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docs/fr/docs/benchmarks.md
* Si on compare Uvicorn, il faut le comparer à d'autre applications de serveurs comme Daphne, Hypercorn, uWSGI, etc. * **Starlette** :
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Thu Jul 27 18:49:56 UTC 2023 - 3.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/post_quantize.cc
private: void runOnOperation() override; }; // TODO: b/305815328 - Consider preserving leading and trailing QDQs for // ModifyIONodesPass in TFLite use cases. // Removes the back-to-back quantize and dequantize ops with volatile attribute. class RemoveVolatileQdqPattern : public OpRewritePattern<quantfork::DequantizeCastOp> { public: explicit RemoveVolatileQdqPattern(MLIRContext* context)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 05 07:39:40 UTC 2024 - 6.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/fake_quant_utils.cc
// and tfl.dequantize pairs before tf.FakeQuant* being foled. LogicalResult ConvertFakeQuantOps(func::FuncOp func, MLIRContext* ctx, bool use_fake_quant_num_bits) { OpBuilder builder(func); if (failed(UnwrapTFCustomOps(func, builder))) { return failure(); } // Insert the tfl.quantize/tfl.dequantize ops after the tf.FakeQuant* ops to
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Jun 03 00:14:05 UTC 2023 - 4.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/prepare_quantize/prepare_quantize_int4.mlir
// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-prepare-quantize=bit-width=4 -verify-diagnostics | FileCheck %s // CHECK-LABEL: func @dot_int4 // CHECK-SAME: (%[[ARG_0:.*]]: tensor<?x3xf32>) -> tensor<?x2xf32> func.func @dot_int4(%arg0: tensor<?x3xf32>) -> tensor<?x2xf32> { // CHECK: %[[cst:.*]] = stablehlo.constant // CHECK: %[[q1:.*]] = "quantfork.qcast"(%[[cst]]) // CHECK-SAME: quant.uniform<i8:f32, 0.0040316890267764818:127>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 08 22:40:14 UTC 2024 - 1.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_quantize_ptq_per_channel.mlir
// RUN: tf-quant-opt %s -split-input-file -quant-prepare-quantize='post-training-quantize=true enable-per-channel-quantization=true' | FileCheck %s module { func.func private @conv_with_bias_and_relu(%arg0: tensor<1x3x4x3xf32>) -> tensor<*xf32> { %cst = "tf.Const"() {device = "", value = dense<[7.11401462, 7.05456924]> : tensor<2xf32>} : () -> tensor<2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 01 10:21:29 UTC 2023 - 4.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/post_quantize_patterns.td
include "mlir/IR/OpBase.td" include "mlir/IR/PatternBase.td" include "mlir/Dialect/Func/IR/FuncOps.td" include "tensorflow/compiler/mlir/lite/ir/tfl_ops.td" // Both Quantize and Dequantize ops have side effects, so we have to define // patterns to remove dead ones after the quantization rewrite. def : Pat<(TFL_QuantizeOp:$op $in, $qt), (replaceWithValue $in), [(HasNoUseOf:$op)]>;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Mar 16 23:20:46 UTC 2022 - 1.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/ir/Passes.td
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jul 29 18:55:28 UTC 2022 - 1.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/quantize_variables.cc
new_q_op.getResult()); assign_variable_op->replaceAllUsesWith(new_assign_variable_op); } assign_variable_op.erase(); dq_op.erase(); } else { // Add quantize op. builder.setInsertionPoint(assign_variable_op); auto new_q_op = builder.create<QuantizeOp>( assign_variable_op.getLoc(), ref_qtype,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/propagate_quantize_type.cc
StringRef getArgument() const final { // This is the argument used to refer to the pass in // the textual format (on the commandline for example). return "quant-propagate-quantize-type"; } StringRef getDescription() const final { // This is a brief description of the pass. return "Propagate quantized type through allowed ops."; } void runOnOperation() override; };
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/transforms/transform_patterns.td
(TFL_AddOp $lhs, (TFL_MulOp $rhs, (Arith_ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "-1.0f">), TFL_AF_None), $act)>; // Squash tfl.dequantize and tfl.quantize pairs. // TODO(b/185915462): Compare the scale of input and output. This can also be // squashed to a requantize op if the scales are different.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Sep 29 21:02:21 UTC 2022 - 1.4K bytes - Viewed (0)