Lazy import + refactor Lora layer addition (#426)

* lazy model import in mlx_lm

* change lora loading

* fix olmo lora

* remove a bunch of unused stuff from plamo

* move phixtral to mlx-lm and out of llms/

llms/mlx_lm/models/phixtral.py

@@ -0,0 +1,218 @@
+import glob
+import inspect
+import json
+import math
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Optional, Tuple
+
+import mlx.core as mx
+import mlx.nn as nn
+import numpy as np
+from huggingface_hub import snapshot_download
+from mlx.utils import tree_unflatten
+from transformers import AutoTokenizer
+
+
+@dataclass
+class ModelArgs:
+    model_type: str
+    max_sequence_length: int = 2048
+    num_vocab: int = 51200
+    model_dim: int = 2560
+    num_heads: int = 32
+    num_layers: int = 32
+    rotary_dim: int = 32
+    num_experts_per_tok: int = 2
+    num_local_experts: int = 4
+
+    @classmethod
+    def from_dict(cls, params):
+        return cls(
+            **{
+                k: v
+                for k, v in params.items()
+                if k in inspect.signature(cls).parameters
+            }
+        )
+
+
+class LayerNorm(nn.LayerNorm):
+    def __call__(self, x: mx.array) -> mx.array:
+        return super().__call__(x.astype(mx.float32)).astype(x.dtype)
+
+
+class RoPEAttention(nn.Module):
+    def __init__(self, dims: int, num_heads: int, rotary_dim: int):
+        super().__init__()
+
+        self.num_heads = num_heads
+
+        self.rope = nn.RoPE(rotary_dim, traditional=False)
+        self.Wqkv = nn.Linear(dims, 3 * dims)
+        self.out_proj = nn.Linear(dims, dims)
+
+    def __call__(self, x, mask=None, cache=None):
+        qkv = self.Wqkv(x)
+        queries, keys, values = mx.split(qkv, 3, axis=-1)
+
+        # Extract some shapes
+        num_heads = self.num_heads
+        B, L, D = queries.shape
+
+        # Prepare the queries, keys and values for the attention computation
+        queries = queries.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
+        keys = keys.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
+        values = values.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
+
+        # Add RoPE to the queries and keys and combine them with the cache
+        if cache is not None:
+            key_cache, value_cache = cache
+            queries = self.rope(queries, offset=key_cache.shape[2])
+            keys = self.rope(keys, offset=key_cache.shape[2])
+            keys = mx.concatenate([key_cache, keys], axis=2)
+            values = mx.concatenate([value_cache, values], axis=2)
+        else:
+            queries = self.rope(queries)
+            keys = self.rope(keys)
+
+        queries = queries.astype(mx.float32)
+        keys = keys.astype(mx.float32)
+
+        # Finally perform the attention computation
+        scale = math.sqrt(1 / queries.shape[-1])
+        scores = (queries * scale) @ keys.transpose(0, 1, 3, 2)
+        if mask is not None:
+            scores = scores + mask
+
+        scores = mx.softmax(scores, axis=-1).astype(values.dtype)
+        values_hat = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
+
+        return self.out_proj(values_hat), (keys, values)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim, hidden_dim):
+        super().__init__()
+        self.fc1 = nn.Linear(dim, hidden_dim)
+        self.fc2 = nn.Linear(hidden_dim, dim)
+        self.act = nn.GELU(approx="precise")
+
+    def __call__(self, x) -> mx.array:
+        return self.fc2(self.act(self.fc1(x)))
+
+
+class MOE(nn.Module):
+    def __init__(self, args: ModelArgs, dim: int, hidden_dim: int):
+        super().__init__()
+        self.dim = dim
+        self.hidden_dim = hidden_dim
+        self.num_experts = args.num_local_experts
+        self.num_experts_per_tok = args.num_experts_per_tok
+        self.mlp = [MLP(self.dim, self.hidden_dim) for _ in range(self.num_experts)]
+        self.gate = nn.Linear(args.model_dim, self.num_experts, bias=False)
+
+    def __call__(self, x: mx.array) -> mx.array:
+        ne = self.num_experts_per_tok
+        orig_shape = x.shape
+        x = x.reshape(-1, x.shape[-1])
+
+        gates = self.gate(x)
+        inds = mx.stop_gradient(mx.argpartition(-gates, kth=ne, axis=-1))[:, :ne]
+        scores = mx.softmax(
+            mx.take_along_axis(gates, inds, axis=-1).astype(mx.float32),
+            axis=-1,
+        ).astype(gates.dtype)
+
+        if self.training:
+            ys = []
+            y = mx.zeros((x.shape[0], ne, x.shape[-1]))
+            for e, expert in enumerate(self.mlp):
+                idx1, idx2 = map(mx.array, np.where(inds == e))
+                if idx1.size == 0:
+                    continue
+                y[idx1, idx2] = expert(x[idx1])
+
+            y = (y * scores[..., None]).sum(axis=1)
+        else:
+            y = []
+            for xt, st, it in zip(x, scores, inds.tolist()):
+                yt = mx.concatenate([self.mlp[e](xt)[:, None] for e in it], axis=-1)
+                yt = (yt * st).sum(axis=-1)
+                y.append(yt[None, :])
+            y = mx.concatenate(y)
+
+        return y.reshape(orig_shape)
+
+
+class ParallelBlock(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        dims = config.model_dim
+        mlp_dims = dims * 4
+        self.mixer = RoPEAttention(dims, config.num_heads, config.rotary_dim)
+        self.ln = LayerNorm(dims)
+        self.moe = MOE(config, dims, mlp_dims)
+
+    def __call__(self, x, mask, cache):
+        h = self.ln(x)
+        attn_h, cache = self.mixer(h, mask, cache)
+        ff_h = self.moe(h)
+        return attn_h + ff_h + x, cache
+
+
+class TransformerDecoder(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.embd = Embd(config)
+        self.h = [ParallelBlock(config) for i in range(config.num_layers)]
+
+    def __call__(self, x, mask, cache):
+        x = self.embd(x)
+        if cache is None:
+            cache = [None] * len(self.h)
+
+        for e, layer in enumerate(self.h):
+            x, cache[e] = layer(x, mask, cache[e])
+        return x, cache
+
+
+class Embd(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.wte = nn.Embedding(config.num_vocab, config.model_dim)
+
+    def __call__(self, x):
+        return self.wte(x)
+
+
+class OutputHead(nn.Module):
+    def __init__(self, config: ModelArgs) -> None:
+        super().__init__()
+        self.ln = LayerNorm(config.model_dim)
+        self.linear = nn.Linear(config.model_dim, config.num_vocab)
+
+    def __call__(self, inputs):
+        return self.linear(self.ln(inputs))
+
+
+class Model(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.model_type = config.model_type
+        self.transformer = TransformerDecoder(config)
+        self.lm_head = OutputHead(config)
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: mx.array = None,
+        cache: mx.array = None,
+    ) -> Tuple[mx.array, mx.array]:
+        mask = None
+        if x.shape[1] > 1:
+            mask = nn.MultiHeadAttention.create_additive_causal_mask(x.shape[1])
+            mask = mask.astype(x.dtype)
+
+        y, cache = self.transformer(x, mask, cache)
+        return self.lm_head(y), cache