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Results 1 - 10 of 603 for rewrites (0.11 sec)
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tensorflow/compiler/aot/codegen.cc
count *= shape.dimensions(dim); } } rewrites->push_back({"{{I}}", absl::StrCat(i)}); rewrites->push_back({"{{TYPE}}", type}); rewrites->push_back({"{{DIM_VARS}}", absl::StrJoin(dim_vars, ", ")}); rewrites->push_back({"{{DIM_SIZES}}", dim_sizes}); rewrites->push_back({"{{INDICES}}", indices}); rewrites->push_back({"{{COUNT}}", absl::StrCat(count)}); return absl::OkStatus(); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 01:20:01 UTC 2024 - 36.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/transforms.cc
// TODO: b/230572023 - Consider improving shape inference for While op instead // of dropping the attribute. This need not be correct for models not trained // on TPU. // Optimizes TF graph via cleanups, merges, rewrites, constant folding, // and edge case handling where possible. pm.addNestedPass<func::FuncOp>(TF::CreateDropWhileShapeInvariantPass()); pm.addNestedPass<func::FuncOp>( tf_executor::CreateTFExecutorGraphPruningPass());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 04:34:23 UTC 2024 - 5.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/defer_activation_transpose.cc
PatternRewriter& rewriter) { return rewriter.create<TransposeOp>( input.getLoc(), input, rewriter.getDenseI64ArrayAttr(permutation)); } // Defers the transpose of the left-hand side (LHS) to the right-hand side and // the result of a binary operation. In detail, this rewrites the // `op(transpose(%rhs), %lhs)` to `transpose(op(%rhs, transpose(%lhs)))`. The
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/rewrite_tpu_embedding_ops.cc
#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.h.inc" // Rewrites RecvTPUEmbeddingActivationsOp and SendTPUEmbeddingGradients ops to // internal variants by introducing XlaRecvTPUEmbeddingDeduplicationData op. struct RewriteTPUEmbeddingOps : public impl::RewriteTPUEmbeddingOpsPassBase<RewriteTPUEmbeddingOps> { void runOnOperation() override; }; // Rewrites the given op to `OpT` op after adding the given operand at the end.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 15 22:55:42 UTC 2024 - 4.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/ir/ConvertSimQuant.cc
LogicalResult matchAndRewrite(FakeQuantOp op, PatternRewriter &rewriter) const override { // TODO: If this pattern comes up more frequently, consider adding core // support for failable rewrites. if (failableRewrite(op, rewriter)) { *hadFailure = true; return failure(); } return success(); } private: bool *hadFailure;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 02:10:16 UTC 2024 - 6K bytes - Viewed (0) -
src/cmd/fix/main.go
"os" "path/filepath" "sort" "strings" "cmd/internal/telemetry" ) var ( fset = token.NewFileSet() exitCode = 0 ) var allowedRewrites = flag.String("r", "", "restrict the rewrites to this comma-separated list") var forceRewrites = flag.String("force", "", "force these fixes to run even if the code looks updated") var allowed, force map[string]bool var (
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Tue May 14 19:41:17 UTC 2024 - 5.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/tf_device_passes.td
let dependentDialects = ["tf_device::TensorFlowDeviceDialect"]; } def XlaRewritePass : Pass<"tf-xla-rewrite", "mlir::ModuleOp"> { let summary = "Rewrites partition calls into Xla launch ops to make the attached function run on XLA."; let description = [{ This pass rewrites `tf.PartitionedCall` and `tf.StatefulPartitionedCall` operations with `_xla_compile_device_type` attribute in a
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 17 18:52:57 UTC 2024 - 12.5K bytes - Viewed (0) -
src/cmd/compile/internal/rangefunc/rewrite.go
// license that can be found in the LICENSE file. /* Package rangefunc rewrites range-over-func to code that doesn't use range-over-funcs. Rewriting the construct in the front end, before noder, means the functions generated during the rewrite are available in a noder-generated representation for inlining by the back end. # Theory of Operation The basic idea is to rewrite for x := range f { ... } into
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Thu May 23 01:05:44 UTC 2024 - 41.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/quantize_preprocess.cc
pm.addPass(mlir::createInlinerPass()); pm.addNestedPass<mlir::func::FuncOp>(mlir::createCanonicalizerPass()); pm.addPass(mlir::quant::CreateCastBf16OpsToF32Pass()); // Optimizes the graph via cleanups, merges, rewrites, constant folding, // and edge case handling where possible. pm.addNestedPass<mlir::func::FuncOp>( mlir::TF::CreateDropWhileShapeInvariantPass()); pm.addNestedPass<mlir::func::FuncOp>(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 12:49:45 UTC 2024 - 9.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/uniform_quantized_stablehlo_to_tfl_pass.cc
CastI64ToI32(window_dims[1]).value()); StringAttr activation_function = rewriter.getStringAttr("NONE"); rewriter.replaceOpWithNewOp<TFL::MaxPool2DOp>( op, result_type, input, padding, stride_w_attr, stride_h_attr, window_w_attr, window_h_attr, activation_function); } };
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 22 09:00:19 UTC 2024 - 99.8K bytes - Viewed (0)