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tensorflow/compiler/mlir/quantization/stablehlo/tests/pipelines/process_nchw_tensor.mlir
%0 = stablehlo.constant dense<2.000000e+00> : tensor<4x2x3x3xf32> // weight %1 = stablehlo.constant dense<3.000000e+00> : tensor<4xf32> // bias %2 = stablehlo.constant dense<0.000000e+00> : tensor<1x4x5x5xf32> // relu %3 = stablehlo.broadcast_in_dim %1, dims = [1] : (tensor<4xf32>) -> tensor<1x4x5x5xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 18 20:32:46 UTC 2024 - 12.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/optimize.mlir
// ----- // CHECK-LABEL: testDotToDotGeneralMatrixVector func.func @testDotToDotGeneralMatrixVector(%arg0: tensor<2x3072xf32>, %arg1: tensor<3072xf32>) -> tensor<2xf32> { %0 = "mhlo.dot"(%arg0, %arg1) : (tensor<2x3072xf32>, tensor<3072xf32>) -> tensor<2xf32> func.return %0 : tensor<2xf32> // CHECK: %[[RES:.*]] = "mhlo.dot_general"(%arg0, %arg1) <{ // CHECK-SAME: dot_dimension_numbers = #mhlo.dot<
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 22.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/defer_activation_transpose.mlir
func.func @add_with_activation_transpose_broadcasted_rhs(%arg0: tensor<1x3x3x4xf32>) -> tensor<1x4x3x3xf32> { %0 = stablehlo.constant dense<2.000000e+00> : tensor<4xf32> %1 = stablehlo.broadcast_in_dim %0, dims = [1] : (tensor<4xf32>) -> tensor<1x4x3x3xf32> %2 = stablehlo.transpose %arg0, dims = [0, 3, 1, 2] : (tensor<1x3x3x4xf32>) -> tensor<1x4x3x3xf32> %3 = stablehlo.add %2, %1 : tensor<1x4x3x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 18 20:32:46 UTC 2024 - 14.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/bridge/optimize.mlir
} // ----- // CHECK-LABEL: func @convolution_add_add_f32 func.func @convolution_add_add_f32( %lhs: tensor<?x3x2x1xf32>, %rhs: tensor<2x1x1x1xf32>, %zp_offset: tensor<?x2x2x1xf32>, %bias: tensor<1xf32> ) -> tensor<?x2x2x1xf32> { // CHECK-DAG: %[[conv:.*]] = mhlo.convolution // CHECK-DAG: %[[combined:.*]] = chlo.broadcast_add %[[conv:.*]], %[[zp_offset:.*]]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Feb 24 02:26:47 UTC 2024 - 10.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/post-quantize-dynamic-range.mlir
// CHECK-LABEL: QuantizeCustomOp // CustomOp-LABEL: QuantizeCustomOp func.func @QuantizeCustomOp(%arg0: tensor<1x1x1x1xf32>) -> (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) attributes {tf.entry_function = {inputs = "input", outputs = "custom_op"}} { %0 = "quantfork.stats"(%arg0) {layerStats = dense<[0.000000e+00, 2.550000e+02]> : tensor<2xf32>} : (tensor<1x1x1x1xf32>) -> tensor<1x1x1x1xf32> %w_1 = arith.constant dense<127.0> : tensor<4096x1x1x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 11.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/insert_weight_param.mlir
func.func @no_qdq_for_non_weight_constant(%arg0: tensor<1x2xf32>, %arg1: tensor<2x3xf32>) -> tensor<1x3xf32> attributes {tf._original_func_name = "main_0"} { %cst = "tf.Const"() {value = dense<4.000000e-02> : tensor<3xf32>} : () -> tensor<3xf32> %0 = "tf.XlaCallModule"(%arg0, %arg1, %cst) { Sout = [#tf_type.shape<1x3>], _entry_function = @composite_dot_general_with_bias_fn, _original_entry_function = "composite_dot_general_with_bias_fn",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 05:56:10 UTC 2024 - 22K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/ir/tfr_ops.td
tensor type, the shape shouldn't be changed during the conversion. Example: ```mlir %1 = tfr.constant_tensor(%0) : f32 -> tensor<f32> %3 = tfr.constant_tensor(%2) : vector<1xf32> -> tensor<1xf32> ``` }]; let arguments = (ins TFR_AllAttrTypes:$arg); let results = (outs TFR_singleTensorType:$out); let hasCanonicalizer = 1; let hasVerifier = 1; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 22 10:54:29 UTC 2024 - 17.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/quantize-dynamic-range.mlir
// CustomOpNotWeightOnly-LABEL: QuantizeCustomOp func.func @QuantizeCustomOp(%arg0: tensor<1x1x1x1xf32>) -> tensor<*xf32> attributes {tf.entry_function = {inputs = "input", outputs = "custom_op"}} { %0 = "quantfork.stats"(%arg0) {layerStats = dense<[0.000000e+00, 2.550000e+02]> : tensor<2xf32>} : (tensor<1x1x1x1xf32>) -> tensor<1x1x1x1xf32> %w = arith.constant dense<127.0> : tensor<1024x1x1x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 23 21:09:00 UTC 2024 - 23.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/region_control_flow_to_functional.cc
while (auto cast_op = llvm::dyn_cast_or_null<CastOp>(value.getDefiningOp())) { // Consider cast compatibility in case // %cast = "tf.Cast"(%0) : (tensor<2xi64>) -> tensor<2xf32> // is skipped. if (cast_op.getSrcT() != cast_op.getDstT()) { break; } value = cast_op.getOperand(); } return value; };
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 28.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_utils.h
// Casts the `target` type to a quantized type by using the quantization // parameters from the type in the `source` type attribute. // Examples: // f32 -> !quant.uniform<i8:f32, 1.0> // tensor<4xf32> -> tensor<4x!quant.uniform<i8:f32, 1.0>> // The result is wrapped by a type attribute. Returns nullptr if the cast // isn't valid. // // `axis` is to specify the quantization dimension in the `target` and only
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 20:30:06 UTC 2024 - 41.7K bytes - Viewed (0)