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Results 1 - 10 of 22 for 8x15xf32 (0.17 sec)
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tensorflow/compiler/mlir/lite/tests/canonicalize.mlir
func.func @RemoveRedundantUnpackPack(%arg0: tensor<2x5xf32>) -> tensor<2x5xf32> { %0:2 = "tfl.unpack"(%arg0) {axis = 0 : i32, num = 2 : i32} : (tensor<2x5xf32>) -> (tensor<5xf32>, tensor<5xf32>) %1 = "tfl.pack"(%0#0, %0#1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<5xf32>, tensor<5xf32>) -> (tensor<2x5xf32>) func.return %1: tensor<2x5xf32> // CHECK-NOT: pack // CHECK: return %arg0 : tensor<2x5xf32> } // -----
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/lower-static-tensor-list.mlir
// CHECK-NEXT: [[NEG_ONE_1:%.*]] = arith.constant dense<-1> : tensor<i32> // CHECK-NEXT: [[RESULT:%.*]] = "tf.Concat"([[ZERO]], [[INPUT]], [[EXTENDED_PART]]) : (tensor<i32>, tensor<3x10xf32>, tensor<?x10xf32>) -> tensor<?x10xf32> // CHECK-NEXT: return [[RESULT]] : tensor<?x10xf32> // CHECK-LABEL: func private @cond_false
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 39.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/tensorflow/tests/fallback_to_flex_ops_default.mlir
func.func @softmax(%arg0: tensor<8x16xf32>) -> tensor<8x16xf32> { %0 = "tf.Softmax"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32> func.return %0 : tensor<8x16xf32> // CHECK: %[[CUSTOM_0:.*]] = "tfl.custom"(%arg0) <{custom_code = "FlexSoftmax", custom_option = #tfl<const_bytes : "0x07536F66746D617800161207536F66746D61781A002A070A0154120230013200000221191414042801">}> : (tensor<8x16xf32>) -> tensor<8x16xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 13.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_composite_functions_weight_only.mlir
func.func @matmul(%arg0: tensor<2x12xf32>) -> (tensor<*xf32>) { %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<12x2xf32>} : () -> tensor<12x2xf32> %1 = "tf.PartitionedCall"(%arg0, %cst_0) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_matmul_fn_1} : (tensor<2x12xf32>, tensor<12x2xf32>) -> tensor<*xf32> func.return %1: tensor<*xf32> }
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/lite/tests/prepare-quantize-post-training-16bits.mlir
%2 = "tfl.pseudo_const"() {value = dense<[[0.2]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> %3 = "tfl.pseudo_const"() {value = dense<[[0.3]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> %4 = "tfl.pseudo_const"() {value = dense<[[0.4]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> %5 = "tfl.pseudo_const"() {value = dense<[[0.5]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 26.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf-fake-quant.mlir
%2 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32> func.return %1, %2 : tensor<2x1xf32>, tensor<2x1xf32> // CHECK: %cst = arith.constant // CHECK: %[[FQ:.*]] = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) // CHECK: %[[R1:.*]] = "tf.Reshape"(%[[FQ]], %cst) // CHECK-SAME: tensor<2x1xf32> // CHECK: %[[R2:.*]] = "tf.Reshape"(%[[FQ]], %cst)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 20.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/replace_stablehlo_ops_in_main_function_with_xla_call_module_ops.mlir
%4:4 = "tf.CustomAggregator"(%3) {calibration_method = 1 : i32, id = "1", max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32, num_bins = 0 : i32} : (tensor<3x11xf32>) -> (tensor<3x11xf32>, tensor<f32>, tensor<f32>, tensor<*xi64>) %5 = stablehlo.add %4#0, %1 : tensor<3x11xf32> %6 = stablehlo.multiply %5, %1 : tensor<3x11xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 01:09:50 UTC 2024 - 39.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf-fake-quant-4bit.mlir
func.func @fakeQuantPerChannelForActivation(%arg0: tensor<8x4xf32>) -> (tensor<8x4xf32>) { %arg1 = arith.constant dense<[0.0, -1.0, 1.0, 0.0]> : tensor<4xf32> %arg2 = arith.constant dense<[15.0, 14.0, 16.0, 1.0e-9]> : tensor<4xf32> %0 = "tf.FakeQuantWithMinMaxVarsPerChannel"(%arg0, %arg1, %arg2) {num_bits = 3, narrow_range = false} : (tensor<8x4xf32>, tensor<4xf32>, tensor<4xf32>) -> tensor<8x4xf32> func.return %0 : tensor<8x4xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 22K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_composite_functions_drq.mlir
func.func @matmul(%arg0: tensor<2x12xf32>) -> (tensor<*xf32>) { %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<12x2xf32>} : () -> tensor<12x2xf32> %1 = "tf.PartitionedCall"(%arg0, %cst_0) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_matmul_fn_1} : (tensor<2x12xf32>, tensor<12x2xf32>) -> tensor<*xf32> func.return %1: tensor<*xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 05 18:35:42 UTC 2024 - 9.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/fused_kernel_matcher.mlir
// CHECK-LABEL: matmulBiasAdd func.func @matmulBiasAdd(%arg0: tensor<64xf32>, %arg1: tensor<8x32xf32>, %arg2: tensor<32x64xf32>) -> (tensor<*xf32>) { // CHECK: %[[VAL_3:.*]] = "tf._FusedMatMul"(%arg1, %arg2, %arg0) <{epsilon = 0.000000e+00 : f32, fused_ops = ["BiasAdd"], transpose_a = false, transpose_b = false}> : (tensor<8x32xf32>, tensor<32x64xf32>, tensor<64xf32>) -> 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)