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tensorflow/compiler/mlir/lite/tests/prepare-quantize-post-training.mlir
// CHECK: %[[lstm:.*]] = "tfl.unidirectional_sequence_lstm"(%[[input_0]], %[[input_1]], %[[input_2]], %[[input_3]], %[[input_4]], %[[input_5]], %[[input_6]], %[[input_7]], %[[input_8]], // CHECK-SAME: %[[input_9]], %[[input_10]], %[[input_11]], %[[input_12]], %[[input_13]], %[[input_14]], %[[input_15]], %[[input_16]], %[[input_17]], %[[input_18]], %[[input_19]], // CHECK-SAME: %[[input_20]], %[[input_21]], %[[input_22]], %[[input_23]])
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 52.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/lite/quantize_model_test.cc
EXPECT_THAT(input2->type, Eq(TensorType_INT8)); // Check if the quantization params of the minimum/maximum inputs match // after requantization EXPECT_THAT(input1->quantization->scale, Eq(input2->quantization->scale)); EXPECT_THAT(input1->quantization->zero_point, Eq(input2->quantization->zero_point)); // Check the input quantization params match the output ones.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 23:15:24 UTC 2024 - 73.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize_patterns.td
(BinaryOp (TFL_ReshapeOp:$lhs $input1, (Arith_ConstantOp:$shape1 $s1)), (TFL_ReshapeOp:$rhs $input2, (Arith_ConstantOp:$shape2 $s2))), (TFL_ReshapeOp (BinaryOp $input1, $input2), $shape1), [(IsTailOfShape $rhs, $lhs), (IsTailOfShape $lhs, $rhs), (IsTailOfShape $input1, $input2), (IsTailOfShape $input2, $input1), (SameElementType $input1, $input2)]>;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 66.4K bytes - Viewed (0) -
platforms/documentation/docs/src/docs/userguide/optimizing-performance/configuration_cache.adoc
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Fri Mar 29 16:24:12 UTC 2024 - 71.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td
let summary = [{ output = input; While (Cond(output)) { output = Body(output) } }]; let description = [{ output = input; While (Cond(output)) { output = Body(output) } input: A list of input tensors whose types are T. output: A list of output tensors whose types are T. cond: A function that takes 'input' and returns a tensor. If the tensor is
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 04:08:35 UTC 2024 - 90.5K bytes - Viewed (0) -
android/guava-tests/test/com/google/common/util/concurrent/AbstractClosingFutureTest.java
ClosingFuture.whenAllComplete(ImmutableList.of(input1, input2Failed)) .call( new CombiningCallable<TestCloseable>() { @Override public TestCloseable call(DeferredCloser closer, Peeker peeker) throws Exception { closer.eventuallyClose(closeable1, closingExecutor); assertThat(peeker.getDone(input1)).isSameInstanceAs("value1");
Registered: Wed Jun 12 16:38:11 UTC 2024 - Last Modified: Tue May 07 12:37:15 UTC 2024 - 75.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/flatbuffer_import.cc
} else if (sparse_index_vector.type == tflite::SparseIndexVector_Uint16Vector) { const auto& inputs = sparse_index_vector.AsUint16Vector()->values; std::vector<int32_t> outputs(inputs.size()); std::transform(inputs.begin(), inputs.end(), outputs.begin(), [](auto x) { return static_cast<int32_t>(x); }); return outputs; } else if (sparse_index_vector.type ==
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 18:21:50 UTC 2024 - 66.8K bytes - Viewed (0) -
tensorflow/c/c_api_test.cc
TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs, ninputs, grad_inputs, s_, grad_outputs); } else { TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs, ninputs, nullptr, s_, grad_outputs); } } void BuildErrorGraph(TF_Output* inputs, TF_Output* outputs) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 15 03:35:10 UTC 2024 - 96.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/lower_static_tensor_list.cc
// Slice the first 'size' rows from the input tensorlist. auto guard = OpBuilder::InsertionGuard(*rewriter); auto inputs = branch_func.getFunctionType().getInputs(); Block *block = rewriter->createBlock( &branch_func.getBody(), branch_func.begin(), inputs, SmallVector<Location>(inputs.size(), branch_func.getLoc())); Value scalar_zero = CreateI32SplatConst(loc, rewriter, {}, 0);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 20:00:43 UTC 2024 - 70.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/internal/passes/extract_outside_compilation.cc
full_output_types.push_back(output.getType()); } // Convert split sharded inputs to MANUAL sharded inputs. // common_split_sharding is the split sharding that is common to all inputs // and outputs. llvm::SmallVector<Value, 4> manual_inputs; manual_inputs.reserve(inputs.size()); for (Value in : inputs) { Type shard_type; if (failed(GetShardShapedType(original_op, num_cores_per_replica,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 21:25:12 UTC 2024 - 68.3K bytes - Viewed (0)