Optimize Tensor::new when called on nested Vec<..>. (#2927)

* Optimize Tensor::new when called on nested Vec<..>.

* Improve performance.

* Similar flattening for the 4d case.

* More tweaks.

* Add some dummy test.

candle-core/benches/benchmarks/copy.rs

@@ -0,0 +1,38 @@
+use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
+use candle_core::{Device, Tensor, WithDType};
+use criterion::{black_box, criterion_group, Criterion, Throughput};
+use std::time::Instant;
+
+fn run_copy_mask_benchmark<D: WithDType>(c: &mut Criterion, device: &Device, name: &str) {
+    let batch_size = 128;
+    let in_seq_len = 1;
+    let kv_seq_len = 1024;
+
+    let attn_mask = vec![vec![vec![D::zero(); kv_seq_len]; in_seq_len]; batch_size];
+    let size_in_bytes = batch_size * in_seq_len * kv_seq_len * D::DTYPE.size_in_bytes();
+
+    let mut group = c.benchmark_group(device.bench_name(name));
+    group.throughput(Throughput::Bytes(size_in_bytes as u64));
+    group.bench_function("iter", move |b| {
+        b.iter_custom(|iters| {
+            let attn_masks = vec![attn_mask.clone(); iters as usize];
+            let start = Instant::now();
+            for attn_mask in attn_masks.into_iter() {
+                let tensor = Tensor::new(black_box(attn_mask), device).unwrap();
+                black_box(tensor);
+            }
+            device.sync().unwrap();
+            start.elapsed()
+        })
+    });
+    group.finish();
+}
+
+fn criterion_benchmark(c: &mut Criterion) {
+    let handler = BenchDeviceHandler::new().unwrap();
+    for device in handler.devices {
+        run_copy_mask_benchmark::<f32>(c, &device, "copy_mask");
+    }
+}
+
+criterion_group!(benches, criterion_benchmark);

candle-core/benches/benchmarks/mod.rs

@@ -1,5 +1,6 @@
 pub(crate) mod affine;
 pub(crate) mod conv_transpose2d;
+pub(crate) mod copy;
 pub(crate) mod matmul;
 pub(crate) mod qmatmul;
 pub(crate) mod random;