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tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_composite_functions_weight_only.mlir
func.return %1: tensor<*xf32> } func.func private @composite_matmul_fn_1(%arg0: tensor<2x12xf32>, %arg1: tensor<12x2xf32>) -> tensor<*xf32> attributes {tf_quant.composite_function} { %0 = "tf.MatMul"(%arg0, %arg1) {attr_map = "0:transpose_a,1:transpose_b", device = "", transpose_a = false, transpose_b = false} : (tensor<2x12xf32>, tensor<12x2xf32>) -> tensor<*xf32> return %0 : tensor<*xf32> } } // PerTensor-LABEL: func @matmul
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 11.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_composite_functions_xla.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Jan 08 01:16:10 UTC 2024 - 25.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/preprocess_op.mlir
} func.func private @composite_depthwise_conv2d_fn(%arg0: tensor<1x3x4x3xf32>, %arg1: tensor<2x3x3x2xf32>) -> tensor<*xf32> attributes {tf_quant.composite_function} { %0 = "tf.DepthwiseConv2dNative"(%arg0, %arg1) { attr_map = "0:strides,1:padding,2:explicit_paddings,3:dilations", data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_composit_functions_debugging.mlir
%0 = "tf.MatMul"(%arg0, %arg1) {attr_map = "0:transpose_a,1:transpose_b", device = "", transpose_a = false, transpose_b = false} : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32> return %0 : tensor<2x2xf32> } func.func private @composite_matmul_fn_2_0(%arg0: tensor<2x2xf32>, %arg1: tensor<2x2xf32>) -> tensor<2x2xf32> attributes {tf_quant.composite_function} {
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/compiler/mlir/quantization/tensorflow/tests/add_dump_tensor_op.mlir
func.func private @composite_conv2d_with_bias_and_relu6_fn_2(%arg0: tensor<1x2x2x3xf32>, %arg1: tensor<2x2x3x2xf32>, %arg2: tensor<2xf32>) -> tensor<*xf32> attributes {tf_quant.composite_function} {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 22 22:55:22 UTC 2024 - 37.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/lift_tflite_flex_ops.cc
auto range = arg_ranges.at(arg.name()); values.push_back(range.second - range.first); } auto attr_value = mlir::DenseI32ArrayAttr::get(tf_op->getContext(), values); tf_op->setAttr(attr_name, attr_value); }; if (tf_op->hasTrait<mlir::OpTrait::AttrSizedOperandSegments>() || tf_op->hasTrait<mlir::OpTrait::AttrSizedResultSegments>()) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 10.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/lift_quantizable_spots_as_functions_drq.mlir
// CHECK-SAME: attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations" // CHECK-NEXT: return %[[CONV2D_0]] // CHECK-LABEL: private @composite_conv2d_fn_1 // CHECK-NEXT: %[[CONV2D_0:.*]] = "tf.Conv2D"(%arg0, %arg1) // CHECK-SAME: attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations"
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 11.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/convert_tf_quant_to_mhlo_int_test.cc
%quant_input, %quant_filter, %input_scale, %input_zp, %filter_scale, %filter_zp, %accum_scale, %accum_zp ) { Tin = "tfdtype$DT_QINT8", Tout = "tfdtype$DT_QINT32", attr_map = "", batch_group_count = 1 : i64, dimension_numbers = "\10\03\1A\02\01\02 \02(\032\02\00\01@\03J\02\01\02", explicit_padding = [], feature_group_count = 1 : i64, lhs_dilation = [1, 1],
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 03 01:03:21 UTC 2024 - 35.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/quantized_function_library_tf_drq.mlir
%2 = "tf.Cast"(%identity) {Truncate = false} : (tensor<*xi8>) -> tensor<*xi32> %3 = "tf.Sub"(%2, %weight_zp) : (tensor<*xi32>, tensor<*xi32>) -> tensor<*xi32> %5 = "tf.MatMul"(%1, %3) { attr_map = "transpose_a:0,transpose_b:1" } : (tensor<*xi32>, tensor<*xi32>) -> tensor<*xi32> func.return %5 : tensor<*xi32> } // Conv2D with int32 accumulation func.func private @internal_conv2d_fn(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 03 15:43:38 UTC 2023 - 12.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_ops_layout_helper.h
(*op)->setAttr("data_format", StringAttr::get(context, target_data_format)); for (auto pair : shuffle_attrs) { StringRef attr_name = pair.first; ArrayAttr attr_value = pair.second; (*op)->setAttr(attr_name, ShuffleArrayAttr(attr_value, reverse_permutation)); } auto fold = cast<FoldOperandsTransposeInterface>(op->getOperation()); for (unsigned idx : fold.GetLayoutDependentResults()) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 08 01:19:25 UTC 2023 - 5.3K bytes - Viewed (0)