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Results 1 - 10 of 15 for 2x7x5x3xf32 (0.33 sec)
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tensorflow/compiler/mlir/tensorflow/tests/einsum.mlir
} func.func @einsum_fourdreducelast(%arg0: tensor<2x5x7x3xf32>, %arg1: tensor<2x3x5x13xf32>) -> tensor<2x7x5x13xf32> { %0 = "tf.Einsum"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", equation = "acbe,aecd->abcd"}: (tensor<2x5x7x3xf32>, tensor<2x3x5x13xf32>) -> tensor<2x7x5x13xf32> func.return %0 : tensor<2x7x5x13xf32> // CHECK-LABEL: einsum_fourdreducelast // CHECK: %[[cst:.*]] = arith.constant dense<[0, 2, 1, 3]> : tensor<4xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 05 18:35:42 UTC 2024 - 25.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/unroll-batch-matmul.mlir
//==== V1 tests ==== func.func @batchMatMulTwoDim(%arg0: tensor<2x3x4x5xf32>, %arg1: tensor<2x3x5x6xf32>) -> tensor<2x3x4x6xf32> { %0 = "tf.BatchMatMul"(%arg0, %arg1) : (tensor<2x3x4x5xf32>, tensor<2x3x5x6xf32>) -> tensor<2x3x4x6xf32> func.return %0 : tensor<2x3x4x6xf32> // CHECK-LABEL: batchMatMulTwoDim
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Dec 06 18:42:28 UTC 2023 - 63.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/quantize/quantize_op_with_region.mlir
%12 = "quantfork.qcast"(%11) {volatile} : (tensor<2x3x1x3xf32>) -> tensor<2x3x1x3x!quant.uniform<i8:f32, 3.000000e-01:1>> %13 = "quantfork.dcast"(%12) : (tensor<2x3x1x3x!quant.uniform<i8:f32, 3.000000e-01:1>>) -> tensor<2x3x1x3xf32> return %13 : tensor<2x3x1x3xf32> } // CHECK: quantized_dot_general_fn_1
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 18 20:32:46 UTC 2024 - 18.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-signed.mlir
%conv2 = "tfl.conv_2d"(%0, %w, %b2) { dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32 } : (tensor<1x5x5x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<1x5x5x3xf32> func.return %conv, %conv2 : tensor<1x5x5x3xf32>, tensor<1x5x5x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 18.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/convert_func_to_bfloat16.mlir
stablehlo.return %2 : tensor<f32> }) {padding = dense<[[0, 0], [1, 1], [1, 1], [0, 0]]> : tensor<4x2xi64>, window_dimensions = array<i64: 1, 3, 3, 1>} : (tensor<2x3x1x3xf32>, tensor<f32>) -> tensor<2x3x1x3xf32> return %1 : tensor<2x3x1x3xf32> } // ----- // CHECK-LABEL: @bitcast_convert_i32_f32(%arg0: tensor<1x256128xi32>) -> tensor<1x256128xbf16>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 08 22:40:14 UTC 2024 - 6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf.mlir
%cst_1 = arith.constant dense<6.000000e+00> : tensor<2x1x3x3xf32> %0 = "tfl.quantize"(%cst_1) {qtype = tensor<2x1x3x3x!quant.uniform<i8<-127:127>:f32:0, {6.587140e-03,1.888450e-02}>>} : (tensor<2x1x3x3xf32>) -> tensor<2x1x3x3x!quant.uniform<i8<-127:127>:f32:0, {6.587140e-03,1.888450e-02}>> %1 = "tfl.dequantize"(%0) : (tensor<2x1x3x3x!quant.uniform<i8<-127:127>:f32:0, {6.587140e-03,1.888450e-02}>>) -> tensor<2x1x3x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 59.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/quantize-numeric-verify.mlir
func.func @CheckNumericVerifyMultipleUsers(%arg0: tensor<1x5x5x3xf32>) -> tensor<1x5x5x3xf32> { %0 = "tfl.quantize"(%arg0) {qtype = tensor<1x5x5x3x!quant.uniform<i8:f32, 0.1>>, volatile} : (tensor<1x5x5x3xf32>) -> tensor<1x5x5x3x!quant.uniform<i8:f32, 0.1>> %1 = "tfl.dequantize"(%0) : (tensor<1x5x5x3x!quant.uniform<i8:f32, 0.1>>) -> tensor<1x5x5x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 15.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/unroll_batch_matmul.pbtxt
value { b: false } } attr { key: "adj_y" value { b: false } } } versions { producer: 175 } # CHECK: func @main(%[[VAL_0:.*]]: tensor<2x5x3xf32>, %[[VAL_1:.*]]: tensor<3x7xf32>) -> tensor<2x5x7xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "Placeholder,Placeholder_1", outputs = "MatMul"}} {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 2.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_lifting.mlir
%cst = "tf.Const"() {value = dense<1.000000e+00> : tensor<2x3x3x3xf32>} : () -> tensor<2x3x3x3xf32> %cst_0 = "tf.Const"() {value = dense<0.500000e+00> : tensor<1x3x2x3xf32>} : () -> tensor<1x3x2x3xf32> %0 = "tf.Conv2D"(%arg0, %cst) {data_format = "NHWC", dilations = [1, 1, 2, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xf32>, tensor<2x3x3x3xf32>) -> tensor<1x3x2x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 14 03:24:59 UTC 2024 - 33.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/replace_cast_hacks_with_tf_xla_ops.mlir
%12 = "tf.Mul"(%11, %cst) {device = ""} : (tensor<2x2x1x3xf32>, tensor<f32>) -> tensor<2x2x1x3xf32> %13 = "tf.Identity"(%12) {device = ""} : (tensor<2x2x1x3xf32>) -> tensor<2x2x1x3xf32> %14 = "tf.Identity"(%13) {device = ""} : (tensor<2x2x1x3xf32>) -> tensor<2x2x1x3xf32> return %14 : tensor<2x2x1x3xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 81K bytes - Viewed (0)