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tensorflow/c/eager/parallel_device/parallel_device_lib.h
// devices of a ParallelDevice. If called, ParallelTensor::Shape inspects // `components` to determine a shape. static std::unique_ptr<ParallelTensor> FromTensorHandles( const ParallelDevice& parallel_device, std::vector<TensorHandlePtr> components, TF_Status* status); // Uses the provided shape without additional checks, which avoids blocking // when ParallelTensor::Shape is called.
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tensorflow/c/eager/parallel_device/parallel_device_lib.cc
if (combined_shape.dims() < 0 || combined_shape.dims() != component_shape.dims()) { PartialTensorShape first_shape; TF_RETURN_IF_ERROR(unwrap(tensors_[0].get())->Shape(&first_shape)); return errors::Unimplemented(absl::StrCat( "Computing the shape of a ParallelTensor when the components do " "not all have the same rank is not supported. One tensor had " "shape ",
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tensorflow/c/eager/parallel_device/parallel_device.cc
// number of dimensions of a parallel tensor. int ParallelTensorNumDims(void* data, TF_Status* status) { const std::vector<int64_t>* shape; Status s = reinterpret_cast<ParallelTensor*>(data)->Shape(&shape); if (!s.ok()) { tsl::Set_TF_Status_from_Status(status, s); return -1; } return shape->size(); } // Used as an argument to TFE_NewCustomDeviceTensorHandle, for computing a // dimension of a parallel tensor.
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tensorflow/c/eager/gradient_checker.cc
AbstractTensorHandlePtr sum_dims; { vector<int32_t> vals(num_dims_out); int64_t vals_shape[] = {num_dims_out}; Range(&vals, 0, num_dims_out); AbstractTensorHandle* sum_dims_raw = nullptr; TF_RETURN_IF_ERROR(TestTensorHandleWithDims<int32_t, TF_INT32>( ctx, vals.data(), vals_shape, 1, &sum_dims_raw)); sum_dims.reset(sum_dims_raw); } // Reduce sum the output on all dimensions.
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tensorflow/c/c_api.h
// setting a shape of [-1, 2] with an existing shape [2, -1] would set // a final shape of [2, 2] based on shape merging semantics. // // Returns an error into `status` if: // * `output` is not in `graph`. // * An invalid shape is being set (e.g., the shape being set // is incompatible with the existing shape). TF_CAPI_EXPORT extern void TF_GraphSetTensorShape(TF_Graph* graph,
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.github/ISSUE_TEMPLATE/tflite-op-request.md
``` # Copy and paste here ``` **Standalone code to reproduce the issue** Provide a reproducible test case that is the bare minimum necessary to generate the problem. If possible, please share a link to Colab/Jupyter/any notebook. Also, please include a link to a GraphDef or the model if possible. **Any other info / logs** Include any logs or source code that would be helpful to diagnose the problem.
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tensorflow/c/eager/c_api_test_util.h
TFE_Op* MatMulOp(TFE_Context* ctx, TFE_TensorHandle* a, TFE_TensorHandle* b); // Return an identity op. TFE_Op* IdentityOp(TFE_Context* ctx, TFE_TensorHandle* a); // Return a shape op fetching the shape of `a`. TFE_Op* ShapeOp(TFE_Context* ctx, TFE_TensorHandle* a); // Return an allreduce op adding up input tensor `in` from `group_size` workers.
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.github/ISSUE_TEMPLATE/tensorflow_issue_template.yaml
id: code-to-reproduce attributes: label: Standalone code to reproduce the issue description: Provide a reproducible test case that is the bare minimum necessary to generate the problem. Please share a link to Colab, Jupyter, or any notebook. placeholder: Tell us what you see! value: render: shell validations: required: true - type: textarea id: logs attributes:
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ci/official/containers/linux_arm64/build.sh
TAG=$(head -n 1 "$KOKORO_PIPER_DIR/presubmit_request.txt" | cut -d" " -f2) else TAG="pr-${KOKORO_GITHUB_PULL_REQUEST_NUMBER}" fi fi # Build for both JAX and TF usage. We do these in one place because they share # almost all of the same cache layers export DOCKER_BUILDKIT=1 for target in jax tf; do IMAGE="gcr.io/tensorflow-sigs/build-arm64:$target-$TAG" docker pull "$IMAGE" || true
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tensorflow/c/eager/parallel_device/parallel_device_testlib.cc
TFE_NewOp(context, "VarHandleOp", status), TFE_DeleteOp); if (TF_GetCode(status) != TF_OK) return nullptr; TFE_OpSetAttrType(op.get(), "dtype", type); TFE_OpSetAttrShape(op.get(), "shape", dims, num_dims, status); TFE_OpSetAttrString(op.get(), "container", "", 0); // Use the special GUID for no buffer sharing // // TODO(allenl): Should we provide a better API for this? AFAIK this is the
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