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Results 1 - 3 of 3 for mat_mul (0.16 sec)
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tensorflow/compiler/mlir/lite/tests/optimize.mlir
func.func @FuseMulWithFullyConnectedWithBias(%arg: tensor<2x512xf32>) -> tensor<2x1024xf32> { %cst_mul = arith.constant dense<2.0> : tensor<512xf32> %cst_weights = arith.constant dense<3.0> : tensor<1024x512xf32> %cst_bias = arith.constant dense<5.0> : tensor<1024xf32> %0 = "tfl.mul"(%arg, %cst_mul) {fused_activation_function = "NONE"} : (tensor<2x512xf32>, tensor<512xf32>) -> tensor<2x512xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 284.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf.mlir
// CHECK: "mhlo.dot"(%[[UPDATED_A]], %[[UPDATED_B]]) %0 = "tf.MatMul"(%a, %b) {transpose_a = true, transpose_b = true} : (tensor<7x5xf32>, tensor<11x7xf32>) -> tensor<5x11xf32> func.return %0 : tensor<5x11xf32> } // Verify that MatMul with ranked inputs are lowered to HLO. // CHECK-LABEL: matmul_ranked
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 06 18:46:23 UTC 2024 - 335.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/transforms/legalize_tf.cc
// - rhs: [RHSBATCHDIMS..., RHSROWS, RHSCOLS] // - result: [broadcast(LHSBATCHDIMS, RHSBATCHDIMS)..., LHSROWS, RHSCOLS] // To perform the matmul, we need to first broadcast lhs and rhs to a common // set of leading dimensions before doing the actual matmul. // That's what the code below does. // In particular, we populate out_lhs and out_rhs to have dimension structure:
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 20:00:43 UTC 2024 - 291.8K bytes - Viewed (0)