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Results 21 - 30 of 34 for broadcastable (0.19 sec)
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tensorflow/compiler/mlir/lite/tests/prepare-tf.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 59.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/canonicalize.mlir
%5 = "tf.AddV2"(%4, %1): (tensor<4x4xf32>, tensor<1xf32>) -> tensor<4x4xf32> %6 = "tf.Log"(%5): (tensor<4x4xf32>) -> tensor<4x4xf32> // This is a legal canonicalization because constant shape 4xf32 is // broadcastable to 4x4xf32, however we currently do not support this case, // and canonicalize only if the constant is a scalar. // CHECK: %[[ADD2:.*]] = "tf.AddV2" // CHECK: %[[LOG2:.*]] = "tf.Log"(%[[ADD2]])
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 22:07:10 UTC 2024 - 132.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/legalize_tf.cc
rewriter.replaceOpWithNewOp<SourceOp>(op, result_type, lhs, rhs); return success(); } }; // This specialization is for TF SelectV2 op. SelectV2 op have three inputs and // they should have broadcastable shapes. template <> class ApplyExplicitBroadcasting<TF::SelectV2Op> : public OpRewritePattern<TF::SelectV2Op> { public: using OpRewritePattern<TF::SelectV2Op>::OpRewritePattern;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 20 20:06:54 UTC 2024 - 45.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_ops_a_m.cc
// Check compatibility of batch dimensions if both input shapes are known. // BatchMatMul should have exactly the same batch dimensions and // BatchMatMulV2 should have broadcastable batch dimensions. // // The last two dimensions are non-batch dimensions that don't need to // participate in batch dimension compatibility check. if (std::is_same<OpT, BatchMatMulOp>()) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 146.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_ops.td
string tflRuntimeDescription = desc; } class TFL_OperandsHaveSameShapesOrBroadcastableShape< list<int> indices, int max_bcast_rank> : TFL_RuntimePredOpTrait<"operands do not have the same shape or " "broadcastable shapes within the rank " # max_bcast_rank, CPred<"TFL::VerifyOperandsHaveSameShapesOrBroadcastableShape(" "$_op, llvm::ArrayRef<unsigned>({" # !interleave(indices, ", ") #
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 186K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/replace_cast_hacks_with_tf_xla_ops.cc
Value output, BoolAttr adj_x, BoolAttr adj_y) { // TensorFlow BatchMatMulOp allows the batch dimensions to be broadcastable // while the XlaDotV2Op doesn't. So we have to broadcast them beforehand. BroadcastBatchDimensionsForBatchMatMul(builder, loc, input, weight); // Both input and weight have the same rank after broadcasting.
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/cc/gradients/math_grad.cc
return Maximum(scope, ids, ZerosLike(scope, ids)); } // Helper function for unsorted segment ops. // Returns a mask of where 'ids' are positive, reshaped so that it will be // broadcastable to the result shape of gathering params by ids. Output GetIsPositive(const Scope& scope, const Output& params, const Output& ids) { Output is_positive = GreaterEqual(scope, ids, ZerosLike(scope, ids));
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Aug 25 18:20:20 UTC 2023 - 50.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_ops_n_z.cc
LogicalResult NotEqualOp::verify() { NotEqualOp op = *this; // If we allow inputs to have incompatible type, then nothing to do. if (!op.getIncompatibleShapeError()) return success(); // Otherwise, check inputs are broadcastable. return mlir::OpTrait::impl::verifyCompatibleOperandBroadcast( op.getOperation()); } void NotEqualOp::build(OpBuilder &builder, OperationState &result, Value x,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 22:07:10 UTC 2024 - 170.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir
// CHECK-LABEL: greater_equal // CHECK: tfl.greater_equal(%arg0, %arg1) : (tensor<8x16xf32>, tensor<8x16xf32>) -> tensor<8x16xi1> // CHECK: return } //TODO(b/136498739): Add failure test for non-broadcastable types, since currently // we can't catch this error. func.func @less_equal(%arg0: tensor<8x16xf32>, %arg1: tensor<8x16xf32>) -> tensor<8x16xi1> {
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/quantization/tensorflow/python/integration_test/quantize_model_test.py
'has_bias': [True, False], 'batch_sizes': [([], []), ([10], [10]), ([2, 3], [2, 3])], 'target_opset': [quant_opts_pb2.XLA], }, # Test broadcastable batch sizes. { 'activation_fn': [None], 'has_bias': [True], 'batch_sizes': [ ([2], []), ([], [2]),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 03:36:50 UTC 2024 - 235.6K bytes - Viewed (0)