mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 01:17:28 +08:00
fca087be49
* fix rotating kv cache for chat use case * reorg + fixes to caching, unify prompt caching across types and use cases for e.g. caching during a chat * nit in chat * fix tests * fix tests * fix tests * docs * chat command * comments + docs * Define meta_state on all Cache implementations * fixes + trim_prompt_cache api * fix default model --------- Co-authored-by: Angelos Katharopoulos <a_katharopoulos@apple.com>
311 lines
9.9 KiB
Python
311 lines
9.9 KiB
Python
# Copyright © 2023-2024 Apple Inc.
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import math
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from dataclasses import dataclass
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from functools import partial
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from typing import Any, Optional
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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, create_attention_mask
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@dataclass
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class ModelArgs(BaseModelArgs):
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model_type: str
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hidden_size: int
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dense_attention_every_n_layers: int
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ff_intermediate_size: int
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gegelu_limit: float
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num_hidden_layers: int
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num_attention_heads: int
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layer_norm_epsilon: float
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vocab_size: int
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num_key_value_heads: int
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mup_attn_multiplier: float = 1.0
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mup_use_scaling: bool = True
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mup_embedding_multiplier: float = 10.0
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mup_width_multiplier: float = 8.0
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rope_embedding_base: float = 1000000
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rope_position_scale: float = 1.0
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blocksparse_block_size: int = 64
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blocksparse_num_local_blocks: int = 16
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blocksparse_vert_stride: int = 8
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@partial(mx.compile, shapeless=True)
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def gegelu_impl(a_gelu, a_linear, limit):
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a_gelu = mx.where(
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mx.isinf(a_gelu),
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a_gelu,
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mx.clip(a_gelu, a_min=None, a_max=limit),
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)
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a_linear = mx.where(
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mx.isinf(a_linear),
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a_linear,
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mx.clip(a_linear, a_min=-limit, a_max=limit),
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)
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out_gelu = a_gelu * mx.sigmoid(1.702 * a_gelu)
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return out_gelu * (a_linear + 1.0)
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def gegelu(x, limit):
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a_gelu, a_linear = x[..., ::2], x[..., 1::2]
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return gegelu_impl(a_gelu, a_linear, limit)
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class Attention(nn.Module):
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def __init__(self, args: ModelArgs, layer_idx):
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super().__init__()
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dim = args.hidden_size
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self.n_heads = n_heads = args.num_attention_heads
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self.n_kv_heads = n_kv_heads = args.num_key_value_heads
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self.n_q_per_kv = n_heads // n_kv_heads
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self.head_dim = head_dim = args.hidden_size // n_heads
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self.query_key_value = nn.Linear(
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dim, (self.n_heads + 2 * self.n_kv_heads) * head_dim
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)
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self.dense = nn.Linear(dim, dim)
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if args.mup_use_scaling:
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norm_factor = head_dim / args.mup_attn_multiplier
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else:
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norm_factor = math.sqrt(head_dim)
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self.scale = 1.0 / norm_factor
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self.rope = nn.RoPE(
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head_dim,
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traditional=False,
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base=args.rope_embedding_base,
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scale=args.rope_position_scale,
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)
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if layer_idx % args.dense_attention_every_n_layers == 0:
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self.block_sparse = True
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self.blocksparse_block_size = args.blocksparse_block_size
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if self.blocksparse_block_size not in (32, 64):
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raise ValueError(
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f"Unsupported block size {self.blocksparse_block_size}"
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)
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self.blocksparse_num_local_blocks = args.blocksparse_num_local_blocks
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self.blocksparse_vert_stride = args.blocksparse_vert_stride
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else:
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self.block_sparse = False
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def _block_sparse_mask(self, q_len, kv_len):
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vert_stride = self.blocksparse_vert_stride
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local_blocks = self.blocksparse_num_local_blocks
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block_size = self.blocksparse_block_size
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n_heads = self.n_heads
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kv_blocks = (kv_len + block_size - 1) // block_size
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q_blocks = (q_len + block_size - 1) // block_size
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q_pos = mx.arange(kv_blocks - q_blocks, kv_blocks)[None, :, None]
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k_pos = mx.arange(kv_blocks)[None, None]
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mask_vert_strided = (
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mx.arange(kv_blocks)[None, :] + mx.arange(1, n_heads + 1)[:, None]
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) % vert_stride
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mask_vert_strided = (mask_vert_strided == 0)[:, None, :]
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block_mask = (q_pos >= k_pos) & (
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(q_pos - k_pos < local_blocks) | mask_vert_strided
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)
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block_mask = block_mask.reshape(
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self.n_kv_heads, self.n_q_per_kv, *block_mask.shape[-2:]
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)
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dense_mask = mx.repeat(
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mx.repeat(block_mask, block_size, axis=-1), block_size, axis=-2
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)
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return block_mask, dense_mask[..., -q_len:, :kv_len]
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def _block_sparse_attention(self, queries, keys, values, scale, mask):
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queries = scale * queries
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B = queries.shape[0]
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L = queries.shape[2]
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queries = mx.reshape(queries, (B, self.n_kv_heads, self.n_q_per_kv, L, -1))
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keys = mx.expand_dims(keys, 2)
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values = mx.expand_dims(values, 2)
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# TODO get rid of dense mask if we have a fill value
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block_mask, dense_mask = self._block_sparse_mask(L, keys.shape[-2])
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scores = queries @ mx.swapaxes(keys, -1, -2)
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# TODO, uncomment when faster
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# scores = mx.block_masked_mm(
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# queries,
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# mx.swapaxes(keys, -1, -2),
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# mask_out=block_mask,
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# block_size=self.blocksparse_block_size,
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# )
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if mask is not None:
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scores = scores + mask
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scores = scores + mx.where(
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dense_mask, mx.array(0, scores.dtype), mx.array(-float("inf"), scores.dtype)
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)
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scores = mx.softmax(scores, axis=-1, precise=True)
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output = scores @ values
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# TODO, uncomment when faster
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# output = mx.block_masked_mm(
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# scores, values, mask_lhs=block_mask, block_size=self.blocksparse_block_size
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# )
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return mx.reshape(output, (B, self.n_heads, L, -1))
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def __call__(
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self,
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x: mx.array,
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mask: Optional[mx.array] = None,
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cache: Optional[Any] = None,
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) -> mx.array:
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B, L, D = x.shape
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qkv = self.query_key_value(x)
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qkv = qkv.reshape(B, L, -1, self.n_q_per_kv + 2, self.head_dim)
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queries = qkv[..., :-2, :].flatten(-3, -2)
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keys = qkv[..., -2, :]
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values = qkv[..., -1, :]
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# Prepare the queries, keys and values for the attention computation
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queries = queries.transpose(0, 2, 1, 3)
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keys = keys.transpose(0, 2, 1, 3)
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values = values.transpose(0, 2, 1, 3)
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if cache is not None:
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queries = self.rope(queries, offset=cache.offset)
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keys = self.rope(keys, offset=cache.offset)
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keys, values = cache.update_and_fetch(keys, values)
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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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if self.block_sparse:
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output = self._block_sparse_attention(
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queries, keys, values, scale=self.scale, mask=mask
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)
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else:
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output = mx.fast.scaled_dot_product_attention(
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queries, keys, values, scale=self.scale, mask=mask
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)
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output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
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return self.dense(output)
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class MLP(nn.Module):
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def __init__(self, args):
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super().__init__()
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dim = args.hidden_size
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hidden_dim = args.ff_intermediate_size
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self.gegelu_limit = args.gegelu_limit
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self.up_proj = nn.Linear(dim, 2 * hidden_dim)
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self.down_proj = nn.Linear(hidden_dim, dim)
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def __call__(self, x) -> mx.array:
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x = self.up_proj(x)
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return self.down_proj(gegelu(x, self.gegelu_limit))
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class TransformerBlock(nn.Module):
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def __init__(self, args: ModelArgs, layer_idx):
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super().__init__()
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self.num_attention_heads = args.num_attention_heads
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self.hidden_size = args.hidden_size
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self.self_attn = Attention(args, layer_idx)
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self.mlp = MLP(args)
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self.input_layernorm = nn.LayerNorm(
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args.hidden_size, eps=args.layer_norm_epsilon
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)
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self.post_attention_layernorm = nn.LayerNorm(
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args.hidden_size,
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eps=args.layer_norm_epsilon,
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)
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self.args = args
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def __call__(
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self,
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x: mx.array,
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mask: Optional[mx.array] = None,
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cache: Optional[Any] = None,
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) -> mx.array:
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r = self.self_attn(self.input_layernorm(x), mask, cache)
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h = x + r
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r = self.mlp(self.post_attention_layernorm(h))
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out = h + r
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return out
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class Phi3Model(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.args = args
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self.vocab_size = args.vocab_size
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self.num_hidden_layers = args.num_hidden_layers
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assert self.vocab_size > 0
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self.mup_embedding_multiplier = args.mup_embedding_multiplier
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self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
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self.layers = [
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TransformerBlock(args=args, layer_idx=l)
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for l in range(args.num_hidden_layers)
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]
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self.final_layernorm = nn.LayerNorm(
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args.hidden_size, eps=args.layer_norm_epsilon
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)
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def __call__(
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self,
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inputs: mx.array,
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cache=None,
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):
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h = self.embed_tokens(inputs)
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if self.mup_embedding_multiplier:
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h = self.mup_embedding_multiplier * h
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mask = create_attention_mask(h, cache)
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if cache is None:
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cache = [None] * len(self.layers)
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for layer, c in zip(self.layers, cache):
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h = layer(h, mask, c)
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return self.final_layernorm(h)
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class Model(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.model_type = args.model_type
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self.model = Phi3Model(args)
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self.args = args
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self.mup_width_multiplier = args.mup_width_multiplier
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self._dummy_tokenizer_ids = mx.array(
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[100256, 100258, 100259, 100260, 100264, 100265]
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+ list(range(100267, 100352))
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)
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def __call__(
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self,
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inputs: mx.array,
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cache=None,
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):
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out = self.model(inputs, cache)
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out = self.model.embed_tokens.as_linear(out)
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if self.mup_width_multiplier:
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out = out / self.mup_width_multiplier
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out[self._dummy_tokenizer_ids] = -float("inf")
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return out
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@property
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def layers(self):
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return self.model.layers
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def sanitize(self, weights):
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# Remove unused precomputed rotary freqs
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return {
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k: v for k, v in weights.items() if "self_attn.rotary_emb.inv_freq" not in k
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}
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