mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-08-30 02:53:41 +08:00
433 lines
15 KiB
Python
433 lines
15 KiB
Python
import argparse
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from dataclasses import dataclass
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from typing import Optional, Tuple, List
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from typing import Optional
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from time import perf_counter_ns
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import numpy as np
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import mlx.core as mx
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import mlx.nn as nn
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from mlx.utils import tree_flatten, tree_unflatten
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from transformers import AutoTokenizer
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@dataclass
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class ModelArgs:
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d_ff: int = 2048
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d_kv: int = 64
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d_model: int = 512
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dropout_rate: int = 0.1
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layer_norm_epsilon: float = 1e-06
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n_positions: int = 512
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relative_attention_num_buckets: int = 32
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relative_attention_max_distance: int = 128
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num_heads: int = 8
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num_layers: int = 6
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decoder_start_token_id: int = 0
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eos_token_id: int = 1
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pad_token_id: int = 0
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vocab_size: int = 32128
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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 = np.log(max_distance / max_exact) * (num_buckets - 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: ModelArgs, bidirectional: bool):
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self.bidirectional = False # 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: ModelArgs):
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super().__init__()
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self.num_heads = config.num_heads
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self.query_proj = nn.Linear(config.d_model, config.d_model, bias=False)
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self.key_proj = nn.Linear(config.d_model, config.d_model, bias=False)
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self.value_proj = nn.Linear(config.d_model, config.d_model, bias=False)
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self.out_proj = nn.Linear(config.d_model, 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: mx.array,
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cache: Optional[Tuple[mx.array, mx.array]] = None,
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) -> [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, 3, 1)
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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=3)
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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
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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, axis=-1)
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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 RMSNorm(nn.Module):
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def __init__(self, dims: int, eps: float = 1e-5):
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super().__init__()
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self.weight = mx.ones((dims,))
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self.eps = eps
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def _norm(self, x):
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return x * mx.rsqrt(x.square().mean(-1, keepdims=True) + self.eps)
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def __call__(self, x):
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output = self._norm(x.astype(mx.float32)).astype(x.dtype)
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return self.weight * output
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class TransformerEncoderLayer(nn.Module):
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def __init__(self, config: ModelArgs):
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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.attention = MultiHeadAttention(config)
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self.ln1 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
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self.ln2 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
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self.linear1 = nn.Linear(config.d_model, mlp_dims, bias=False)
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self.linear2 = nn.Linear(mlp_dims, config.d_model, bias=False)
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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.linear1(y)
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y = mx.maximum(y, 0)
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y = self.linear2(y)
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return x + y
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class TransformerEncoder(nn.Module):
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def __init__(self, config: ModelArgs):
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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 = 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: ModelArgs):
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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.self_attention = MultiHeadAttention(config)
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self.cross_attention = MultiHeadAttention(config)
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self.ln1 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
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self.ln2 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
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self.ln3 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
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self.linear1 = nn.Linear(config.d_model, mlp_dims, bias=False)
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self.linear2 = nn.Linear(mlp_dims, config.d_model, bias=False)
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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[List[Tuple[mx.array, mx.array]]] = None,
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):
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y = self.ln1(x)
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y, 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(x, 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.linear1(y)
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y = mx.maximum(y, 0)
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y = self.linear2(y)
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x = x + y
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return x, cache
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class TransformerDecoder(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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self.layers = [
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TransformerDecoderLayer(config) for i in range(config.num_layers)
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]
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self.ln = 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, mask, memory_mask, cache=None):
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if cache is not None:
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offset = cache[0][0].shape[3]
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else:
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offset = 0
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cache = [None] * len(self.layers)
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T = offset + x.shape[1]
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pos_bias = self.relative_attention_bias(T, T, 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, memory_mask, 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: ModelArgs):
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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 T5(nn.Module):
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def __init__(self, config: ModelArgs):
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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.lm_head = OutputHead(config)
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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 decode(
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self,
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inputs: mx.array,
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memory: mx.array,
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cache=None,
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):
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inputs = self.wte(inputs)
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T = inputs.shape[1]
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if T > 1:
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mask = nn.MultiHeadAttention.create_additive_causal_mask(T)
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mask = mask.astype(inputs.dtype)
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else:
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mask = None
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y, cache = self.decoder(
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inputs, memory=memory, mask=mask, memory_mask=None, cache=cache
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)
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return self.lm_head(y), cache
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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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def generate(
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inputs: mx.array, decoder_inputs: mx.array, model: T5, temp: Optional[float] = 0.0
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):
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def sample(logits):
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if temp == 0:
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return mx.argmax(logits, axis=-1)
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else:
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return mx.random.categorical(logits * (1 / temp))
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memory = model.encode(inputs)
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cache = None
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y = decoder_inputs
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while True:
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logits, cache = model.decode(y[None], memory, cache=cache)
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y = sample(logits[:, -1, :])
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yield y.squeeze()
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def load_model(model_config):
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model = T5(model_config)
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weights = mx.load("weights.npz")
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current_weights = tree_flatten(model.parameters())
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weights_to_load = list(weights.items())
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current_weights_dict = dict(current_weights)
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current_weights_keys = set(current_weights_dict.keys())
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weights_to_load_dict = dict(weights_to_load)
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weights_to_load_keys = set(weights_to_load_dict.keys())
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print("Missing weights: ", sorted(current_weights_keys - weights_to_load_keys))
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print()
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print("Weights ignored: ", sorted(weights_to_load_keys - current_weights_keys))
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for key in current_weights_keys & weights_to_load_keys:
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if weights_to_load_dict[key].shape != current_weights_dict[key].shape:
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print("Shape mismatch for key: ", key)
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print("Expected shape: ", current_weights_dict[key].shape)
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print("Loading shape: ", weights_to_load_dict[key].shape)
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model.update(tree_unflatten(weights_to_load))
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mx.eval(model.parameters())
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tokenizer = AutoTokenizer.from_pretrained("t5-small", trust_remote_code=True)
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return model, tokenizer
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="T5 Inference script")
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parser.add_argument(
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"--prompt",
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help="",
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default="translate English to German: That is good.",
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)
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parser.add_argument(
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"--encode-only",
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action="store_true",
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default=False,
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help="Whether to decode or not",
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)
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parser.add_argument(
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"--max_tokens",
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"-m",
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type=int,
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default=100,
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help="Maximum number of tokens to generate",
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)
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parser.add_argument(
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"--temp",
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help="The sampling temperature.",
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type=float,
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default=0.0,
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)
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parser.add_argument("--seed", type=int, default=0, help="The PRNG seed")
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args = parser.parse_args()
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mx.random.seed(args.seed)
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config = ModelArgs()
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model, tokenizer = load_model(config)
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prompt = tokenizer(
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args.prompt,
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return_tensors="np",
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return_attention_mask=False,
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)["input_ids"]
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prompt = mx.array(prompt)
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if args.encode_only:
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print("[INFO] Encoding with T5...", flush=True)
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print(args.prompt, flush=True)
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embeddings = model.wte(prompt)
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encoder_output = model.encoder(embeddings, mask=None)
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print(encoder_output, flush=True)
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exit(0)
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print("[INFO] Generating with T5...", flush=True)
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print("Input: ", args.prompt, flush=True)
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decoder_inputs = mx.array([config.decoder_start_token_id])
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start = perf_counter_ns()
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tokens = []
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for token, n_tokens in zip(
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generate(prompt, decoder_inputs, model, args.temp), range(args.max_tokens)
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):
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if token.item() == tokenizer.eos_token_id:
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break
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tokens.append(token.item())
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# For some reason using the following line doesn't give spaces
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# print(tokenizer.decode(token.item(), clean_up_tokenization_spaces=False), end="", flush=True)
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print(tokenizer.decode(tokens), end="", flush=True)
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end = perf_counter_ns()
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elapsed = (end - start) / 1.0e9
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print()
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print(f"Time: {elapsed:.2f} seconds, tokens/s: {n_tokens / elapsed:.2f}")
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