mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 01:17:28 +08:00
4c9f9f9be7
* Made mypy compatible * reformatted * Added more fixes * Added fixes to speculative-decoding * Fixes * fix circle * revert some stuff --------- Co-authored-by: Awni Hannun <awni@apple.com>
327 lines
12 KiB
Python
327 lines
12 KiB
Python
from typing import List, Optional, Tuple
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import mlx.core as mx
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import mlx.nn as nn
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import numpy as np
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from mlx.utils import tree_map
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from transformers import T5Config
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def _relative_position_bucket(
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relative_position, bidirectional=True, num_buckets=32, max_distance=128
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):
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"""
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Adapted from HF Tensorflow:
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https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py
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Translate relative position to a bucket number for relative attention. The relative position is defined as
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memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
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position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
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small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
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positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
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This should allow for more graceful generalization to longer sequences than the model has been trained on
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Args:
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relative_position: an int32 Tensor
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bidirectional: a boolean - whether the attention is bidirectional
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num_buckets: an integer
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max_distance: an integer
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Returns:
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a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
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"""
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relative_buckets = 0
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if bidirectional:
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num_buckets //= 2
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relative_buckets += (relative_position > 0).astype(mx.int16) * num_buckets
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relative_position = mx.abs(relative_position)
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else:
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relative_position = -mx.minimum(
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relative_position, mx.zeros_like(relative_position)
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)
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# now relative_position is in the range [0, inf)
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# half of the buckets are for exact increments in positions
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max_exact = num_buckets // 2
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is_small = relative_position < max_exact
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# The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
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scale = (num_buckets - max_exact) / np.log(max_distance / max_exact)
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relative_position_if_large = max_exact + (
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mx.log(relative_position.astype(mx.float32) / max_exact) * scale
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).astype(mx.int16)
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relative_position_if_large = mx.minimum(relative_position_if_large, num_buckets - 1)
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relative_buckets += mx.where(
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is_small, relative_position, relative_position_if_large
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)
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return relative_buckets
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class RelativePositionBias(nn.Module):
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def __init__(self, config: T5Config, bidirectional: bool):
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self.bidirectional = bidirectional
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self.num_buckets = config.relative_attention_num_buckets
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self.max_distance = config.relative_attention_max_distance
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self.n_heads = config.num_heads
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self.embeddings = nn.Embedding(
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config.relative_attention_num_buckets, config.num_heads
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)
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def __call__(self, query_length: int, key_length: int, offset: int = 0):
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"""Compute binned relative position bias"""
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context_position = mx.arange(offset, query_length)[:, None]
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memory_position = mx.arange(key_length)[None, :]
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# shape (query_length, key_length)
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relative_position = memory_position - context_position
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relative_position_bucket = _relative_position_bucket(
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relative_position,
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bidirectional=self.bidirectional,
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num_buckets=self.num_buckets,
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max_distance=self.max_distance,
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)
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# shape (query_length, key_length, num_heads)
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values = self.embeddings(relative_position_bucket)
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# shape (num_heads, query_length, key_length)
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return values.transpose(2, 0, 1)
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class MultiHeadAttention(nn.Module):
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def __init__(self, config: T5Config):
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super().__init__()
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inner_dim = config.d_kv * config.num_heads
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self.num_heads = config.num_heads
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self.query_proj = nn.Linear(config.d_model, inner_dim, bias=False)
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self.key_proj = nn.Linear(config.d_model, inner_dim, bias=False)
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self.value_proj = nn.Linear(config.d_model, inner_dim, bias=False)
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self.out_proj = nn.Linear(inner_dim, config.d_model, bias=False)
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def __call__(
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self,
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queries: mx.array,
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keys: mx.array,
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values: mx.array,
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mask: Optional[mx.array],
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cache: Optional[Tuple[mx.array, mx.array]] = None,
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) -> Tuple[mx.array, Tuple[mx.array, mx.array]]:
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queries = self.query_proj(queries)
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keys = self.key_proj(keys)
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values = self.value_proj(values)
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num_heads = self.num_heads
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B, L, _ = queries.shape
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_, S, _ = keys.shape
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queries = queries.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
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keys = keys.reshape(B, S, num_heads, -1).transpose(0, 2, 1, 3)
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values = values.reshape(B, S, num_heads, -1).transpose(0, 2, 1, 3)
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if cache is not None:
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key_cache, value_cache = cache
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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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# Dimensions are [batch x num heads x sequence x hidden dim]
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scores = queries @ keys.transpose(0, 1, 3, 2)
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if mask is not None:
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scores = scores + mask.astype(scores.dtype)
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scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
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values_hat = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
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return self.out_proj(values_hat), (keys, values)
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class DenseActivation(nn.Module):
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def __init__(self, config: T5Config):
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super().__init__()
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mlp_dims = config.d_ff or config.d_model * 4
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self.gated = config.feed_forward_proj.startswith("gated")
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if self.gated:
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self.wi_0 = nn.Linear(config.d_model, mlp_dims, bias=False)
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self.wi_1 = nn.Linear(config.d_model, mlp_dims, bias=False)
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else:
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self.wi = nn.Linear(config.d_model, mlp_dims, bias=False)
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self.wo = nn.Linear(mlp_dims, config.d_model, bias=False)
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activation = config.feed_forward_proj.removeprefix("gated-")
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if activation == "relu":
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self.act = nn.relu
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elif activation == "gelu":
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self.act = nn.gelu
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elif activation == "silu":
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self.act = nn.silu
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else:
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raise ValueError(f"Unknown activation: {activation}")
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def __call__(self, x):
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if self.gated:
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hidden_act = self.act(self.wi_0(x))
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hidden_linear = self.wi_1(x)
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x = hidden_act * hidden_linear
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else:
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x = self.act(self.wi(x))
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return self.wo(x)
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class TransformerEncoderLayer(nn.Module):
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def __init__(self, config: T5Config):
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super().__init__()
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self.attention = MultiHeadAttention(config)
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self.ln1 = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
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self.ln2 = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
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self.dense = DenseActivation(config)
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def __call__(self, x, mask):
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y = self.ln1(x)
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y, _ = self.attention(y, y, y, mask=mask)
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x = x + y
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y = self.ln2(x)
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y = self.dense(y)
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return x + y
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class TransformerEncoder(nn.Module):
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def __init__(self, config: T5Config):
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super().__init__()
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self.layers = [
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TransformerEncoderLayer(config) for i in range(config.num_layers)
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]
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self.ln = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
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self.relative_attention_bias = RelativePositionBias(config, bidirectional=True)
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def __call__(self, x: mx.array):
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pos_bias = self.relative_attention_bias(x.shape[1], x.shape[1])
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for layer in self.layers:
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x = layer(x, mask=pos_bias)
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return self.ln(x)
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class TransformerDecoderLayer(nn.Module):
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def __init__(self, config: T5Config):
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super().__init__()
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self.self_attention = MultiHeadAttention(config)
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self.cross_attention = MultiHeadAttention(config)
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self.ln1 = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
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self.ln2 = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
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self.ln3 = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
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self.dense = DenseActivation(config)
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def __call__(
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self,
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x: mx.array,
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memory: mx.array,
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mask: mx.array,
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memory_mask: mx.array,
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cache: Optional[Tuple[mx.array, mx.array]] = None,
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) -> Tuple[mx.array, Tuple[mx.array, mx.array]]:
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y = self.ln1(x)
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y, new_cache = self.self_attention(y, y, y, mask, cache)
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x = x + y
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y = self.ln2(x)
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y, _ = self.cross_attention(y, memory, memory, memory_mask)
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x = x + y
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y = self.ln3(x)
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y = self.dense(y)
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x = x + y
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return x, new_cache
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def create_additive_causal_mask(N: int, offset: int = 0):
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rinds = mx.arange(offset + N)
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linds = mx.arange(offset, offset + N) if offset else rinds
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mask = linds[:, None] < rinds[None]
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return mask * -1e9
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class TransformerDecoder(nn.Module):
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def __init__(self, config: T5Config):
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super().__init__()
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n_layers = getattr(config, "num_decoder_layers", config.num_layers)
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self.layers = [TransformerDecoderLayer(config) for i in range(n_layers)]
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self.ln = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
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self.relative_attention_bias = RelativePositionBias(config, bidirectional=False)
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def __call__(self, x, memory, cache=None):
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if cache[0] is not None:
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offset = cache[0][0].shape[2]
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else:
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offset = 0
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T = x.shape[1]
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if T > 1:
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mask = create_additive_causal_mask(T, offset)
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else:
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mask = None
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pos_bias = self.relative_attention_bias(T + offset, T + offset, offset=offset)
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if mask is not None:
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mask += pos_bias
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else:
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mask = pos_bias
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for e, layer in enumerate(self.layers):
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x, cache[e] = layer(x, memory, mask, None, cache=cache[e])
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x = self.ln(x)
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return x, cache
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class OutputHead(nn.Module):
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def __init__(self, config: T5Config):
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self.linear = nn.Linear(config.d_model, config.vocab_size, bias=False)
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def __call__(self, inputs):
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return self.linear(inputs)
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class Model(nn.Module):
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def __init__(self, config: T5Config):
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self.wte = nn.Embedding(config.vocab_size, config.d_model)
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self.encoder = TransformerEncoder(config)
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self.decoder = TransformerDecoder(config)
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self.tie_word_embeddings = config.tie_word_embeddings
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if not self.tie_word_embeddings:
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self.lm_head = OutputHead(config)
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self.model_dim = config.d_model
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self.reset_cache()
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def encode(self, inputs: mx.array):
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return self.encoder(self.wte(inputs))
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def truncate_cache(self, num_to_truncate):
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if num_to_truncate <= 0:
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return
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cache_length = self.cache[0][0].shape[2]
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if num_to_truncate < cache_length:
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self.cache = tree_map(lambda x: x[:, :, :-num_to_truncate, :], self.cache)
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else:
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self.reset_cache()
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def reset_cache(self):
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self.cache = [None] * len(self.decoder.layers)
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def decode(
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self,
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inputs: mx.array,
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memory: mx.array,
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):
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inputs = self.wte(inputs)
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y, self.cache = self.decoder(inputs, memory=memory, cache=self.cache)
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if not self.tie_word_embeddings:
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y *= self.model_dim**-0.5
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y = self.lm_head(y)
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else:
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y = y @ self.wte.weight.T
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return y
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def __call__(
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self,
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inputs: mx.array,
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decoder_inputs: mx.array,
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):
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return self.decode(decoder_inputs, self.encode(inputs))[0]
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