Adding more details on how to load things.

- Loading with memmap
- Loading a sharded tensor
- Moved some snippets to `candle-examples/src/lib.rs` This is because
managing book specific dependencies is a pain rust-lang/mdBook#706
- This causes a non aligned inclusion  rust-lang/mdBook#1856 which we have
to ignore fmt to remove.

mdbook might need some more love :)

candle-book/src/inference/hub.md

@@ -25,6 +25,8 @@ let weights = candle::safetensors::load(weights, &Device::Cpu);
 
 We now have access to all the [tensors](https://huggingface.co/bert-base-uncased?show_tensors=true) within the file.
 
+You can check all the names of the tensors [here](https://huggingface.co/bert-base-uncased?show_tensors=true)
+
 
 ## Using async 
 
@@ -35,17 +37,9 @@ cargo add hf-hub --features tokio
 ```
 
 ```rust,ignore
-# extern crate candle;
-# extern crate hf_hub;
-use hf_hub::api::tokio::Api;
-use candle::Device;
-
-let api = Api::new().unwrap();
-let repo = api.model("bert-base-uncased".to_string());
-
-let weights = repo.get("model.safetensors").await.unwrap();
-
-let weights = candle::safetensors::load(weights, &Device::Cpu);
+# This is tested directly in examples crate because it needs external dependencies unfortunately:
+# See [this](https://github.com/rust-lang/mdBook/issues/706)
+{{#include ../../../candle-examples/src/lib.rs:book_hub_1}}
 ```
 
 
@@ -78,3 +72,33 @@ let output = linear.forward(&input_ids);
 ```
 
 For a full reference, you can check out the full [bert](https://github.com/LaurentMazare/candle/tree/main/candle-examples/examples/bert) example.
+
+## Memory mapping
+
+For more efficient loading, instead of reading the file, you could use [`memmap2`](https://docs.rs/memmap2/latest/memmap2/)
+
+**Note**: Be careful about memory mapping it seems to cause issues on [Windows, WSL](https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/5893)
+and will definitely be slower on network mounted disk, because it will issue more read calls.
+
+```rust,ignore
+{{#include ../../../candle-examples/src/lib.rs:book_hub_2}}
+```
+
+**Note**: This operation is **unsafe**. [See the safety notice](https://docs.rs/memmap2/latest/memmap2/struct.Mmap.html#safety).
+In practice model files should never be modified, and the mmaps should be mostly READONLY anyway, so the caveat most likely does not apply, but always keep it in mind.
+
+
+## Tensor Parallel Sharding
+
+When using multiple GPUs to use in Tensor Parallel in order to get good latency, you can load only the part of the Tensor you need.
+
+For that you need to use [`safetensors`](https://crates.io/crates/safetensors) directly.
+
+```bash
+cargo add safetensors
+```
+
+
+```rust,ignore
+{{#include ../../../candle-examples/src/lib.rs:book_hub_3}}
+```

candle-core/src/safetensors.rs

@@ -242,7 +242,11 @@ fn convert_back(tensor: &Tensor) -> Result<Vec<u8>> {
 
 pub fn load<P: AsRef<Path>>(filename: P, device: &Device) -> Result<HashMap<String, Tensor>> {
     let data = std::fs::read(filename.as_ref())?;
-    let st = safetensors::SafeTensors::deserialize(&data)?;
+    load_buffer(&data[..], device)
+}
+
+pub fn load_buffer(data: &[u8], device: &Device) -> Result<HashMap<String, Tensor>> {
+    let st = safetensors::SafeTensors::deserialize(data)?;
     st.tensors()
         .into_iter()
         .map(|(name, view)| Ok((name, view.load(device)?)))

candle-examples/Cargo.toml

@@ -26,14 +26,17 @@ half = { workspace = true, optional = true }
 [dev-dependencies]
 anyhow = { workspace = true }
 byteorder = { workspace = true }
-hf-hub = { workspace = true}
+hf-hub = { workspace = true, features=["tokio"]}
 clap = { workspace = true }
 rand = { workspace = true }
 tokenizers = { workspace = true, features = ["onig"] }
 tracing = { workspace = true }
 tracing-chrome = { workspace = true }
 tracing-subscriber = { workspace = true }
 wav = { workspace = true }
+# Necessary to disambiguate with tokio in wasm examples which are 1.28.1
+tokio = "1.29.1"
+memmap2.workspace = true
 
 [build-dependencies]
 anyhow = { workspace = true }

candle-examples/src/lib.rs

@@ -11,3 +11,102 @@ pub fn device(cpu: bool) -> Result<Device> {
         Ok(device)
     }
 }
+
+#[cfg(test)]
+mod tests {
+    // NOTE: Waiting on https://github.com/rust-lang/mdBook/pull/1856
+    #[rustfmt::skip]
+    #[tokio::test]
+    async fn book_hub_1() {
+// ANCHOR: book_hub_1
+use candle::Device;
+use hf_hub::api::tokio::Api;
+
+let api = Api::new().unwrap();
+let repo = api.model("bert-base-uncased".to_string());
+
+let weights_filename = repo.get("model.safetensors").await.unwrap();
+
+let weights = candle::safetensors::load(weights_filename, &Device::Cpu).unwrap();
+// ANCHOR_END: book_hub_1
+        assert_eq!(weights.len(), 206);
+    }
+
+    #[rustfmt::skip]
+    #[test]
+    fn book_hub_2() {
+// ANCHOR: book_hub_2
+use candle::Device;
+use hf_hub::api::sync::Api;
+use memmap2::Mmap;
+use std::fs;
+
+let api = Api::new().unwrap();
+let repo = api.model("bert-base-uncased".to_string());
+let weights_filename = repo.get("model.safetensors").unwrap();
+
+let file = fs::File::open(weights_filename).unwrap();
+let mmap = unsafe { Mmap::map(&file).unwrap() };
+let weights = candle::safetensors::load_buffer(&mmap[..], &Device::Cpu).unwrap();
+// ANCHOR_END: book_hub_2
+        assert_eq!(weights.len(), 206);
+    }
+
+    #[rustfmt::skip]
+    #[test]
+    fn book_hub_3() {
+// ANCHOR: book_hub_3
+use candle::{DType, Device, Tensor};
+use hf_hub::api::sync::Api;
+use memmap2::Mmap;
+use safetensors::slice::IndexOp;
+use safetensors::SafeTensors;
+use std::fs;
+
+let api = Api::new().unwrap();
+let repo = api.model("bert-base-uncased".to_string());
+let weights_filename = repo.get("model.safetensors").unwrap();
+
+let file = fs::File::open(weights_filename).unwrap();
+let mmap = unsafe { Mmap::map(&file).unwrap() };
+
+// Use safetensors directly
+let tensors = SafeTensors::deserialize(&mmap[..]).unwrap();
+let view = tensors
+.tensor("bert.encoder.layer.0.attention.self.query.weight")
+.unwrap();
+
+// We're going to load shard with rank 1, within a world_size of 4
+// We're going to split along dimension 0 doing VIEW[start..stop, :]
+let rank = 1;
+let world_size = 4;
+let dim = 0;
+let dtype = view.dtype();
+let mut tp_shape = view.shape().to_vec();
+let size = tp_shape[0];
+
+if size % world_size != 0 {
+panic!("The dimension is not divisble by `world_size`");
+}
+let block_size = size / world_size;
+let start = rank * block_size;
+let stop = (rank + 1) * block_size;
+
+// Everything is expressed in tensor dimension
+// bytes offsets is handled automatically for safetensors.
+
+let iterator = view.slice(start..stop).unwrap();
+
+tp_shape[dim] = block_size;
+
+// Convert safetensors Dtype to candle DType
+let dtype: DType = dtype.try_into().unwrap();
+
+// TODO: Implement from_buffer_iterator to we can skip the extra CPU alloc.
+let raw: Vec<u8> = iterator.into_iter().flatten().cloned().collect();
+let tp_tensor = Tensor::from_raw_buffer(&raw, dtype, &tp_shape, &Device::Cpu).unwrap();
+// ANCHOR_END: book_hub_3
+        assert_eq!(view.shape(), &[768, 768]);
+        assert_eq!(tp_tensor.dims(), &[192, 768]);
+    }
+}