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tensorflow/cc/gradients/functional_grad.cc
} std::vector<Output> func_inputs; std::vector<DataType> input_dtypes; const int num_inputs = op.num_inputs(); func_inputs.reserve(num_inputs + grad_inputs.size()); input_dtypes.reserve(num_inputs); for (int i = 0; i < num_inputs; i++) { func_inputs.push_back(op.input(i)); input_dtypes.push_back(op.input_type(i)); } func_inputs.insert(std::end(func_inputs), std::begin(grad_inputs),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Oct 15 20:09:06 UTC 2021 - 2.1K bytes - Viewed (0) -
tensorflow/compiler/jit/xla_kernel_creator_test.cc
EXPECT_EQ("XTimesY", kernel_->name()); EXPECT_EQ("XTimesY", kernel_->type_string()); EXPECT_EQ(2, kernel_->num_inputs()); EXPECT_EQ(DT_FLOAT, kernel_->input_type(0)); EXPECT_EQ(DT_RESOURCE, kernel_->input_type(1)); EXPECT_EQ(DEVICE_MEMORY, kernel_->input_memory_types()[0]); EXPECT_EQ(HOST_MEMORY, kernel_->input_memory_types()[1]); EXPECT_EQ(1, kernel_->num_outputs());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 16 01:39:55 UTC 2023 - 5.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_ops_a_m.cc
llvm::SmallVector<int64_t, 4> input_shape(4, ShapedType::kDynamic); auto input_type = mlir::cast<TensorType>(op.getInput().getType()); if (input_type.hasRank()) { if (input_type.getRank() != 4) return op.emitOpError() << "requires input to be a 4D tensor, but got " << input_type; int64_t input_batch = input_type.getDimSize(0); if (input_batch != ShapedType::kDynamic &&
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 146.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/optimize.cc
if (!reshape_type.hasStaticShape()) return failure(); ArrayRef<int64_t> reshape_shape = reshape_type.getShape(); auto input_type = mlir::cast<ShapedType>(op.getInput().getType()); auto output_type = mlir::cast<ShapedType>(op.getOutput().getType()); if (!input_type.hasRank() || !output_type.hasRank()) return failure(); // The pattern attempts to reduce the rank of the input to BroadcastTo.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_tf.cc
return failure(); Value input = tf_op.getInput(); RankedTensorType input_type = mlir::dyn_cast<RankedTensorType>(input.getType()); // Only rank size four input will be only available by the tf.Conv2D // operator verification. if (!input_type || input_type.isDynamicDim(3)) { return failure(); } // Check if the given op is based on grouped convolution.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 21:49:50 UTC 2024 - 64.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/ir/UniformSupport.h
// process. struct ExpressedToQuantizedConverter { // Creates a converter for the given input type. static ExpressedToQuantizedConverter forInputType(Type input_type); // Converts the inputType to be based on the given elemental type, // returning the new type (or nullptr and emit an error on failure). Type convert(quant::QuantizedType elemental_type) const; // Whether the conversion is legal.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 02:10:16 UTC 2024 - 9.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_quantize_helper.h
if (dyn_cast_or_null<quantfork::QuantizeCastOp>(next_op)) return failure(); auto input_type = mlir::cast<ShapedType>(transpose_op.getInput().getType()); auto perm_type = mlir::cast<ShapedType>(transpose_op.getPerm().getType()); if (input_type.hasStaticShape() && perm_type.hasStaticShape()) { if (perm_type.getNumElements() != input_type.getRank()) { return transpose_op.emitOpError(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 18:01:23 UTC 2024 - 28K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/uniform_quantized_stablehlo_to_tfl_pass.cc
return failure(); } const auto input_type = op.getLhs().getType().cast<TensorType>(); if (!(input_type.getRank() == 2 || input_type.getRank() == 3)) { LLVM_DEBUG(llvm::dbgs() << "Input expected to have rank of 2 or 3. Got: " << input_type << ".\n"); return failure(); } const Value filter = op.getRhs();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 22 09:00:19 UTC 2024 - 99.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_quantize.cc
BoolAttr narrow_range = builder.getBoolAttr(false); auto add_quantize_op = [&](Location loc, Type input_type, Block* block, Block::iterator insertion_point, Value arg, int i) { if (auto shaped = mlir::dyn_cast<ShapedType>(input_type)) { if (mlir::isa<FloatType>(shaped.getElementType())) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 17.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/quantize_composite_functions.cc
break; default: return nullptr; // Not yet supported } } else { return nullptr; // Not yet supported } input_type = input_type.clone(new_storage_type); return input_type; } // Replaces quant.qcast op to composite quantize_i8 function. class ReplaceQuantizePattern : public mlir::OpRewritePattern<quantfork::QuantizeCastOp> { public:
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 54.5K bytes - Viewed (0)