Update the Phi model to use the updated architecture. (#1580)

* Update the Phi model to use the updated architecture.

* Add more of the phi model.

* Repeat KV + caching.

* Apply the rotary embeddings.

* Add support for the new phi model in the phi example.

* Fix a couple glitches.

* Fix a couple more glitches.

candle-transformers/src/models/phi.rs

@@ -0,0 +1,365 @@
+use crate::models::with_tracing::{layer_norm, linear, Embedding, LayerNorm, Linear};
+/// Phi model.
+/// https://huggingface.co/microsoft/phi-2
+/// There is an alternative implementation of the phi model in mixformers.rs.
+/// This corresponds to the model update made with the following commit:
+/// https://huggingface.co/microsoft/phi-2/commit/cb2f4533604d8b67de604e7df03bfe6f3ca22869
+use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
+use candle_nn::{Activation, VarBuilder};
+use serde::Deserialize;
+
+// https://huggingface.co/microsoft/phi-2/blob/main/configuration_phi.py
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+pub struct Config {
+    pub(crate) vocab_size: usize,
+    pub(crate) hidden_size: usize,
+    pub(crate) intermediate_size: usize,
+    pub(crate) num_hidden_layers: usize,
+    pub(crate) num_attention_heads: usize,
+    pub(crate) num_key_value_heads: Option<usize>,
+    pub(crate) hidden_act: Activation,
+    pub(crate) max_position_embeddings: usize,
+    pub(crate) layer_norm_eps: f64,
+    pub(crate) tie_word_embeddings: bool,
+    pub(crate) rope_theta: f32,
+    pub(crate) partial_rotary_factor: f64,
+    pub(crate) qk_layernorm: bool,
+}
+
+impl Config {
+    fn num_key_value_heads(&self) -> usize {
+        self.num_key_value_heads.unwrap_or(self.num_attention_heads)
+    }
+
+    fn head_dim(&self) -> usize {
+        self.hidden_size / self.num_attention_heads
+    }
+}
+
+#[derive(Debug, Clone)]
+struct RotaryEmbedding {
+    sin: Tensor,
+    cos: Tensor,
+}
+
+impl RotaryEmbedding {
+    fn new(cfg: &Config, dev: &Device) -> Result<Self> {
+        let dim = (cfg.partial_rotary_factor * cfg.head_dim() as f64) as usize;
+        let inv_freq: Vec<_> = (0..dim)
+            .step_by(2)
+            .map(|i| 1f32 / cfg.rope_theta.powf(i as f32 / dim as f32))
+            .collect();
+        let inv_freq_len = inv_freq.len();
+        let inv_freq = Tensor::from_vec(inv_freq, (1, inv_freq_len), dev)?;
+        let t = Tensor::arange(0u32, cfg.max_position_embeddings as u32, dev)?
+            .to_dtype(DType::F32)?
+            .reshape((cfg.max_position_embeddings, 1))?;
+        let freqs = t.matmul(&inv_freq)?;
+        Ok(Self {
+            sin: freqs.sin()?,
+            cos: freqs.cos()?,
+        })
+    }
+
+    fn apply_rotary_emb(&self, xs: &Tensor, seqlen_offset: usize) -> Result<Tensor> {
+        let (_b_size, seqlen, _, _headdim) = xs.dims4()?;
+        let (_rotary_seqlen, rotary_dim) = self.cos.dims2()?;
+        let rotary_dim = rotary_dim * 2;
+        let xs_rot = xs.i((.., .., .., ..rotary_dim))?;
+        let xs_pass = xs.i((.., .., .., rotary_dim..))?;
+        let xs12 = xs_rot.chunk(2, D::Minus1)?;
+        let (xs1, xs2) = (&xs12[0], &xs12[1]);
+        let c = self.cos.narrow(0, seqlen_offset, seqlen)?.unsqueeze(1)?;
+        let s = self.sin.narrow(0, seqlen_offset, seqlen)?.unsqueeze(1)?;
+        let xs_rot = Tensor::cat(
+            &[
+                (xs1.broadcast_mul(&c)? - xs2.broadcast_mul(&s)?)?,
+                (xs1.broadcast_mul(&s)? + xs2.broadcast_mul(&c)?)?,
+            ],
+            D::Minus1,
+        )?;
+        Tensor::cat(&[&xs_rot, &xs_pass], D::Minus1)
+    }
+}
+
+#[derive(Debug, Clone)]
+#[allow(clippy::upper_case_acronyms)]
+struct MLP {
+    fc1: Linear,
+    fc2: Linear,
+    act: Activation,
+}
+
+impl MLP {
+    fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let fc1 = linear(cfg.hidden_size, cfg.intermediate_size, vb.pp("fc1"))?;
+        let fc2 = linear(cfg.intermediate_size, cfg.hidden_size, vb.pp("fc2"))?;
+        Ok(Self {
+            fc1,
+            fc2,
+            act: cfg.hidden_act,
+        })
+    }
+}
+
+impl Module for MLP {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.apply(&self.fc1)?.apply(&self.act)?.apply(&self.fc2)
+    }
+}
+
+#[derive(Clone)]
+struct Attention {
+    q_proj: Linear,
+    k_proj: Linear,
+    v_proj: Linear,
+    dense: Linear,
+    kv_cache: Option<(Tensor, Tensor)>,
+    q_layernorm: Option<LayerNorm>,
+    k_layernorm: Option<LayerNorm>,
+    rotary_emb: RotaryEmbedding,
+    softmax_scale: f64,
+    num_heads: usize,
+    num_kv_heads: usize,
+    head_dim: usize,
+    span: tracing::Span,
+}
+
+fn get_mask(size: usize, device: &Device) -> Result<Tensor> {
+    let mask: Vec<_> = (0..size)
+        .flat_map(|i| (0..size).map(move |j| u8::from(j > i)))
+        .collect();
+    Tensor::from_slice(&mask, (size, size), device)
+}
+
+fn masked_fill(on_false: &Tensor, mask: &Tensor, on_true: f32) -> Result<Tensor> {
+    let shape = mask.shape();
+    let on_true = Tensor::new(on_true, on_false.device())?.broadcast_as(shape.dims())?;
+    let m = mask.where_cond(&on_true, on_false)?;
+    Ok(m)
+}
+
+impl Attention {
+    fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let num_heads = cfg.num_attention_heads;
+        let num_kv_heads = cfg.num_key_value_heads();
+        let head_dim = cfg.head_dim();
+        let q_proj = linear(cfg.hidden_size, num_heads * head_dim, vb.pp("q_proj"))?;
+        let k_proj = linear(cfg.hidden_size, num_kv_heads * head_dim, vb.pp("k_proj"))?;
+        let v_proj = linear(cfg.hidden_size, num_kv_heads * head_dim, vb.pp("v_proj"))?;
+        let dense = linear(num_heads * head_dim, cfg.hidden_size, vb.pp("dense"))?;
+        // Alternative rope scalings are not supported.
+        let rotary_emb = RotaryEmbedding::new(cfg, vb.device())?;
+        let (q_layernorm, k_layernorm) = if cfg.qk_layernorm {
+            let q_layernorm = layer_norm(head_dim, cfg.layer_norm_eps, vb.pp("q_layernorm"))?;
+            let k_layernorm = layer_norm(head_dim, cfg.layer_norm_eps, vb.pp("k_layernorm"))?;
+            (Some(q_layernorm), Some(k_layernorm))
+        } else {
+            (None, None)
+        };
+        let softmax_scale = 1f64 / (head_dim as f64).sqrt();
+        Ok(Self {
+            q_proj,
+            k_proj,
+            v_proj,
+            dense,
+            kv_cache: None,
+            q_layernorm,
+            k_layernorm,
+            rotary_emb,
+            softmax_scale,
+            num_heads,
+            num_kv_heads,
+            head_dim,
+            span: tracing::span!(tracing::Level::TRACE, "attention"),
+        })
+    }
+
+    fn repeat_kv(&self, xs: Tensor) -> Result<Tensor> {
+        let n_rep = self.num_heads / self.num_kv_heads;
+        if n_rep == 1 {
+            Ok(xs)
+        } else {
+            let (b_sz, num_kv_heads, seq_len, head_dim) = xs.dims4()?;
+            xs.unsqueeze(2)?
+                .expand((b_sz, num_kv_heads, n_rep, seq_len, head_dim))?
+                .reshape((b_sz, num_kv_heads * n_rep, seq_len, head_dim))
+        }
+    }
+
+    fn forward(&mut self, xs: &Tensor, mask: Option<&Tensor>) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        let (b_size, seq_len, _n_embd) = xs.dims3()?;
+        let query_states = self.q_proj.forward(xs)?;
+        let key_states = self.k_proj.forward(xs)?;
+        let value_states = self.v_proj.forward(xs)?;
+
+        let query_states = match &self.q_layernorm {
+            None => query_states,
+            Some(ln) => query_states.apply(ln)?,
+        };
+        let key_states = match &self.k_layernorm {
+            None => key_states,
+            Some(ln) => key_states.apply(ln)?,
+        };
+
+        let query_states = query_states
+            .reshape((b_size, seq_len, self.num_heads, self.head_dim))?
+            .transpose(1, 2)?;
+        let key_states = key_states
+            .reshape((b_size, seq_len, self.num_kv_heads, self.head_dim))?
+            .transpose(1, 2)?;
+        let value_states = value_states
+            .reshape((b_size, seq_len, self.num_kv_heads, self.head_dim))?
+            .transpose(1, 2)?;
+
+        // Rotary embeddings.
+        let seqlen_offset = match &self.kv_cache {
+            None => 0,
+            Some((prev_k, _)) => prev_k.dim(1)?,
+        };
+        let query_states = self
+            .rotary_emb
+            .apply_rotary_emb(&query_states, seqlen_offset)?;
+        let key_states = self
+            .rotary_emb
+            .apply_rotary_emb(&key_states, seqlen_offset)?;
+
+        // KV cache.
+        let (key_states, value_states) = match &self.kv_cache {
+            None => (key_states, value_states),
+            Some((prev_k, prev_v)) => {
+                let k = Tensor::cat(&[prev_k, &key_states], 2)?;
+                let v = Tensor::cat(&[prev_v, &value_states], 2)?;
+                (k, v)
+            }
+        };
+        self.kv_cache = Some((key_states.clone(), value_states.clone()));
+
+        // Repeat kv.
+        let key_states = self.repeat_kv(key_states)?.contiguous()?;
+        let value_states = self.repeat_kv(value_states)?.contiguous()?;
+
+        let attn_weights = (query_states
+            .to_dtype(DType::F32)?
+            .contiguous()?
+            .matmul(&key_states.to_dtype(DType::F32)?.t()?)?
+            * self.softmax_scale)?;
+        let attn_weights = match mask {
+            None => attn_weights,
+            Some(mask) => masked_fill(
+                &attn_weights,
+                &mask.broadcast_left((b_size, self.num_heads))?,
+                f32::NEG_INFINITY,
+            )?,
+        };
+        let attn_weights =
+            candle_nn::ops::softmax_last_dim(&attn_weights)?.to_dtype(value_states.dtype())?;
+        let attn_output = attn_weights.matmul(&value_states)?;
+        let attn_output = attn_output
+            .transpose(1, 2)?
+            .reshape((b_size, seq_len, ()))?;
+        attn_output.apply(&self.dense)
+    }
+
+    fn clear_kv_cache(&mut self) {
+        self.kv_cache = None
+    }
+}
+
+#[derive(Clone)]
+struct DecoderLayer {
+    self_attn: Attention,
+    mlp: MLP,
+    input_layernorm: LayerNorm,
+    span: tracing::Span,
+}
+
+impl DecoderLayer {
+    fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let self_attn = Attention::new(cfg, vb.pp("self_attn"))?;
+        let mlp = MLP::new(cfg, vb.pp("mlp"))?;
+        let input_layernorm = layer_norm(
+            cfg.hidden_size,
+            cfg.layer_norm_eps,
+            vb.pp("input_layernorm"),
+        )?;
+        Ok(Self {
+            self_attn,
+            mlp,
+            input_layernorm,
+            span: tracing::span!(tracing::Level::TRACE, "block"),
+        })
+    }
+
+    fn forward(&mut self, xs: &Tensor, mask: Option<&Tensor>) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        let residual = xs;
+        let xs = xs.apply(&self.input_layernorm)?;
+        let attn_outputs = self.self_attn.forward(&xs, mask)?;
+        let feed_forward_hidden_states = self.mlp.forward(&xs)?;
+        attn_outputs + feed_forward_hidden_states + residual
+    }
+
+    fn clear_kv_cache(&mut self) {
+        self.self_attn.clear_kv_cache()
+    }
+}
+
+#[derive(Clone)]
+pub struct Model {
+    embed_tokens: Embedding,
+    layers: Vec<DecoderLayer>,
+    final_layernorm: LayerNorm,
+    lm_head: Linear,
+    span: tracing::Span,
+}
+
+impl Model {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let vb_m = vb.pp("model");
+        let embed_tokens =
+            Embedding::new(cfg.vocab_size, cfg.hidden_size, vb_m.pp("embed_tokens"))?;
+        let final_layernorm = layer_norm(
+            cfg.hidden_size,
+            cfg.layer_norm_eps,
+            vb_m.pp("final_layernorm"),
+        )?;
+        let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
+        let vb_m = vb_m.pp("layers");
+        for layer_idx in 0..cfg.num_hidden_layers {
+            let layer = DecoderLayer::new(cfg, vb_m.pp(layer_idx))?;
+            layers.push(layer)
+        }
+        let lm_head = linear(cfg.hidden_size, cfg.vocab_size, vb.pp("lm_head"))?;
+        Ok(Self {
+            embed_tokens,
+            layers,
+            final_layernorm,
+            lm_head,
+            span: tracing::span!(tracing::Level::TRACE, "model"),
+        })
+    }
+
+    pub fn forward(&mut self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        let (_b_size, seq_len) = xs.dims2()?;
+        let mut xs = xs.apply(&self.embed_tokens)?;
+        let mask = if seq_len <= 1 {
+            None
+        } else {
+            Some(get_mask(seq_len, xs.device())?)
+        };
+        for layer in self.layers.iter_mut() {
+            xs = layer.forward(&xs, mask.as_ref())?
+        }
+        xs.apply(&self.final_layernorm)?
+            .narrow(1, seq_len - 1, 1)?
+            .apply(&self.lm_head)?
+            .squeeze(1)
+    }
+
+    pub fn clear_kv_cache(&mut self) {
+        self.layers.iter_mut().for_each(|b| b.clear_kv_cache())
+    }
+}