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tensorflow/compiler/mlir/g3doc/_includes/tf_passes.md
}, { "tf.Yield"() : () -> () }) { is_stateless = true } : (tensor<i1>) -> () ``` ### `-tf-move-transposes` _Move transposes pass._ #### Options ``` -fold-transpose-in-ops : Whether to fold transposes in ops which can support folding. -direction : Move transposes to the beginning or the end of the block where they are defined. ``` ### `-tf-name-anonymous-iterators`
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Aug 02 02:26:39 UTC 2023 - 96.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf.mlir
// CHECK: %[[TRANSPOSE:.*]] = "tf.Transpose"(%[[DEQUANT]], %[[CST]]) : (tensor<3x4xf32>, tensor<?xi32>) -> tensor<*xf32> // CHECK: %[[MATMUL:.*]] = "tf.MatMul"(%arg0, %[[TRANSPOSE]]) <{grad_a = false, grad_b = false, transpose_a = false, transpose_b = true}> : (tensor<2x3xf32>, tensor<*xf32>) -> tensor<2x4xf32> // CHECK: return %[[MATMUL]] : tensor<2x4xf32> }
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/unroll-batch-matmul.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Dec 06 18:42:28 UTC 2023 - 63.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_composit_functions_debugging.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Nov 06 01:23:21 UTC 2023 - 80.5K bytes - Viewed (0) -
tensorflow/cc/gradients/math_grad.cc
std::vector<Output>* grad_outputs) { if (is_batch == false) { auto dx = MatMul(scope, x0, x1, MatMul::TransposeA(adj_x0).TransposeB(adj_x1)); grad_outputs->push_back(dx); auto dy = MatMul(scope, y0, y1, MatMul::TransposeA(adj_y0).TransposeB(adj_y1)); grad_outputs->push_back(dy); } else { auto dx = BatchMatMulV3(scope, x0, x1, x_data_type,
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/lite/transforms/optimize_patterns.td
[(HasRank<2> $input), (AreLastTwoDimsTransposed $perm_value), (IsBoolAttrEqual<"false"> $adj_y)]>; // Replace conv-->transpose-->add with conv-->add-->transpose // The bias needs only reshape (i.e. ReshapeNCHWBiasToNHWC) and not transpose // because the bias's shape simply changes from NxCx1x1 to Nx1x1xC. def ReorderNCHWTransposeAdd : Pat < (TFL_AddOp:$add_op
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 66.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/lower_tf.cc
rewriter.replaceOpWithNewOp<ConcatV2Op>(op, op.getType(), expanded_inputs, axis_value); return success(); } }; // Lowers SpaceToBatchND by reducing to reshape(transpose(reshape(pad(input)))). // // Before rewrite: // output = SpaceToBatchND(input, block_shape, paddings) // Let: // [batch] + spatial_shape + remaining_shape = input.shape // M = spatial_shape.rank
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 74.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/replace_cast_hacks_with_tf_xla_ops.mlir
%8 = "tf.Cast"(%7) {Truncate = false, device = ""} : (tensor<1024x3xi8>) -> tensor<1024x3xi32> %9 = "tf.MatMul"(%6, %8) {device = "", transpose_a = false, transpose_b = false} : (tensor<1x1024xi32>, tensor<1024x3xi32>) -> tensor<1x3xi32> %10 = "tf.Cast"(%9) {Truncate = false, device = ""} : (tensor<1x3xi32>) -> tensor<1x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 81K bytes - Viewed (0) -
tensorflow/c/c_api_test.cc
TF_Operation* r, const char* name, bool transpose_a = false, bool transpose_b = false) { TF_OperationDescription* desc = TF_NewOperation(graph, "MatMul", name); if (transpose_a) { TF_SetAttrBool(desc, "transpose_a", 1); } if (transpose_b) { TF_SetAttrBool(desc, "transpose_b", 1); } TF_AddInput(desc, {l, 0}); TF_AddInput(desc, {r, 0});
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 15 03:35:10 UTC 2024 - 96.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_tf.cc
TFL::populateWithGenerated(patterns); // TODO(fengliuai): Implement similar rule in the QuantizePass if the constant // folding hook of tfl.transpose and tfl.reshape are implemented. patterns.add<ReorderFakeQuantPattern<TF::ReshapeOp>, ReorderFakeQuantPattern<TF::TransposeOp>>(ctx); // Remove redundant reshape ops. TF::ReshapeOp::getCanonicalizationPatterns(patterns, ctx);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 21:49:50 UTC 2024 - 64.6K bytes - Viewed (0)