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Results 121 - 130 of 137 for matmul_0 (0.15 sec)
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tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo.cc
auto matmul = rewriter.create<TF::BatchMatMulV3Op>( loc, RankedTensorType::get(matmul_shape, result_type.getElementType()), lhs_flattend, rhs_flattend); if (result_type.hasStaticShape()) { auto reshaped = rewriter.create<mhlo::ReshapeOp>(loc, result_type, matmul.getResult()); return reshaped.getResult(); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 154.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tpu_sharding_identification.mlir
return %1#0, %1#1 : tensor<1x2xf32>, tensor<1x2xf32> } func.func @_func(%arg0: tensor<2x4xf32>, %arg1: tensor<4x2xf32>) -> tensor<2x2xf32> { %0 = "tf.MatMul"(%arg0, %arg1) {_XlaSharding = "\08\03\1A\02\02\01\22\02\00\01"} : (tensor<2x4xf32>, tensor<4x2xf32>) -> tensor<2x2xf32> return %0 : tensor<2x2xf32> } // -----
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Feb 20 19:07:52 UTC 2024 - 47.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/passes.h
// Guarantee that all FuncOp's have a single use. std::unique_ptr<OperationPass<ModuleOp>> CreateGuaranteeAllFuncsOneUsePass(); // Optional pass which will unroll BatchMatMul and use only MatMul std::unique_ptr<OperationPass<func::FuncOp>> CreateUnrollBatchMatMulPassPass(); // Optional pass which will map TF BatchMatMul to TF Einsum std::unique_ptr<OperationPass<func::FuncOp>> CreateBatchMatMulToEinsumPass();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 21:18:05 UTC 2024 - 31.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/optimize.cc
// %1 = mhlo.reshape %param : (1xCxZ) -> CxZ // mhlo.dot_general %input, %1 {batch_dims = []} // To: // mhlo.dot_general %input, %param {batch_dims = [0]} // // This usage will mostly come from tf-unroll-batch-matmul, so it's fine to only // handle the case where batching dim is the leftmost dim. LogicalResult ConvertReshapeDotRhsToBatchedDot(mhlo::DotGeneralOp dot, PatternRewriter &rewriter) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 26.9K bytes - Viewed (0) -
tensorflow/compiler/jit/xla_launch_util.cc
// // 2. Old fashion Tensor with raw device memory pointer. This case occurs // when the producer is a non-XLA TF GPU kernel or function (e.g. // tf.matmul). // // 3. AsyncValueTensor, containing a PjRtBuffer. This is the legacy mode // and certain device type (e.g. TPU) still uses this path. AsyncValueTensor* av_tensor = AsyncValueTensor::FromTensor(tensor);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 00:36:08 UTC 2024 - 40.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/convert_tf_quant_to_mhlo_int_test.cc
quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64 } : (tensor<9x10x!tf_type.qint8>, tensor<f32>, tensor<i32>) -> tensor<9x10xf32> %0 = "tf.MatMul"(%input, %filter_new) { } : (tensor<8x9xf32>, tensor<9x10xf32>) -> tensor<8x10xf32> return %0 : tensor<8x10xf32> })mlir"; constexpr absl::string_view kProgram = R"mlir(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 03 01:03:21 UTC 2024 - 35.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.td
``` The pass also works across control flow and functional calls. }]; } def UnrollBatchMatMulPass : Pass<"tf-unroll-batch-matmul", "mlir::func::FuncOp"> { let summary = "Unroll TF BatchMatMul op into Reshape, Slice, MatMul, Pack ops."; let constructor = "TF::CreateUnrollBatchMatMulPassPass()"; } def ClusterFormationPass : Pass<"tf-device-cluster-formation", "mlir::ModuleOp"> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 21:18:05 UTC 2024 - 99.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/g3doc/_includes/tf_passes.md
[%state_var0, %state_var1] as %rstate) { tf.TPUReshardVariablesOp(%rvar, %default_format, %rstate) } ``` ### `-tf-unroll-batch-matmul` _Unroll TF BatchMatMul op into Reshape, Slice, MatMul, Pack ops._ ### `-tf-verify-for-export` _Verify module is suitable for export back to TF Graph_ Verifies whether all functions in module are of single tf_executor.graph and
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Aug 02 02:26:39 UTC 2023 - 96.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize.cc
if (fc_op.getFusedActivationFunction() != "NONE") return failure(); // Only fuse multiplier if all dimensions other than the depth dimension // are equal to 1 since otherwise // `matmul(x, filter) * cst != matmul(x, filter * cst)` // even if `filter` and `cst` are be broadcastable. auto shape = cst.getType().getShape(); if (!IsDimensionsDegenerateExceptLastOne(shape)) return failure();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 00:40:15 UTC 2024 - 102.3K bytes - Viewed (1) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf.mlir
// CHECK: "mhlo.dot"(%[[UPDATED_A]], %[[UPDATED_B]]) %0 = "tf.MatMul"(%a, %b) {transpose_a = true, transpose_b = true} : (tensor<7x5xf32>, tensor<11x7xf32>) -> tensor<5x11xf32> func.return %0 : tensor<5x11xf32> } // Verify that MatMul with ranked inputs are lowered to HLO. // CHECK-LABEL: matmul_ranked
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 06 18:46:23 UTC 2024 - 335.5K bytes - Viewed (0)