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Results 91 - 100 of 200 for requantize (0.13 sec)
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tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_config.h
// weights but will dequantize them back at runtime which is useful for // memory bound case without kernel support available in lower precisions. // Used in MLIR dynamic range quantizer. bool weight_only_quantization = false; // The minimum number of elements in a weights array required to apply // quantization. This is especially useful not to quantize small tensors as
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Mar 13 10:16:19 UTC 2024 - 10.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize_op_order.cc
}; // If the op is the pass-through op with (3x) smaller output, the dequantize // op can be pushed down to the single result of this op. if (!llvm::dyn_cast<mlir::SameScalesOpInterface>(passthrough_op) || passthrough_op->getNumResults() != 1) { return failure(); } // Only push down the dequantize op when the output is smaller, so that it // can have smaller memory usage.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 5.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/mlir2flatbuffer/depthwise_conv2d_v2.mlir
// CHECK: { // CHECK-NEXT: version: 3, // CHECK-NEXT: operator_codes: [ { // CHECK-NEXT: deprecated_builtin_code: 6, // CHECK-NEXT: version: 1, // CHECK-NEXT: builtin_code: DEQUANTIZE // CHECK-NEXT: }, { // CHECK-NEXT: deprecated_builtin_code: 4, // CHECK-NEXT: version: 2, // CHECK-NEXT: builtin_code: DEPTHWISE_CONV_2D // CHECK-NEXT: } ], // CHECK-NEXT: subgraphs: [ {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jul 14 16:41:28 UTC 2022 - 9.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/lite/quantize_weights_test.cc
const auto float_graph = model_->subgraphs()->Get(subgraph_idx); // The output graph should have an extra tensor from the added dequantize // op. ASSERT_EQ(quantized_graph->tensors()->size(), float_graph->tensors()->size() + 1); // Check that a dequantize op exists. int32_t dequant_input_idx = -1; int32_t dequant_output_idx = -1;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 23:15:24 UTC 2024 - 32.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/mlir2flatbuffer/depthwise_conv2d.mlir
// CHECK: { // CHECK-NEXT: version: 3, // CHECK-NEXT: operator_codes: [ { // CHECK-NEXT: deprecated_builtin_code: 6, // CHECK-NEXT: version: 1 // CHECK-NEXT: builtin_code: DEQUANTIZE // CHECK-NEXT: }, { // CHECK-NEXT: deprecated_builtin_code: 4, // CHECK-NEXT: version: 1 // CHECK-NEXT: builtin_code: DEPTHWISE_CONV_2D // CHECK-NEXT: } ], // CHECK-NEXT: subgraphs: [ {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jul 14 16:41:28 UTC 2022 - 9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/modify_io_nodes.cc
returned_type = quant::ConvertSignedQuantizedToUnsigned( dequantize_input.getType(), dequantize_op.getLoc()); // replace the dequantize op by a quantize op TypeAttr type_attr = TypeAttr::get(returned_type); auto quantize_op = builder.create<QuantizeOp>( dequantize_op.getLoc(), returned_type, dequantize_input, type_attr);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/passes/decompose_patterns.td
include "mlir/IR/PatternBase.td" include "mlir/Dialect/Arith/IR/ArithOps.td" include "mlir/Dialect/Func/IR/FuncOps.td" include "tensorflow/compiler/mlir/tfr/ir/tfr_ops.td" class Quantize<string value> : NativeCodeCall<"TFR::Quantize(" # value # ", $0, $1, $_builder)">; class HasStringAttr<string value> : AttrConstraint< CPred<"$_self.cast<StringAttr>().getValue() == \"" # value # "\"">>; def QuantActRangeNonePattern :
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Sep 29 21:02:21 UTC 2022 - 2.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/uniform-quantized-stablehlo-to-tfl.mlir
} // CHECK-LABEL: uniform_dequantize_op_ui16_storage_input // CHECK: stablehlo.uniform_dequantize // CHECK-NOT: tfl.dequantize // ----- // Tests that the pattern doesn't match when the input quantized tensor's // storage type is i32. i32 storage type is not compatible with // `tfl.dequantize`. func.func @uniform_dequantize_op_i32_storage_input(%arg: tensor<2x2x!quant.uniform<i32:f32, 1.000000e+0:8>>) -> tensor<2x2xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 17:10:32 UTC 2024 - 106.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize_batch_matmul.mlir
%0 = arith.constant dense<[[1.0], [2.0]]> : tensor<2x1xf32> %1 = "tfl.quantize"(%0) {qtype = tensor<2x1x!quant.uniform<i8:f32, 0.024986599940879671:92>>} : (tensor<2x1xf32>) -> tensor<2x1x!quant.uniform<i8:f32, 0.024986599940879671:92>> %2 = "tfl.dequantize"(%1) : (tensor<2x1x!quant.uniform<i8:f32, 0.024986599940879671:92>>) -> tensor<2x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test.py
): quantize_model.quantize( self._input_saved_model_path, self._output_saved_model_path, quantization_options=quantization_options, representative_dataset=representative_dataset, ) converted_model = quantize_model.quantize( self._input_saved_model_path, self._output_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)