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tensorflow/compiler/mlir/tensorflow/tests/tf-ops.mlir
// Test valid tf.ConcatV2 func.func @testConcatV2(%arg: tensor<8x16xf32>, %axis: tensor<i32>) -> tensor<?xf32> { %0 = "tf.ConcatV2"(%arg, %arg, %axis) : (tensor<8x16xf32>, tensor<8x16xf32>, tensor<i32>) -> tensor<?xf32> func.return %0 : tensor<?xf32> } // ----- // tf.ConcatV2 with wrong 'axis' element type func.func @testConcatV2(%arg: tensor<8x16xf32>, %axis: tensor<f32>) -> tensor<?xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 23 14:40:35 UTC 2023 - 236.4K bytes - Viewed (0) -
src/math/sincos.go
j++ y++ } j &= 7 // octant modulo 2Pi radians (360 degrees) z = ((x - y*PI4A) - y*PI4B) - y*PI4C // Extended precision modular arithmetic } if j > 3 { // reflect in x axis j -= 4 sinSign, cosSign = !sinSign, !cosSign } if j > 1 { cosSign = !cosSign } zz := z * z cos = 1.0 - 0.5*zz + zz*zz*((((((_cos[0]*zz)+_cos[1])*zz+_cos[2])*zz+_cos[3])*zz+_cos[4])*zz+_cos[5])
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Mon Apr 11 16:34:30 UTC 2022 - 1.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/examples/mnist/ops_defs.py
input_value_shape = tf.shape(op.inputs[2]) _, reduction_axes = tf.raw_ops.BroadcastGradientArgs( s0=broadcast_shape, s1=input_value_shape) updates_grad_reshaped = tf.reduce_sum( grad, axis=reduction_axes, keepdims=True) bias_grad = tf.reshape(updates_grad_reshaped, input_value_shape) dilations = [1, op.get_attr('dilation_w'), op.get_attr('dilation_h'), 1]
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/tf2xla/tests/legalize-tf.mlir
func.func @expand_dims(%arg0: tensor<2xf32>, %axis: tensor<i32>) -> tensor<1x2xf32> { // CHECK: mhlo.reshape %0 = "tf.ExpandDims"(%arg0, %axis) : (tensor<2xf32>, tensor<i32>) -> tensor<1x2xf32> func.return %0 : tensor<1x2xf32> } // ----- // CHECK-LABEL: expand_dims_dynamic func.func @expand_dims_dynamic(%arg0: tensor<?x?xf32>) -> tensor<?x1x?xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 06 18:46:23 UTC 2024 - 335.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/unroll_batch_matmul.pbtxt
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/lite/tests/quantize-variables.mlir
%2 = "tfl.var_handle"() {container = "", shared_name = "read_assign2/states"} : () -> tensor<!tf_type.resource> %3 = "tfl.read_variable"(%2) : (tensor<!tf_type.resource>) -> tensor<1x2x1x3xf32> %4 = "tfl.concatenation"(%3, %1) {axis = 1 : i32, fused_activation_function = "NONE"} : (tensor<1x2x1x3xf32>, tensor<1x32x1x3xf32>) -> tensor<1x34x1x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 20.3K bytes - Viewed (0) -
tensorflow/c/eager/c_api_test_util.h
// Return a 1-D INT32 tensor containing a single value 1. TFE_TensorHandle* TestAxisTensorHandle(TFE_Context* ctx); // Return an op taking minimum of `input` long `axis` dimension. TFE_Op* MinOp(TFE_Context* ctx, TFE_TensorHandle* input, TFE_TensorHandle* axis); // If there is a device of type `device_type`, returns true // and sets 'device_name' accordingly. // `device_type` must be either "GPU" or "TPU".
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Jul 17 23:43:59 UTC 2023 - 7.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td
specified by `axis[i]`. Negative shifts will roll the elements in the opposite direction.}]>:$shift, Arg<TF_I32OrI64Tensor, [{Dimension must be 0-D or 1-D. `axis[i]` specifies the dimension that the shift `shift[i]` should occur. If the same axis is referenced more than once, the total shift for that axis will be the sum of all the shifts that belong to that axis.}]>:$axis ); let results = (outs
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 793K bytes - Viewed (0) -
tensorflow/compiler/aot/tests/make_test_graphs.py
x0, x1 = array_ops.split(x, 2, 0) y0, y1 = array_ops.split(y, 2, 0) x0 += 1 y0 += 1 z = math_ops.matmul(x, y, name='x_y_prod') a = array_ops.concat([x0, y1], axis=0, name='concat_x0_y1') b = array_ops.concat([y0, x1], axis=0, name='concat_y0_x1') x = math_ops.matmul(a, b, name='a_b') y = math_ops.add(x, z) array_ops.identity(y, name='result') def tftop_k(_):
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 15 15:25:23 UTC 2023 - 7.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/python/tfr_gen_test.py
@composite.Composite('TestTwoInputsOp') def _tfr_tensor_tensor_list_split(x, y, pred): z, _ = array_ops.Split(axis=0, value=x, num_split=2) (y, pred) # pylint: disable=pointless-statement return z @composite.Composite('TestTwoOutputsOp') def _tfr_tensor_two_output(x): z = array_ops.Split(axis=0, value=x, num_split=2) return z[0], z[1] @composite.Composite('TestNumAttrsOp')
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Oct 13 16:33:28 UTC 2021 - 28.8K bytes - Viewed (0)