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tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_composite_functions_weight_only.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 11.3K bytes - Viewed (0) -
tensorflow/c/experimental/ops/README.md
category names correspond to generated source file names, and should be consistent with the original source files registering each operator. For example since `REGISTER_OP("MatMul")` appears in ***core/math_ops.cc***, the "MatMul" operator in the script should be in the "math" category, and it will be generated in the output file `c/experimental/ops/math_ops.cc`.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jul 28 17:21:01 UTC 2021 - 993 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) -
tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/multi_variables_v1.py
# CHECK-NEXT: [[R1:%.*]] = "tf.ReadVariableOp"([[ARG1]]) {{{.*}}} : (tensor<!tf_type.resource<tensor<3x5xf32>>>) -> tensor<3x5xf32> # CHECK-NEXT: [[R2:%.*]] = "tf.MatMul"([[R0]], [[R1]]) <{{{.*}}}> {{{.*}}} : (tensor<5x3xf32>, tensor<3x5xf32>) -> tensor<5x5xf32> def Test(): x = tf.compat.v1.get_variable( name='x', shape=(5, 3),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Oct 31 08:49:35 UTC 2023 - 2.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_quantize_drq.mlir
%0 = "tf.MatMul"(%arg0, %arg1) {attr_map = "0:transpose_a,1:transpose_a", device = "", transpose_a = false, transpose_b = false} : (tensor<1x2x2x3xf32>, tensor<2x1024xf32>) -> tensor<*xf32> return %0 : tensor<*xf32> } // CHECK-LABEL: func @matmul // CHECK-DAG: %[[CONST:.*]] = arith.constant dense<0.000000e+00> : tensor<2x1024xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 6.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/device_assignment_by_func_attr.mlir
// CHECK: device = "xpu" %0 = "tf.Const"() {value = dense<[[1.0, 2.0, 3.0]]> : tensor<1x3xf32>} : () -> tensor<1x3xf32> // CHECK: device = "xpu" %1 = "tf.MatMul"(%arg0, %0) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32> // CHECK: device = "cpu"
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 10 00:30:05 UTC 2022 - 1.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/lift_quantizable_spots_as_functions.mlir
// CHECK-LABEL: private @composite_matmul_with_bias_and_relu6_fn_1 // CHECK-NEXT: %[[matmul:.*]] = "tf.MatMul"(%arg0, %arg1) // CHECK-SAME: attr_map = "0:transpose_a,1:transpose_b" // CHECK-NEXT: tf.BiasAdd // CHECK-NEXT: tf.Relu6 // CHECK-NEXT: return // CHECK-LABEL: private @composite_matmul_with_bias_and_relu_fn_1 // CHECK-NEXT: tf.MatMul"(%arg0, %arg1) // CHECK-SAME: attr_map = "0:transpose_a,1:transpose_b"
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 26.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/python/integration_test/quantize_model_test_base.py
out = math_ops.matmul(input_tensor, self.filters, name='sample/matmul') if bias_fn is not None: out = bias_fn(out, self.bias) if activation_fn is not None: out = activation_fn(out) return {'output': out} model = MatmulModel(weight_shape) saved_model_save.save( model, saved_model_path, signatures=model.matmul.get_concrete_function(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 06:31:57 UTC 2024 - 18.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_quantize_drq_per_channel.mlir
%0 = "tf.MatMul"(%arg0, %arg1) {attr_map = "0:transpose_a,1:transpose_a", device = "", transpose_a = false, transpose_b = false} : (tensor<1x2x2x3xf32>, tensor<2x1024xf32>) -> tensor<*xf32> return %0 : tensor<*xf32> } // CHECK-LABEL: func @matmul // CHECK-DAG: %[[CONST:.*]] = arith.constant dense<0.000000e+00> : tensor<2x1024xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 6.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/basic_v1.py
# CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["key"] # CHECK-NEXT: [[R0:%.*]] = "tf.ReadVariableOp"([[ARG1]]) {{{.*}}} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32> # CHECK-NEXT: [[R1:%.*]] = "tf.MatMul"([[ARG0]], [[R0]]) <{{{.*}}}> {device = ""} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32> # CHECK-NEXT: return [[R1]] : tensor<3x3xf32> def Test(): x = tf.constant([[1.0], [1.0], [1.0]])
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Oct 31 08:49:35 UTC 2023 - 2.7K bytes - Viewed (0)