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Results 51 - 60 of 116 for mat_mul (0.18 sec)
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tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo_conversions/dot_general.cc
auto matmul = rewriter.create<TFL::BatchMatMulOp>( loc, RankedTensorType::get(matmul_shape, result_type.getElementType()), lhs_flattend, rhs_flattend, /*adj_x*/ false_attr, /*adj_y*/ false_attr, /*asym_quant_input*/ false_attr); if (result_type.hasStaticShape()) { auto reshaped = rewriter.create<mhlo::ReshapeOp>(loc, result_type, matmul.getResult()); return reshaped.getResult();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 19.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/batchmatmul_to_einsum.mlir
// RUN: tf-opt %s -tf-batch-matmul-to-tf-einsum | FileCheck %s func.func @test_batch_matmul_to_einsum(%arg0: tensor<1x2x3xf32>, %arg1: tensor<3x4xf32>) -> tensor<1x2x4xf32> { // CHECK-LABEL: test_batch_matmul_to_einsum // CHECK: "tf.Einsum"(%arg0, %arg1) <{equation = "...mk,...kn->...mn"}> : (tensor<1x2x3xf32>, tensor<3x4xf32>) -> tensor<1x2x4xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/import_restore_v1.py
def Test(): x = tf.constant([[1.0], [1.0], [1.0]]) y = tf.compat.v1.get_variable( name='y', shape=(1, 3), initializer=tf.random_normal_initializer(), trainable=True) r = tf.matmul(x, y) tensor_info_x = tf.compat.v1.saved_model.utils.build_tensor_info(x) tensor_info_r = tf.compat.v1.saved_model.utils.build_tensor_info(r) return {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Oct 31 08:49:35 UTC 2023 - 2.8K bytes - Viewed (0) -
tensorflow/c/eager/c_api_distributed_test.cc
ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_Op* matmul = MatMulOp(ctx, h0_task1, h1_task1); TFE_OpSetDevice(matmul, remote_device_name, status); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_TensorHandle* retvals[1]; int num_retvals = 1; TFE_Execute(matmul, &retvals[0], &num_retvals, status); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 15 09:49:45 UTC 2024 - 23.5K bytes - Viewed (0) -
tensorflow/cc/framework/scope.h
/// int idx = 3; /// auto b = Variable(linear.WithOpName("b_", idx), /// {2}, DT_FLOAT); /// auto x = Const(linear, {...}); // name: "linear/Const" /// auto m = MatMul(linear, x, W); // name: "linear/MatMul" /// auto r = BiasAdd(linear, m, b); // name: "linear/BiasAdd" /// /// Scope lifetime: /// /// A new scope is created by calling Scope::NewRootScope. This creates some
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 13 09:08:33 UTC 2024 - 10.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/replace_cast_hacks_with_tf_xla_ops.mlir
// CHECK-DAG: %[[CONST:.*]] = "tf.Const"() <{value = dense<-131072> : tensor<1x3xi32>}> : () -> tensor<1x3xi32> // CHECK: %[[MATMUL:.*]] = "tf.XlaDotV2"({{.*}}, %[[WEIGHT]]) // CHECK-SAME: (tensor<1x1024xi8>, tensor<1024x3xi8>) -> tensor<1x3xi32> // CHECK: %[[SUB:.*]] = "tf.Sub"(%[[MATMUL]], %[[CONST]]) : (tensor<1x3xi32>, tensor<1x3xi32>) -> tensor<1x3xi32> } // ----- module attributes {} {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 81K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize_batch_matmul.mlir
// Run optimize-batch-matmul pass only and check the results. // RUN: tf-opt %s -tfl-optimize-batch-matmul | FileCheck %s // CHECK-LABEL: FuseTransposeFCRhsToBatchMatmul func.func @FuseTransposeFCRhsToBatchMatmul(%arg0: tensor<16x1024xf32>, %arg1: tensor<1024x128xf32>, %arg2: none) -> tensor<16x128xf32> { %cst = arith.constant dense<[1, 0]> : tensor<2xi32> %0 = "tfl.transpose"(%arg1, %cst) : (tensor<1024x128xf32>, tensor<2xi32>) -> tensor<128x1024xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/tfrt_fallback/batching_fallback.mlir
%ch1 = tfrt.merge.chains %ch, %ch0 : !tfrt.chain, !tfrt.chain %ch2 = tfrt_fallback_async.createop(%ch1) key(0) device("/CPU:0") "tf.MatMul"() {T = i32} num_args(2) %ch3, %result = tfrt_fallback_async.executeop.seq(%ch2) key(0) cost(100) device("/CPU:0") "tf.MatMul"(%a, %b) {T = i32} : 1 %s = "tfrt_test.get_string"() { value = "Running @matmul_cpu" } : () -> !tfrt.string
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jul 18 22:58:56 UTC 2023 - 8.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/shared_variable_v1.py
def Test(): x = tf.constant([[1.0], [1.0], [1.0]]) y = tf.get_variable( name='y', shape=(1, 3), initializer=tf.random_normal_initializer(), trainable=True) r = tf.matmul(x, y) tensor_info_x = tf.saved_model.utils.build_tensor_info(x) tensor_info_r = tf.saved_model.utils.build_tensor_info(r) signature_def = tf.saved_model.signature_def_utils.build_signature_def(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Oct 31 08:49:35 UTC 2023 - 2.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/decompose_hybrid_quantization.cc
==============================================================================*/ // This transformation pass decomposes dense operations that assume // support for hybrid quantization. These cases cover when a dense operation // (e.g. matmul) has both quantized and unquantized inputs by dequantizing // the quantized inputs, performing the operation in the expressed type, then // requantizing if a quantized output is required. //
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 5.8K bytes - Viewed (0)