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Results 1 - 10 of 39 for mat_mul (0.19 sec)
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tensorflow/compiler/mlir/tfr/examples/mnist/ops_defs.py
bias_grad = tf.reshape(updates_grad_reshaped, input_value_shape) a = math_ops.conj(op.inputs[0]) b = math_ops.conj(op.inputs[1]) grad_a = gen_math_ops.mat_mul(grad, b) grad_b = gen_math_ops.mat_mul(grad, a, transpose_a=True) return [grad_a, grad_b, bias_grad] @Composite( 'NewMaxPool', inputs=['input_: T'], attrs=[
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Aug 31 20:23:51 UTC 2023 - 6.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/matmul.mlir
Christian Sigg <******@****.***> 1714640622 -0700
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.8K bytes - Viewed (0) -
tensorflow/compiler/jit/xla_activity_listener_test.cc
"/job:localhost/replica:0/task:0/device:CPU:0"); Output a = ops::Placeholder(root.WithOpName("A"), DT_FLOAT); for (int i = 0; i < 5; i++) { a = ops::MatMul(root.WithOpName(absl::StrCat("matmul_", i)), a, a); a = ops::Add(root.WithOpName(absl::StrCat("add_", i)), a, a); } GraphDef graph_def; root.graph()->ToGraphDef(&graph_def); return graph_def; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 22 08:47:20 UTC 2024 - 5.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/unroll_batch_matmul_disabled.pbtxt
# RUN: tf_tfl_translate -unfold_batchmatmul=false -tf-input-arrays=Placeholder,Placeholder_1 -tf-input-shapes=2,5,3:3,7 -tf-input-data-types=DT_FLOAT,DT_FLOAT -tf-output-arrays=MatMul -output-mlir %s -o - 2>&1 | FileCheck %s node { name: "Placeholder" op: "Placeholder" attr { key: "dtype" value { type: DT_FLOAT } } attr { key: "shape" value { shape { dim { size: 2
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/propagate_quantize_type.mlir
// CHECK: %[[IDENTITY:.*]] = "tf.Identity"(%cst) : (tensor<2x1024xi8>) -> tensor<2x1024xi8> // CHECK: %[[MATMUL:.*]] = "tf.XlaDotV2"(%arg0, %[[IDENTITY]]) <{dimension_numbers = "\12\01\00\0A\01\03", precision_config = ""}> {device = ""} : (tensor<1x2x2x2xbf16>, tensor<2x1024xi8>) -> tensor<1x2x2x1024xbf16>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 6.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_drq.mlir
// RUN: tf-quant-opt %s -split-input-file -quant-lift-quantizable-spots-as-functions -quant-prepare-quantize-drq -quant-quantize='weight-quantization=true' -verify-each=false | FileCheck %s // ----- module { func.func @matmul(%arg0: tensor<1x2x2x3xf32>) -> (tensor<*xf32>) { %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2x1024xf32>} : () -> tensor<2x1024xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 19:32:28 UTC 2024 - 1.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/unroll_batch_matmul.pbtxt
# RUN: tf_tfl_translate -tf-input-arrays=Placeholder,Placeholder_1 -tf-input-shapes=2,5,3:3,7 -tf-input-data-types=DT_FLOAT,DT_FLOAT -tf-output-arrays=MatMul -unfold_batchmatmul=true -output-mlir %s -o - 2>&1 | FileCheck %s node { name: "Placeholder" op: "Placeholder" attr { key: "dtype" value { type: DT_FLOAT } } attr { key: "shape" value { shape { dim { size: 2 }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 2.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/tf_to_corert/fallback.mlir
// CHECK: tfrt_fallback_async.executeop key(2) cost({{.*}}) device("/device:CPU:0") "tf.MatMul" %0 = "tf.ReadVariableOp"(%arg1) {device = "/device:CPU:0", dtype = f32} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32> %1 = "tf.MatMul"(%arg0, %0) {T = f32, device = "/device:CPU:0", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 00:18:59 UTC 2024 - 9.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/lift_quantizable_spots_as_functions_drq_min_elements.mlir
%cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<512x512xf32>} : () -> tensor<512x512xf32> %out_1 = "tf.MatMul"(%arg0, %cst) { device = "", transpose_a = false, transpose_b = false } : (tensor<1x12x12x512xf32>, tensor<512x512xf32>) -> tensor<*xf32> %out_2 = "tf.MatMul"(%arg0, %arg0) { device = "", transpose_a = false, transpose_b = true } : (tensor<1x12x12x512xf32>, tensor<1x12x12x512xf32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 2.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/mlrt/rewrite_ifrt_load_variable.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 22 21:35:32 UTC 2024 - 1.7K bytes - Viewed (0)