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Results 21 - 30 of 35 for 3x1x1x2xf32 (0.49 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_lifting.mlir
%cst_0 = "tf.Const"() {value = dense<0.400000e+00> : tensor<1x1x1x2xf32>} : () -> tensor<1x1x1x2xf32> %cst_1 = "tf.Const"() {value = dense<0.200000e+00> : tensor<1x1x1x2xf32>} : () -> tensor<1x1x1x2xf32> %cst_2 = "tf.Const"() {value = dense<0.300000e+00> : tensor<1x1x1x2xf32>} : () -> tensor<1x1x1x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 14 03:24:59 UTC 2024 - 33.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/cast_bf16_ops_to_f32.mlir
%2 = "tf.Cast"(%1) {Truncate = false} : (tensor<1x1x2xbf16>) -> tensor<1x1x2xf32> %3 = "tf.IdentityN"(%2) {device = ""} : (tensor<1x1x2xf32>) -> tensor<1x1x2xf32> return %3 : tensor<1x1x2xf32> } // CHECK: func @cast_bf16_batch_matmul_v2_to_fp32 // CHECK-DAG: %[[cst:.*]] = "tf.Const"() <{value = dense<{{.*}}> : tensor<10x2xf32>}> : () -> tensor<10x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 8.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-dynamic-range.mlir
// MinElement-LABEL: QuantizeCustomOp func.func @QuantizeCustomOp(%arg0: tensor<1x1x1x1xf32>) -> (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) attributes {tf.entry_function = {inputs = "input", outputs = "custom_op"}} { %0 = "quantfork.stats"(%arg0) {layerStats = dense<[0.000000e+00, 2.550000e+02]> : tensor<2xf32>} : (tensor<1x1x1x1xf32>) -> tensor<1x1x1x1xf32> %w_1 = arith.constant dense<127.0> : tensor<4096x1x1x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 38.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/strip_tf_attributes.mlir
// CHECK-LABEL: strips_attributes // CHECK-NOT: tf func.func @strips_attributes(%arg0: tensor<32x28x28x1xf32> {tf._user_specified_name = "x"}, %arg1: tensor<3x3x1x5xf32> {tf._user_specified_name = "w1"}, %arg2: tensor<5xf32> {tf._user_specified_name = "b1"}, %arg3: tensor<3920x10xf32> {tf._user_specified_name = "w2"},
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Oct 25 20:04:10 UTC 2022 - 1.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/bridge/optimize.mlir
return %2 : tensor<?x2x2x1xi8> } // ----- // CHECK-LABEL: func @convolution_add_add_f32 func.func @convolution_add_add_f32( %lhs: tensor<?x3x2x1xf32>, %rhs: tensor<2x1x1x1xf32>, %zp_offset: tensor<?x2x2x1xf32>, %bias: tensor<1xf32> ) -> tensor<?x2x2x1xf32> { // CHECK-DAG: %[[conv:.*]] = mhlo.convolution // CHECK-DAG: %[[combined:.*]] = chlo.broadcast_add %[[conv:.*]], %[[zp_offset:.*]]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Feb 24 02:26:47 UTC 2024 - 10.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/quantize-dynamic-range.mlir
// CustomOpNotWeightOnly-LABEL: QuantizeCustomOp func.func @QuantizeCustomOp(%arg0: tensor<1x1x1x1xf32>) -> tensor<*xf32> attributes {tf.entry_function = {inputs = "input", outputs = "custom_op"}} { %0 = "quantfork.stats"(%arg0) {layerStats = dense<[0.000000e+00, 2.550000e+02]> : tensor<2xf32>} : (tensor<1x1x1x1xf32>) -> tensor<1x1x1x1xf32> %w = arith.constant dense<127.0> : tensor<1024x1x1x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 23 21:09:00 UTC 2024 - 23.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize.mlir
// CHECK-LABEL: squeezeToReshape func.func @squeezeToReshape(%arg0: tensor<1x1x2xf32>) -> tensor<2xf32> { %0 = "tfl.squeeze"(%arg0) : (tensor<1x1x2xf32>) -> tensor<2xf32> func.return %0 : tensor<2xf32> // CHECK-DAG: [[CONST:.*]] = arith.constant dense<2> : tensor<1xi32> // CHECK: %[[RESULT:.*]] = "tfl.reshape"(%arg0, %[[CONST:.*]]) : (tensor<1x1x2xf32>, tensor<1xi32>) -> tensor<2xf32> // CHECK: return %[[RESULT]] }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 284.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/convert_tf_xla_op_to_tf_op.cc
// {0}, then it returns: tensor<1x3x5xi32>. // * If `xla_gather_op_output_type` == tensor<3x5xf32> and `collapsed_dims` == // {1, 3}, then it returns: tensor<3x1x5x1xf32>. Type GetSliceOpOutputType(Type xla_gather_op_output_type, const absl::flat_hash_set<int64_t>& collapsed_dims) { if (auto ranked_output_type =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 13.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/ops.mlir
func.func @testSpaceToDepthF32(%arg0: tensor<1x2x2x1xf32>) -> tensor<1x1x1x4xf32> { // CHECK: %[[ARG:.*]]: tensor<1x2x2x1xf32> // CHECK: "tfl.space_to_depth"(%[[ARG]]) <{block_size = 2 : i32}> : (tensor<1x2x2x1xf32>) -> tensor<1x1x1x4xf32> %0 = "tfl.space_to_depth"(%arg0) {block_size = 2: i32} : (tensor<1x2x2x1xf32>) -> tensor<1x1x1x4xf32> func.return %0 : tensor<1x1x1x4xf32> } // -----
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 189.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/replace_cast_hacks_with_tf_xla_ops.mlir
// CHECK-DAG: %[[CONST_6:.*]] = "tf.Const"() <{value = dense<{{.*}}> : tensor<2x3x3x2xi8>}> : () -> tensor<2x3x3x2xi8> // CHECK-DAG: %[[CONST_7:.*]] = "tf.Const"() <{value = dense<{{.*}}> : tensor<1x1x1x2xi32>}> : () -> tensor<1x1x1x2xi32> // CHECK-DAG-SAME{LITERAL}: value = dense<[[[[-22016, -23680]]]]> // CHECK-DAG: %[[CONST_8:.*]] = "tf.Const"() <{value = dense<[162, 160]> : tensor<2xi32>}> : () -> tensor<2xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 81K bytes - Viewed (0)