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tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test.py
@test_util.run_in_graph_and_eager_modes def test_qat_gather_and_conv_model( self, ): input_type = dtypes.int32 model = self._create_simple_gather_and_conv_model( input_type, filter_shape=(2, 3, 3, 1024), is_qat_model=True, ) saved_model_save.save(model, self._input_saved_model_path)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 03:36:50 UTC 2024 - 235.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/lower_tf.cc
// as its an input requirement. if (!input_ty.hasRank() || input_ty.getRank() != 4) { return failure(); } int64_t batch_cst = input_ty.getShape()[0]; int64_t channels_cst = input_ty.getShape()[3]; int64_t in_y_cst = input_ty.getShape()[1]; int64_t in_x_cst = input_ty.getShape()[2]; int64_t in_spatial_cst =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 74.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/post_quantize.cc
mlir::OpBuilder builder(func.getBody()); auto& bb = func.front(); auto loc = func.getLoc(); int num_args = bb.getNumArguments(); llvm::SmallVector<Type, 4> input_types; input_types.reserve(num_args); // Edit the block arguments and create the new input ops in place to replace // the old input ops and quantize ops. for (int i = 0; i != num_args; ++i) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 17.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize.cc
static bool AreInputDimensionsOneInAxes(Value input, const mlir::Attribute &axes) { RankedTensorType input_type = mlir::dyn_cast_or_null<RankedTensorType>(input.getType()); if (!input_type) return false; auto type_shape = input_type.getShape(); DenseIntElementsAttr axes_attr = mlir::dyn_cast_or_null<DenseIntElementsAttr>(axes); if (!axes_attr) return false;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 00:40:15 UTC 2024 - 102.3K bytes - Viewed (0) -
tensorflow/cc/framework/ops.h
class Operation { public: Operation() : node_(nullptr) {} explicit Operation(Node* n); int32 num_inputs() const { return node_->num_inputs(); } DataType input_type(int32_t o) const { return node_->input_type(o); } Output input(int32_t i) const; int32 num_outputs() const { return node_->num_outputs(); } DataType output_type(int32_t o) const { return node_->output_type(o); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 13 05:57:22 UTC 2024 - 10.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_utils.cc
return attr_enforced_quantizable || trait_enforced_quantizable; } // Returns the quantized type for the // input_type/min/max/storag_type_width/narrow_range. // This is entry point to the Quant dialect and used for both quantizing // activations and weights. Type GetQuantizedType(Builder builder, const Type input_type, const ArrayRef<double> min, const ArrayRef<double> max,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 02:10:16 UTC 2024 - 43.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/ir/ConvertSimQuant.cc
auto qbarrier = rewriter.create<QuantizeCastOp>(op.getLoc(), quantizedType, op.getInputs()); rewriter.replaceOpWithNewOp<DequantizeCastOp>(op, converter.input_type, qbarrier.getResult()); return false; } }; class ConstFakeQuantRewrite : public FakeQuantRewrite<ConstFakeQuantRewrite, ConstFakeQuant> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 02:10:16 UTC 2024 - 6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/python/converter_python_api.cc
const tflite::TensorType input_type = FromTocoDataTypeToTflitToTensorType(input_data_type); const tflite::TensorType output_type = FromTocoDataTypeToTflitToTensorType(output_data_type); std::string output_model; const absl::string_view input_model_buffer(buf, length); auto status = mlir::lite::QuantizeModel( input_model_buffer, input_type, output_type, inference_tensor_type,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 23:15:24 UTC 2024 - 19.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/legalize_tf.cc
// Create a tfl.transpose op that performs ZX transpose on `input`. auto create_z_x_transpose_op = [&](Value input) -> Value { RankedTensorType input_type = mlir::cast<RankedTensorType>(input.getType()); const int input_rank = input_type.getRank(); // Create a 1D I32 tensor for representing the dimension permutation. auto permuation_tensor_type =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 20 20:06:54 UTC 2024 - 45.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/python/integration_test/quantize_model_test_base.py
shape=input_shape, dtype=dtypes.float32, name='input_tensor' ) ), options=save_options, ) return model def _create_gather_model(self, input_type, use_variable) -> module.Module: class GatherModel(module.Module): """A simple model with a single gather.""" def __init__(self, use_variable): """Initializes a GatherModel.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 06:31:57 UTC 2024 - 18.2K bytes - Viewed (0)