import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple, Union import json import tempfile from functools import partial import numpy as np import jax import jax.numpy as jnp from jax import lax from jax.sharding import PartitionSpec as PS import flax.linen as nn from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.linen import combine_masks, make_causal_mask from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from flax.linen import partitioning as nn_partitioning import einops import sentencepiece as spm from transformers import AutoTokenizer from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging from transformers.tokenization_utils import PreTrainedTokenizer from transformers.modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutput from transformers.modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging from ml_collections import ConfigDict from ml_collections.config_dict import config_dict from mlxu import function_args_to_config, load_pickle, open_file from EasyLM.bpt import blockwise_ffn, blockwise_attn from EasyLM.jax_utils import ( with_sharding_constraint, get_jax_mesh, get_gradient_checkpoint_policy ) LLAMA_STANDARD_CONFIGS = { 'small': { 'vocab_size': 64256, 'hidden_size': 768, 'intermediate_size': 3072, 'num_hidden_layers': 12, 'num_attention_heads': 12, 'max_sequence_length': 2048, 'initializer_range': 0.02, 'rms_norm_eps': 1e-6, 'use_cache': True, 'tie_word_embeddings': False, }, 'medium': { 'vocab_size': 64256, 'hidden_size': 1024, 'intermediate_size': 4096, 'num_hidden_layers': 24, 'num_attention_heads': 16, 'max_sequence_length': 2048, 'initializer_range': 0.02, 'rms_norm_eps': 1e-6, 'use_cache': True, 'tie_word_embeddings': False, }, 'large': { 'vocab_size': 64256, 'hidden_size': 1536, 'intermediate_size': 6144, 'num_hidden_layers': 24, 'num_attention_heads': 16, 'max_sequence_length': 2048, 'initializer_range': 0.02, 'rms_norm_eps': 1e-6, 'use_cache': True, 'tie_word_embeddings': False, }, 'xlarge': { 'vocab_size': 64256, 'hidden_size': 2048, 'intermediate_size': 8192, 'num_hidden_layers': 24, 'num_attention_heads': 32, 'max_sequence_length': 2048, 'initializer_range': 0.02, 'rms_norm_eps': 1e-6, 'use_cache': True, 'tie_word_embeddings': False, }, '1b': { 'vocab_size': 64256, 'hidden_size': 2048, 'intermediate_size': 5504, 'num_hidden_layers': 22, 'num_attention_heads': 16, 'max_sequence_length': 2048, 'initializer_range': 0.02, 'rms_norm_eps': 1e-6, 'use_cache': True, 'tie_word_embeddings': False, }, '3b': { 'vocab_size': 64256, 'hidden_size': 3200, 'intermediate_size': 8640, 'num_hidden_layers': 26, 'num_attention_heads': 32, 'max_sequence_length': 2048, 'initializer_range': 0.02, 'rms_norm_eps': 1e-6, 'use_cache': True, 'tie_word_embeddings': False, }, '7b': { 'vocab_size': 64256, 'hidden_size': 4096, 'intermediate_size': 11008, 'num_hidden_layers': 32, 'num_attention_heads': 32, 'max_sequence_length': 2048, 'initializer_range': 0.02, 'rms_norm_eps': 1e-6, 'use_cache': True, 'tie_word_embeddings': False, }, '13b': { 'vocab_size': 64256, 'hidden_size': 5120, 'intermediate_size': 13824, 'num_hidden_layers': 40, 'num_attention_heads': 40, 'max_sequence_length': 2048, 'initializer_range': 0.02, 'rms_norm_eps': 1e-6, 'use_cache': True, 'tie_word_embeddings': False, }, '30b': { 'vocab_size': 64256, 'hidden_size': 6656, 'intermediate_size': 17920, 'num_hidden_layers': 60, 'num_attention_heads': 52, 'max_sequence_length': 2048, 'initializer_range': 0.02, 'rms_norm_eps': 1e-6, 'use_cache': True, 'tie_word_embeddings': False, }, '65b': { 'vocab_size': 64256, 'hidden_size': 8192, 'intermediate_size': 22016, 'num_hidden_layers': 80, 'num_attention_heads': 64, 'max_sequence_length': 2048, 'initializer_range': 0.02, 'rms_norm_eps': 1e-5, 'use_cache': True, 'tie_word_embeddings': False, }, 'debug': { # A small model for debugging 'vocab_size': 64256, 'hidden_size': 128, 'intermediate_size': 256, 'num_hidden_layers': 2, 'num_attention_heads': 4, 'max_sequence_length': 2048, 'initializer_range': 0.02, 'rms_norm_eps': 1e-6, 'use_cache': True, 'tie_word_embeddings': False, }, } class LLaMAConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`~LLaMAModel`]. It is used to instantiate an LLaMA model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the LLaMA-7B. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 32000): Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`~LLaMAModel`] or [`~TFLLaMAModel`]. hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 11008): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_sequence_length (`int`, *optional*, defaults to 2048): Max sequence length for model (for RoPE computation) initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. tie_word_embeddings(`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings Example: ```python >>> from transformers import LLaMAModel, LLaMAConfig >>> # Initializing a LLaMA llama-7b style configuration >>> configuration = LLaMAConfig() >>> # Initializing a model from the llama-7b style configuration >>> model = LLaMAModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "llama" def __init__( self, vocab_size=32000, hidden_size=4096, intermediate_size=11008, num_hidden_layers=32, num_attention_heads=32, max_sequence_length=2048, rms_norm_eps=1e-6, initializer_range=0.02, use_cache=True, # pad_token_id=-1, bos_token_id=0, eos_token_id=1, resid_pdrop=0.0, embd_pdrop=0.0, attn_pdrop=0.0, tie_word_embeddings=False, remat_block='nothing_saveable', remat_attention='', remat_mlp='', scan_attention=False, scan_mlp=False, scan_query_chunk_size=1024, scan_key_chunk_size=1024, scan_mlp_chunk_size=1024, fcm_min_ratio=0.0, fcm_max_ratio=0.0, **kwargs, ): self.vocab_size = vocab_size self.hidden_size = hidden_size self.initializer_range = initializer_range self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.max_sequence_length = max_sequence_length self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.resid_pdrop = resid_pdrop self.embd_pdrop = embd_pdrop self.attn_pdrop = attn_pdrop self.remat_block = remat_block self.remat_attention = remat_attention self.remat_mlp = remat_mlp self.scan_attention = scan_attention self.scan_mlp = scan_mlp self.scan_query_chunk_size = scan_query_chunk_size self.scan_key_chunk_size = scan_key_chunk_size self.scan_mlp_chunk_size = scan_mlp_chunk_size self.fcm_min_ratio = fcm_min_ratio self.fcm_max_ratio = fcm_max_ratio super().__init__( # pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, ) @classmethod def get_default_config(cls, updates=None): config = function_args_to_config(cls.__init__) if updates is not None: config.update(ConfigDict(updates).copy_and_resolve_references()) return config @staticmethod def get_jax_mesh(axis_dims): return get_jax_mesh(axis_dims, ('dp', 'fsdp', 'mp')) @staticmethod def get_partition_rules(): """ Parition rules for GPTJ. Note that these rules are orderd, so that the beginning rules match first. It is important to use PartitionSpec() instead of None here because JAX does not treat None as a pytree leaf. """ return ( # embeddings ("transformer/wte/embedding", PS("mp", "fsdp")), # atention ("attention/(wq|wk|wv)/kernel", PS("fsdp", "mp")), ("attention/wo/kernel", PS("mp", "fsdp")), # mlp ("feed_forward/w1/kernel", PS("fsdp", "mp")), ("feed_forward/w2/kernel", PS("mp", "fsdp")), ("feed_forward/w3/kernel", PS("fsdp", "mp")), # layer norms ("attention_norm/kernel", PS(None)), ("ffn_norm/kernel", PS(None)), # output head ("transformer/ln_f/kernel", PS(None)), ("lm_head/kernel", PS("fsdp", "mp")), ('.*', PS(None)), ) @staticmethod def get_weight_decay_exclusions(): return ( "attention_norm/kernel", "ffn_norm/kernel", "transformer/ln_f/kernel", ) @staticmethod def rng_keys(): return ('params', 'dropout', 'fcm') @staticmethod def get_tokenizer_config(updates=None): config = ConfigDict() config.vocab_file = '' config.pretrained_model_name_or_path = '' config.add_bos_token = False config.add_eos_token = False if updates is not None: config.update(ConfigDict(updates).copy_and_resolve_references()) return config @classmethod def get_tokenizer(cls, config, padding_side='left', truncation_side='right'): config = cls.get_tokenizer_config(config) if config.vocab_file == '': assert config.pretrained_model_name_or_path != '', 'vocab_file or pretrained_model_name_or_path must be specified' if config.pretrained_model_name_or_path != '': tokenizer = AutoTokenizer.from_pretrained( config.pretrained_model_name_or_path, add_bos_token=config.add_bos_token, add_eos_token=config.add_eos_token, padding_side=padding_side, truncation_side=truncation_side, ) else: tokenizer = LLaMATokenizer( vocab_file=config.vocab_file, add_bos_token=config.add_bos_token, add_eos_token=config.add_eos_token, padding_side=padding_side, truncation_side=truncation_side, ) return tokenizer @classmethod def load_config(cls, path): if path in LLAMA_STANDARD_CONFIGS: return cls.from_dict(LLAMA_STANDARD_CONFIGS[path]) load_type, load_path = path.split('::', 1) if load_type == 'pickle': return cls.from_dict(load_pickle(load_path)['llama_config']) elif load_type == 'json': with open_file(load_path, 'r') as fin: raw_config = fin.read() return cls.from_dict(json.loads(raw_config)) else: raise ValueError(f'Unsupported load config type: {load_type}') remat = nn_partitioning.remat logger = logging.get_logger(__name__) class RMSNorm(nn.Module): dim: int eps: float=1e-6 dtype: jnp.dtype=jnp.float32 param_dtype: jnp.dtype=jnp.float32 def setup(self) -> None: self.weight = self.param( 'kernel', nn.initializers.ones, (self.dim,), self.param_dtype, ) def _norm(self, x: jnp.ndarray) -> jnp.ndarray: return x * jax.lax.rsqrt(jnp.square(x).mean(-1, keepdims=True) + self.eps) def __call__(self, x: jnp.ndarray) -> jnp.ndarray: x = x.astype(jnp.promote_types(self.dtype, jnp.float32)) output = self._norm(x).astype(self.dtype) weight = jnp.asarray(self.weight, self.dtype) return output * weight def precompute_freqs_cis(dim: int, end: int, theta: float=10000.0, dtype: jnp.dtype=jnp.float32) -> jnp.ndarray: freqs = 1.0 / (theta ** (np.arange(0, dim, 2)[: (dim // 2)].astype(dtype) / dim)) t = np.arange(end) # type: ignore freqs = np.outer(t, freqs).astype(dtype) # type: ignore sin, cos = np.sin(freqs), np.cos(freqs) freqs_cis = np.complex64(cos + 1j * sin) return jnp.asarray(freqs_cis) def apply_rotary_emb( xq: jnp.ndarray, xk: jnp.ndarray, freqs_cis: jnp.ndarray, dtype: jnp.dtype=jnp.float32, ) -> Tuple[jnp.ndarray, jnp.ndarray]: reshape_xq = xq.astype(jnp.float32).reshape(*xq.shape[:-1], -1, 2) reshape_xk = xk.astype(jnp.float32).reshape(*xk.shape[:-1], -1, 2) xq_ = jax.lax.complex(reshape_xq[..., 0], reshape_xq[..., 1]) xk_ = jax.lax.complex(reshape_xk[..., 0], reshape_xk[..., 1]) # add head dim freqs_cis = jnp.reshape(freqs_cis, (*freqs_cis.shape[:2], 1, *freqs_cis.shape[2:])) xq_out = xq_ * freqs_cis xq_out = jnp.stack((jnp.real(xq_out), jnp.imag(xq_out)), axis=-1).reshape(*xq_out.shape[:-1], -1) xk_out = xk_ * freqs_cis xk_out = jnp.stack((jnp.real(xk_out), jnp.imag(xk_out)), axis=-1).reshape(*xk_out.shape[:-1], -1) return xq_out.astype(dtype), xk_out.astype(dtype) class FlaxLLaMAAttention(nn.Module): config: LLaMAConfig dtype: jnp.dtype=jnp.float32 param_dtype: jnp.dtype=jnp.float32 precision: Optional[Union[jax.lax.Precision, str]]=None def setup(self): config = self.config self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.embed_dim // self.num_heads self.wq = nn.Dense( config.num_attention_heads*self.head_dim, dtype=self.dtype, param_dtype=self.param_dtype, use_bias=False, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), precision=self.precision, ) self.wk = nn.Dense( config.num_attention_heads*self.head_dim, dtype=self.dtype, param_dtype=self.param_dtype, use_bias=False, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), precision=self.precision, ) self.wv = nn.Dense( config.num_attention_heads*self.head_dim, dtype=self.dtype, param_dtype=self.param_dtype, use_bias=False, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), precision=self.precision, ) self.wo = nn.Dense( config.hidden_size, dtype=self.dtype, param_dtype=self.param_dtype, use_bias=False, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), precision=self.precision, ) self.resid_dropout = nn.Dropout(rate=config.resid_pdrop) self.causal_mask = make_causal_mask(jnp.ones((1, config.max_sequence_length), dtype="bool"), dtype="bool") self.freqs_cis = precompute_freqs_cis( self.head_dim, config.max_sequence_length * 2, dtype=self.dtype, ) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) @nn.compact def _concatenate_to_cache(self, key, value, query, attention_mask): """ This function takes projected key, value states from a single input token and concatenates the states to cached states from previous steps. This function is slighly adapted from the official Flax repository: https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252 """ # detect if we're initializing by absence of existing cache data. is_initialized = self.has_variable("cache", "cached_key") cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype) cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype) cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) if is_initialized: *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape # update key, value caches with our new 1d spatial slices cur_index = cache_index.value indices = (0,) * len(batch_dims) + (cur_index, 0, 0) key = lax.dynamic_update_slice(cached_key.value, key, indices) value = lax.dynamic_update_slice(cached_value.value, value, indices) cached_key.value = key cached_value.value = value num_updated_cache_vectors = query.shape[1] cache_index.value = cache_index.value + num_updated_cache_vectors # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements. pad_mask = jnp.broadcast_to( jnp.arange(max_length) < cur_index + num_updated_cache_vectors, tuple(batch_dims) + (1, num_updated_cache_vectors, max_length), ) attention_mask = combine_masks(pad_mask, attention_mask) return key, value, attention_mask def __call__( self, hidden_states, attention_mask, position_ids, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, fcm_mask=None, ): xq, xk, xv = self.wq(hidden_states), self.wk(hidden_states), self.wv(hidden_states) xq = with_sharding_constraint(xq, PS(("dp", "fsdp"), None, "mp")) xk = with_sharding_constraint(xk, PS(("dp", "fsdp"), None, "mp")) xv = with_sharding_constraint(xv, PS(("dp", "fsdp"), None, "mp")) xq = self._split_heads(xq) xk = self._split_heads(xk) xv = self._split_heads(xv) freqs_cis = jnp.take(self.freqs_cis, position_ids, axis=0) xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis, dtype=self.dtype) dropout_rng = None if not deterministic and self.config.attn_pdrop > 0.0: dropout_rng = self.make_rng("dropout") if self.config.scan_attention and not (self.has_variable("cache", "cached_key") or init_cache): # doesn't need blockwise attention if we are doing autoregressive decoding since no quadratic memory # attention mask without nxn materlization, blockwise_attn will handle the rest attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) # transform boolean mask into float mask attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype), ) attn_weights = None attn_output = blockwise_attn( xq, xk, xv, bias=attention_bias, deterministic=deterministic, dropout_rng=dropout_rng, attn_pdrop=self.config.attn_pdrop, causal=True, query_chunk_size=self.config.scan_query_chunk_size, key_chunk_size=self.config.scan_key_chunk_size, dtype=self.dtype, policy=get_gradient_checkpoint_policy('nothing_saveable'), precision=self.precision, float32_logits=True, prevent_cse=True, ) attn_output = with_sharding_constraint(attn_output, PS(("dp", "fsdp"), None, "mp", None)) else: query_length, key_length = xq.shape[1], xk.shape[1] if self.has_variable("cache", "cached_key"): mask_shift = self.variables["cache"]["cache_index"] max_decoder_length = self.variables["cache"]["cached_key"].shape[1] causal_mask = lax.dynamic_slice( self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length) ) else: causal_mask = self.causal_mask[:, :, :query_length, :key_length] batch_size = hidden_states.shape[0] causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:]) attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape) attention_mask = combine_masks(attention_mask, causal_mask, fcm_mask) # During fast autoregressive decoding, we feed one position at a time, # and cache the keys and values step by step. if self.has_variable("cache", "cached_key") or init_cache: xk, xv, attention_mask = self._concatenate_to_cache(xk, xv, xq, attention_mask) # transform boolean mask into float mask attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype), ) attn_weights = dot_product_attention_weights( xq, xk, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.config.attn_pdrop, deterministic=deterministic, dtype=jnp.promote_types(self.dtype, jnp.float32), precision=self.precision, ) attn_weights = with_sharding_constraint(attn_weights, PS(("dp", "fsdp"), "mp", None, None)) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, xv, precision=self.precision) attn_output = self._merge_heads(attn_output) attn_output = self.wo(attn_output) attn_output = self.resid_dropout(attn_output, deterministic=deterministic) outputs = (attn_output, attn_weights) if output_attentions else (attn_output,) return outputs class FlaxLLaMAMLP(nn.Module): config: LLaMAConfig dtype: jnp.dtype=jnp.float32 param_dtype: jnp.dtype=jnp.float32 precision: Optional[Union[jax.lax.Precision, str]]=None def setup(self) -> None: config = self.config self.w1 = nn.Dense( config.intermediate_size, dtype=self.dtype, param_dtype=self.param_dtype, use_bias=False, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), precision=self.precision, ) self.w2 = nn.Dense( config.hidden_size, dtype=self.dtype, param_dtype=self.param_dtype, use_bias=False, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), precision=self.precision, ) self.w3 = nn.Dense( config.intermediate_size, dtype=self.dtype, param_dtype=self.param_dtype, use_bias=False, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), precision=self.precision, ) self.dropout = nn.Dropout(rate=self.config.resid_pdrop) def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray: x = self.w2(nn.silu(self.w1(x)) * self.w3(x)) x = self.dropout(x, deterministic=deterministic) return x class FlaxLLaMABlock(nn.Module): config: LLaMAConfig dtype: jnp.dtype=jnp.float32 param_dtype: jnp.dtype=jnp.float32 precision: Optional[Union[jax.lax.Precision, str]]=None def setup(self) -> None: attention_module = FlaxLLaMAAttention mlp_module = FlaxLLaMAMLP if self.config.remat_attention != '': attention_module = remat( FlaxLLaMAAttention, static_argnums=(3, 4, 5), policy=get_gradient_checkpoint_policy(self.config.remat_attention), prevent_cse=True, ) if self.config.remat_mlp != '': mlp_module = remat( FlaxLLaMAMLP, static_argnums=(1,), policy=get_gradient_checkpoint_policy(self.config.remat_mlp), prevent_cse=True, ) self.attention = attention_module( self.config, dtype=self.dtype, param_dtype=self.param_dtype, precision=self.precision, ) self.feed_forward = mlp_module( self.config, dtype=self.dtype, param_dtype=self.param_dtype, precision=self.precision, ) self.attention_norm = RMSNorm( self.config.hidden_size, eps=self.config.rms_norm_eps, dtype=self.dtype, param_dtype=self.param_dtype, ) self.ffn_norm = RMSNorm( self.config.hidden_size, eps=self.config.rms_norm_eps, dtype=self.dtype, param_dtype=self.param_dtype, ) def __call__( self, hidden_states, attention_mask=None, position_ids=None, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, fcm_mask: Optional[jnp.ndarray] = None, ): attn_outputs = self.attention( self.attention_norm(hidden_states), attention_mask, position_ids, deterministic, init_cache, output_attentions, fcm_mask, ) attn_output = attn_outputs[0] hidden_states = hidden_states + attn_output feed_forward_input = self.ffn_norm(hidden_states) if self.config.scan_mlp: feed_forward_hidden_states = blockwise_ffn( self.feed_forward, feed_forward_input, self.config.scan_mlp_chunk_size, deterministic, ) else: feed_forward_hidden_states = self.feed_forward( feed_forward_input, deterministic, ) feed_forward_hidden_states = with_sharding_constraint(feed_forward_hidden_states, PS(("dp", "fsdp"), None, "mp")) hidden_states = hidden_states + feed_forward_hidden_states return (hidden_states,) + attn_outputs[1:] class FlaxLLaMAPreTrainedModel(FlaxPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = LLaMAConfig base_model_prefix = "transformer" module_class: nn.Module = None def __init__( self, config: LLaMAConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs, ): module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_ids = jnp.zeros(input_shape, dtype="i4") attention_mask = jnp.ones_like(input_ids) position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} if self.config.add_cross_attention: encoder_hidden_states = jnp.zeros(input_shape + (self.config.hidden_size,)) encoder_attention_mask = attention_mask module_init_outputs = self.module.init( rngs, input_ids, attention_mask, position_ids, encoder_hidden_states, encoder_attention_mask, return_dict=False, ) else: module_init_outputs = self.module.init(rngs, input_ids, attention_mask, position_ids, return_dict=False) random_params = module_init_outputs["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def init_cache(self, batch_size, max_length): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. """ # init input variables to retrieve cache input_ids = jnp.ones((batch_size, max_length)) attention_mask = jnp.ones_like(input_ids) position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) init_variables = self.module.init( jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True ) return init_variables["cache"] @add_start_docstrings_to_model_forward("") def __call__( self, input_ids, attention_mask=None, position_ids=None, params: dict = None, past_key_values: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict batch_size, sequence_length = input_ids.shape if position_ids is None: if past_key_values is not None: raise ValueError("Make sure to provide `position_ids` when passing `past_key_values`.") position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) if attention_mask is None: attention_mask = jnp.ones((batch_size, sequence_length)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be changed by FlaxGPTJAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False outputs = self.module.apply( inputs, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), jnp.array(position_ids, dtype="i4"), not train, False, output_attentions, output_hidden_states, return_dict, rngs=rngs, mutable=mutable, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past_key_values = outputs outputs["past_key_values"] = unfreeze(past_key_values["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past_key_values = outputs outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:] return outputs class FlaxLLaMABlockCollection(nn.Module): config: LLaMAConfig dtype: jnp.dtype = jnp.float32 param_dtype: jnp.dtype=jnp.float32 precision: Optional[Union[jax.lax.Precision, str]]=None def setup(self): block = FlaxLLaMABlock if self.config.remat_block != '': block = remat( FlaxLLaMABlock, static_argnums=(3, 4, 5), policy=get_gradient_checkpoint_policy(self.config.remat_block) ) self.blocks = [ block( self.config, name=str(i), dtype=self.dtype, param_dtype=self.param_dtype, precision=self.precision ) for i in range(self.config.num_hidden_layers) ] def __call__( self, hidden_states, attention_mask=None, position_ids=None, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None if not deterministic and self.config.fcm_max_ratio > 0: # Apply forgetful causal mask batch_size, seq_length = hidden_states.shape[0], hidden_states.shape[1] fcm_ratio = jax.random.uniform( self.make_rng('fcm'), shape=(batch_size, 1, 1, 1), minval=self.config.fcm_min_ratio, maxval=self.config.fcm_max_ratio ) fcm_mask = jax.random.uniform( self.make_rng('fcm'), shape=(batch_size, 1, 1, seq_length) ) > fcm_ratio fcm_mask = fcm_mask.at[:, :, :, 0].set(True) fcm_mask = fcm_mask.astype('bool') else: fcm_mask = None for block in self.blocks: if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = block( hidden_states, attention_mask, position_ids, deterministic, init_cache, output_attentions, fcm_mask, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions += (layer_outputs[1],) # this contains possible `None` values - `FlaxGPTJModule` will filter them out outputs = (hidden_states, all_hidden_states, all_attentions) return outputs class FlaxLLaMAModule(nn.Module): config: LLaMAConfig dtype: jnp.dtype = jnp.float32 param_dtype: jnp.dtype=jnp.float32 precision: Optional[Union[jax.lax.Precision, str]]=None def setup(self): self.embed_dim = self.config.hidden_size self.wte = nn.Embed( self.config.vocab_size, self.config.hidden_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), dtype=self.dtype, param_dtype=self.param_dtype, ) self.dropout = nn.Dropout(rate=self.config.embd_pdrop) self.h = FlaxLLaMABlockCollection(self.config, dtype=self.dtype, param_dtype=self.param_dtype, precision=self.precision) self.ln_f = RMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps, dtype=self.dtype, param_dtype=self.param_dtype) def __call__( self, input_ids, attention_mask, position_ids, deterministic=True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): input_embeds = self.wte(input_ids.astype("i4")) hidden_states = self.dropout(input_embeds, deterministic=deterministic) outputs = self.h( hidden_states, attention_mask, position_ids=position_ids, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] hidden_states = self.ln_f(hidden_states) if output_hidden_states: all_hidden_states = outputs[1] + (hidden_states,) outputs = (hidden_states, all_hidden_states) + outputs[2:] else: outputs = (hidden_states,) + outputs[1:] if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=hidden_states, hidden_states=outputs[1], attentions=outputs[-1], ) @add_start_docstrings("", "") class FlaxLLaMAModel(FlaxLLaMAPreTrainedModel): module_class = FlaxLLaMAModule # append_call_sample_docstring( # FlaxLLaMAModel, # _TOKENIZER_FOR_DOC, # _CHECKPOINT_FOR_DOC, # FlaxCausalLMOutput, # _CONFIG_FOR_DOC, # ) class FlaxLLaMAForCausalLMModule(nn.Module): config: LLaMAConfig dtype: jnp.dtype = jnp.float32 param_dtype: jnp.dtype=jnp.float32 precision: Optional[Union[jax.lax.Precision, str]]=None def setup(self): self.transformer = FlaxLLaMAModule(self.config, dtype=self.dtype) self.lm_head = nn.Dense( self.config.vocab_size, dtype=self.dtype, param_dtype=self.param_dtype, use_bias=False, kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), precision=self.precision, ) def __call__( self, input_ids, attention_mask=None, position_ids=None, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): batch_size, seq_length = input_ids.shape if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if position_ids is None: position_ids = jnp.broadcast_to( jnp.clip(jnp.cumsum(attention_mask, axis=-1) - 1, a_min=0), (batch_size, seq_length) ) outputs = self.transformer( input_ids, attention_mask, position_ids, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_kernel = self.transformer.variables["params"]["wte"]["embedding"].T lm_logits = self.lm_head.apply({"params": {"kernel": shared_kernel}}, hidden_states) else: lm_logits = self.lm_head(hidden_states) if not return_dict: return (lm_logits,) + outputs[1:] return FlaxCausalLMOutput(logits=lm_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions) @add_start_docstrings("", "") class FlaxLLaMAForCausalLM(FlaxLLaMAPreTrainedModel): module_class = FlaxLLaMAForCausalLMModule def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None): # initializing the cache batch_size, seq_length = input_ids.shape past_key_values = self.init_cache(batch_size, max_length) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since GPTJ uses a causal mask, those positions are masked anyways. # Thus we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if attention_mask is not None: position_ids = attention_mask.cumsum(axis=-1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0)) else: position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)) return { "past_key_values": past_key_values, "attention_mask": extended_attention_mask, "position_ids": position_ids, } def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1 return model_kwargs # append_call_sample_docstring( # FlaxGPTJForCausalLM, # _TOKENIZER_FOR_DOC, # _CHECKPOINT_FOR_DOC, # FlaxCausalLMOutput, # _CONFIG_FOR_DOC, # ) VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"} PRETRAINED_VOCAB_FILES_MAP = {} class LLaMATokenizer(PreTrainedTokenizer): """ Construct a LLaMA tokenizer. Based on byte-level Byte-Pair-Encoding. Args: vocab_file (`str`): Path to the vocabulary file. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, unk_token="", bos_token="", eos_token="", sp_model_kwargs: Optional[Dict[str, Any]] = None, add_bos_token=False, add_eos_token=False, **kwargs, ): self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs) self.vocab_file = vocab_file self.add_bos_token = add_bos_token self.add_eos_token = add_eos_token self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) with tempfile.NamedTemporaryFile() as tfile: with open_file(self.vocab_file, 'rb') as fin: tfile.write(fin.read()) tfile.flush() tfile.seek(0) self.sp_model.Load(tfile.name) """ Initialisation""" self.add_special_tokens(dict( unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, )) self.pad_token_id = self.unk_token_id @property def vocab_size(self): """Returns vocab size""" return self.sp_model.get_piece_size() @property def bos_token_id(self) -> Optional[int]: return self.sp_model.bos_id() @property def eos_token_id(self) -> Optional[int]: return self.sp_model.eos_id() def get_vocab(self): """Returns vocab as a dict""" vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab def _tokenize(self, text): """Returns a tokenized string.""" return self.sp_model.encode(text, out_type=str) def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" return self.sp_model.piece_to_id(token) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" token = self.sp_model.IdToPiece(index) return token def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" current_sub_tokens = [] out_string = "" prev_is_special = False for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: if not prev_is_special: out_string += " " out_string += self.sp_model.decode(current_sub_tokens) + token prev_is_special = True current_sub_tokens = [] else: current_sub_tokens.append(token) prev_is_special = False out_string += self.sp_model.decode(current_sub_tokens) return out_string.strip() def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]: """ Save the vocabulary and special tokens file to a directory. Args: save_directory (`str`): The directory in which to save the vocabulary. Returns: `Tuple(str)`: Paths to the files saved. """ if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): copyfile(self.vocab_file, out_vocab_file) elif not os.path.isfile(self.vocab_file): with open(out_vocab_file, "wb") as fi: content_spiece_model = self.sp_model.serialized_model_proto() fi.write(content_spiece_model) return (out_vocab_file,) def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): if self.add_bos_token: bos_token_ids = [self.bos_token_id] else: bos_token_ids = [] output = bos_token_ids + token_ids_0 if token_ids_1 is not None: output = output + token_ids_1 if self.add_eos_token: output = output + [self.eos_token_id] return output def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ eos = [self.eos_token_id] if token_ids_1 is None: return len(token_ids_0 + eos) * [0] return len(token_ids_0 + eos + token_ids_1 + eos) * [0]