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Results 41 - 49 of 49 for zero_point (0.24 sec)
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tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/convert_tf_quant_ops_to_mhlo.cc
return rewriter.notifyMatchFailure(op, "scales must be constant"); } // Check whether the zero_points operand has constant op. DenseIntElementsAttr zero_points; if (!matchPattern(zero_points_value, m_Constant(&zero_points))) { return rewriter.notifyMatchFailure(op, "zero_points must be constant"); } auto original_element_type = getElementTypeOrSelf(original_type);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 17:58:54 UTC 2024 - 30.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test.py
) _DebuggerConfig = stablehlo_quant_config_pb2.DebuggerConfig # Lists of ops whose channel dimension should be changed if per_channel # quantization is enabled. Respectively refers to (scale, zero_point). _SUFFIXES = ('/filter1', '/filter2') _PER_CHANNEL_OP_NAMES = ( f'{op}{suffix}' for op, suffix in itertools.product(_PER_CHANNEL_QUANTIZED_OPS, _SUFFIXES) )
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 03:36:50 UTC 2024 - 235.6K bytes - Viewed (0) -
src/main/resources/fess_indices/_cloud/fess/doc.json
Registered: Wed Jun 12 13:08:18 UTC 2024 - Last Modified: Tue Aug 15 11:50:35 UTC 2023 - 11.7K bytes - Viewed (0) -
src/main/resources/fess_indices/fess/doc.json
Registered: Wed Jun 12 13:08:18 UTC 2024 - Last Modified: Tue Aug 15 11:50:35 UTC 2023 - 11.8K bytes - Viewed (0) -
src/main/resources/fess_indices/_aws/fess/doc.json
Registered: Wed Jun 12 13:08:18 UTC 2024 - Last Modified: Tue Aug 15 11:50:35 UTC 2023 - 11.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_quantize_helper.h
quant_type = UniformQuantizedType::getChecked( const_op->getLoc(), quant::QuantizationFlags::Signed, rewriter.getIntegerType(16), attr.getType().getElementType(), scale, /*zeroPoint=*/0, llvm::minIntN(10), -llvm::minIntN(10)); } else { quant_type = mlir::dyn_cast<quant::UniformQuantizedType>( quant::GetUniformQuantizedTypeForWeight( attr, /*symmetric=*/true,
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/tensorflow/ir/tf_ops_n_z.cc
LogicalResult VerifyScalesAndZeroPoints(UniformQuantizedOp op, Value scales, Value zero_points, int32_t quantization_axis) { ShapedType scales_type = scales.getType().cast<ShapedType>(); ShapedType zero_points_type = zero_points.getType().cast<ShapedType>(); if (quantization_axis == -1) { if (scales_type.hasRank() && scales_type.getRank() != 0) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 22:07:10 UTC 2024 - 170.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/replace_cast_hacks_with_tf_xla_ops.cc
auto mul_op = builder.create<TF::MulOp>(loc, zp, reduced); SmallVector<Value> folded_results = ConstantFoldOpIfPossible(mul_op); return folded_results.front(); } // Add two contributions, and a zeropoint modification term // Consider two quantized matrices P, Q with zero points z, w. Let's say the // dimensions are l X n, n X m. // What we want to calculate is: R = matmul(P-z, Q-w).
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 47.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tf-ops.mlir
// expected-error @below {{'tf.UniformQuantizedDotHybrid' op quantization_axis is -1, zero_points must have 0 rank.}} %0 = "tf.UniformQuantizedDotHybrid"(%lhs, %rhs, %rhs_scales, %rhs_zps) { rhs_quantization_axis = -1 : i64, rhs_quantization_min_val = -128 : i64, rhs_quantization_max_val = 127 : i64
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 23 14:40:35 UTC 2023 - 236.4K bytes - Viewed (0)