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Results 1 - 10 of 10 for quantization_dimension (0.22 sec)
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tensorflow/compiler/mlir/quantization/common/uniform_quantized_types_test.cc
CreateI8F32UniformQuantizedPerAxisType( UnknownLoc::get(&ctx_), ctx_, /*scales=*/SmallVector<double, 2>{1.0, 1.0}, /*zero_points=*/SmallVector<int64_t, 2>{0, 0}, /*quantization_dimension=*/0); // Storage type of `i8` is currently verifiable as `unsigned` in `Types.cpp`. EXPECT_TRUE(quantized_type.getStorageType().isSignlessInteger(8)); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 28.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/uniform_quantized_types.cc
const ArrayRef<int64_t> zero_points, const int quantization_dimension, const bool narrow_range) { return UniformQuantizedPerAxisType::getChecked( loc, /*flags=*/QuantizationFlags::Signed, /*storageType=*/IntegerType::get(&context, /*width=*/8), /*expressedType=*/FloatType::getF32(&context), SmallVector<double>(scales), SmallVector<int64_t>(zero_points), quantization_dimension,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/uniform_quantized_types.h
UniformQuantizedPerAxisType CreateI8F32UniformQuantizedPerAxisType( Location loc, MLIRContext& context, ArrayRef<double> scales, ArrayRef<int64_t> zero_points, int quantization_dimension, bool narrow_range = false); // Creates a `UniformQuantizedPerAxisType` with the given `scales` and // `zero_points` values. The produced type has f32 as its expressed type and
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 5.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/insert_weight_param.cc
/*narrow_range=*/true, /*legacy_float_scale=*/false)); } else { int quantization_dimension = GetQuantizationDimension( weight_only_ptq, cast<TF::XlaCallModuleOp>(quantizable_op)); weight_type = quant::GetUniformQuantizedPerAxisTypeForWeight( attr, quantization_dimension, /*symmetric=*/true, /*num_bits=*/8, /*is_signed=*/true,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 05:56:10 UTC 2024 - 10.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/attrs_and_constraints.cc
// - size(rhs_contracting_dimensions) = 1 // - lhs_contracting_dimension = last dimension of lhs. // - `stablehlo.dot_general` should not have `lhs_batching_dim`. // - quantization_dimension(rhs) should not be in // `rhs_contracting_dimensions`. // https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dot_general const bool has_proper_rank =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 6.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/uniform_quantized_stablehlo_to_tfl_pass.cc
// // StableHLO Quantizer output: // * input: per-tensor qi8 // * filter: per-channel qi8 (`quantization_dimension` = 3) // * output: per-channel qi32 (`quantization_dimension` = 3) // JAX Quantizer output: // * input: per-tensor qi8 // * filter: per-channel qi8 (`quantization_dimension` = 3) // * output: per-tensor qi8 // // Conditions for the conversion:
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 22 09:00:19 UTC 2024 - 99.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/compose_uniform_quantized_type_pass.cc
CreateI8F32UniformQuantizedPerAxisType( filter_op->getLoc(), *rewriter.getContext(), filter_scale_values, filter_zero_point_values, /*quantization_dimension=*/3); // Create a new constant op for the filter in i8. auto quantized_filter_constant_op = rewriter.create<stablehlo::ConstantOp>( filter_op->getLoc(), /*output=*/
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 64.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/quantization_patterns.cc
const int64_t quantization_dimension = mlir::cast<ShapedType>(filter_type).getShape().size() - 1; accumulation_quantized_element_type = CreateI32F32UniformQuantizedPerAxisType( gemm_style_op->getLoc(), *rewriter.getContext(), result_scales, zero_points, quantization_dimension);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 06:04:36 UTC 2024 - 41.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/schema/schema_v3b.fbs
// zero_points correspond to. For example, a tensor t, with dims=[4, 3, 2, 1] // with quantization params: // scale=[1.0, 2.0, 3.0], zero_point=[1, 2, 3], quantization_dimension=1 // will be quantized across the second dimension of t. // t[:, 0, :, :] will have scale[0]=1.0, zero_point[0]=1 // t[:, 1, :, :] will have scale[1]=2.0, zero_point[0]=2
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 14:28:27 UTC 2024 - 30K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/schema/schema.fbs
// zero_points correspond to. For example, a tensor t, with dims=[4, 3, 2, 1] // with quantization params: // scale=[1.0, 2.0, 3.0], zero_point=[1, 2, 3], quantization_dimension=1 // will be quantized across the second dimension of t. // t[:, 0, :, :] will have scale[0]=1.0, zero_point[0]=1 // t[:, 1, :, :] will have scale[1]=2.0, zero_point[0]=2
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 18:01:23 UTC 2024 - 41.7K bytes - Viewed (0)