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tensorflow/compiler/mlir/quantization/tensorflow/tests/insert_quantized_functions.mlir
// CHECK: func private @quantized_conv2d_with_relu6_fn // CHECK: func private @quantized_depthwise_conv2d_with_bias_and_relu_float_output_fn // CHECK-SAME: tf_quant.quantized_ops = ["DepthwiseConv2D", "BiasAdd", "Relu"] // CHECK: func private @quantized_matmul_with_bias_fn // 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
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Aug 29 01:13:58 UTC 2023 - 3.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-signed.mlir
dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32 } : (tensor<1x5x5x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<1x5x5x3xf32> %conv2 = "tfl.conv_2d"(%0, %w, %b2) { dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 18.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/tests/decompose.mlir
%none_attr = tfr.constant "NONE" -> !tfr.attr %relu_attr = tfr.constant "RELU" -> !tfr.attr %relu6_attr = tfr.constant "RELU6" -> !tfr.attr %reluN1_1_attr = tfr.constant "RELU_N1_TO_1" -> !tfr.attr %none:2 = "tfr.quant_act_range"(%none_attr, %scale, %zp) : (!tfr.attr, f32, i64) -> (!tfr.tensor, !tfr.tensor) %relu:2 = "tfr.quant_act_range"(%relu_attr, %scale, %zp) : (!tfr.attr, f32, i64) -> (!tfr.tensor, !tfr.tensor)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 16.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/fused_kernel_matcher.cc
// Currently, GPU only supports Conv2D+BiasAdd+Relu fusion. if (IsGpuDevice(conv)) { auto activation = GetActivation(bias_add); if (!activation || activation->getName().stripDialect() != "Relu" || !bias_add.getOutput().hasOneUse()) { (void)rewriter.notifyMatchFailure(conv, [&](Diagnostic &diag) { diag << "GPU only supports Conv2D+BiasAdd+Relu fusion"; }); return false;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 14.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/integration/graph_decompose_test.py
t1 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]]) t2 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]]) t3 = constant_op.constant([[-10.0, -10.0], [-10.0, -10.0]]) sq = biased_dense(t1, t2, t3, act='relu') self.assertAllEqual(sq.numpy().reshape(-1), [0, 0, 5, 12]) def testWithKnownKernel(self): @def_function.function def biasd_dense_elu(x, y, z): dot = gen_composite_ops.my_biased_dense(x, y, z)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Sep 28 21:37:05 UTC 2021 - 3.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test.py
('none', None, False, False, quant_opts_pb2.TF, False, 'SAME'), ('relu', nn_ops.relu, False, False, quant_opts_pb2.TF, False, 'SAME'), ('relu6', nn_ops.relu6, False, False, quant_opts_pb2.TF, False, 'SAME'), ('with_bias', None, True, False, quant_opts_pb2.TF, False, 'SAME'), ( 'with_bias_and_relu', nn_ops.relu, True, False, quant_opts_pb2.TF,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 03:36:50 UTC 2024 - 235.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/integration/node_expansion_test.py
t1 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]]) t2 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]]) t3 = constant_op.constant([[-10.0, -10.0], [-10.0, -10.0]]) sq = gen_composite_ops.my_biased_dense(t1, t2, t3, act='relu') self.assertAllEqual(sq.numpy().reshape(-1), [0, 0, 5, 12]) def testWithKnownKernel(self): def biasd_dense_elu(x, y, z): dot = gen_composite_ops.my_biased_dense(x, y, z)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Sep 28 21:37:05 UTC 2021 - 3.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/mlir2flatbuffer/custom_op_with_tflite_op.mlir
// tf.MyCustomOp is the result of conversion to a Custom op %2 = "tf.MyCustomOp"(%1, %0) {fused_activation_function = "RELU", int_attr = 2 : i32} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("MyCustomOp") %3 = "tfl.exp"(%2) : (tensor<4xf32>) -> tensor<4xf32> loc("exp") func.return %3 : tensor<4xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jul 14 16:41:28 UTC 2022 - 4.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize_no_verify.mlir
%cst = arith.constant dense<0.0> : tensor<2x3xbf16> %0 = "tfl.maximum"(%arg0, %cst) : (tensor<2x3xbf16>, tensor<2x3xbf16>) -> tensor<2x3xbf16> func.return %0 : tensor<2x3xbf16> // CHECK: %[[RESULT:.*]] = "tfl.relu"(%arg0) // CHECK: return %[[RESULT]] } // CHECK-LABEL: fuseScalarAddIntoConv2dBf16 func.func @fuseScalarAddIntoConv2dBf16(%arg0: tensor<256x32x32x3xbf16>, %arg1: tensor<16x3x3x3xbf16>) -> tensor<256x8x7x16xbf16> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 5.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_op_enums.td
} // Allowed activation function cases // These should match the ActivationFunctionType enum in TFLite schema. def TFL_AFEnum_None : I32EnumAttrCase<"NONE", 0>; def TFL_AFEnum_Relu : I32EnumAttrCase<"RELU", 1>; def TFL_AFEnum_Relu1 : I32EnumAttrCase<"RELU_N1_TO_1", 2>; def TFL_AFEnum_Relu6 : I32EnumAttrCase<"RELU6", 3>; def TFL_AFEnum_Tanh : I32EnumAttrCase<"TANH", 4>; def TFL_AFEnum_Sign : I32EnumAttrCase<"SIGN_BIT", 5>;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Oct 20 00:05:24 UTC 2022 - 6.4K bytes - Viewed (0)