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tensorflow/compiler/mlir/quantization/tensorflow/tests/lift_quantizable_spots_as_functions.mlir
} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32> %4 = "tf.BiasAdd"(%3, %cst) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<2xf32>) -> tensor<*xf32> %5 = "tf.Relu"(%4) {device = ""} : (tensor<*xf32>) -> tensor<*xf32> %6 = "tf.Conv2D"(%arg0, %arg1) { data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [],
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 26.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/lift_quantizable_spots_as_functions_drq.mlir
// CHECK-SAME: f = @composite_conv3d_fn_1}> // CHECK-NOT: {_tfl_quant_trait = "fully_quantizable" // CHECK: %[[RELU:.*]] = "tf.Relu"(%[[PARTITIONEDCALL_0]]) // CHECK: return %[[RELU]] // CHECK-LABEL: private @composite_conv3d_fn_1 // WEIGHTONLY-DAG: %[[CST:.*]] = "tf.Const"() {{.*}} : () -> tensor<2x3x3x3x2xf32> // WEIGHTONLY: %[[PARTITIONEDCALL_0:.*]] = "tf.PartitionedCall"(%arg0, %[[CST]])
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/lite/tests/end2end/back2back_fake_quant.pbtxt
key: "T" value { type: DT_FLOAT } } attr { key: "data_format" value { s: "NHWC" } } } node { name: "sequential/quant_dense/Relu" op: "Relu" input: "sequential/quant_dense/BiasAdd" attr { key: "T" value { type: DT_FLOAT } } } node {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Nov 15 19:42:47 UTC 2021 - 25.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/quantized_function_library_uniform_quantized.mlir
parameters[ {"quantized_ops": ["${main_op}", "BiasAdd"], "act_func": "internal_requantize_no_activation_fn", "output_type": "!tf_type.qint8"}, {"quantized_ops": ["${main_op}", "BiasAdd", "Relu"], "act_func": "internal_requantize_and_relu_fn", "output_type": "!tf_type.qint8"}, {"quantized_ops": ["${main_op}", "BiasAdd", "Relu6"], "act_func": "internal_requantize_and_relu6_fn", "output_type": "!tf_type.qint8"}, ]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Aug 29 01:13:58 UTC 2023 - 19.3K bytes - Viewed (0) -
tensorflow/cc/gradients/nn_grad_test.cc
RunTest(x, x_init_value, y, shape); } TEST_F(NNGradTest, ReluGrad) { TensorShape shape({5, 2}); auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape)); auto y = Relu(scope_, x); // Avoid input values where ReLU gradient is not well defined (around zero). Tensor x_init_value = test::AsTensor<float>( {-0.9f, -0.7f, -0.5f, -0.3f, -0.1f, 0.1f, 0.3f, 0.5f, 0.7f, 0.9f}, {5, 2});
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 22 20:45:22 UTC 2022 - 15K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/canonicalize.td
// Canonicalize tf.Maximum of zero to tf.Relu //===----------------------------------------------------------------------===// def IsInteger32Pred: CPred< "getElementTypeOrSelf($0.getType()).isInteger(32)">; // Whether the transformation is compatible with the device if given. // Currently, Relu with int32 is not supported on GPU. def IsDeviceCompatible: Constraint<
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Dec 06 18:42:28 UTC 2023 - 17K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/fused_kernel_matcher.mlir
// CHECK: %[[VAL_0:.*]] = "tf._FusedConv2D"(%arg2, %arg1, %arg0) <{data_format = "NHWC", dilations = [1, 1, 1, 1], epsilon = 0.000000e+00 : f32, explicit_paddings = [], fused_ops = ["BiasAdd", "Relu"], num_args = 1 : i64, operandSegmentSizes = array<i32: 1, 1, 1, 0>, padding = "SAME", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}> {TArgs = [f32]} : (tensor<8x32x32x3xf32>, tensor<1x1x3x128xf32>, tensor<128xf32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 13.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/quantized_function_library.mlir
{"quantized_ops": ["${main_op}", "Relu"], "act_func": "internal_requantize_and_relu_fn", "output_type": "i8"}, {"quantized_ops": ["${main_op}", "Relu6"], "act_func": "internal_requantize_and_relu6_fn", "output_type": "i8"}, {"quantized_ops": ["${main_op}"], "act_func": "internal_dequantize_no_activation_fn", "output_type": "f32"},
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Jan 08 01:16:10 UTC 2024 - 30.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-signed.mlir
dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32 } : (tensor<1x5x5x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<1x5x5x3xf32> %conv2 = "tfl.conv_2d"(%0, %w, %b2) { dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 18.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/tests/decompose.mlir
%none_attr = tfr.constant "NONE" -> !tfr.attr %relu_attr = tfr.constant "RELU" -> !tfr.attr %relu6_attr = tfr.constant "RELU6" -> !tfr.attr %reluN1_1_attr = tfr.constant "RELU_N1_TO_1" -> !tfr.attr %none:2 = "tfr.quant_act_range"(%none_attr, %scale, %zp) : (!tfr.attr, f32, i64) -> (!tfr.tensor, !tfr.tensor) %relu:2 = "tfr.quant_act_range"(%relu_attr, %scale, %zp) : (!tfr.attr, f32, i64) -> (!tfr.tensor, !tfr.tensor)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 16.7K bytes - Viewed (0)