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tensorflow/compiler/mlir/lite/tests/shape-inference.mlir
// RUN: tf-opt -split-input-file -verify-diagnostics --tf-shape-inference %s | FileCheck %s module attributes {tf.versions = {producer = 888 : i32}} { // CHECK-LABEL: testConv2dShapeValidPadding func.func @testConv2dShapeValidPadding(%arg0: tensor<1x112x80x128xf32>, %arg1: tensor<128x3x3x128xf32>, %arg2: tensor<128xf32>) -> tensor<1x?x?x128xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 11.5K bytes - Viewed (0) -
src/runtime/mbarrier.go
// this will shade it. // // 3. Once a goroutine's stack is black, the shade(ptr) becomes // unnecessary. shade(ptr) prevents hiding an object by moving it from // the stack to the heap, but this requires first having a pointer // hidden on the stack. Immediately after a stack is scanned, it only // points to shaded objects, so it's not hiding anything, and the
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Wed May 29 17:58:53 UTC 2024 - 15.7K bytes - Viewed (0) -
src/runtime/slice.go
} } else { // Note: can't use rawmem (which avoids zeroing of memory), because then GC can scan uninitialized memory. to = mallocgc(tomem, et, true) if copymem > 0 && writeBarrier.enabled { // Only shade the pointers in old.array since we know the destination slice to // only contains nil pointers because it has been cleared during alloc. // // It's safe to pass a type to this function as an optimization because
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Wed May 29 16:25:21 UTC 2024 - 12.2K bytes - Viewed (0) -
src/runtime/proc.go
runnext := atomic.Loaduintptr((*uintptr)(unsafe.Pointer(&pp.runnext))) if tail == atomic.Load(&pp.runqtail) { return head == tail && runnext == 0 } } } // To shake out latent assumptions about scheduling order, // we introduce some randomness into scheduling decisions // when running with the race detector. // The need for this was made obvious by changing the
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Wed May 29 17:58:53 UTC 2024 - 207.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/transforms/device_transform_patterns.cc
if (pack_axis < 0) { pack_axis += rank; } // Concat out shape. for (int i = 0; i < rank; ++i) { int64_t dim_size = input_type.getDimSize(i); if (i == pack_axis) { dim_size *= count; } concat_out_shape.push_back(dim_size); } // Pack out shape. int j = 0; for (int i = 0; i < rank + 1; ++i) { if (i == pack_axis) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 25.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/utils.h
return transposed_type; } // Returns shape of a ranked tensor. // Precondition: output_val's is ranked tensor. // Returns a truncated shape when `truncate` is set to true. inline DenseElementsAttr GetShape(Value output_val, bool truncate = false) { auto output_shape = output_val.getType().dyn_cast<ShapedType>().getShape(); SmallVector<int32_t> shape; shape.reserve(output_shape.size());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 00:40:15 UTC 2024 - 11.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/api/v1/compile_mlir_util.cc
// Extracts shape from XlaArgument as TensorShape. If shape is a xla::Shape, // that is converted to a TensorShape. absl::StatusOr<TensorShape> GetTensorShapeFromXlaArgument( const XlaArgument& arg) { if (absl::holds_alternative<xla::Shape>(arg.shape)) { TensorShape arg_shape; TF_RETURN_IF_ERROR( XLAShapeToTensorShape(std::get<xla::Shape>(arg.shape), &arg_shape)); return arg_shape;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 17:24:39 UTC 2024 - 45.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/lstm.json
{ "tensors": [ { "shape": [1, 5, 2], "name": "input0" }, { "shape": [2, 5], "buffer": 1, "name": "input2input_weights1" }, { "shape": [2, 5], "buffer": 2, "name": "input2forget_weights2" }, { "shape": [2, 5], "buffer": 3,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 01 06:25:50 UTC 2024 - 9.1K bytes - Viewed (0) -
tensorflow/compiler/jit/shape_inference.cc
// Merge node causes a loop so we remove NextIteration->Merge edge before // performing shape inference. But removing those edges also prevents us // from inferring output shape for Merge node (we need shapes for all its // inputs). // For loop invariant resource input's Merge node, we set output resource // shape as Enter node's resource shape. // TODO(b/129367850): clean this up.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 31 00:41:19 UTC 2024 - 13K bytes - Viewed (0) -
tensorflow/compiler/jit/xla_host_send_device_context.h
// se::DeviceMemoryBase gpu_dst{device_tensor.data(), 4 * sizeof(float)}; // xla::Shape shape(xla::F32, {2, 2}, {}, {}) // tsl::AsyncValueRef<std::unique_ptr<se::Event>> done_event = // tsl::MakeConstructedAsyncValueRef<std::unique_ptr<se::Event>>(stream.parent()); // done_event->Init(); // // XlaHostSendDeviceContext device_context(&stream, &gpu_dst, // shape, done_event); // device_context.CopyCPUTensorToDeviceSync(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 22:46:36 UTC 2024 - 3.7K bytes - Viewed (0)