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https://github.com/ml-explore/mlx-examples.git
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7575125d5d
* Added lora support for Phi-2 * Added Phi-2 support in fuse and convert * format + readme --------- Co-authored-by: Awni Hannun <awni@apple.com>
139 lines
4.3 KiB
Python
139 lines
4.3 KiB
Python
import math
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from dataclasses import dataclass
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import mlx.core as mx
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import mlx.nn as nn
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from .base import BaseModelArgs
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@dataclass
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class ModelArgs(BaseModelArgs):
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n_positions: int = 2048
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vocab_size: int = 51200
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n_embd: int = 2560
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n_head: int = 32
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n_layer: int = 32
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rotary_dim: int = 32
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class LayerNorm(nn.LayerNorm):
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def __call__(self, x: mx.array) -> mx.array:
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return super().__call__(x.astype(mx.float32)).astype(x.dtype)
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class RoPEAttention(nn.Module):
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def __init__(self, dims: int, n_head: int, rotary_dim: int):
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super().__init__()
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self.n_head = n_head
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self.q_proj = nn.Linear(dims, dims)
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self.k_proj = nn.Linear(dims, dims)
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self.v_proj = nn.Linear(dims, dims)
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self.dense = nn.Linear(dims, dims)
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self.rope = nn.RoPE(rotary_dim, traditional=False)
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def __call__(self, x, mask=None, cache=None):
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queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
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# Extract some shapes
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n_head = self.n_head
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B, L, D = queries.shape
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# Prepare the queries, keys and values for the attention computation
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queries = queries.reshape(B, L, n_head, -1).transpose(0, 2, 1, 3)
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keys = keys.reshape(B, L, n_head, -1).transpose(0, 2, 1, 3)
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values = values.reshape(B, L, n_head, -1).transpose(0, 2, 1, 3)
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# Add RoPE to the queries and keys and combine them with the cache
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if cache is not None:
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key_cache, value_cache = cache
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queries = self.rope(queries, offset=key_cache.shape[2])
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keys = self.rope(keys, offset=key_cache.shape[2])
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keys = mx.concatenate([key_cache, keys], axis=2)
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values = mx.concatenate([value_cache, values], axis=2)
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else:
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queries = self.rope(queries)
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keys = self.rope(keys)
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queries = queries.astype(mx.float32)
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keys = keys.astype(mx.float32)
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# Finally perform the attention computation
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scale = math.sqrt(1 / queries.shape[-1])
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scores = (queries * scale) @ keys.transpose(0, 1, 3, 2)
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if mask is not None:
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scores = scores + mask
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scores = mx.softmax(scores, axis=-1).astype(values.dtype)
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values_hat = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
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return self.dense(values_hat), (keys, values)
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class MLP(nn.Module):
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def __init__(self, dim, hidden_dim):
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super().__init__()
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self.fc1 = nn.Linear(dim, hidden_dim)
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self.fc2 = nn.Linear(hidden_dim, dim)
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self.act = nn.GELU(approx="precise")
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def __call__(self, x) -> mx.array:
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return self.fc2(self.act(self.fc1(x)))
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class ParallelBlock(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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dims = config.n_embd
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mlp_dims = dims * 4
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self.self_attn = RoPEAttention(dims, config.n_head, config.rotary_dim)
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self.input_layernorm = LayerNorm(dims)
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self.mlp = MLP(dims, mlp_dims)
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def __call__(self, x, mask, cache):
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h = self.input_layernorm(x)
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attn_h, cache = self.self_attn(h, mask, cache)
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ff_h = self.mlp(h)
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return attn_h + ff_h + x, cache
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class Transformer(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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self.embed_tokens = nn.Embedding(config.vocab_size, config.n_embd)
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self.layers = [ParallelBlock(config) for i in range(config.n_layer)]
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self.final_layernorm = LayerNorm(config.n_embd)
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def __call__(self, x, mask, cache):
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x = self.embed_tokens(x)
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if cache is None:
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cache = [None] * len(self.layers)
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for e, layer in enumerate(self.layers):
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x, cache[e] = layer(x, mask, cache[e])
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return self.final_layernorm(x), cache
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class Model(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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self.model = Transformer(config)
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self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
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def __call__(
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self,
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x: mx.array,
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mask: mx.array = None,
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cache: mx.array = None,
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) -> tuple[mx.array, mx.array]:
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mask = None
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if x.shape[1] > 1:
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mask = nn.MultiHeadAttention.create_additive_causal_mask(x.shape[1])
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mask = mask.astype(x.dtype)
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y, cache = self.model(x, mask, cache)
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return self.lm_head(y), cache
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