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Results 41 - 50 of 77 for mat_mul (0.24 sec)
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tensorflow/compiler/mlir/lite/transforms/prepare_composite_functions_tf.cc
return func_.emitWarning() << "Invalid number of arguments in the embedding " "matmul composite function"; } if (func_.getFunctionType().getNumResults() != 1) { return func_.emitWarning() << "Invalid number of results in the " "embedding matmul composite function"; } return success(); } private: func::FuncOp func_; };
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 17.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir
} func.func @matmul(%arg0: tensor<40x37xf32>, %arg1: tensor<37x40xf32>) -> tensor<40x40xf32> { %0 = "tf.MatMul"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", device = "/device:CPU:0", name = "MatMul", transpose_a = false, transpose_b = false} : (tensor<40x37xf32>, tensor<37x40xf32>) -> tensor<40x40xf32> func.return %0 : tensor<40x40xf32> // CHECK-LABEL: matmul
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 05 01:54:33 UTC 2024 - 153.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/tf_to_corert/tf_to_corert_pipeline.mlir
%outputs_16, %control_17 = tf_executor.island wraps "tf.Reshape"(%outputs_14, %outputs_6) {device = ""} : (tensor<16x16x16x?xf32>, tensor<2xi32>) -> tensor<?x16384xf32> %outputs_18, %control_19 = tf_executor.island wraps "tf.MatMul"(%outputs_16, %outputs_4) {device = "", transpose_a = false, transpose_b = false} : (tensor<?x16384xf32>, tensor<*xf32>) -> tensor<?x?xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 00:18:59 UTC 2024 - 7.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize_batch_matmul.cc
Value input_rhs = bmm_op.getY(); Value output_lhs = bmm_op.getAdjX() ? create_z_x_transpose_op(input_lhs) : input_lhs; // The rhs need to be transposed if adj_y == false AND this matmul will be // legalized to tfl.fully_connected Value output_rhs = !bmm_op.getAdjY() ? create_z_x_transpose_op(input_rhs) : input_rhs; Type output_type = bmm_op.getResult().getType();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 9.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/prepare_lifting.td
def MakeOneDimValueBroadcastable : NativeCodeCall< "MakeOneDimValueBroadcastable($_builder, $_loc, $0, $1.getType().cast<ShapedType>())">; // Match convolution op with "NHWC" data format or matmul op. def SupportedAffineOpMatcher : NativeCodeCall< "MatchSupportedAffineOp($_self, $0, $1, $2)">; // Checks if a value can be symetrically quantized.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 14 03:24:59 UTC 2024 - 8.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/replace_cast_hacks_with_tf_xla_ops.cc
#include "xla/xla_data.pb.h" namespace mlir::quant { namespace { constexpr StringRef kTfQuantCreatedEinsum = "__tf_quant_created_einsum"; // Replaces mixed-type Conv and Matmul cast hacks with TF XLA ops. // TODO(b/228403741): Support conversion for dynamic-shaped TF ops. class ReplaceCastHacksWithTFXLAOpsPass : public PassWrapper<ReplaceCastHacksWithTFXLAOpsPass,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 47.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/attrs_and_constraints_test.cc
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 17:10:32 UTC 2024 - 22.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/passes.h
bool enable_canonicalization, bool disable_fuse_mul_and_fc = false); std::unique_ptr<OperationPass<func::FuncOp>> CreateOptimizePass(); // Creates an instance of the Tensorflow Lite batch matmul Optimize pass. std::unique_ptr<OperationPass<func::FuncOp>> CreateOptimizeBatchMatmulPass(); // Creates an instance of the TensorFlow Lite dialect PrepareTF pass. std::unique_ptr<OperationPass<func::FuncOp>> CreatePrepareTFPass(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 07 21:29:34 UTC 2024 - 10.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/prepare_lifting.cc
loc, value, Create1DConstValue(builder, loc, new_shape)); } return ConstantFoldOpIfPossible(value.getDefiningOp()).front(); } // Matches convolution op with "NHWC" data format or matmul op with false adj_y. // The list of supported ops in this function is: // - Conv2DOp // - Conv3DOp // - DepthwiseConv2dNativeOp // - MatMulOp // - BatchMatMulV2Op
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 17:58:54 UTC 2024 - 13.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/passes.td
"bool", "false", "Disable folding mul and fully connected ops during optimization pass.">, ]; } def OptimizeBatchMatmulPass : Pass<"tfl-optimize-batch-matmul", "mlir::func::FuncOp"> { let summary = "Optimize FC with BatchMatmul within the TensorFlow Lite dialect"; let constructor = "CreateOptimizeBatchMatmulPass()"; let dependentDialects = ["TFL::TensorFlowLiteDialect"]; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 20:30:06 UTC 2024 - 22.6K bytes - Viewed (0)