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Results 41 - 50 of 137 for matmul_0 (0.14 sec)
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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) -
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/tfrt/tests/ifrt/sink_variable_as_named_array.mlir
// CHECK: "tf.VarHandleOp" // CHECK-NOT: [[VARIABLE:%.*]] = "tf.ReadVariableOp" // CHECK-NEXT: [[KEY:%.*]], [[FUTURE:%.*]] = "tf.IfrtLoadVariable" // CHECK-SAME: used_by_host = true // CHECK-NEXT: [[MATRES:%.*]] = "tf.MatMul"(%arg0, [[FUTURE]]) // CHECK-NEXT: [[RES:%.*]] = "tf.IfrtCall"(%arg0, [[KEY]]) <{program_id = 6515870160938153680 : i64, variable_arg_indices = [1 : i32]}> // CHECK-NEXT: return [[RES]], [[MATRES]] : tensor<1x1xf32>, tensor<1x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 15:33:17 UTC 2024 - 5.3K bytes - Viewed (0) -
tensorflow/c/experimental/gradients/math_grad_test.cc
absl::Span<AbstractTensorHandle* const> inputs, absl::Span<AbstractTensorHandle*> outputs) -> Status { return ops::MatMul(ctx, inputs[0], inputs[1], &outputs[0], transpose_a, transpose_b, "MatMul"); }; ASSERT_NO_FATAL_FAILURE(CompareNumericalAndAutodiffGradients( MatMulModel, BuildGradModel(MatMulModel, registry_),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 13 17:32:14 UTC 2023 - 16.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/ops/tf_op_quant_spec.cc
TF::SqueezeOp, TF::TransposeOp>(op); } bool IsOpWithQuantizableTrait(Operation* op) { // Supported quantizable ops. return isa<TF::XlaConvV2Op, TF::XlaDotV2Op, TF::MatMulOp, TF::Conv2DOp, TF::GatherOp, TF::GatherV2Op, TF::XlaGatherOp, TF::ResourceGatherOp, TF::DepthwiseConv2dNativeOp, TF::Conv3DOp, TF::BatchMatMulV2Op, TF::EinsumOp>(op); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 6.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_composite_functions_drq.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 05 18:35:42 UTC 2024 - 9.8K bytes - Viewed (0) -
tensorflow/cc/framework/cc_ops_test.cc
// It's being used here ONLY to ensure that, that style is tested. MatMul m(root, c, {{41}, {1}}); TF_EXPECT_OK(root.status()); Tensor out; test::GetTensor(root, m, &out); test::ExpectTensorEqual<int>(out, test::AsTensor<int>({42}, {1, 1})); } TEST(CCOpTest, Attrs) { Scope root = Scope::NewRootScope(); auto m = MatMul(root, {{1}, {1}}, {{41}, {1}}, MatMul::TransposeA(true)); TF_EXPECT_OK(root.status()); Tensor out;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 15 15:13:38 UTC 2023 - 8.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/lift_quantizable_spots_as_functions_drq.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 - 11.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/insert_quantized_functions.mlir
// CHECK: func private @quantized_matmul_with_bias_and_relu_fn // CHECK: func private @quantized_matmul_with_bias_and_relu6_fn // CHECK: func private @quantized_matmul_fn // CHECK-SAME: tf_quant.quantized_ops = ["MatMul"] // CHECK: func private @quantized_matmul_with_relu_fn // CHECK: func private @quantized_matmul_with_relu6_fn // CHECK: func private @quantized_conv3d_with_bias_fn // CHECK-SAME: tf_quant.quantized_ops = ["Conv3D", "BiasAdd"]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Aug 29 01:13:58 UTC 2023 - 3.3K bytes - Viewed (0)