Quantized GGUF style (#1523)

* Metal quantized modifications proposal.

- Add a device param, wherever needed.
- Create new QMetal storage thing that implements QuantizedType.
- Update everywhere needed.

Fix Python.

Fixing examples.

Fix: fmt + clippy + stub.

Moving everything around.

Only missing the actual implems.

Fixing everything + adding dequantized kernels.

More work.

Fixing matmul.

Fmt + Clippy

Some clippy fixes.

Working state.

Q2K Metal -> Bugged (also present in GGML).
Q4K CPU -> Bugged (present previously, new test catch it).
Q5K CPU -> Bugged (present previously).
Q8_1 Both -> Never really implemented it seems
Q8K metal -> Never implemented in metal

Fixing Q2K bug (present in ggml).

* Cleanup.

* Fix the rebase.

* Removing the fences speeds everything up and *is* correct this time...

* Cleanup the fence.

* After rebase.

* Bad code removal.

* Rebase after phi2 merge + fix replit default to CPU.

* Making the CI happy.

* More happy tests.

---------

Co-authored-by: Nicolas Patry <nicolas@Nicolass-MacBook-Pro.local>

candle-examples/examples/llama2-c/main.rs

@@ -262,7 +262,7 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
         .extension()
         .map_or(false, |v| v == "safetensors");
     let (model, config) = if is_gguf {
-        let vb = qmodel::VarBuilder::from_gguf(config_path)?;
+        let vb = qmodel::VarBuilder::from_gguf(config_path, &device)?;
         let (_vocab_size, dim) = vb
             .get_no_shape("model.embed_tokens.weight")?
             .shape()
@@ -279,13 +279,13 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
                 (config.seq_len, config.head_size() / 2),
                 "rot.freq_cis_real",
             )?
-            .dequantize(&candle::Device::Cpu)?;
+            .dequantize(&device)?;
         let freq_cis_imag = vb
             .get(
                 (config.seq_len, config.head_size() / 2),
                 "rot.freq_cis_imag",
             )?
-            .dequantize(&candle::Device::Cpu)?;
+            .dequantize(&device)?;
 
         let fake_vb = candle_nn::VarBuilder::from_tensors(
             [
@@ -295,7 +295,7 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
             .into_iter()
             .collect(),
             candle::DType::F32,
-            &candle::Device::Cpu,
+            &device,
         );
         let cache = model::Cache::new(true, &config, fake_vb)?;
         let model = Model::QLlama(QLlama::load(vb, &cache, config.clone())?);