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Results 1 - 7 of 7 for UniformQuantizedDot (0.27 sec)
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tensorflow/compiler/mlir/tf2xla/tests/legalize-tf-quant.mlir
} : (tensor<2x2x!tf_type.qint8>, tensor<2xf32>, tensor<2xi32>) -> tensor<2x2xf32> func.return %2 : tensor<2x2xf32> } //===----------------------------------------------------------------------===// // tf.UniformQuantizedDot legalization //===----------------------------------------------------------------------===// // ----- // CHECK-LABEL: func @uniform_quantized_dot
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 01:25:29 UTC 2024 - 37.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/quantized_function_library_uniform_quantized.mlir
%filter_scale : tensor<*xf32>, %filter_zp : tensor<*xi32>, %out_scale : tensor<*xf32>, %out_zp : tensor<*xi32>) -> tensor<*x!tf_type.qint32> { %dot_out = "tf.UniformQuantizedDot"(%input, %filter, %input_scale, %input_zp, %filter_scale, %filter_zp, %out_scale, %out_zp) { Tin = "tfdtype$DT_QINT8", Tout = "tfdtype$DT_QINT32",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Aug 29 01:13:58 UTC 2023 - 19.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/convert_tf_quant_to_mhlo_int_test.cc
%quant_input = "tf.Cast"(%input) {} : (tensor<8x9xi8>) -> tensor<8x9x!tf_type.qint8> %quant_filter = "tf.Cast"(%filter) {} : (tensor<9x10xi8>) -> tensor<9x10x!tf_type.qint8> %0 = "tf.UniformQuantizedDot"( %quant_input, %quant_filter, %input_scale, %input_zp, %filter_scale, %filter_zp, %accum_scale, %accum_zp ) { Tin = "tfdtype$DT_QINT8", Tout = "tfdtype$DT_QINT32", attr_map = "",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 03 01:03:21 UTC 2024 - 35.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tf-ops.mlir
%weight_scales: tensor<f32>, %weight_zps: tensor<i32>, %output_scales: tensor<f32>, %output_zps: tensor<i32>) -> () { // expected-error @below {{'tf.UniformQuantizedDot' op quantization_axis is -1, scales must have 0 rank.}} %1 = "tf.UniformQuantizedDot"( %input, %weight, %input_scales, %input_zps, %weight_scales, %weight_zps, %output_scales, %output_zps) { lhs_quantization_axis = -1 : 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) -
tensorflow/compiler/jit/mark_for_compilation_pass.cc
"TruncatedNormal", "UniformDequantize", "UniformQuantize", "UniformQuantizedAdd", "UniformQuantizedClipByValue", "UniformQuantizedConvolution", "UniformQuantizedDot", "UniformRequantize", "Unique", "UniqueV2", "UpperBound", "UnsortedSegmentMax", "UnsortedSegmentMin", "UnsortedSegmentProd",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 21 12:19:41 UTC 2024 - 85.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test.py
) ) elif target_opset == quant_opts_pb2.UNIFORM_QUANTIZED: self.assertTrue( self._contains_op( output_graphdef, 'UniformQuantizedDot', node_name='sample/matmul.*', ) ) new_outputs = converted_model.signatures['serving_default']( input_tensor=ops.convert_to_tensor(input_data) )
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 03:36:50 UTC 2024 - 235.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td
TF_DerivedOperandTypeAttr Trhs = TF_DerivedOperandTypeAttr<1>; TF_DerivedResultTypeAttr Tout = TF_DerivedResultTypeAttr<0>; let hasVerifier = 1; } def TF_UniformQuantizedDotOp : TF_Op<"UniformQuantizedDot", [Pure]> { let summary = [{ Perform quantized dot of quantized Tensor `lhs` and quantized Tensor `rhs` to make quantized `output`. }]; let description = [{
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 793K bytes - Viewed (0)