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tensorflow/compiler/mlir/tf2xla/tests/legalize-tf-with-tf2xla-hlo-importer.mlir
// CHECK: %[[mul2:.*]] = mhlo.multiply %arg2, %[[scr1]] : tensor<8xf32> // CHECK: %[[bcast_mul2:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[mul2]], {{.*}}) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<8xf32>, tensor<4xindex>) -> tensor<8x8x8x8xf32> // CHECK: %[[mul3:.*]] = mhlo.multiply %[[grad]], %[[bcast_mul2]] : tensor<8x8x8x8xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 38.6K bytes - Viewed (1) -
android/guava/src/com/google/common/math/LongMath.java
// It's definitely safe to multiply into numerator and denominator. numerator *= n; denominator *= i; numeratorBits += nBits; } else { // It might not be safe to multiply into numerator and denominator, // so multiply (numerator / denominator) into result.
Registered: Wed Jun 12 16:38:11 UTC 2024 - Last Modified: Wed Feb 07 17:50:39 UTC 2024 - 44.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/python/integration_test/quantize_model_test.py
module_str = self._extract_first_xla_call_module_op( self._output_saved_model_path ) # Tests that the output graph contains multiply for symmetric # dequantization. self.assertTrue(re.search('stablehlo.multiply', module_str)) # Tests that the output graph contains float dot_general. self.assertTrue( re.search('stablehlo.dot_general.*xf32>.*xf32>.*xf32>', module_str)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 06:31:57 UTC 2024 - 51.4K bytes - Viewed (0) -
src/internal/fuzz/mutator.go
} max = 100 if v < 0 && maxValue+v < max { // Don't let v drop below -maxValue max = maxValue + v } v -= float64(1 + m.rand(int(max))) return v case 2: // Multiply by a random number absV := math.Abs(v) if v == 0 || absV >= maxValue { continue } max = 10 if maxValue/absV < max { // Don't let v go beyond the minimum or maximum value
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Mon Sep 18 20:01:34 UTC 2023 - 6.6K bytes - Viewed (0) -
staging/src/k8s.io/apiserver/pkg/storage/feature/feature_support_checker.go
runtime.HandleError(fmt.Errorf("feature %q is not implemented in DefaultFeatureSupportChecker", feature)) } } func (f *defaultFeatureSupportChecker) checkClient(ctx context.Context, c client) { // start with 10 ms, multiply by 2 each step, until 15 s and stays on 15 seconds. delayFunc := wait.Backoff{ Duration: 10 * time.Millisecond, Cap: 15 * time.Second, Factor: 2.0, Steps: 11}.DelayFunc() f.lock.Lock()
Registered: Sat Jun 15 01:39:40 UTC 2024 - Last Modified: Fri May 10 11:56:42 UTC 2024 - 6.2K bytes - Viewed (0) -
src/net/rpc/server.go
"Arith.Divide". To invoke one, a client first dials the server: client, err := rpc.DialHTTP("tcp", serverAddress + ":1234") if err != nil { log.Fatal("dialing:", err) } Then it can make a remote call: // Synchronous call args := &server.Args{7,8} var reply int err = client.Call("Arith.Multiply", args, &reply) if err != nil {
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Wed Jan 10 03:29:50 UTC 2024 - 21.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/fold_broadcast.cc
matmul_op.getAdjY() ? shape_y.back() : *(shape_y.rbegin() + 1); const int y_col = !matmul_op.getAdjY() ? shape_y.back() : *(shape_y.rbegin() + 1); // Checks that matrix multiply can perform a valid contraction. if (x_col != y_row) { result_shape.clear(); return false; } result_shape.push_back(x_row); result_shape.push_back(y_col);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 7.9K bytes - Viewed (0) -
android/guava-tests/test/com/google/common/primitives/UnsignedLongTest.java
for (long b : TEST_LONGS) { UnsignedLong aUnsigned = UnsignedLong.fromLongBits(a); UnsignedLong bUnsigned = UnsignedLong.fromLongBits(b); long expected = aUnsigned.bigIntegerValue().multiply(bUnsigned.bigIntegerValue()).longValue(); UnsignedLong unsignedMul = aUnsigned.times(bUnsigned); assertThat(unsignedMul.longValue()).isEqualTo(expected); } } }
Registered: Wed Jun 12 16:38:11 UTC 2024 - Last Modified: Tue Feb 06 16:10:08 UTC 2024 - 10.5K bytes - Viewed (0) -
src/runtime/sys_windows_386.s
TEXT runtime·nanotime1(SB),NOSPLIT,$0-8 loop: MOVL (_INTERRUPT_TIME+time_hi1), AX MOVL (_INTERRUPT_TIME+time_lo), CX MOVL (_INTERRUPT_TIME+time_hi2), DI CMPL AX, DI JNE loop // wintime = DI:CX, multiply by 100 MOVL $100, AX MULL CX IMULL $100, DI ADDL DI, DX // wintime*100 = DX:AX MOVL AX, ret_lo+0(FP) MOVL DX, ret_hi+4(FP) RET
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Thu Sep 21 15:56:43 UTC 2023 - 6.5K bytes - Viewed (0) -
tensorflow/cc/gradients/array_grad.cc
accum_dim_value = Multiply(scope, accum_dim_value, Slice(scope, casted_params_shape, {dim}, {1})); auto start = ConstHelper(scope, 0, indices.type()); auto step = ConstHelper(scope, 1, indices.type()); Output dim_indices = Range(scope, start, Squeeze(scope, dim_value), step); dim_indices = Multiply(scope, dim_indices, accum_dim_value);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Oct 10 23:33:32 UTC 2023 - 31.7K bytes - Viewed (0)