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Results 1 - 10 of 10 for 1x2xi64 (0.17 sec)
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tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/quantize/quantize_op_with_region.mlir
// CHECK: %[[REDUCE:.*]] = "stablehlo.reduce_window"(%[[CALL]], %[[Q0]]) // CHECK{LITERAL}: padding = dense<[[0, 0], [1, 1], [1, 1], [0, 0]]> : tensor<4x2xi64> // CHECK-SAME: window_dimensions = array<i64: 1, 3, 3, 1> // CHECK: %[[ARG1:.*]]: tensor<!quant.uniform<i8:f32, 3.000000e-01:1>>, %[[ARG2:.*]]: tensor<!quant.uniform<i8:f32, 3.000000e-01:1>>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 18 20:32:46 UTC 2024 - 18.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/compose-uniform-quantized-type.mlir
func.func private @uniform_quantize_0(%arg0: tensor<1x2xf32>, %arg1: tensor<1x1xf32>, %arg2: tensor<1x1xi8>) -> tensor<1x2xi32> { %0 = stablehlo.convert %arg0 : (tensor<1x2xf32>) -> tensor<1x2xi32> return %0 : tensor<1x2xi32> } // CHECK: @uniform_quantize_0 func.func private @uniform_quantize_1(%arg0: tensor<1x3xf32>, %arg1: tensor<1x1xf32>, %arg2: tensor<1x1xi8>) -> tensor<1x3xi8> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 17:10:32 UTC 2024 - 37K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/insert_custom_aggregation_ops.mlir
// HISTOGRAM-PERCENTILE-CHECK-NEXT: [[lhs:%.*]], {{.*}}, {{.*}}, {{.*}} = "tf.CustomAggregator"(%arg0) <{calibration_method = 3 : i32, id = "composite_conv2d_with_relu6_fn_arg_0_calibration_method_3", max_percentile = 9.999900e+01 : f32, min_percentile = 1.000000e-03 : f32, num_bins = 512 : i32}> : (tensor<*xf32>) -> (tensor<*xf32>, tensor<f32>, tensor<f32>, tensor<512xi64>)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 32.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/const-fold.mlir
%0 = "tfl.pseudo_const"() {value = dense<0> : tensor<1x2xi32>} : () -> tensor<1x2xi32> %1 = "tfl.pad"(%arg0, %0) : (tensor<15600xf32>, tensor<1x2xi32>) -> tensor<15600xf32> func.return %1 : tensor<15600xf32> // CHECK: return %arg0 } func.func @ConstFoldPadV2(%arg0: tensor<15600xf32>) -> tensor<15600xf32> { %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<1x2xi32>} : () -> tensor<1x2xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 45.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/internal/passes/clustering_passes.td
}) : () -> tensor<f32> "tf_device.cluster"() ({ %group = "tf.Const"() <{value = dense<[[0, 1]]> : tensor<1x2xi32>}> : () -> tensor<1x2xi32> %arg1_reduced = "tf.XlaAllReduce"(%arg1_id, %group) <{mode = "CrossReplica", reduce_op = "Add"}> : (tensor<f32>, tensor<1x2xi32>) -> tensor<f32> "tf.OpA"(%arg1_reduced) : (tensor<f32>) -> () tf_device.return }) : () -> ()
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 02:01:13 UTC 2024 - 19.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/api/v2/legalize_tf_test.cc
func.func @main() -> () { %cst0 = "tf.Const"(){ value = dense<0> : tensor<3x5xi1>} : () -> tensor<3x5xi1> %0 = "tf.Where"(%cst0) : (tensor<3x5xi1>) -> tensor<?x2xi64> func.return } })"; absl::StatusOr<XlaCompiler::CompilationResult> CompileMlirModule( const char* mlir_module_str, ConfigProto::Experimental::MlirBridgeRollout rollout_state) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 13 23:59:33 UTC 2024 - 16.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/defer_activation_transpose.mlir
stablehlo.return %3 : tensor<f32> }) { window_dimensions = array<i64: 1, 1, 2, 2>, window_strides = array<i64: 1, 1, 2, 2>, padding = dense<[[0, 0], [0, 0], [1, 1], [1, 1]]> : tensor<4x2xi64> } : (tensor<1x4x16x16xf32>, tensor<f32>) -> tensor<1x4x9x9xf32> return %2 : tensor<1x4x9x9xf32> } // CHECK-SAME: %[[ARG:.+]]: tensor<1x16x16x4xf32> // CHECK-DAG: stablehlo.constant
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 18 20:32:46 UTC 2024 - 14.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/canonicalize.mlir
func.func @reshape_vector_shape(tensor<4x4x4xf32>) -> tensor<16x4xf32> { ^bb0(%arg0: tensor<4x4x4xf32>) : %shape0 = arith.constant dense<[[16, 4]]> : tensor<1x2xi32> // expected-error @+1 {{'tfl.reshape' op requires 'shape' to be rank 1, but got 2}} %1 = "tfl.reshape"(%arg0, %shape0) : (tensor<4x4x4xf32>, tensor<1x2xi32>) -> tensor<16x4xf32> func.return %1 : tensor<16x4xf32> } // -----
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 20.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/post-quantize.mlir
%cell_input = "tfl.pseudo_qconst"() {qtype = tensor<1x20x!quant.uniform<i16:f32, 0.006:-34>>, value = dense<1> : tensor<1x20xi6>} : () -> tensor<1x20x!quant.uniform<i16:f32, 0.006:-34>> %0 = "tfl.unidirectional_sequence_lstm"(%input, %cst_11, %cst_11, %cst_11, %cst_11, %cst_3, %cst_3, %cst_3, %cst_3, %cst_2, %cst_2, %cst_2,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 19.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/quantize.mlir
func.return %0 : tensor<1x2x!quant.uniform<i8:f32, 0.0078431372549019607:-128>> // LEGACY: "tfl.pseudo_qconst"() <{qtype = tensor<1x2x!quant.uniform<i8:f32, 0.0078431372549019607:-128>>, value = dense<{{\[\[}}-1, 127]]> : tensor<1x2xi8>}> } func.func private @testIfThen(tensor<*xf32>) -> tensor<*xf32> func.func private @testIfElse(tensor<*xf32>) -> tensor<*xf32> // CHECK-LABEL: NotQuantizeIf func.func @NotQuantizeIf(%arg0: tensor<i1>,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 23:10:13 UTC 2024 - 39.7K bytes - Viewed (0)