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Results 21 - 30 of 58 for mat_mul (0.12 sec)
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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/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/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/compiler/mlir/quantization/stablehlo/tests/passes/insert_calibration_statistics_saver.mlir
} func.func private @composite_matmul_with_bias_fn_1(%arg0: tensor<1x4xf32>, %arg1: tensor<4x3xf32>, %arg2: tensor<3xf32>) -> tensor<1x3xf32> attributes {tf_quant.composite_function} { %0 = "tf.MatMul"(%arg0, %arg1) <{grad_a = false, grad_b = false, transpose_a = false, transpose_b = false}> {attr_map = "0:transpose_a,1:transpose_b", device = ""} : (tensor<1x4xf32>, tensor<4x3xf32>) -> tensor<1x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 01:09:50 UTC 2024 - 24.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/api/v2/legalize_tf_test.cc
// May have been filtered so check for lack of failure instead of success. EXPECT_EQ(compilation_status.Delta(kMlirWithFallbackModeFailure), 0); } TEST(LegalizeTFTest, MatMul) { static constexpr char kMatMulModuleStr[] = R"( module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} { func.func @main() -> (tensor<5x11xf32>) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 13 23:59:33 UTC 2024 - 16.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/legalize-tf-while.mlir
%4 = "tf.Sub"(%3, %cst_2) : (tensor<?xi32>, tensor<i32>) -> tensor<?xi32> %5 = "tf.Transpose"(%arg3, %4) : (tensor<*xf32>, tensor<?xi32>) -> tensor<*xf32> %6 = "tf.MatMul"(%1, %5) {transpose_a = false, transpose_b = true} : (tensor<?x?xf32>, tensor<*xf32>) -> tensor<?x?xf32> %7 = "tf.AddV2"(%arg4, %6) {T = f32, device = ""} : (tensor<*xf32>, tensor<?x?xf32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/lift_quantizable_spots_as_functions.cc
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 16.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/insert_custom_aggregation_ops.mlir
func.func private @composite_matmul_with_bias_fn_1(%arg0: tensor<1x4xf32>, %arg1: tensor<4x3xf32>, %arg2: tensor<3xf32>) -> tensor<1x3xf32> attributes {tf_quant.composite_function} { %0 = "tf.MatMul"(%arg0, %arg1) <{grad_a = false, grad_b = false, transpose_a = false, transpose_b = false}> {attr_map = "0:transpose_a,1:transpose_b", device = ""} : (tensor<1x4xf32>, tensor<4x3xf32>) -> tensor<1x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 32.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/ops/tf_op_quant_spec.cc
if (function_name.contains("with_bias")) { spec->biases_params[2] = {{0, 1}, quant::GetUniformQuantizedTypeForBias}; } } else if (function_name.contains("matmul")) { spec->coeff_op_quant_dim[1] = -1; if (function_name.contains("with_bias") || function_name.contains("and_bias")) { spec->biases_params[2] = {{0, 1},
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 6.3K bytes - Viewed (0)