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tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/vhlo.mlir
// CHECK: return %0 : tensor<1xf32> // CHECK:} func.func @multiply(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> { %0 = "vhlo.multiply_v1"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> func.return %0 : tensor<1xf32> } // CHECK:func.func private @multiply(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 14 19:15:40 UTC 2024 - 31.9K bytes - Viewed (1) -
src/image/color/ycbcr.go
// https://www.w3.org/Graphics/JPEG/jfif3.pdf says Y but means Y'. // // Those formulae use non-integer multiplication factors. When computing, // integer math is generally faster than floating point math. We multiply // all of those factors by 1<<16 and round to the nearest integer: // 91881 = roundToNearestInteger(1.40200 * 65536). // 22554 = roundToNearestInteger(0.34414 * 65536). // 46802 = roundToNearestInteger(0.71414 * 65536).
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Thu Oct 19 12:02:45 UTC 2023 - 10.8K bytes - Viewed (0) -
src/runtime/softfloat64.go
var trunc uint32 for mant >= 1<<32 { trunc |= uint32(mant) & 1 mant >>= 1 exp++ } return fpack32(uint32(fs>>32), uint32(mant), exp, trunc) } // 64x64 -> 128 multiply. // adapted from hacker's delight. func mullu(u, v uint64) (lo, hi uint64) { const ( s = 32 mask = 1<<s - 1 ) u0 := u & mask u1 := u >> s v0 := v & mask v1 := v >> s
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Fri Oct 08 17:58:41 UTC 2021 - 11.5K bytes - Viewed (0) -
src/crypto/internal/nistec/p256_asm_ppc64le.s
// equivalent function from the corresponding s390x // instruction for vector multiply high, low, and add, // since there aren't exact equivalent instructions. // The corresponding s390x instructions appear in the // comments. // Implementation for big endian would have to be // investigated, I think it would be different. // // // Vector multiply word // // VMLF x0, x1, out_low // VMLHF x0, x1, out_hi
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Mon Mar 04 17:29:44 UTC 2024 - 56.5K bytes - Viewed (0) -
guava/src/com/google/common/math/StatsAccumulator.java
// These fields must satisfy the requirements of Stats' constructor as well as those of the stat // methods of this class. private long count = 0; private double mean = 0.0; // any finite value will do, we only use it to multiply by zero for sum private double sumOfSquaresOfDeltas = 0.0; private double min = NaN; // any value will do private double max = NaN; // any value will do /** Adds the given value to the dataset. */
Registered: Wed Jun 12 16:38:11 UTC 2024 - Last Modified: Fri May 12 17:02:53 UTC 2023 - 15.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf-prefer-tf2xla.mlir
// CHECK-NEXT: %[[v10:.*]] = "mhlo.broadcast_in_dim"(%9) <{broadcast_dimensions = dense<> : tensor<0xi64>}> : (tensor<f32>) -> tensor<1x300x300x40xf32> // CHECK-NEXT: %11 = mhlo.multiply %8, %10 : tensor<1x300x300x40xf32> // CHECK-NEXT: %12 = mhlo.convert %arg2 : tensor<40xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 15.8K bytes - Viewed (0) -
okhttp/src/main/kotlin/okhttp3/Response.kt
*/ open fun header( name: String, value: String, ) = commonHeader(name, value) /** * Adds a header with [name] to [value]. Prefer this method for multiply-valued * headers like "Set-Cookie". */ open fun addHeader( name: String, value: String, ) = commonAddHeader(name, value)
Registered: Sun Jun 16 04:42:17 UTC 2024 - Last Modified: Tue Jan 23 14:31:42 UTC 2024 - 15.5K bytes - Viewed (0) -
src/cmd/compile/internal/ssa/_gen/generic.rules
(Div64 n (Const64 [-1<<63])) && isNonNegative(n) => (Const64 [0]) // Unsigned divide, not a power of 2. Strength reduce to a multiply. // For 8-bit divides, we just do a direct 9-bit by 8-bit multiply. (Div8u x (Const8 [c])) && umagicOK8(c) => (Trunc32to8 (Rsh32Ux64 <typ.UInt32> (Mul32 <typ.UInt32> (Const32 <typ.UInt32> [int32(1<<8+umagic8(c).m)])
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Thu May 16 22:21:05 UTC 2024 - 135.3K bytes - Viewed (0) -
src/math/rand/v2/rand.go
// and want to reduce it to the range [0,n) preserving exact uniformity. // We can simulate a scaling arbitrary precision x * (n/2⁶⁴) by // the high bits of a double-width multiply of x*n, meaning (x*n)/2⁶⁴. // Since there are 2⁶⁴ possible inputs x and only n possible outputs, // the output is necessarily biased if n does not divide 2⁶⁴. // In general (x*n)/2⁶⁴ = k for x*n in [k*2⁶⁴,(k+1)*2⁶⁴).
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Wed May 22 02:25:49 UTC 2024 - 12.8K bytes - Viewed (0) -
android/guava/src/com/google/common/math/StatsAccumulator.java
// These fields must satisfy the requirements of Stats' constructor as well as those of the stat // methods of this class. private long count = 0; private double mean = 0.0; // any finite value will do, we only use it to multiply by zero for sum private double sumOfSquaresOfDeltas = 0.0; private double min = NaN; // any value will do private double max = NaN; // any value will do /** Adds the given value to the dataset. */
Registered: Wed Jun 12 16:38:11 UTC 2024 - Last Modified: Fri May 12 17:02:53 UTC 2023 - 14.2K bytes - Viewed (0)