.
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Add CompileSingleOp streamz metrics.
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PR_kwDOArmXAs5pRrgD
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[xla:gpu] Add runtime optimization using frontend attribute
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[xla:gpu] Add runtime optimization using frontend attribute
_xla_send_recv_validation.
A collective-permute instruction inside a loop may not always send or receive
data that affect the output of the whole module in all iterations. Assume this
information is encoded in frontend attribute _xla_send_recv_validation attached
to the Send and Recv instructions decomposed from such a collective-permute
instruction, the runtime can use this information to skip the invocation of the
NCCL API that performs the Send and Recv operations.
Add tests.
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[XLA:GPU] Explicit algorithm support for dot instructions
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[XLA:GPU] Explicit algorithm support for dot instructions
Now we can set PrecisionConfig::algorithm instead of PrecisionConfig::operand_precision.
The old PrecisionConfig::operand_precision is also still supported.
It would be a big change to remove it or even convert it to algorithm internally (because we support many type combinations and many tests would need to be updated).
PrecisionConfig::algorithm is not yet added to StableHLO, so we can only use it if we feed an HLO to XLA directly.
Future plans:
- We would like to support it in StableHLO as well.
- There are some plans to add similar algorithm support for convolution as well.
- The code around matmul_utils.cc is a bit convoluted, so it may be cleaned up later.
- We may write better tests for the algorithm support (actually checking if the correct code runs).
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10011 from ROCm:ci_cusolver_fix_240227 d965fd3a817838c4b7440868afb6884ea011b078
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Avoid computing determinants in numpy involving inf/nan.
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Avoid computing determinants in numpy involving inf/nan.
Newer versions of numpy print out a runtime warning and return zero
when encountering a NaN during the determinant computation. This
leads to incorrect comparisons.
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Addressing TF query analysis errors for @local_tsl repository
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Crash fatally on input HLOs with sharding annotations that suggest an ideal >2D device mesh for the HLO.
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Crash fatally on input HLOs with sharding annotations that suggest an ideal >2D device mesh for the HLO.
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PR_kwDOArmXAs5pQ81O
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Support elemental reduce with unsigned types.
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Support elemental reduce with unsigned types.
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Typo and Grammatical changes in data_flow_ops.py
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Some typo and Grammatical changes are updated in the file
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Integrate LLVM at llvm/llvm-project@f1aa7837884c
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Integrate LLVM at llvm/llvm-project@f1aa7837884c
Updates LLVM usage to match
[f1aa7837884c](https://github.com/llvm/llvm-project/commit/f1aa7837884c)
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Don't simplify mods to subtractions.
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NONE
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Don't simplify mods to subtractions.
The subtractions typically result in more complex expressions that are harder
to analyze. This solution isn't pretty, but I couldn't find anything better.
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PR_kwDOArmXAs5pQmNl
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[XLA:Python] Port py_array and py_executable to nanobind.
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NONE
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[XLA:Python] Port py_array and py_executable to nanobind.
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PR_kwDOArmXAs5pQcUb
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Integrate LLVM at llvm/llvm-project@f1aa7837884c
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Integrate LLVM at llvm/llvm-project@f1aa7837884c
Updates LLVM usage to match
[f1aa7837884c](https://github.com/llvm/llvm-project/commit/f1aa7837884c)
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PR_kwDOArmXAs5pQPht
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[XLA:CPU Next] Flatten tuple shardings
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[XLA:CPU Next] Flatten tuple shardings
Function arguments and results that are tuples, might have an `mhlo.sharding` attribute that encodes a tuple sharding, we should expand those as well when expanding the tuple arguments and results.
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PR_kwDOArmXAs5pQJir
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Integrate LLVM at llvm/llvm-project@f1aa7837884c
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Integrate LLVM at llvm/llvm-project@f1aa7837884c
Updates LLVM usage to match
[f1aa7837884c](https://github.com/llvm/llvm-project/commit/f1aa7837884c)
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PR_kwDOArmXAs5pQF4K
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#shlo_ref Add `cosine` op.
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#shlo_ref Add `cosine` op.
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#shlo_ref Add `cbrt` op.
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#shlo_ref Add `cbrt` op.
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PR_kwDOArmXAs5pQFcW
| 63,415 |
#shlo_ref Add a printer for `DataType` typed tests.
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#shlo_ref Add a printer for `DataType` typed tests.
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PR_kwDOArmXAs5pQFOv
| 63,414 |
#shlo_ref Add `ceil` op.
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#shlo_ref Add `ceil` op.
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PR_kwDOArmXAs5pPyHq
| 63,413 |
Only log if a valid logging context is available.
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NONE
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Only log if a valid logging context is available.
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PR_kwDOArmXAs5pPpjQ
| 63,412 |
Do not apply delegates in testDisablePerChannelQuantizationForDenseLayers as XNNPack converts per tensor to per channel
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NONE
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Do not apply delegates in testDisablePerChannelQuantizationForDenseLayers as XNNPack converts per tensor to per channel
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| 2,179,152,417 |
PR_kwDOArmXAs5pPpZN
| 63,411 |
Fix bug in Horner's rule for Zeta
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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/63411/checks?check_run_id=22513846895) 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."
] | 2024-03-11T13:34:36 | 2024-03-13T18:10:21 | 2024-03-13T18:10:20 |
NONE
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Fix bug in Horner's rule for Zeta
This change used to produce an invalid result for the Hurwitz Zeta function.
It matches https://github.com/openxla/xla/blob/7a067a7b88d2ffb15b1dc5e3c06f701a15f0391d/xla/client/lib/math.cc#L1967-L1968 C++ implementation as well.
Example zeta(2, q=1):
jax32/zeta : 1.6449342966079712
jax64/zeta : 1.6449342726140739
scipy/zeta : 1.6449340668482266
pi^2 / 6 : 1.6449340668482264
After fixing:
jax64/zeta : 1.6449340668482264
Fixes #5838
Co-authored-by: pparuzel <[email protected]>
Co-authored-by: Adam-Banas <[email protected]>
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PR_kwDOArmXAs5pPk17
| 63,410 |
Clean up some duplicate code in fusions.cc.
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NONE
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Clean up some duplicate code in fusions.cc.
Also don't try to emit scatters that contain unsupported instructions
in their update computation.
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PR_kwDOArmXAs5pPi-3
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Modify QuantizeCompositeFunctionPass to produce hybrid quantized op
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Modify QuantizeCompositeFunctionPass to produce hybrid quantized op
* Added a boolean option to QuantizationCompositeFunctionPass to control hybrid quantization
* Modified StableHloQuantizationPattern for hybrid quantization
* Removed unused interface functions in StableHloQuantizationBase to reduce clutter
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PR_kwDOArmXAs5pPYf2
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Integrate int8 per-tensor weight-only quantization for server
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Integrate int8 per-tensor weight-only quantization for server
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PR_kwDOArmXAs5pPV-a
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Integrate LLVM at llvm/llvm-project@f1aa7837884c
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Integrate LLVM at llvm/llvm-project@f1aa7837884c
Updates LLVM usage to match
[f1aa7837884c](https://github.com/llvm/llvm-project/commit/f1aa7837884c)
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PR_kwDOArmXAs5pPU9D
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Indexing analysis: move utility for epilogue indexing to main library.
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Indexing analysis: move utility for epilogue indexing to main library.
This is needed for transpose, reduce, concat.
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PR_kwDOArmXAs5pPGJ1
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[XLA:GPU] Support type[] broadcast of parameter (no dims) in softmax rewriter.
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[XLA:GPU] Support type[] broadcast of parameter (no dims) in softmax rewriter.
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I_kwDOArmXAs6B4A8c
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Error Loading Model with Bidirectional GRU layers
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[
"@kreouzisv,\r\nI ran the code and face a different error, Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/508d5841c05beb7031238a44dae82f07/untitled1755.ipynb) and share all dependencies to replicate the issue or share a colab gist with the reported error. Thank you!\r\n \r\n\r\n\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"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/63404\">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/63404\">No</a>\n"
] | 2024-03-11T11:54:02 | 2024-03-29T01:46:57 | 2024-03-29T01:46:53 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
v2.15.0-2-g0b15fdfcb3f 2.15.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
model saving with the layers specified errors in loading ; unexpected since other architectures are supported for saving .keras models with effective loading afterwards with current tf version and keras > 3.0.0
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, Bidirectional, GRU, Dense, Input
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.utils import to_categorical
# Data Pre omitted
# Define the model
embedding_dim = 100
model = Sequential([
Input(shape=(maxlen,)),
Embedding(max_features, embedding_dim),
Bidirectional(GRU(64, return_sequences=True)),
Bidirectional(GRU(64)),
Dense(3, activation='softmax') # Adjusted for 3 categories
])
# Compile the model
model.compile(optimizer='adam',
loss='categorical_crossentropy', # Adjusted for multi-class classification
metrics=['accuracy'])
# Model summary
model.summary()
# Train the model
model.fit(train_x, train_y,
batch_size=100,
epochs=10,
validation_data=(val_x, val_y))
# Save the model in the native Keras format
model.save('my_model.keras')
# Load the model
loaded_model = tf.keras.models.load_model('my_model.keras')
```
### Relevant log output
```shell
Traceback (most recent call last):
File "/data/host-category-classification/data/databases/t.py", line 101, in <module>
loaded_model = tf.keras.models.load_model('my_model.keras')
File "/ENTER/envs/tf-gpu/lib/python3.10/site-packages/keras/src/saving/saving_api.py", line 176, in load_model
return saving_lib.load_model(
File "/ENTER/envs/tf-gpu/lib/python3.10/site-packages/keras/src/saving/saving_lib.py", line 192, in load_model
_raise_loading_failure(error_msgs)
File "/ENTER/envs/tf-gpu/lib/python3.10/site-packages/keras/src/saving/saving_lib.py", line 273, in _raise_loading_failure
raise ValueError(msg)
ValueError: A total of 2 objects could not be loaded. Example error message for object <GRUCell name=gru_cell, built=True>:
Layer 'gru_cell' expected 3 variables, but received 0 variables during loading. Expected: ['kernel', 'recurrent_kernel', 'bias']
```
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PR_kwDOArmXAs5pOysT
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PR #9873: [ROCm] Don't use CUDA PTX for ROCM in ComputationIdCmd
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PR #9873: [ROCm] Don't use CUDA PTX for ROCM in ComputationIdCmd
Imported from GitHub PR https://github.com/openxla/xla/pull/9873
Copybara import of the project:
--
818077159230e06ce8e94b3c556d1d68fa125b09 by Dragan Mladjenovic <[email protected]>:
[ROCm] Don't use CUDA PTX for ROCM in ComputationIdCmd
Merging this change closes #9873
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/9873 from ROCm:comp_id 818077159230e06ce8e94b3c556d1d68fa125b09
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PR_kwDOArmXAs5pOsaO
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PR #10082: [ROCm] Enable GpuCommandBuffer::Trace
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PR #10082: [ROCm] Enable GpuCommandBuffer::Trace
Imported from GitHub PR https://github.com/openxla/xla/pull/10082
Copybara import of the project:
--
e7c8de8fcdadd7bf9b02e4e79afe941afc35d190 by Dragan Mladjenovic <[email protected]>:
[ROCm] Enable GpuCommandBuffer::Trace
--
b66e16f53a9a6d6a44f51439a5b39e2670effaf7 by Dragan Mladjenovic <[email protected]>:
[ROCm] Allow capturing operations to command buffer that require tracing
Merging this change closes #10082
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10082 from ROCm:cmmand_trace b66e16f53a9a6d6a44f51439a5b39e2670effaf7
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PR_kwDOArmXAs5pOqgl
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Fix materializeZeta algorithm #5838.
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Fix materializeZeta algorithm #5838.
Commit ea0cde0 fixed the Zeta formula in XlaOp code. This change applies the same fix to materializeZeta function.
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PR_kwDOArmXAs5pOnCo
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WIP - testing out JAX fix
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WIP - testing out JAX fix
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PR_kwDOArmXAs5pOdw1
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Integrate LLVM at llvm/llvm-project@f1aa7837884c
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Integrate LLVM at llvm/llvm-project@f1aa7837884c
Updates LLVM usage to match
[f1aa7837884c](https://github.com/llvm/llvm-project/commit/f1aa7837884c)
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PR_kwDOArmXAs5pOSf9
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Fix checkfail in UnicodeEncode Op
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The Op UnicodeEncode segfaults when passed 2D tensor to `input_splits`.
It has the below check in `SetShapeFn` which supposed to raise exception if `rank !=1` AFAIk. This seems not working for reason unknown to me.
https://github.com/tensorflow/tensorflow/blob/6f64ad5d767a034df45a5eaab8b36fd688cd1217/tensorflow/core/ops/string_ops.cc#L316-L317
Same with `input_values` argument also.
Added an explicit check in Op.
Ref issue #63379
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| 63,397 |
Fix mapping for unsigned popcount.
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Fix mapping for unsigned popcount.
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PR_kwDOArmXAs5pNn_J
| 63,396 |
Migrate DebuggerOptions to DebuggerConfig
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Migrate DebuggerOptions to DebuggerConfig
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PR_kwDOArmXAs5pNmAk
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Let `StableHloQuantizationPattern` inherit `OpRewritePattern` instead of `RewritePattern`.
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Let `StableHloQuantizationPattern` inherit `OpRewritePattern` instead of `RewritePattern`.
`StableHloQuantizationPattern` is showing the same behavior as `OpRewritePattern<RootOpT>`.
Also includes minor cleanup of removing `llvm::` prefixes when possible.
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PR_kwDOArmXAs5pNfaE
| 63,394 |
Adds implementation for folding `stablehlo.constant`->`stablehlo.transpose` patterns.
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Adds implementation for folding `stablehlo.constant`->`stablehlo.transpose` patterns.
Introduces a new pattern `FoldConstantTransposePass` that folds constants followed by a transpose op.
This is required to correctly compose quantized TFLite ops legalized from quantized stablehlo graphs where weights have `stablehlo.constant`->`stablehlo.transpose` patterns.
Since this should be handled early on before quantization, this pass is added to the pre-calibration component.
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PR_kwDOArmXAs5pNZEr
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Add unit tests in add_dump_tensor_op_stablehlo for whole model and float per layer option
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Add unit tests in add_dump_tensor_op_stablehlo for whole model and float per layer option
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I_kwDOArmXAs6B1-Q-
| 63,392 |
conv2d op produces output without nan with nan inputs
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[
"Hi @pzy2000 ,\r\n\r\nCould you help me with a minimal reproducible code snippet? Given code is lengthy and takes time.",
"Sorry, but it is already a minimal reproducible code snippet, at least to my knowledge:)"
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NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.10.0
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04.4
### Mobile device
_No response_
### Python version
3.7.6
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.2
### GPU model and memory
_No response_
### Current behavior?

we expect layer 5 output to be also nan, since layer 5 input is nan
[lenet5-fashion-mnist_origin-SpecialI5-LMerg34-MDtype57.zip](https://github.com/tensorflow/tensorflow/files/14554923/lenet5-fashion-mnist_origin-SpecialI5-LMerg34-MDtype57.zip)
### Standalone code to reproduce the issue
```shell
import keras
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Model
def _init_input(input_shape):
input_shape = list(input_shape)
input_shape[0] = 10
input_shape = tuple(input_shape)
input = np.random.rand(*input_shape)
return input
class CustomCastLayer(keras.layers.Layer):
def __init__(self, target_dtype, **kwargs):
self.target_dtype = target_dtype
super().__init__(**kwargs)
def build(self, input_shape):
"""
Do Nothing
"""
super().build(input_shape)
def call(self, inputs, **kwargs):
output = tf.cast(inputs, self.target_dtype)
return output
def get_config(self):
config = super().get_config()
config.update({"target_dtype": self.target_dtype})
return config
def compute_output_shape(self, input_shape):
"""
Do Nothing
"""
return input_shape
class CustomPadLayer(keras.layers.Layer):
def __init__(self, padding=[[0, 0]], constant_values=0, **kwargs):
self.padding = padding # add [0,0] to padding so we will not change the shape of batch dimension
self.constant_values = constant_values
super().__init__(**kwargs)
def build(self, input_shape):
super().build(input_shape)
def call(self, inputs, **kwargs):
output = tf.pad(inputs, paddings=tf.constant([[0, 0]] + self.padding), mode="CONSTANT",
constant_values=self.constant_values)
return output
def get_config(self):
config = super().get_config()
config.update({"padding": self.padding, "constant_values": self.constant_values})
return config
def compute_output_shape(self, input_shape):
"""
Formula to calculate the output shape
Suppose the input_shape is (None, N, W, C), the `paddings` is [[a, b], [c, d]].
The output_shape is (None, N+a+b, W+c+d, C).
"""
input_shape_list = list(input_shape)
padding = [[0, 0]] + self.padding
assert len(input_shape_list) == len(padding) # Two dimensions should match.
output_shape = [None]
for i, pad in zip(input_shape_list[1:], padding[1:]):
output_shape.append(i + pad[0] + pad[1])
return tuple(output_shape)
class CustomCropLayer(keras.layers.Layer):
def __init__(self, cropping, **kwargs):
self.cropping = cropping
super().__init__(**kwargs)
def build(self, input_shape):
super().build(input_shape)
def call(self, inputs, **kwargs):
input_shape = inputs.shape.as_list()
indices = [slice(None)]
cropping = [[0, 0]] + self.cropping # add [0,0] to padding so we will not change the shape of batch dimension
for shape, crop in zip(input_shape[1:], cropping[1:]):
indices.append(slice(0 + crop[0], shape - crop[1]))
return inputs[indices]
def get_config(self):
config = super().get_config()
config.update({"cropping": self.cropping})
return config
def compute_output_shape(self, input_shape):
"""
Formula to calculate the output shape
Suppose the input_shape is (None, N, W, C), the `cropping` is [[a, b], [c, d]].
The output_shape is (None, N-a-b, W-c-d, C).
"""
input_shape_list = list(input_shape)
cropping = [[0, 0]] + self.cropping
assert len(input_shape_list) == len(cropping) # Two dimensions should match.
output_shape = [None]
for i, crop in zip(input_shape[1:], cropping[1:]):
output_shape.append(i - crop[0] - crop[1])
return tuple(output_shape)
class CustomExpandLayer(keras.layers.Layer):
def __init__(self, axis=1, **kwargs):
self.axis = axis
super().__init__(**kwargs)
def build(self, input_shape):
super().build(input_shape)
def call(self, inputs, **kwargs):
return tf.expand_dims(inputs, self.axis)
def get_config(self):
config = super().get_config()
config.update({"axis": self.axis})
return config
def compute_output_shape(self, input_shape):
"""
Formula to calculate the output shape
Suppose the input_shape is [None, N, W, C]:
axis=0:
output_shape: [1, None, N, W, C]
axis=1 (default):
output_shape: [None, 1, N, W, C]
axis=2:
output_shape: [None, N, 1, W, C]
axis=3:
output_shape: [None, N, W, 1, C]
axis=4:
output_shape: [None, N, W, C, 1]
axis=5:
raise Exception
"""
input_shape_list = list(input_shape)
if self.axis > len(input_shape_list):
raise ValueError(f"axis {self.axis} should be smaller than input_shape + 1: {len(input_shape_list) + 1}")
output_shape = input_shape_list[0:self.axis] + [1] + input_shape_list[self.axis:]
return tuple(output_shape) # we should use tuple!!! not list !!!
class CustomDropDimLayer(keras.layers.Layer):
def __init__(self, axis=1, **kwargs):
self.axis = axis
super().__init__(**kwargs)
def build(self, input_shape):
super().build(input_shape)
def call(self, inputs, **kwargs):
"""
Something magic to automatically generate indices for array slicing.
To determine a specific axis, we can use slice(None) to replace `:`
"""
dim = len(inputs.shape)
if self.axis > dim - 1 or self.axis < 1:
raise ValueError(f"axis: {self.axis} should be within the range: [1, {dim - 1}] for {dim}D tensor")
indices = [slice(None) for i in range(dim)]
indices[self.axis] = 0
return inputs[indices]
def get_config(self):
config = super().get_config()
config.update({"axis": self.axis})
return config
def compute_output_shape(self, input_shape):
"""
Formula to calculate the output shape
Suppose the input_shape is [None, N, W, C]:
axis=0: # Although it is feasible, we don't allow this to happen
Raise Exception
axis=1 (default):
output_shape: [None, W, C]
axis=2:
output_shape: [None, N, C]
axis=3:
output_shape: [None, N, W]
axis=4:
Raise Exception
"""
input_shape_list = list(input_shape)
output_shape = input_shape_list[0:self.axis] + input_shape_list[self.axis + 1:]
return tuple(output_shape)
def custom_objects(mode="custom"):
def no_activation(x):
return x
def leakyrelu(x):
import keras.backend as K
return K.relu(x, alpha=0.01)
# objects = {}
objects = {'no_activation': no_activation, 'leakyrelu': leakyrelu}
if mode == "custom":
objects['CustomPadLayer'] = CustomPadLayer
objects['CustomCropLayer'] = CustomCropLayer
objects['CustomDropDimLayer'] = CustomDropDimLayer
objects['CustomExpandLayer'] = CustomExpandLayer
objects['CustomCastLayer'] = CustomCastLayer
return objects
# 加载预训练模型
model = keras.models.load_model(
"lenet5-fashion-mnist_origin-SpecialI5-LMerg34-MDtype57.h5",
custom_objects=custom_objects())
input_layer = model.input
layer_outputs = [layer.output for layer in model.layers]
print(layer_outputs)
print("===================================")
new_model = Model(inputs=input_layer, outputs=layer_outputs)
# 创建一个具有特定形状的随机tensor,例如形状为(1, 3, 32, 32)
with tf.device('cpu'):
input_tensor = _init_input(model.input_shape)
# Use this model to predict
all_layer_outputs = new_model.predict(input_tensor)
# print(all_layer_outputs)
# print(type(all_layer_outputs))
# print(all_layer_outputs[0])
print("===================================")
for i, layer_output in enumerate(all_layer_outputs):
# print(f"Output shape of layer {i}: {layer_output.shape}")
print(f"Name of layer {i}: {model.layers[i].name}")
print(f"Output if nan of layer {i}: {np.isnan(layer_output).any()}")
print()
```
### Relevant log output
```shell
Name of layer 0: conv2d_1_input
Output if nan of layer 0: False
Name of layer 1: conv2d_1_copy_SpecialI_copy_LMerg_copy_MDtype
Output if nan of layer 1: False
Name of layer 2: max_pooling2d_1_copy_SpecialI_copy_LMerg_copy_MDtype
Output if nan of layer 2: False
Name of layer 3: lambda_copy_LMerg_copy_MDtype
Output if nan of layer 3: True
Name of layer 4: dropout_1_copy_SpecialI_copy_LMerg_copy_MDtype
Output if nan of layer 4: True
Name of layer 5: conv2d_2_copy_SpecialI_copy_LMerg_copy_MDtype
Output if nan of layer 5: False
Name of layer 6: max_pooling2d_2_copy_SpecialI_copy_LMerg_copy_MDtype
Output if nan of layer 6: False
Name of layer 7: dropout_2_copy_SpecialI_copy_LMerg_merge1_copy_MDtype
Output if nan of layer 7: False
Name of layer 8: subtract_copy_ML_copy_MDtype
Output if nan of layer 8: False
Name of layer 9: custom_cast_layer
Output if nan of layer 9: False
Name of layer 10: flatten_1_copy_SpecialI_copy_LMerg_copy_MDtype
Output if nan of layer 10: False
Name of layer 11: custom_cast_layer_1
Output if nan of layer 11: False
Name of layer 12: dense_1_copy_SpecialI_copy_LMerg_copy_MDtype
Output if nan of layer 12: False
Name of layer 13: dense_2_copy_SpecialI_copy_LMerg_copy_MDtype
Output if nan of layer 13: False
Name of layer 14: dense_3_copy_SpecialI_copy_LMerg_copy_MDtype
Output if nan of layer 14: False
Name of layer 15: dense_insert
Output if nan of layer 15: False
```
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