From d7c64c621a96ad77a39eb90a8c13fa3fda5f9c07 Mon Sep 17 00:00:00 2001 From: Prince Canuma Date: Sat, 14 Dec 2024 16:15:24 +0100 Subject: [PATCH] add support for cohere2 --- llms/mlx_lm/models/cohere2.py | 207 ++++++++++++++++++++++++++++++++++ 1 file changed, 207 insertions(+) create mode 100644 llms/mlx_lm/models/cohere2.py diff --git a/llms/mlx_lm/models/cohere2.py b/llms/mlx_lm/models/cohere2.py new file mode 100644 index 00000000..ae19f4d8 --- /dev/null +++ b/llms/mlx_lm/models/cohere2.py @@ -0,0 +1,207 @@ +# Copyright © 2023-2024 Apple Inc. + +from dataclasses import dataclass +from typing import Optional, Tuple + +import mlx.core as mx +import mlx.nn as nn + +from .base import BaseModelArgs, create_attention_mask + + +@dataclass +class ModelArgs(BaseModelArgs): + model_type: str + hidden_size: int = 4096 + head_dim: int = 128 + num_hidden_layers: int = 32 + intermediate_size: int = 14336 + num_attention_heads: int = 32 + num_key_value_heads: int = 8 + rope_theta: float = 50000.0 + vocab_size: int = 256000 + layer_norm_eps: float = 1e-05 + logit_scale: float = 0.0625 + attention_bias: bool = False + layer_norm_bias: bool = False + sliding_window: int = 4096 + sliding_window_pattern: int = 4 + + +class LayerNorm2D(nn.Module): + + def __init__(self, d1, d2, eps): + super().__init__() + self.weight = mx.zeros((d1, d2)) + self.eps = eps + + def __call__(self, x): + return self.weight * mx.fast.layer_norm(x, None, None, self.eps) + + +class Attention(nn.Module): + def __init__(self, args: ModelArgs, layer_idx: int): + super().__init__() + self.args = args + self.layer_idx = layer_idx + + dim = args.hidden_size + self.n_heads = n_heads = args.num_attention_heads + self.n_kv_heads = n_kv_heads = args.num_key_value_heads + + head_dim = args.hidden_size // args.num_attention_heads + self.scale = head_dim**-0.5 + + attetion_bias = args.attention_bias + + self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=attetion_bias) + self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=attetion_bias) + self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=attetion_bias) + self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=attetion_bias) + + self.rope = nn.RoPE(head_dim, traditional=True, base=args.rope_theta) + + self.sliding_window = ( + args.sliding_window + if (layer_idx + 1) % args.sliding_window_pattern != 0 + else None + ) + + def __call__( + self, + x: mx.array, + mask: Optional[mx.array] = None, + cache: Optional[Tuple[mx.array, mx.array]] = None, + ) -> mx.array: + B, L, D = x.shape + + queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x) + + queries = queries.reshape(B, L, self.n_heads, -1) + keys = keys.reshape(B, L, self.n_kv_heads, -1) + + queries = queries.transpose(0, 2, 1, 3) + keys = keys.transpose(0, 2, 1, 3) + values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3) + + if cache is not None: + queries = self.rope(queries, offset=cache.offset) + keys = self.rope(keys, offset=cache.offset) + keys, values = cache.update_and_fetch(keys, values) + else: + queries = self.rope(queries) + keys = self.rope(keys) + + # sliding window attention + if self.sliding_window is not None: + keys = keys[:, : -self.sliding_window :, :] + values = values[:, : -self.sliding_window :, :] + if mask is not None: + mask = mask[:, : -self.sliding_window, :] + + output = mx.fast.scaled_dot_product_attention( + queries, keys, values, scale=self.scale, mask=mask + ) + + output = output.transpose(0, 2, 1, 3).reshape(B, L, -1) + return self.o_proj(output) + + +class MLP(nn.Module): + def __init__(self, dim, hidden_dim): + super().__init__() + self.gate_proj = nn.Linear(dim, hidden_dim, bias=False) + self.up_proj = nn.Linear(dim, hidden_dim, bias=False) + self.down_proj = nn.Linear(hidden_dim, dim, bias=False) + + def __call__(self, x): + return self.down_proj(nn.gelu(self.gate_proj(x)) * self.up_proj(x)) + + +class TransformerBlock(nn.Module): + def __init__(self, args: ModelArgs, layer_idx: int): + super().__init__() + self.hidden_size = args.hidden_size + self.n_heads = args.num_attention_heads + + self.self_attn = Attention(args, layer_idx) + self.mlp = MLP(args.hidden_size, args.intermediate_size) + self.input_layernorm = nn.LayerNorm( + args.hidden_size, eps=args.layer_norm_eps, bias=args.layer_norm_bias + ) + self.args = args + + def __call__( + self, + x: mx.array, + mask: Optional[mx.array] = None, + cache: Optional[Tuple[mx.array, mx.array]] = None, + ) -> mx.array: + h = self.input_layernorm(x) + attn_h = self.self_attn(h, mask, cache) + ff_h = self.mlp(h) + return attn_h + ff_h + x + + +class CohereModel(nn.Module): + def __init__(self, args: ModelArgs): + super().__init__() + self.args = args + self.vocab_size = args.vocab_size + self.num_hidden_layers = args.num_hidden_layers + assert self.vocab_size > 0 + self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size) + self.layers = [ + TransformerBlock(args=args, layer_idx=i) + for i in range(args.num_hidden_layers) + ] + self.norm = nn.LayerNorm( + args.hidden_size, eps=args.layer_norm_eps, bias=args.layer_norm_bias + ) + + def __call__( + self, + inputs: mx.array, + cache=None, + ): + h = self.embed_tokens(inputs) + + mask = create_attention_mask(h, cache) + + if cache is None: + cache = [None] * len(self.layers) + + for layer, c in zip(self.layers, cache): + h = layer(h, mask, c) + + return self.norm(h) + + +class Model(nn.Module): + def __init__(self, args: ModelArgs): + super().__init__() + self.model_type = args.model_type + self.model = CohereModel(args) + self.args = args + + def __call__( + self, + inputs: mx.array, + cache=None, + ): + out = self.model(inputs, cache) + out = self.model.embed_tokens.as_linear(out) + out = out * self.model.args.logit_scale + return out + + @property + def layers(self): + return self.model.layers + + @property + def head_dim(self): + return self.args.hidden_size // self.args.num_attention_heads + + @property + def n_kv_heads(self): + return self.args.num_key_value_heads