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tensorflow/compiler/mlir/lite/experimental/tac/tests/e2e/simple-graph.mlir
func.return %3 : tensor<2x1xf32> } // CHECK: %[[CST:.*]] = arith.constant dense<1> : tensor<4xi32> // CHECK: [[VAL_0:%.*]] = "tfl.reshape"(%1, %[[CST]]) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32> // CHECK: [[VAL_1:%.*]] = "tfl.reshape"(%2, %[[CST]]) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.6K bytes - Viewed (0) -
platforms/software/dependency-management/src/main/java/org/gradle/api/internal/artifacts/ModuleComponentSelectorSerializer.java
} public void writeVersionConstraint(Encoder encoder, VersionConstraint cst) throws IOException { encoder.writeString(cst.getRequiredVersion()); encoder.writeString(cst.getPreferredVersion()); encoder.writeString(cst.getStrictVersion()); List<String> rejectedVersions = cst.getRejectedVersions(); encoder.writeSmallInt(rejectedVersions.size());
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Tue Oct 10 21:10:11 UTC 2023 - 5.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/external_constant.mlir
func.func @main(tensor<40x37xf32>, tensor<40x37xf32>) -> tensor<40x40xf32> { ^bb0(%arg0: tensor<40x37xf32>, %arg1: tensor<40x37xf32>): %cst = arith.constant dense<1.0> : tensor<40xf32> %0:2 = "tfl.fully_connected"(%arg0, %arg1, %cst) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<40x37xf32>, tensor<40x37xf32>, tensor<40xf32>) -> (tensor<40x40xf32>, tensor<40x40xf32>)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/lift_quantizable_spots_as_functions_xla.mlir
%cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32> %0 = "tf.Conv2D"(%arg0, %arg1) {data_format = "NHWC", dilations = [1, 1, 2, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 8.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/quantize_composite_functions_weight_only.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 05:56:10 UTC 2024 - 9.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/legalize-tf-while.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/fake_quant_e2e_flow.mlir
// CHECK-NEXT: %[[V1:.*]] = "tf.PartitionedCall"(%[[ARG0]], %[[CST]], %[[CST_0]]) <{config = "", config_proto = "", executor_type = "", f = @quantize_i8}> : (tensor<1x3x4x3xf32>, tensor<f32>, tensor<i32>) -> tensor<1x3x4x3xi8>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 3.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/prepare_quantize/prepare_quantize.mlir
func.func @dot(%arg0: tensor<?x3xf32>) -> tensor<?x2xf32> { // CHECK: %[[cst:.*]] = stablehlo.constant // CHECK: %[[q1:.*]] = "quantfork.qcast"(%[[cst]]) // CHECK-SAME: quant.uniform<i8:f32, 0.0040316890267764818:127> // CHECK: %[[dq1:.*]] = "quantfork.dcast"(%[[q1]]) // CHECK-SAME: quant.uniform<i8:f32, 0.0040316890267764818:127>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 22 19:52:06 UTC 2024 - 8.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/quantization.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 4.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/insert_calibration_statistics_saver_with_skipping.mlir
%cst = stablehlo.constant dense<0.000000e+00> : tensor<10x1x3xf32> %0 = stablehlo.dot_general %arg0, %arg1, batching_dims = [0] x [0], contracting_dims = [2] x [1], precision = [DEFAULT, DEFAULT] {mhlo.frontend_attributes = {grad_x = "false", grad_y = "false"}} : (tensor<10x1x1024xf32>, tensor<10x1024x3xf32>) -> tensor<10x1x3xf32> %1 = stablehlo.maximum %0, %cst : tensor<10x1x3xf32> return %1 : tensor<10x1x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 06:31:57 UTC 2024 - 6.3K bytes - Viewed (0)