mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-08-30 02:53:41 +08:00
435 lines
15 KiB
Python
435 lines
15 KiB
Python
import argparse
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from dataclasses import dataclass
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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.min(relative_position, mx.zeros_like(relative_position))
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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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relative_position_if_large = max_exact + (
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mx.log(relative_position.astype(mx.float32) / max_exact)
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/ np.log(max_distance / max_exact)
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* (num_buckets - max_exact)
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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, is_decoder: bool = False):
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self.bidirectional = not is_decoder
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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, key_length):
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"""Compute binned relative position bias"""
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context_position = mx.arange(query_length, dtype=mx.int32)[:, None]
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memory_position = mx.arange(key_length, dtype=mx.int32)[None, :]
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relative_position = (
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memory_position - context_position
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) # shape (query_length, key_length)
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relative_position_bucket = _relative_position_bucket(
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relative_position, # shape (query_length, key_length)
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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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values = self.embeddings(
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relative_position_bucket
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) # shape (query_length, key_length, num_heads)
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values = mx.expand_dims(
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values.transpose(2, 0, 1), 0
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) # shape (1, num_heads, query_length, key_length)
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return values
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class MultiHeadAttention(nn.Module):
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def __init__(self, config: ModelArgs, has_relative_attention_bias: bool = False):
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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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self.has_relative_attention_bias = has_relative_attention_bias
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if has_relative_attention_bias:
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self.relative_attention_bias = RelativePositionBias(config)
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def __call__(self, queries, keys, values, mask=None, position_bias=None):
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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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# 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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if self.has_relative_attention_bias:
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position_bias = self.relative_attention_bias(L, S)
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if position_bias is not None:
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scores += position_bias
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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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out = self.out_proj(values_hat)
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return out, position_bias
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@staticmethod
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def create_additive_causal_mask(N: int, dtype: mx.Dtype = mx.float32):
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indices = mx.arange(N)
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mask = indices[:, None] < indices[None]
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# usually inf but 1e9 is as good and softmax(full(1e9)) != nan
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# TODO: Should replace this with finfo(dtype).min
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mask = mask.astype(dtype) * -1e9
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return mask
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class LayerNorm(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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self.dims = dims
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def __call__(self, x):
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var = x.var(axis=-1, keepdims=True)
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x = x * mx.rsqrt(var + self.eps)
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return x * self.weight
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class TransformerEncoderLayer(nn.Module):
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def __init__(self, config: ModelArgs, has_relative_attention_bias: bool = False):
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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(
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config, has_relative_attention_bias=has_relative_attention_bias
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)
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self.ln1 = LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
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self.ln2 = LayerNorm(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, position_bias=None):
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y = self.ln1(x)
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y, position_bias = self.attention(
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queries=y, keys=y, values=y, mask=mask, position_bias=position_bias
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)
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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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x = x + y
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return x, position_bias
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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, has_relative_attention_bias=i == 0)
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for i in range(config.num_layers)
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]
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self.ln = LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
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def __call__(self, x, mask):
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position_bias = None
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for layer in self.layers:
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x, position_bias = layer(x, mask, position_bias=position_bias)
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x = self.ln(x)
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return x
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class TransformerDecoderLayer(nn.Module):
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def __init__(self, config: ModelArgs, has_relative_attention_bias: bool = False):
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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(
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config, has_relative_attention_bias=has_relative_attention_bias
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)
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self.cross_attention = MultiHeadAttention(config)
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self.ln1 = LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
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self.ln2 = LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
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self.ln3 = LayerNorm(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, memory, x_mask, memory_mask, position_bias=None):
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y = self.ln1(x)
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y, position_bias = self.self_attention(y, y, y, x_mask, position_bias=position_bias)
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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, position_bias
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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, has_relative_attention_bias=i == 0)
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for i in range(config.num_layers)
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]
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self.ln = LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
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def __call__(self, x, memory, x_mask, memory_mask):
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position_bias = None
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for layer in self.layers:
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x, position_bias = layer(
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x, memory, x_mask, memory_mask, position_bias=position_bias
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)
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x = self.ln(x)
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return x
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class OutputHead(nn.Module):
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def __init__(self, config: ModelArgs) -> None:
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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 __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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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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x = self.wte(inputs)
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y = self.encoder(x, mask=None) # , cache)
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decoder_inputs = self.wte(decoder_inputs)
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decoder_n_tokens = decoder_inputs.shape[1]
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if decoder_n_tokens > 1 and mask is None:
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mask = MultiHeadAttention.create_additive_causal_mask(decoder_n_tokens)
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mask = mask.astype(x.dtype)
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y = self.decoder(
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x=decoder_inputs, x_mask=mask, memory=y, memory_mask=None
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) # , cache)
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return self.lm_head(y), cache
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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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while True:
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# TODO: add cache
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logits, _ = model(inputs, decoder_inputs)
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y = mx.expand_dims(sample(logits[:, -1, :]), 0)
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decoder_inputs = mx.concatenate([decoder_inputs, y], axis=1)
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yield y
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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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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]]).astype(mx.uint32)
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start = perf_counter_ns()
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n_tokens = 0
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tokens = []
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for token, _ in zip(
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generate(prompt, decoder_inputs, model, args.temp),
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range(args.max_tokens)
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):
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tokens.append(token)
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if (len(tokens) % 10) == 0:
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mx.eval(tokens)
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eos_index = next(
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(i for i, t in enumerate(tokens) if t.item() == tokenizer.eos_token_id),
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None,
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)
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if eos_index is not None:
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tokens = tokens[:eos_index]
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s = tokenizer.decode([t.item() for t in tokens])
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print(s, end="", flush=True)
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n_tokens += len(tokens)
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tokens = []
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if eos_index is not None:
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break
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end = perf_counter_ns()
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mx.eval(tokens)
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s = tokenizer.decode([t.item() for t in tokens])
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n_tokens += len(tokens)
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elapsed = (end - start) / 1.0e9
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print(s, flush=True)
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print(f"Time: {elapsed:.2f} seconds, tokens/s: {n_tokens / elapsed:.2f}") |