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Results 11 - 20 of 23 for quantize_i8 (0.31 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_composite_functions_drq.mlir
// CHECK: %[[quantize_1:.*]] = "tf.PartitionedCall"(%arg0, %[[q_w]], %[[w_scale]], %[[w_zp]]) <{config = "", config_proto = "", executor_type = "", f = @quantized_conv2d_fn_1}> : (tensor<1x2x2x3xf32>, tensor<2x3x3x2x!tf_type.qint8>, tensor<f32>, tensor<i32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 05 18:35:42 UTC 2024 - 9.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/components/post_calibration_component.mlir
// CHECK-NO-UNPACK: %[[QUANTIZE_0:.+]] = stablehlo.uniform_quantize %[[ARG_0]] : (tensor<1x1024xf32>) -> tensor<1x1024x!quant.uniform<i8:f32, {{.*}}>> // CHECK-NO-UNPACK: %[[DOT:.+]] = stablehlo.dot_general %[[QUANTIZE_0]], %[[CONST]]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 01:09:50 UTC 2024 - 6.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/modify_io_nodes.cc
quantize_op.setOperand(new_arg); } else { input_type.print(llvm::errs() << "Requested input type "); quantize_op.emitError(" Couldn't be modified to the requested type."); return failure(); } new_input_types[i] = arg_type; arg.dropAllUses(); if (quantize_op.use_empty()) { quantize_op.erase(); } } else {
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/lite/transforms/prepare_quantize_dynamic_range.cc
int bit_width = quant_specs_.GetQuantizationTypeWidth(); Operation* quantize_op = quant_op.first; int quantize_operand_num = quant_op.second; auto affine_user = dyn_cast<AffineQuantizedOpInterface>(quantize_op); bool op_with_per_axis_support = false; if (!llvm::dyn_cast_or_null<CustomOp>(quantize_op)) { bool op_with_narrow_range = affine_user &&
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 20.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/post_quantize.cc
auto arg = bb.getArgument(0); auto remove_quantize_op = [&](QuantizeOp quantize_op) { auto quantize_output = quantize_op.getOutput(); auto quantize_type = quantize_output.getType(); input_types.push_back(quantize_type); auto new_arg = bb.addArgument(quantize_type, loc); quantize_output.replaceAllUsesWith(new_arg); quantize_op.erase(); arg.dropAllUses(); bb.eraseArgument(0); };
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/quantization/stablehlo/passes/quantization_patterns.h
} // Collects all candidate ops for quantization, which is the operand of // `quantize_op`. If successful, this always returns one element which is the // operand of `quantize_op`. FailureOr<SmallVector<Operation*>> CollectCandidateOps( QuantizeOpT quantize_op) const { Value operand = quantize_op->getOperand(0); if (QuantizedType::getQuantizedElementType(operand.getType())) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 10.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/prepare_quantize_drq.cc
bool insertQDQ(PatternRewriter& rewriter, arith::ConstantOp op, QuantizedType quant_type, QuantizationUnit quant_op) const { if (!quant_type) return false; Operation* quantize_op = quant_op.first; int quantize_operand_num = quant_op.second; Type expressed_type = op.getResult().getType(); Type cast_type = quant_type.castFromExpressedType(expressed_type);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/ops/stablehlo_op_quant_spec_test.cc
auto test_func = module_op->lookupSymbol<func::FuncOp>("same_scale_after_composite"); ASSERT_THAT(test_func, NotNull()); auto quantize_op = FindOperationOfType<quantfork::QuantizeCastOp>(test_func); EXPECT_FALSE(IsOpQuantizableStableHlo(quantize_op)); auto dequantize_op = FindOperationOfType<quantfork::DequantizeCastOp>(test_func); EXPECT_FALSE(IsOpQuantizableStableHlo(dequantize_op)); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 04 07:19:09 UTC 2024 - 14.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/insert_quantized_functions.cc
METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8) { // Uniform quantized opset is not supported for weight-only as inputs for // weight quantization are floats. And only dequantize_i8 is used from the // quantized function library. function_library_map = { {OpSet::TF, kQuantizedFunctionLibraryInMLIR}, {OpSet::XLA, kQuantizedFunctionLibraryInMLIR_XLA_WEIGHT_ONLY}}; } else {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 22 05:52:39 UTC 2024 - 8.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/quantized_function_library_xla_weight_only.mlir
} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32> func.return %out : tensor<*xf32> } // Used for legacy weight-only func.func @dequantize_i8(%input : tensor<*xi8>, %scale : tensor<*xf32>, %zp : tensor<*xi32>) -> tensor<*xf32> { // Use identity op to avoid the weight being constant-folded. %identity = "tf.Identity"(%input) : (tensor<*xi8>) -> tensor<*xi8>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 03 15:43:38 UTC 2023 - 7K bytes - Viewed (0)