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Results 101 - 110 of 163 for matmult (0.13 sec)
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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/quantization/tensorflow/passes/quantized_function_library_uniform_quantized_drq.mlir
%input : tensor<*xf32>, %weight : tensor<*x!tf_type.qint8>, %weight_scale : tensor<*xf32>, %weight_zp : tensor<*xi32>) -> tensor<*xf32> attributes {tf_quant.quantized_ops = ["MatMul"]} { %out = "tf.UniformQuantizedDotHybrid"(%input, %weight, %weight_scale, %weight_zp) { Tlhs = "tfdtype$DT_FLOAT", Trhs = "tfdtype$DT_QINT8",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Dec 01 12:06:54 UTC 2022 - 3.9K 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/cc/framework/gradient_checker_test.cc
#include "tensorflow/core/platform/test.h" #include "tensorflow/core/util/equal_graph_def.h" namespace tensorflow { namespace { using ops::Complex; using ops::Const; using ops::Div; using ops::MatMul; using ops::Placeholder; using ops::Real; using ops::Split; using ops::Square; using ops::Stack; using ops::Sub; using ops::Unstack; TEST(GradientCheckerTest, BasicFloat) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Aug 06 15:54:08 UTC 2018 - 6.7K 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/quantization/tensorflow/quantization_options.proto
// NEXT ID: 7 message UnitWiseQuantizationSpec { // Quantization unit granularity. // NEXT ID: 4 message QuantizationUnit { // Type of the op, ex: Conv2D, MatMul, Einsum... The node_name field can // be omitted if it is intended to match all nodes with this type. string op_type = 1; // Name of the node. This field accepts re2 regex format. If the node name
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 19 06:31:19 UTC 2024 - 9.2K 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) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_xla.mlir
%dq_weight = "quantfork.dcast"(%q_weight) : (tensor<144x12x!quant.uniform<i8:f32, 0.074855112561992565:-1>>) -> tensor<144x12xf32> %9 = "tf.MatMul"(%7, %dq_weight) {transpose_a = false, transpose_b = false} : (tensor<*xf32>, tensor<144x12xf32>) -> tensor<*xf32> %10 = "quantfork.qcast"(%9) {volatile} : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 4.000000e-03:-12>>
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/c/experimental/ops/math_ops.h
AbstractTensorHandle* const y, AbstractTensorHandle** z, const char* name = nullptr, const char* raw_device_name = nullptr); // Multiply the matrix "a" by the matrix "b". Status MatMul(AbstractContext* ctx, AbstractTensorHandle* const a, AbstractTensorHandle* const b, AbstractTensorHandle** product, bool transpose_a = false, bool transpose_b = false,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 10 19:11:36 UTC 2022 - 4.4K bytes - Viewed (0)