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tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/convert_tf_quant_to_mhlo_int_test.cc
} : (tensor<9x10x!tf_type.qint8>, tensor<f32>, tensor<i32>) -> tensor<9x10xf32> %0 = "tf.MatMul"(%input, %filter_new) { } : (tensor<8x9xf32>, tensor<9x10xf32>) -> tensor<8x10xf32> return %0 : tensor<8x10xf32> })mlir"; constexpr absl::string_view kProgram = R"mlir( func.func @main(%input: tensor<8x9xf32>, %filter: tensor<9x10xi8>) -> tensor<8x10xf32> { %filter_scale = "tf.Const"() { value = dense<0.0235> : tensor<f32> } : ()
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 03 01:03:21 UTC 2024 - 35.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/shape-inference.mlir
%arg17, %arg18, %arg19, %arg20, %arg21, %arg22, %arg23) <{fused_activation_function = "NONE", time_major = false}> : (tensor<600x?x20xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<40xf32>, tensor<?x?xf32>, tensor<?xf32>, tensor<600x40xf32>, tensor<600x40xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>)...
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 11.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf.mlir
%4 = "tf.MatMul"(%arg0, %3) {device = "", transpose_a = false, transpose_b = false} : (tensor<2x3xf32>, tensor<3x4xf32>) -> tensor<2x4xf32> %5 = "tf.Identity"(%4) {device = ""} : (tensor<2x4xf32>) -> tensor<2x4xf32> %6 = "tf.Identity"(%5) {device = ""} : (tensor<2x4xf32>) -> tensor<2x4xf32> func.return %6 : tensor<2x4xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 59.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/const-fold.mlir
%cst_weights = arith.constant dense<[[1.0, 1.0e38, 1.0, -1.0e38]]> : tensor<1x4xf32> %cst_bias = arith.constant dense<0.0> : tensor<1xf32> %0 = "tfl.fully_connected" (%cst_input, %cst_weights, %cst_bias) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<4xf32>, tensor<1x4xf32>, tensor<1xf32>) -> tensor<1xf32> func.return %0 : tensor<1xf32>
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/tensorflow/tests/tpu_sharding_identification.mlir
return %1#0, %1#1 : tensor<1x2xf32>, tensor<1x2xf32> } func.func @_func(%arg0: tensor<2x4xf32>, %arg1: tensor<4x2xf32>) -> tensor<2x2xf32> { %0 = "tf.MatMul"(%arg0, %arg1) {_XlaSharding = "\08\03\1A\02\02\01\22\02\00\01"} : (tensor<2x4xf32>, tensor<4x2xf32>) -> tensor<2x2xf32> return %0 : tensor<2x2xf32> } // -----
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Feb 20 19:07:52 UTC 2024 - 47.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf.mlir
// CHECK-NEXT: %[[DIV:.*]] = mhlo.divide %{{.*}}, %[[ADD]] : tensor<4x10xf32> // CHECK-NEXT: return %[[DIV]] : tensor<4x10xf32> %0 = "tf.Softsign"(%arg0) : (tensor<4x10xf32>) -> tensor<4x10xf32> func.return %0 : tensor<4x10xf32> } // ----- // CHECK-LABEL: func @softsign_grad func.func @softsign_grad(%arg0: tensor<4x10xf32>, %arg1: tensor<4x10xf32>) -> tensor<4x10xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 06 18:46:23 UTC 2024 - 335.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/legalize_hlo.mlir
// CHECK: return %[[VALUES]], %[[INDICES]] : tensor<1x4xf32>, tensor<1x4xi32> // CHECK: } func.func @convert_approx_top_k_custom_call(%arg0: tensor<1x4xf32>, %arg1: tensor<1x4xi32>, %arg2: tensor<f32>, %arg3: tensor<i32>) -> (tensor<1x4xf32>, tensor<1x4xi32>) { %0:2 = mhlo.custom_call @ApproxTopK(%arg0, %arg1, %arg2, %arg3) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 340.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tf-ops.mlir
} // ----- // CHECK-LABEL: func @testBatchMatMulV2NoBatchDimension func.func @testBatchMatMulV2NoBatchDimension(%lhs: tensor<5x10xf32>, %rhs: tensor<10x10xf32>) -> (tensor<5x10xf32>) { %0 = "tf.BatchMatMulV2"(%lhs, %rhs) : (tensor<5x10xf32>, tensor<10x10xf32>) -> tensor<5x10xf32> func.return %0 : tensor<5x10xf32> } // -----
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 23 14:40:35 UTC 2023 - 236.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/shape_inference.cc
} } return new_arg_type; } // Combination of value producer and port of value produced (e.g., // <value result output>:<value in output tensor>, // so for tf.Const -> tensor<10x20xf32>, [0,2,18] would point to a unique output // scalar value). struct ValuePort { PointerUnion<Operation*, BlockArgument> producer; SmallVector<unsigned int, 2> port;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Jun 08 07:28:49 UTC 2024 - 134.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td
mlir_module = '''python func @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> { %add = "magic.op"(%arg0, %arg1) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32> return %ret : tensor<10x10xf32> } ''' @tf.function def foo(x, y): return mlir_passthrough_op([x, y], mlir_module, Toutputs=[tf.float32])
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 793K bytes - Viewed (0)