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Results 11 - 20 of 33 for 1x5x5x3xf32 (0.6 sec)
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tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/optimize_graph.mlir
// CHECK-SAME: %[[ARG_0:.*]]: tensor<1x3x4x3xf32> func.func @dont_merge_quantization_followed_by_quantization(%arg0: tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xf32> { // CHECK: %[[QUANT_ARG_0:.*]] = stablehlo.uniform_quantize %[[ARG_0]] // CHECK: %[[DEQUANT:.*]] = stablehlo.uniform_dequantize %[[QUANT_ARG_0]] // CHECK: return %[[DEQUANT]]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 08 22:40:14 UTC 2024 - 2.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/prepare_quantize/prepare_quantize_per_channel.mlir
%2 = stablehlo.convolution(%1, %0) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = { stride = [1, 1], pad = [[0, 0], [1, 1]], lhs_dilate = [1, 1], rhs_dilate = [1, 1] } { batch_group_count = 1 : i64, feature_group_count = 1 : i64 } : (tensor<1x3x2x3xf32>, tensor<2x3x3x2xf32>)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 26 07:48:15 UTC 2024 - 8.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/composite-lowering.mlir
// CHECK: %[[VAL_2:.*]] = "tfl.transpose"(%[[VAL_0]], %[[VAL_1]]) : (tensor<1x3x6x6xf32>, tensor<4xi32>) -> tensor<1x6x6x3xf32> // CHECK: %[[VAL_3:.*]] = arith.constant dense<0> : tensor<4x2xi32> // CHECK: %[[VAL_4:.*]] = "tfl.pad"(%[[VAL_2]], %[[VAL_3]]) : (tensor<1x6x6x3xf32>, tensor<4x2xi32>) -> tensor<1x6x6x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 18:45:51 UTC 2024 - 32.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/legalize-skip-partitioned-calls.mlir
} : (tensor<1x2x2x3xf32>) -> tensor<1x2x2x3xf32> // CHECK-SKIP: tf.PartitionedCall // CHECK-NOSKIP: call @some_other_func // CHECK-NOSKIP-NOT: tf.PartitionedCall func.return %1: tensor<1x2x2x3xf32> } // CHECK-SKIP: func.func private @some_func func.func private @some_func(%arg0: tensor<1x2x2x3xf32>) -> tensor<1x2x2x3xf32> attributes {tf._noinline = true} {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 08 20:05:12 UTC 2024 - 1.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/defer_activation_transpose.mlir
%0 = stablehlo.constant dense<2.000000e+00> : tensor<1x4x3x3xf32> %1 = stablehlo.transpose %arg0, dims = [0, 3, 1, 2] : (tensor<1x3x3x4xf32>) -> tensor<1x4x3x3xf32> %2 = stablehlo.add %1, %0 : tensor<1x4x3x3xf32> return %2 : tensor<1x4x3x3xf32> } // CHECK-SAME: (%[[ARG_0:.+]]: tensor<1x3x3x4xf32>) -> tensor<1x4x3x3xf32> // CHECK-DAG: %[[CONST_0:.+]] = 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/quantization/tensorflow/tests/quantize_drq.mlir
// ----- module { func.func @matmul(%arg0: tensor<1x2x2x3xf32>) -> (tensor<*xf32>) { %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2x1024xf32>} : () -> tensor<2x1024xf32> %1 = "tf.PartitionedCall"(%arg0, %cst_0) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_matmul_fn} : (tensor<1x2x2x3xf32>, tensor<2x1024xf32>) -> tensor<*xf32> func.return %1: tensor<*xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 19:32:28 UTC 2024 - 1.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/quantize-variables.mlir
%3 = "tfl.quantize"(%2) {qtype = tensor<1x2x1x3x!quant.uniform<i8:f32, 1.0>>, volatile} : (tensor<1x2x1x3xf32>) -> tensor<1x2x1x3x!quant.uniform<i8:f32, 1.0>> %5 = "tfl.dequantize"(%arg0) : (tensor<1x2x1x3x!quant.uniform<i8:f64, 1.0>>) -> tensor<1x2x1x3xf32> "tfl.assign_variable"(%1, %5) : (tensor<!tf_type.resource>, tensor<1x2x1x3xf32>) -> ()
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 20.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/fold_broadcast.mlir
%0 = "mhlo.broadcast_in_dim"(%cst0) <{broadcast_dimensions = dense<[1, 3]> : tensor<2xi64>}> : (tensor<2x3xf32>) -> tensor<1x2x2x3xf32> %1 = mhlo.multiply %0, %cst1 : tensor<1x2x2x3xf32> // CHECK: return %[[RES]] : tensor<1x2x2x3xf32> func.return %1 : tensor<1x2x2x3xf32> } // CHECK-LABEL: @foldBroadcastInDimBeforeMulOp_bcast_dim_1D_int
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 4.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_xla.mlir
func.func private @conv(%input: tensor<1x3x4x3xf32> {tf._user_specified_name = "input_tensor"}) -> tensor<*xf32> attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<1x3x4x3>]} { %weight = arith.constant dense_resource<__elided__> : tensor<2x3x3x2xf32> %bias = arith.constant dense<[7.11401462, 7.05456924]> : tensor<2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 19:32:28 UTC 2024 - 11.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/lift_quantizable_spots_as_functions.mlir
func.func @float_conv_strides_equals_to_dilations(%arg0: tensor<1x3x4x3xf32>, %arg1: tensor<2x3x3x2xf32>) -> tensor<*xf32> { %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32> %0 = "tf.Conv2D"(%arg0, %arg1) {data_format = "NHWC", device = "", 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: Fri May 10 04:07:09 UTC 2024 - 26.5K bytes - Viewed (0)