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Results 11 - 16 of 16 for linefeed (0.23 sec)
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tensorflow/compiler/mlir/tensorflow/transforms/set_tpu_infeed_layout.cc
#include "xla/translate/mhlo_to_hlo/type_to_shape.h" namespace mlir { static FailureOr<std::vector<int64_t>> GetTPUInfeedLayoutFromAPI( RankedTensorType t) { // Call the TPU API to determine the right infeed layout. Note that // this can fail if we're not running on a TPU-enabled node. // TODO(kramm): Move this into a separate pass. See b/184944903 xla::Shape old_shape = xla::TypeToShape(t); XLA_Shape old_shape_c = {};
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 6.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/transforms/legalize_tf.cc
} }; // Converts InfeedDequeueTuple to XLA HLO create_token, infeed and // get_tuple_element ops. // // All HLO infeed ops expect a HLO token type operand and produce a tuple // containing a token. This HLO token type is used to order multiple infeed // operations within a computation. The token type can come from other // infeed/outfeed/send/recv ops or can be generated using create_token op with
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 20:00:43 UTC 2024 - 291.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td
TF_Tensor:$output ); TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>; } def TF_InfeedDequeueTupleOp : TF_Op<"InfeedDequeueTuple", []> { let summary = "Fetches multiple values from infeed as an XLA tuple."; let arguments = (ins OptionalAttr<StrAttr>:$_XlaSharding, OptionalAttr<ArrayAttr>:$layouts ); let results = (outs Variadic<TF_Tensor>:$outputs );
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 04:08:35 UTC 2024 - 90.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf.mlir
// CHECK: [[TOKEN:%.*]] = mhlo.create_token : !mhlo.token // CHECK: [[INFEED:%.*]]:3 = "mhlo.infeed"([[TOKEN]]) <{infeed_config = ""{{.*}}}> : (!mhlo.token) -> (tensor<1x8x4x4xi32>, tensor<1x100x1xf32>, !mhlo.token) // CHECK: return [[INFEED]]#0, [[INFEED]]#1 %0:2 = "tf.InfeedDequeueTuple"() : () -> (tensor<1x8x4x4xi32>, tensor<1x100x1xf32>)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 06 18:46:23 UTC 2024 - 335.5K bytes - Viewed (0) -
src/internal/trace/traceviewer/static/trace_viewer_full.html
g,p,v,d=t(i),m=u.invert(r[0],r[1]),y={point:o,lineStart:c,lineEnd:s,polygonStart:function(){y.point=l,y.lineStart=f,y.lineEnd=h,g=[],p=[],i.polygonStart()},polygonEnd:function(){y.point=o,y.lineStart=c,y.lineEnd=s,g=Xo.merge(g);var n=Le(m,p);g.length?we(g,Ne,n,e,i):n&&(i.lineStart(),e(null,null,1,i),i.lineEnd()),i.polygonEnd(),g=p=null},sphere:function(){i.polygonStart(),i.lineStart(),e(null,null,1,i),i.lineEnd(),i.polygonEnd()}},x=Ce(),M=t(x);return y}}function Ae(n){return n.length>1}function Ce(){var n,t=[];...
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Tue Nov 21 20:45:06 UTC 2023 - 2.5M bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td
}]; let arguments = (ins TF_ShapeAttr:$shape ); let results = (outs Res<TF_Tensor, [{A tensor that will be provided using the infeed mechanism.}]>:$output ); TF_DerivedResultTypeAttr dtype = TF_DerivedResultTypeAttr<0>; } def TF_InitializeTableOp : TF_Op<"InitializeTable", []> { let summary = [{
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 793K bytes - Viewed (0)