Remove python files, add info to readme, fix slow loading

README.md

@@ -11,6 +11,8 @@ inference: false
 
 # MPT-7B-StoryWriter-65k+
 
+Quantized for [KoboldAI (4bit-fork)](https://github.com/0cc4m/koboldAI)
+
 MPT-7B-StoryWriter-65k+ is a model designed to read and write fictional stories with super long context lengths.
 It was built by finetuning MPT-7B with a context length of 65k tokens on a filtered fiction subset of the [books3 dataset](https://huggingface.co/datasets/the_pile_books3).
 At inference time, thanks to [ALiBi](https://arxiv.org/abs/2108.12409), MPT-7B-StoryWriter-65k+ can extrapolate even beyond 65k tokens.

adapt_tokenizer.py

@@ -1,41 +0,0 @@
-from typing import Union
-from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
-Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
-NUM_SENTINEL_TOKENS: int = 100
-
-def adapt_tokenizer_for_denoising(tokenizer: Tokenizer):
-    """Adds sentinel tokens and padding token (if missing).
-
-    Expands the tokenizer vocabulary to include sentinel tokens
-    used in mixture-of-denoiser tasks as well as a padding token.
-
-    All added tokens are added as special tokens. No tokens are
-    added if sentinel tokens and padding token already exist.
-    """
-    sentinels_to_add = [f'<extra_id_{i}>' for i in range(NUM_SENTINEL_TOKENS)]
-    tokenizer.add_tokens(sentinels_to_add, special_tokens=True)
-    if tokenizer.pad_token is None:
-        tokenizer.add_tokens('<pad>', special_tokens=True)
-        tokenizer.pad_token = '<pad>'
-        assert tokenizer.pad_token_id is not None
-    sentinels = ''.join([f'<extra_id_{i}>' for i in range(NUM_SENTINEL_TOKENS)])
-    _sentinel_token_ids = tokenizer(sentinels, add_special_tokens=False).input_ids
-    tokenizer.sentinel_token_ids = _sentinel_token_ids
-
-class AutoTokenizerForMOD(AutoTokenizer):
-    """AutoTokenizer + Adaptation for MOD.
-
-    A simple wrapper around AutoTokenizer to make instantiating
-    an MOD-adapted tokenizer a bit easier.
-
-    MOD-adapted tokenizers have sentinel tokens (e.g., <extra_id_0>),
-    a padding token, and a property to get the token ids of the
-    sentinel tokens.
-    """
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        """See `AutoTokenizer.from_pretrained` docstring."""
-        tokenizer = super().from_pretrained(*args, **kwargs)
-        adapt_tokenizer_for_denoising(tokenizer)
-        return tokenizer

attention.py

@@ -1,276 +0,0 @@
-"""Attention layers."""
-import math
-import warnings
-from typing import Optional
-import torch
-import torch.nn as nn
-from einops import rearrange
-from torch import nn
-from .norm import LPLayerNorm
-
-def _reset_is_causal(num_query_tokens: int, num_key_tokens: int, original_is_causal: bool):
-    if original_is_causal and num_query_tokens != num_key_tokens:
-        if num_query_tokens != 1:
-            raise NotImplementedError('MPT does not support query and key with different number of tokens, unless number of query tokens is 1.')
-        else:
-            return False
-    return original_is_causal
-
-def scaled_multihead_dot_product_attention(query, key, value, n_heads, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False):
-    q = rearrange(query, 'b s (h d) -> b h s d', h=n_heads)
-    k = rearrange(key, 'b s (h d) -> b h d s', h=1 if multiquery else n_heads)
-    v = rearrange(value, 'b s (h d) -> b h s d', h=1 if multiquery else n_heads)
-    min_val = torch.finfo(q.dtype).min
-    (b, _, s_q, d) = q.shape
-    s_k = k.size(-1)
-    if softmax_scale is None:
-        softmax_scale = 1 / math.sqrt(d)
-    attn_weight = q.matmul(k) * softmax_scale
-    if attn_bias is not None:
-        if attn_bias.size(-1) != 1 and attn_bias.size(-1) != s_k or (attn_bias.size(-2) != 1 and attn_bias.size(-2) != s_q):
-            raise RuntimeError(f'attn_bias (shape: {attn_bias.shape}) is expected to broadcast to shape: {attn_weight.shape}.')
-        attn_weight = attn_weight + attn_bias
-    if key_padding_mask is not None:
-        if attn_bias is not None:
-            warnings.warn('Propogating key_padding_mask to the attention module ' + 'and applying it within the attention module can cause ' + 'unneccessary computation/memory usage. Consider integrating ' + 'into attn_bias once and passing that to each attention ' + 'module instead.')
-        attn_weight = attn_weight.masked_fill(~key_padding_mask.view((b, 1, 1, s_k)), min_val)
-    if is_causal:
-        s = max(s_q, s_k)
-        causal_mask = attn_weight.new_ones(s, s, dtype=torch.float16)
-        causal_mask = causal_mask.tril()
-        causal_mask = causal_mask.to(torch.bool)
-        causal_mask = ~causal_mask
-        causal_mask = causal_mask[-s_q:, -s_k:]
-        attn_weight = attn_weight.masked_fill(causal_mask.view(1, 1, s_q, s_k), min_val)
-    attn_weight = torch.softmax(attn_weight, dim=-1)
-    if dropout_p:
-        attn_weight = torch.nn.functional.dropout(attn_weight, p=dropout_p, training=training, inplace=True)
-    out = attn_weight.matmul(v)
-    out = rearrange(out, 'b h s d -> b s (h d)')
-    if needs_weights:
-        return (out, attn_weight)
-    return (out, None)
-
-def check_valid_inputs(*tensors, valid_dtypes=[torch.float16, torch.bfloat16]):
-    for tensor in tensors:
-        if tensor.dtype not in valid_dtypes:
-            raise TypeError(f'tensor.dtype={tensor.dtype!r} must be in valid_dtypes={valid_dtypes!r}.')
-        if not tensor.is_cuda:
-            raise TypeError(f'Inputs must be cuda tensors (tensor.is_cuda={tensor.is_cuda!r}).')
-
-def flash_attn_fn(query, key, value, n_heads, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False):
-    try:
-        from flash_attn import bert_padding, flash_attn_interface
-    except:
-        raise RuntimeError('Please install flash-attn==1.0.3.post0')
-    check_valid_inputs(query, key, value)
-    if attn_bias is not None:
-        raise NotImplementedError(f'attn_bias not implemented for flash attn.')
-    (batch_size, seqlen) = query.shape[:2]
-    if key_padding_mask is None:
-        key_padding_mask = torch.ones_like(key[:, :, 0], dtype=torch.bool)
-    query_padding_mask = key_padding_mask[:, -query.size(1):]
-    (query_unpad, indices_q, cu_seqlens_q, max_seqlen_q) = bert_padding.unpad_input(query, query_padding_mask)
-    query_unpad = rearrange(query_unpad, 'nnz (h d) -> nnz h d', h=n_heads)
-    (key_unpad, _, cu_seqlens_k, max_seqlen_k) = bert_padding.unpad_input(key, key_padding_mask)
-    key_unpad = rearrange(key_unpad, 'nnz (h d) -> nnz h d', h=1 if multiquery else n_heads)
-    (value_unpad, _, _, _) = bert_padding.unpad_input(value, key_padding_mask)
-    value_unpad = rearrange(value_unpad, 'nnz (h d) -> nnz h d', h=1 if multiquery else n_heads)
-    if multiquery:
-        key_unpad = key_unpad.expand(key_unpad.size(0), n_heads, key_unpad.size(-1))
-        value_unpad = value_unpad.expand(value_unpad.size(0), n_heads, value_unpad.size(-1))
-    dropout_p = dropout_p if training else 0.0
-    reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)
-    output_unpad = flash_attn_interface.flash_attn_unpadded_func(query_unpad, key_unpad, value_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p, softmax_scale=softmax_scale, causal=reset_is_causal, return_attn_probs=needs_weights)
-    output = bert_padding.pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'), indices_q, batch_size, seqlen)
-    return (output, None)
-
-def triton_flash_attn_fn(query, key, value, n_heads, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False):
-    try:
-        from flash_attn import flash_attn_triton
-    except:
-        raise RuntimeError('Please install flash-attn==1.0.3.post0 and triton==2.0.0.dev20221202')
-    check_valid_inputs(query, key, value)
-    if dropout_p:
-        raise NotImplementedError(f'Dropout not implemented for attn_impl: triton.')
-    if needs_weights:
-        raise NotImplementedError(f'attn_impl: triton cannot return attn weights.')
-    if key_padding_mask is not None:
-        warnings.warn('Propagating key_padding_mask to the attention module ' + 'and applying it within the attention module can cause ' + 'unnecessary computation/memory usage. Consider integrating ' + 'into attn_bias once and passing that to each attention ' + 'module instead.')
-        (b_size, s_k) = key_padding_mask.shape[:2]
-        if attn_bias is None:
-            attn_bias = query.new_zeros(b_size, 1, 1, s_k)
-        attn_bias = attn_bias.masked_fill(~key_padding_mask.view((b_size, 1, 1, s_k)), torch.finfo(query.dtype).min)
-    query = rearrange(query, 'b s (h d) -> b s h d', h=n_heads)
-    key = rearrange(key, 'b s (h d) -> b s h d', h=1 if multiquery else n_heads)
-    value = rearrange(value, 'b s (h d) -> b s h d', h=1 if multiquery else n_heads)
-    if multiquery:
-        key = key.expand(*key.shape[:2], n_heads, key.size(-1))
-        value = value.expand(*value.shape[:2], n_heads, value.size(-1))
-    reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)
-    attn_output = flash_attn_triton.flash_attn_func(query, key, value, attn_bias, reset_is_causal, softmax_scale)
-    output = attn_output.view(*attn_output.shape[:2], -1)
-    return (output, None)
-
-class MultiheadAttention(nn.Module):
-    """Multi-head self attention.
-
-    Using torch or triton attention implemetation enables user to also use
-    additive bias.
-    """
-
-    def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, low_precision_layernorm: bool=False, device: Optional[str]=None):
-        super().__init__()
-        self.attn_impl = attn_impl
-        self.clip_qkv = clip_qkv
-        self.qk_ln = qk_ln
-        self.d_model = d_model
-        self.n_heads = n_heads
-        self.softmax_scale = softmax_scale
-        if self.softmax_scale is None:
-            self.softmax_scale = 1 / math.sqrt(self.d_model / self.n_heads)
-        self.attn_dropout_p = attn_pdrop
-        self.Wqkv = nn.Linear(self.d_model, 3 * self.d_model, device=device)
-        fuse_splits = (d_model, 2 * d_model)
-        self.Wqkv._fused = (0, fuse_splits)
-        if self.qk_ln:
-            layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm
-            self.q_ln = layernorm_class(self.d_model, device=device)
-            self.k_ln = layernorm_class(self.d_model, device=device)
-        if self.attn_impl == 'flash':
-            self.attn_fn = flash_attn_fn
-        elif self.attn_impl == 'triton':
-            self.attn_fn = triton_flash_attn_fn
-            warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.')
-        elif self.attn_impl == 'torch':
-            self.attn_fn = scaled_multihead_dot_product_attention
-            if torch.cuda.is_available():
-                warnings.warn('Using `attn_impl: torch`. If your model does not use `alibi` or ' + '`prefix_lm` we recommend using `attn_impl: flash` otherwise ' + 'we recommend using `attn_impl: triton`.')
-        else:
-            raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
-        self.out_proj = nn.Linear(self.d_model, self.d_model, device=device)
-        self.out_proj._is_residual = True
-
-    def forward(self, x, past_key_value=None, attn_bias=None, attention_mask=None, is_causal=True, needs_weights=False):
-        qkv = self.Wqkv(x)
-        if self.clip_qkv:
-            qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
-        (query, key, value) = qkv.chunk(3, dim=2)
-        key_padding_mask = attention_mask
-        if self.qk_ln:
-            dtype = query.dtype
-            query = self.q_ln(query).to(dtype)
-            key = self.k_ln(key).to(dtype)
-        if past_key_value is not None:
-            if len(past_key_value) != 0:
-                key = torch.cat([past_key_value[0], key], dim=1)
-                value = torch.cat([past_key_value[1], value], dim=1)
-            past_key_value = (key, value)
-        if attn_bias is not None:
-            attn_bias = attn_bias[:, :, -query.size(1):, -key.size(1):]
-        (context, attn_weights) = self.attn_fn(query, key, value, self.n_heads, softmax_scale=self.softmax_scale, attn_bias=attn_bias, key_padding_mask=key_padding_mask, is_causal=is_causal, dropout_p=self.attn_dropout_p, training=self.training, needs_weights=needs_weights)
-        return (self.out_proj(context), attn_weights, past_key_value)
-
-class MultiQueryAttention(nn.Module):
-    """Multi-Query self attention.
-
-    Using torch or triton attention implemetation enables user to also use
-    additive bias.
-    """
-
-    def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, low_precision_layernorm: bool=False, device: Optional[str]=None):
-        super().__init__()
-        self.attn_impl = attn_impl
-        self.clip_qkv = clip_qkv
-        self.qk_ln = qk_ln
-        self.d_model = d_model
-        self.n_heads = n_heads
-        self.head_dim = d_model // n_heads
-        self.softmax_scale = softmax_scale
-        if self.softmax_scale is None:
-            self.softmax_scale = 1 / math.sqrt(self.head_dim)
-        self.attn_dropout_p = attn_pdrop
-        self.Wqkv = nn.Linear(d_model, d_model + 2 * self.head_dim, device=device)
-        fuse_splits = (d_model, d_model + self.head_dim)
-        self.Wqkv._fused = (0, fuse_splits)
-        if self.qk_ln:
-            layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm
-            self.q_ln = layernorm_class(d_model, device=device)
-            self.k_ln = layernorm_class(self.head_dim, device=device)
-        if self.attn_impl == 'flash':
-            self.attn_fn = flash_attn_fn
-        elif self.attn_impl == 'triton':
-            self.attn_fn = triton_flash_attn_fn
-            warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.')
-        elif self.attn_impl == 'torch':
-            self.attn_fn = scaled_multihead_dot_product_attention
-            if torch.cuda.is_available():
-                warnings.warn('Using `attn_impl: torch`. If your model does not use `alibi` or ' + '`prefix_lm` we recommend using `attn_impl: flash` otherwise ' + 'we recommend using `attn_impl: triton`.')
-        else:
-            raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
-        self.out_proj = nn.Linear(self.d_model, self.d_model, device=device)
-        self.out_proj._is_residual = True
-
-    def forward(self, x, past_key_value=None, attn_bias=None, attention_mask=None, is_causal=True, needs_weights=False):
-        qkv = self.Wqkv(x)
-        if self.clip_qkv:
-            qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
-        (query, key, value) = qkv.split([self.d_model, self.head_dim, self.head_dim], dim=2)
-        key_padding_mask = attention_mask
-        if self.qk_ln:
-            dtype = query.dtype
-            query = self.q_ln(query).to(dtype)
-            key = self.k_ln(key).to(dtype)
-        if past_key_value is not None:
-            if len(past_key_value) != 0:
-                key = torch.cat([past_key_value[0], key], dim=1)
-                value = torch.cat([past_key_value[1], value], dim=1)
-            past_key_value = (key, value)
-        if attn_bias is not None:
-            attn_bias = attn_bias[:, :, -query.size(1):, -key.size(1):]
-        (context, attn_weights) = self.attn_fn(query, key, value, self.n_heads, softmax_scale=self.softmax_scale, attn_bias=attn_bias, key_padding_mask=key_padding_mask, is_causal=is_causal, dropout_p=self.attn_dropout_p, training=self.training, needs_weights=needs_weights, multiquery=True)
-        return (self.out_proj(context), attn_weights, past_key_value)
-
-def attn_bias_shape(attn_impl, n_heads, seq_len, alibi, prefix_lm, causal, use_sequence_id):
-    if attn_impl == 'flash':
-        return None
-    elif attn_impl in ['torch', 'triton']:
-        if alibi:
-            if (prefix_lm or not causal) or use_sequence_id:
-                return (1, n_heads, seq_len, seq_len)
-            return (1, n_heads, 1, seq_len)
-        elif prefix_lm or use_sequence_id:
-            return (1, 1, seq_len, seq_len)
-        return None
-    else:
-        raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
-
-def build_attn_bias(attn_impl, attn_bias, n_heads, seq_len, causal=False, alibi=False, alibi_bias_max=8):
-    if attn_impl == 'flash':
-        return None
-    elif attn_impl in ['torch', 'triton']:
-        if alibi:
-            (device, dtype) = (attn_bias.device, attn_bias.dtype)
-            attn_bias = attn_bias.add(build_alibi_bias(n_heads, seq_len, full=not causal, alibi_bias_max=alibi_bias_max, device=device, dtype=dtype))
-        return attn_bias
-    else:
-        raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
-
-def gen_slopes(n_heads, alibi_bias_max=8, device=None):
-    _n_heads = 2 ** math.ceil(math.log2(n_heads))
-    m = torch.arange(1, _n_heads + 1, dtype=torch.float32, device=device)
-    m = m.mul(alibi_bias_max / _n_heads)
-    slopes = 1.0 / torch.pow(2, m)
-    if _n_heads != n_heads:
-        slopes = torch.concat([slopes[1::2], slopes[::2]])[:n_heads]
-    return slopes.view(1, n_heads, 1, 1)
-
-def build_alibi_bias(n_heads, seq_len, full=False, alibi_bias_max=8, device=None, dtype=None):
-    alibi_bias = torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, 1, seq_len)
-    if full:
-        alibi_bias = alibi_bias - torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, seq_len, 1)
-        alibi_bias = alibi_bias.abs().mul(-1)
-    slopes = gen_slopes(n_heads, alibi_bias_max, device=device)
-    alibi_bias = alibi_bias * slopes
-    return alibi_bias.to(dtype=dtype)
-ATTN_CLASS_REGISTRY = {'multihead_attention': MultiheadAttention, 'multiquery_attention': MultiQueryAttention}

blocks.py

@@ -1,41 +0,0 @@
-"""GPT Blocks used for the GPT Model."""
-from typing import Dict, Optional, Tuple
-import torch
-import torch.nn as nn
-from .attention import ATTN_CLASS_REGISTRY
-from .norm import NORM_CLASS_REGISTRY
-
-class MPTMLP(nn.Module):
-
-    def __init__(self, d_model: int, expansion_ratio: int, device: Optional[str]=None):
-        super().__init__()
-        self.up_proj = nn.Linear(d_model, expansion_ratio * d_model, device=device)
-        self.act = nn.GELU(approximate='none')
-        self.down_proj = nn.Linear(expansion_ratio * d_model, d_model, device=device)
-        self.down_proj._is_residual = True
-
-    def forward(self, x):
-        return self.down_proj(self.act(self.up_proj(x)))
-
-class MPTBlock(nn.Module):
-
-    def __init__(self, d_model: int, n_heads: int, expansion_ratio: int, attn_config: Dict={'attn_type': 'multihead_attention', 'attn_pdrop': 0.0, 'attn_impl': 'triton', 'qk_ln': False, 'clip_qkv': None, 'softmax_scale': None, 'prefix_lm': False, 'attn_uses_sequence_id': False, 'alibi': False, 'alibi_bias_max': 8}, resid_pdrop: float=0.0, norm_type: str='low_precision_layernorm', device: Optional[str]=None, **kwargs):
-        del kwargs
-        super().__init__()
-        norm_class = NORM_CLASS_REGISTRY[norm_type.lower()]
-        attn_class = ATTN_CLASS_REGISTRY[attn_config['attn_type']]
-        self.norm_1 = norm_class(d_model, device=device)
-        self.attn = attn_class(attn_impl=attn_config['attn_impl'], clip_qkv=attn_config['clip_qkv'], qk_ln=attn_config['qk_ln'], softmax_scale=attn_config['softmax_scale'], attn_pdrop=attn_config['attn_pdrop'], d_model=d_model, n_heads=n_heads, device=device)
-        self.norm_2 = norm_class(d_model, device=device)
-        self.ffn = MPTMLP(d_model=d_model, expansion_ratio=expansion_ratio, device=device)
-        self.resid_attn_dropout = nn.Dropout(resid_pdrop)
-        self.resid_ffn_dropout = nn.Dropout(resid_pdrop)
-
-    def forward(self, x: torch.Tensor, past_key_value: Optional[Tuple[torch.Tensor]]=None, attn_bias: Optional[torch.Tensor]=None, attention_mask: Optional[torch.ByteTensor]=None, is_causal: bool=True) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor]]]:
-        a = self.norm_1(x)
-        (b, _, past_key_value) = self.attn(a, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=is_causal)
-        x = x + self.resid_attn_dropout(b)
-        m = self.norm_2(x)
-        n = self.ffn(m)
-        x = x + self.resid_ffn_dropout(n)
-        return (x, past_key_value)

config.json

@@ -33,7 +33,7 @@
     "name": "kaiming_normal_",
     "verbose": 0
   },
-  "init_device": "cpu",
+  "init_device": "cuda",
   "learned_pos_emb": true,
   "logit_scale": null,
   "max_seq_len": 65536,

configuration_mpt.py

@@ -1,118 +0,0 @@
-"""A HuggingFace-style model configuration."""
-from typing import Dict, Optional, Union
-from transformers import PretrainedConfig
-attn_config_defaults: Dict = {'attn_type': 'multihead_attention', 'attn_pdrop': 0.0, 'attn_impl': 'triton', 'qk_ln': False, 'clip_qkv': None, 'softmax_scale': None, 'prefix_lm': False, 'attn_uses_sequence_id': False, 'alibi': False, 'alibi_bias_max': 8}
-init_config_defaults: Dict = {'name': 'kaiming_normal_', 'fan_mode': 'fan_in', 'init_nonlinearity': 'relu'}
-
-class MPTConfig(PretrainedConfig):
-    model_type = 'mpt'
-
-    def __init__(self, d_model: int=2048, n_heads: int=16, n_layers: int=24, expansion_ratio: int=4, max_seq_len: int=2048, vocab_size: int=50368, resid_pdrop: float=0.0, emb_pdrop: float=0.0, learned_pos_emb: bool=True, attn_config: Dict=attn_config_defaults, init_device: str='cpu', logit_scale: Optional[Union[float, str]]=None, no_bias: bool=False, verbose: int=0, embedding_fraction: float=1.0, norm_type: str='low_precision_layernorm', use_cache: bool=False, init_config: Dict=init_config_defaults, **kwargs):
-        """The MPT configuration class.
-
-        Args:
-            d_model (int): The size of the embedding dimension of the model.
-            n_heads (int): The number of attention heads.
-            n_layers (int): The number of layers in the model.
-            expansion_ratio (int): The ratio of the up/down scale in the MLP.
-            max_seq_len (int): The maximum sequence length of the model.
-            vocab_size (int): The size of the vocabulary.
-            resid_pdrop (float): The dropout probability applied to the attention output before combining with residual.
-            emb_pdrop (float): The dropout probability for the embedding layer.
-            learned_pos_emb (bool): Whether to use learned positional embeddings
-            attn_config (Dict):  A dictionary used to configure the model's attention module:
-                attn_type (str): type of attention to use. Options: multihead_attention, multiquery_attention
-                attn_pdrop (float): The dropout probability for the attention layers.
-                attn_impl (str): The attention implementation to use. One of 'torch', 'flash', or 'triton'.
-                qk_ln (bool): Whether to apply layer normalization to the queries and keys in the attention layer.
-                clip_qkv (Optional[float]): If not None, clip the queries, keys, and values in the attention layer to
-                    this value.
-                softmax_scale (Optional[float]): If not None, scale the softmax in the attention layer by this value. If None,
-                    use the default scale of ``1/sqrt(d_keys)``.
-                prefix_lm (Optional[bool]): Whether the model should operate as a Prefix LM. This requires passing an
-                    extra `prefix_mask` argument which indicates which tokens belong to the prefix. Tokens in the prefix
-                    can attend to one another bi-directionally. Tokens outside the prefix use causal attention.
-                attn_uses_sequence_id (Optional[bool]): Whether to restrict attention to tokens that have the same sequence_id.
-                    When the model is in `train` mode, this requires passing an extra `sequence_id` argument which indicates
-                    which sub-sequence each token belongs to.
-                    Defaults to ``False`` meaning any provided `sequence_id` will be ignored.
-                alibi (bool): Whether to use the alibi bias instead of position embeddings.
-                alibi_bias_max (int): The maximum value of the alibi bias.
-            init_device (str): The device to use for parameter initialization.
-            logit_scale (Optional[Union[float, str]]): If not None, scale the logits by this value.
-            no_bias (bool): Whether to use bias in all layers.
-            verbose (int): The verbosity level. 0 is silent.
-            embedding_fraction (float): The fraction to scale the gradients of the embedding layer by.
-            norm_type (str): choose type of norm to use
-            multiquery_attention (bool): Whether to use multiquery attention implementation.
-            use_cache (bool): Whether or not the model should return the last key/values attentions
-            init_config (Dict): A dictionary used to configure the model initialization:
-                init_config.name: The parameter initialization scheme to use. Options: 'default_', 'baseline_',
-                    'kaiming_uniform_', 'kaiming_normal_', 'neox_init_', 'small_init_', 'xavier_uniform_', or
-                    'xavier_normal_'. These mimic the parameter initialization methods in PyTorch.
-                init_div_is_residual (Union[int, float, str, bool]): Value to divide initial weights by if ``module._is_residual`` is True.
-                emb_init_std (Optional[float]): The standard deviation of the normal distribution used to initialize the embedding layer.
-                emb_init_uniform_lim (Optional[Union[Tuple[float, float], float]]): The lower and upper limits of the uniform distribution
-                    used to initialize the embedding layer. Mutually exclusive with ``emb_init_std``.
-                init_std (float): The standard deviation of the normal distribution used to initialize the model,
-                    if using the baseline_ parameter initialization scheme.
-                init_gain (float): The gain to use for parameter initialization with kaiming or xavier initialization schemes.
-                fan_mode (str): The fan mode to use for parameter initialization with kaiming initialization schemes.
-                init_nonlinearity (str): The nonlinearity to use for parameter initialization with kaiming initialization schemes.
-                ---
-                See llmfoundry.models.utils.param_init_fns.py for info on other param init config options
-        """
-        self.d_model = d_model
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.expansion_ratio = expansion_ratio
-        self.max_seq_len = max_seq_len
-        self.vocab_size = vocab_size
-        self.resid_pdrop = resid_pdrop
-        self.emb_pdrop = emb_pdrop
-        self.learned_pos_emb = learned_pos_emb
-        self.attn_config = attn_config
-        self.init_device = init_device
-        self.logit_scale = logit_scale
-        self.no_bias = no_bias
-        self.verbose = verbose
-        self.embedding_fraction = embedding_fraction
-        self.norm_type = norm_type
-        self.use_cache = use_cache
-        self.init_config = init_config
-        if 'name' in kwargs:
-            del kwargs['name']
-        if 'loss_fn' in kwargs:
-            del kwargs['loss_fn']
-        super().__init__(**kwargs)
-        self._validate_config()
-
-    def _set_config_defaults(self, config, config_defaults):
-        for (k, v) in config_defaults.items():
-            if k not in config:
-                config[k] = v
-        return config
-
-    def _validate_config(self):
-        self.attn_config = self._set_config_defaults(self.attn_config, attn_config_defaults)
-        self.init_config = self._set_config_defaults(self.init_config, init_config_defaults)
-        if self.d_model % self.n_heads != 0:
-            raise ValueError('d_model must be divisible by n_heads')
-        if any((prob < 0 or prob > 1 for prob in [self.attn_config['attn_pdrop'], self.resid_pdrop, self.emb_pdrop])):
-            raise ValueError("self.attn_config['attn_pdrop'], resid_pdrop, emb_pdrop are probabilities and must be between 0 and 1")
-        if self.attn_config['attn_impl'] not in ['torch', 'flash', 'triton']:
-            raise ValueError(f"Unknown attn_impl={self.attn_config['attn_impl']}")
-        if self.attn_config['prefix_lm'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
-            raise NotImplementedError('prefix_lm only implemented with torch and triton attention.')
-        if self.attn_config['alibi'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
-            raise NotImplementedError('alibi only implemented with torch and triton attention.')
-        if self.attn_config['attn_uses_sequence_id'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
-            raise NotImplementedError('attn_uses_sequence_id only implemented with torch and triton attention.')
-        if self.embedding_fraction > 1 or self.embedding_fraction <= 0:
-            raise ValueError('model.embedding_fraction must be between 0 (exclusive) and 1 (inclusive)!')
-        if isinstance(self.logit_scale, str) and self.logit_scale != 'inv_sqrt_d_model':
-            raise ValueError(f"self.logit_scale={self.logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.")
-        if self.init_config.get('name', None) is None:
-            raise ValueError(f"self.init_config={self.init_config!r} 'name' needs to be set.")
-        if not self.learned_pos_emb and (not self.attn_config['alibi']):
-            raise ValueError(f'Positional information must be provided to the model using either learned_pos_emb or alibi.')

hf_prefixlm_converter.py

@@ -1,415 +0,0 @@
-"""Converts Huggingface Causal LM to Prefix LM.
-
-Conversion does lightweight surgery on a HuggingFace
-Causal LM to convert it to a Prefix LM.
-
-Prefix LMs accepts a `bidirectional_mask` input in `forward`
-and treat the input prompt as the prefix in `generate`.
-"""
-import math
-import warnings
-from types import MethodType
-from typing import Any, Dict, List, Optional, Tuple, Union
-import torch
-from transformers.models.bloom.modeling_bloom import BaseModelOutputWithPastAndCrossAttentions, BloomForCausalLM, BloomModel, CausalLMOutputWithCrossAttentions, CrossEntropyLoss
-from transformers.models.bloom.modeling_bloom import _expand_mask as _expand_mask_bloom
-from transformers.models.bloom.modeling_bloom import _make_causal_mask as _make_causal_mask_bloom
-from transformers.models.bloom.modeling_bloom import logging
-from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
-from transformers.models.gpt_neo.modeling_gpt_neo import GPTNeoForCausalLM
-from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXForCausalLM
-from transformers.models.gptj.modeling_gptj import GPTJForCausalLM
-from transformers.models.opt.modeling_opt import OPTForCausalLM
-from transformers.models.opt.modeling_opt import _expand_mask as _expand_mask_opt
-from transformers.models.opt.modeling_opt import _make_causal_mask as _make_causal_mask_opt
-logger = logging.get_logger(__name__)
-_SUPPORTED_GPT_MODELS = (GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM)
-CAUSAL_GPT_TYPES = Union[GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM]
-
-def _convert_gpt_causal_lm_to_prefix_lm(model: CAUSAL_GPT_TYPES) -> CAUSAL_GPT_TYPES:
-    """Converts a GPT-style Causal LM to a Prefix LM.
-
-    Supported HuggingFace model classes:
-        - `GPT2LMHeadModel`
-        - `GPTNeoForCausalLM`
-        - `GPTNeoXForCausalLM`
-        - `GPTJForCausalLM`
-
-    See `convert_hf_causal_lm_to_prefix_lm` for more details.
-    """
-    if hasattr(model, '_prefix_lm_converted'):
-        return model
-    assert isinstance(model, _SUPPORTED_GPT_MODELS)
-    assert model.config.add_cross_attention == False, 'Only supports GPT-style decoder-only models'
-
-    def _get_attn_modules(model: CAUSAL_GPT_TYPES) -> List[torch.nn.Module]:
-        """Helper that gets a list of the model's attention modules.
-
-        Each module has a `bias` buffer used for causal masking. The Prefix LM
-        conversion adds logic to dynamically manipulate these biases to support
-        Prefix LM attention masking.
-        """
-        attn_modules = []
-        if isinstance(model, GPTNeoXForCausalLM):
-            blocks = model.gpt_neox.layers
-        else:
-            blocks = model.transformer.h
-        for block in blocks:
-            if isinstance(model, GPTNeoForCausalLM):
-                if block.attn.attention_type != 'global':
-                    continue
-                attn_module = block.attn.attention
-            elif isinstance(model, GPTNeoXForCausalLM):
-                attn_module = block.attention
-            else:
-                attn_module = block.attn
-            attn_modules.append(attn_module)
-        return attn_modules
-    setattr(model, '_original_forward', getattr(model, 'forward'))
-    setattr(model, '_original_generate', getattr(model, 'generate'))
-
-    def forward(self: CAUSAL_GPT_TYPES, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]]=None, attention_mask: Optional[torch.FloatTensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None):
-        """Wraps original forward to enable PrefixLM attention."""
-
-        def call_og_forward():
-            if isinstance(self, GPTNeoXForCausalLM):
-                return self._original_forward(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
-            else:
-                return self._original_forward(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
-        if bidirectional_mask is None:
-            return call_og_forward()
-        assert isinstance(bidirectional_mask, torch.Tensor)
-        attn_modules = _get_attn_modules(model)
-        (b, s) = bidirectional_mask.shape
-        max_length = attn_modules[0].bias.shape[-1]
-        if s > max_length:
-            raise ValueError(f'bidirectional_mask sequence length (={s}) exceeds the ' + f'max length allowed by the model ({max_length}).')
-        assert s <= max_length
-        if s < max_length:
-            pad = torch.zeros((int(b), int(max_length - s)), dtype=bidirectional_mask.dtype, device=bidirectional_mask.device)
-            bidirectional_mask = torch.cat([bidirectional_mask, pad], dim=1)
-        bidirectional = bidirectional_mask.unsqueeze(1).unsqueeze(1)
-        for attn_module in attn_modules:
-            attn_module.bias.data = torch.logical_or(attn_module.bias.data, bidirectional)
-        output = call_og_forward()
-        for attn_module in attn_modules:
-            attn_module.bias.data = torch.tril(attn_module.bias.data[0, 0])[None, None]
-        return output
-
-    def generate(self: CAUSAL_GPT_TYPES, *args: tuple, **kwargs: Dict[str, Any]):
-        """Wraps original generate to enable PrefixLM attention."""
-        attn_modules = _get_attn_modules(model)
-        for attn_module in attn_modules:
-            attn_module.bias.data[:] = 1
-        output = self._original_generate(*args, **kwargs)
-        for attn_module in attn_modules:
-            attn_module.bias.data = torch.tril(attn_module.bias.data[0, 0])[None, None]
-        return output
-    setattr(model, 'forward', MethodType(forward, model))
-    setattr(model, 'generate', MethodType(generate, model))
-    setattr(model, '_prefix_lm_converted', True)
-    return model
-
-def _convert_bloom_causal_lm_to_prefix_lm(model: BloomForCausalLM) -> BloomForCausalLM:
-    """Converts a BLOOM Causal LM to a Prefix LM.
-
-    Supported HuggingFace model classes:
-        - `BloomForCausalLM`
-
-    See `convert_hf_causal_lm_to_prefix_lm` for more details.
-    """
-    if hasattr(model, '_prefix_lm_converted'):
-        return model
-    assert isinstance(model, BloomForCausalLM)
-    assert model.config.add_cross_attention == False, 'Only supports BLOOM decoder-only models'
-
-    def _prepare_attn_mask(self: BloomModel, attention_mask: torch.Tensor, bidirectional_mask: Optional[torch.Tensor], input_shape: Tuple[int, int], past_key_values_length: int) -> torch.BoolTensor:
-        combined_attention_mask = None
-        device = attention_mask.device
-        (_, src_length) = input_shape
-        if src_length > 1:
-            combined_attention_mask = _make_causal_mask_bloom(input_shape, device=device, past_key_values_length=past_key_values_length)
-            if bidirectional_mask is not None:
-                assert attention_mask.shape == bidirectional_mask.shape
-                expanded_bidirectional_mask = _expand_mask_bloom(bidirectional_mask, tgt_length=src_length)
-                combined_attention_mask = torch.logical_and(combined_attention_mask, expanded_bidirectional_mask)
-        expanded_attn_mask = _expand_mask_bloom(attention_mask, tgt_length=src_length)
-        combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask
-        return combined_attention_mask
-
-    def _build_alibi_tensor(self: BloomModel, batch_size: int, query_length: int, key_length: int, dtype: torch.dtype, device: torch.device) -> torch.Tensor:
-        num_heads = self.config.n_head
-        closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
-        base = torch.tensor(2 ** (-2 ** (-(math.log2(closest_power_of_2) - 3))), device=device, dtype=torch.float32)
-        powers = torch.arange(1, 1 + closest_power_of_2, device=device, dtype=torch.int32)
-        slopes = torch.pow(base, powers)
-        if closest_power_of_2 != num_heads:
-            extra_base = torch.tensor(2 ** (-2 ** (-(math.log2(2 * closest_power_of_2) - 3))), device=device, dtype=torch.float32)
-            num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
-            extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=device, dtype=torch.int32)
-            slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
-        qa = torch.arange(query_length, device=device, dtype=torch.int32).view(-1, 1)
-        ka = torch.arange(key_length, device=device, dtype=torch.int32).view(1, -1)
-        diffs = qa - ka + key_length - query_length
-        diffs = -diffs.abs()
-        alibi = slopes.view(1, num_heads, 1, 1) * diffs.view(1, 1, query_length, key_length)
-        alibi = alibi.expand(batch_size, -1, -1, -1).reshape(-1, query_length, key_length)
-        return alibi.to(dtype)
-    KeyValueT = Tuple[torch.Tensor, torch.Tensor]
-
-    def forward(self: BloomModel, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[KeyValueT, ...]]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, **deprecated_arguments) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
-        if deprecated_arguments.pop('position_ids', False) is not False:
-            warnings.warn('`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. ' + 'You can safely ignore passing `position_ids`.', FutureWarning)
-        if len(deprecated_arguments) > 0:
-            raise ValueError(f'Got unexpected arguments: {deprecated_arguments}')
-        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
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time')
-        elif input_ids is not None:
-            (batch_size, seq_length) = input_ids.shape
-        elif inputs_embeds is not None:
-            (batch_size, seq_length, _) = inputs_embeds.shape
-        else:
-            raise ValueError('You have to specify either input_ids or inputs_embeds')
-        if past_key_values is None:
-            past_key_values = tuple([None] * len(self.h))
-        head_mask = self.get_head_mask(head_mask, self.config.n_layer)
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-        hidden_states = self.word_embeddings_layernorm(inputs_embeds)
-        presents = () if use_cache else None
-        all_self_attentions = () if output_attentions else None
-        all_hidden_states = () if output_hidden_states else None
-        seq_length_with_past = seq_length
-        past_key_values_length = 0
-        if past_key_values[0] is not None:
-            tmp = past_key_values[0][0]
-            past_key_values_length = tmp.shape[2]
-            seq_length_with_past = seq_length_with_past + past_key_values_length
-        if attention_mask is None:
-            attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
-        else:
-            attention_mask = attention_mask.to(hidden_states.device)
-        alibi = self._build_alibi_tensor(batch_size=batch_size, query_length=seq_length, key_length=seq_length_with_past, dtype=hidden_states.dtype, device=hidden_states.device)
-        causal_mask = self._prepare_attn_mask(attention_mask, bidirectional_mask, input_shape=(batch_size, seq_length), past_key_values_length=past_key_values_length)
-        for (i, (block, layer_past)) in enumerate(zip(self.h, past_key_values)):
-            if output_hidden_states:
-                hst = (hidden_states,)
-                all_hidden_states = all_hidden_states + hst
-            if self.gradient_checkpointing and self.training:
-                if use_cache:
-                    logger.warning('`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...')
-                    use_cache = False
-
-                def create_custom_forward(module):
-
-                    def custom_forward(*inputs):
-                        return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)
-                    return custom_forward
-                outputs = torch.utils.checkpoint.checkpoint(create_custom_forward(block), hidden_states, alibi, causal_mask, head_mask[i])
-            else:
-                outputs = block(hidden_states, layer_past=layer_past, attention_mask=causal_mask, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, alibi=alibi)
-            hidden_states = outputs[0]
-            if use_cache is True:
-                presents = presents + (outputs[1],)
-            if output_attentions:
-                oa = (outputs[2 if use_cache else 1],)
-                all_self_attentions = all_self_attentions + oa
-        hidden_states = self.ln_f(hidden_states)
-        if output_hidden_states:
-            hst = (hidden_states,)
-            all_hidden_states = all_hidden_states + hst
-        if not return_dict:
-            return tuple((v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None))
-        return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attentions)
-    setattr(model.transformer, '_prepare_attn_mask', MethodType(_prepare_attn_mask, model.transformer))
-    setattr(model.transformer, '_build_alibi_tensor', MethodType(_build_alibi_tensor, model.transformer))
-    setattr(model.transformer, 'forward', MethodType(forward, model.transformer))
-    KeyValueT = Tuple[torch.Tensor, torch.Tensor]
-
-    def forward(self: BloomForCausalLM, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[KeyValueT, ...]]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.Tensor]=None, inputs_embeds: Optional[torch.Tensor]=None, labels: Optional[torch.Tensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, **deprecated_arguments) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
-        """Replacement forward method for BloomCausalLM."""
-        if deprecated_arguments.pop('position_ids', False) is not False:
-            warnings.warn('`position_ids` have no functionality in BLOOM and will be removed ' + 'in v5.0.0. You can safely ignore passing `position_ids`.', FutureWarning)
-        if len(deprecated_arguments) > 0:
-            raise ValueError(f'Got unexpected arguments: {deprecated_arguments}')
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        transformer_outputs = self.transformer(input_ids, past_key_values=past_key_values, attention_mask=attention_mask, bidirectional_mask=bidirectional_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
-        hidden_states = transformer_outputs[0]
-        lm_logits = self.lm_head(hidden_states)
-        loss = None
-        if labels is not None:
-            shift_logits = lm_logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            (batch_size, seq_length, vocab_size) = shift_logits.shape
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length))
-        if not return_dict:
-            output = (lm_logits,) + transformer_outputs[1:]
-            return (loss,) + output if loss is not None else output
-        return CausalLMOutputWithCrossAttentions(loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions)
-
-    def prepare_inputs_for_generation(self: BloomForCausalLM, input_ids: torch.LongTensor, past: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, **kwargs) -> dict:
-        if past:
-            input_ids = input_ids[:, -1].unsqueeze(-1)
-            bidirectional_mask = None
-            if past[0][0].shape[0] == input_ids.shape[0]:
-                past = self._convert_to_bloom_cache(past)
-        else:
-            bidirectional_mask = torch.ones_like(input_ids)
-        return {'input_ids': input_ids, 'past_key_values': past, 'use_cache': True, 'attention_mask': attention_mask, 'bidirectional_mask': bidirectional_mask}
-    setattr(model, 'forward', MethodType(forward, model))
-    setattr(model, 'prepare_inputs_for_generation', MethodType(prepare_inputs_for_generation, model))
-    setattr(model, '_prefix_lm_converted', True)
-    return model
-
-def _convert_opt_causal_lm_to_prefix_lm(model: OPTForCausalLM) -> OPTForCausalLM:
-    """Converts an OPT Causal LM to a Prefix LM.
-
-    Supported HuggingFace model classes:
-        - `OPTForCausalLM`
-
-    See `convert_hf_causal_lm_to_prefix_lm` for more details.
-    """
-    if hasattr(model, '_prefix_lm_converted'):
-        return model
-    assert isinstance(model, OPTForCausalLM)
-    assert model.config.add_cross_attention == False, 'Only supports OPT decoder-only models'
-    setattr(model, '_original_forward', getattr(model, 'forward'))
-    setattr(model, '_original_generate', getattr(model, 'generate'))
-    model.model.decoder.bidirectional_mask = None
-
-    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
-        combined_attention_mask = None
-        if input_shape[-1] > 1:
-            if self.bidirectional_mask == 'g':
-                (bsz, src_length) = input_shape
-                combined_attention_mask = torch.zeros((bsz, 1, src_length, src_length + past_key_values_length), dtype=inputs_embeds.dtype, device=inputs_embeds.device)
-            else:
-                combined_attention_mask = _make_causal_mask_opt(input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length).to(inputs_embeds.device)
-                if self.bidirectional_mask is not None:
-                    assert attention_mask.shape == self.bidirectional_mask.shape
-                    expanded_bidirectional_mask = _expand_mask_opt(self.bidirectional_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(inputs_embeds.device)
-                    combined_attention_mask = torch.maximum(expanded_bidirectional_mask, combined_attention_mask)
-        if attention_mask is not None:
-            expanded_attn_mask = _expand_mask_opt(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(inputs_embeds.device)
-            combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
-        return combined_attention_mask
-    setattr(model.model.decoder, '_prepare_decoder_attention_mask', MethodType(_prepare_decoder_attention_mask, model.model.decoder))
-
-    def forward(self: OPTForCausalLM, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.ByteTensor]=None, head_mask: Optional[torch.Tensor]=None, past_key_values: Optional[List[torch.FloatTensor]]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None):
-
-        def call_og_forward():
-            return self._original_forward(input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
-        if bidirectional_mask is None:
-            return call_og_forward()
-        self.model.decoder.bidirectional_mask = bidirectional_mask
-        try:
-            outputs = call_og_forward()
-        except:
-            self.model.decoder.bidirectional_mask = None
-            raise
-        self.model.decoder.bidirectional_mask = None
-        return outputs
-
-    def generate(self: OPTForCausalLM, *args: tuple, **kwargs: Dict[str, Any]):
-        """Wraps original generate to enable PrefixLM-style attention."""
-        self.model.decoder.bidirectional_mask = 'g'
-        try:
-            output = self._original_generate(*args, **kwargs)
-        except:
-            self.model.decoder.bidirectional_mask = None
-            raise
-        self.model.decoder.bidirectional_mask = None
-        return output
-    setattr(model, 'forward', MethodType(forward, model))
-    setattr(model, 'generate', MethodType(generate, model))
-    setattr(model, '_prefix_lm_converted', True)
-    return model
-_SUPPORTED_HF_MODELS = _SUPPORTED_GPT_MODELS + (BloomForCausalLM, OPTForCausalLM)
-CAUSAL_LM_TYPES = Union[GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM, BloomForCausalLM, OPTForCausalLM]
-
-def convert_hf_causal_lm_to_prefix_lm(model: CAUSAL_LM_TYPES) -> CAUSAL_LM_TYPES:
-    """Converts a HuggingFace Causal LM to a Prefix LM.
-
-    Supported HuggingFace model classes:
-        - `GPT2LMHeadModel`
-        - `GPTNeoForCausalLM`
-        - `GPTNeoXForCausalLM`
-        - `GPTJForCausalLM`
-        - `BloomForCausalLM`
-        - `OPTForCausalLM`
-
-    Conversion to a Prefix LM is done by modifying the `forward` method, and possibly also the
-    `generate` method and/or select underlying methods depending on the model class.
-
-    These changes preserve the model API, but add a new input to `forward`: "bidirectional_mask".
-
-    Notes on training:
-        To actually train the converted model as a Prefix LM, training batches will need to indicate
-        the prefix/target structure by including `bidirectional_mask` as part of the batch inputs.
-
-        **This is not a standard input and requires custom layers either within or after your dataloader.**
-
-        In addition to adding `bidirectional_mask` to the batch, this custom code should modify `labels`
-        such that `batch['labels'][batch['bidirectional_mask'] == 1] == -100`.
-        That is, the prefix portion of the sequence should not generate any loss. Loss should only be
-        generated by the target portion of the sequence.
-
-    Notes on `GPTNeoForCausalLM`:
-        To simplify the implementation, "global" and "local" attention layers are handled differently.
-        For "global" layers, we handle conversion as described above. For "local" layers, which use a
-        causal attention mask within a restricted local window, we do not alter the masking.
-
-    Notes on `forward` method conversion:
-        After conversion, the `forward` method will handle a new input, `bidirectional_mask`,
-        which should be a [batch_size, seq_length] byte tensor, where 1 indicates token positions
-        belonging to the prefix (prefix tokens can attend to one another bidirectionally), and
-        0 indicates token positions belonging to the target.
-
-        The new `forward` method will incorporate `bidirectional_mask` (if supplied) into the existing
-        causal mask, call the original `forward` method, and (if the causal mask is a buffer) reset
-        the causal masks before returning the result.
-
-    Notes on `generate` method conversion:
-        After conversion, the `generate` method will have the same signature but will internally
-        convert all causal masks to be purely bidirectional, call the original `generate` method, and
-        (where appropriate) reset the causal masks before returning the result.
-
-        This works thanks to the logic of the HuggingFace `generate` API, which first encodes the token
-        "prompt" passed to `generate` (which is treated as the prefix) and then sequentially generates
-        each new token. Encodings are cached as generation happens, so all prefix tokens can attend to one
-        another (as expected in a Prefix LM) and generated tokens can only attend to prefix tokens and
-        previously-generated tokens (also as expected in a Prefix LM).
-
-    To preserve the API, the original methods are renamed to `_original_forward` and
-    `_original_generate`, and replaced with new `forward` and `generate` methods that wrap
-    them, respectively. Although implementation details vary by model class.
-    """
-    if isinstance(model, _SUPPORTED_GPT_MODELS):
-        return _convert_gpt_causal_lm_to_prefix_lm(model)
-    elif isinstance(model, BloomForCausalLM):
-        return _convert_bloom_causal_lm_to_prefix_lm(model)
-    elif isinstance(model, OPTForCausalLM):
-        return _convert_opt_causal_lm_to_prefix_lm(model)
-    else:
-        raise TypeError(f'Cannot convert model to Prefix LM. ' + f'Model does not belong to set of supported HF models:' + f'\n{_SUPPORTED_HF_MODELS}')
-
-def add_bidirectional_mask_if_missing(batch: Dict[str, Any]):
-    """Attempts to add bidirectional_mask to batch if missing.
-
-    Raises:
-        KeyError if bidirectional_mask is missing and can't be inferred
-    """
-    if 'bidirectional_mask' not in batch:
-        if batch.get('mode', None) == 'icl_task':
-            batch['bidirectional_mask'] = batch['attention_mask'].clone()
-            for (i, continuation_indices) in enumerate(batch['continuation_indices']):
-                batch['bidirectional_mask'][i, continuation_indices] = 0
-        elif 'labels' in batch and 'attention_mask' in batch:
-            batch['bidirectional_mask'] = torch.logical_and(torch.eq(batch['attention_mask'], 1), torch.eq(batch['labels'], -100)).type_as(batch['attention_mask'])
-        else:
-            raise KeyError('No bidirectional_mask in batch and not sure how to construct one.')

meta_init_context.py

@@ -1,94 +0,0 @@
-from contextlib import contextmanager
-import torch
-import torch.nn as nn
-
-@contextmanager
-def init_empty_weights(include_buffers: bool=False):
-    """Meta initialization context manager.
-
-    A context manager under which models are initialized with all parameters
-    on the meta device, therefore creating an empty model. Useful when just
-    initializing the model would blow the available RAM.
-
-    Args:
-        include_buffers (`bool`, *optional*, defaults to `False`): Whether or
-            not to also put all buffers on the meta device while initializing.
-
-    Example:
-    ```python
-    import torch.nn as nn
-
-    # Initialize a model with 100 billions parameters in no time and without using any RAM.
-    with init_empty_weights():
-        tst = nn.Sequential(*[nn.Linear(10000, 10000) for _ in range(1000)])
-    ```
-
-    <Tip warning={true}>
-
-    Any model created under this context manager has no weights. As such you can't do something like
-    `model.to(some_device)` with it. To load weights inside your empty model, see [`load_checkpoint_and_dispatch`].
-
-    </Tip>
-    """
-    with init_on_device(torch.device('meta'), include_buffers=include_buffers) as f:
-        yield f
-
-@contextmanager
-def init_on_device(device: torch.device, include_buffers: bool=False):
-    """Device initialization context manager.
-
-    A context manager under which models are initialized with all parameters
-    on the specified device.
-
-    Args:
-        device (`torch.device`): Device to initialize all parameters on.
-        include_buffers (`bool`, *optional*, defaults to `False`): Whether or
-            not to also put all buffers on the meta device while initializing.
-
-    Example:
-    ```python
-    import torch.nn as nn
-
-    with init_on_device(device=torch.device("cuda")):
-        tst = nn.Liner(100, 100)  # on `cuda` device
-    ```
-    """
-    old_register_parameter = nn.Module.register_parameter
-    if include_buffers:
-        old_register_buffer = nn.Module.register_buffer
-
-    def register_empty_parameter(module, name, param):
-        old_register_parameter(module, name, param)
-        if param is not None:
-            param_cls = type(module._parameters[name])
-            kwargs = module._parameters[name].__dict__
-            module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs)
-
-    def register_empty_buffer(module, name, buffer):
-        old_register_buffer(module, name, buffer)
-        if buffer is not None:
-            module._buffers[name] = module._buffers[name].to(device)
-    if include_buffers:
-        tensor_constructors_to_patch = {torch_function_name: getattr(torch, torch_function_name) for torch_function_name in ['empty', 'zeros', 'ones', 'full']}
-    else:
-        tensor_constructors_to_patch = {}
-
-    def patch_tensor_constructor(fn):
-
-        def wrapper(*args, **kwargs):
-            kwargs['device'] = device
-            return fn(*args, **kwargs)
-        return wrapper
-    try:
-        nn.Module.register_parameter = register_empty_parameter
-        if include_buffers:
-            nn.Module.register_buffer = register_empty_buffer
-        for torch_function_name in tensor_constructors_to_patch.keys():
-            setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name)))
-        yield
-    finally:
-        nn.Module.register_parameter = old_register_parameter
-        if include_buffers:
-            nn.Module.register_buffer = old_register_buffer
-        for (torch_function_name, old_torch_function) in tensor_constructors_to_patch.items():
-            setattr(torch, torch_function_name, old_torch_function)

modeling_mpt.py

@@ -1,291 +0,0 @@
-"""A simple, flexible implementation of a GPT model.
-
-Inspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
-"""
-import math
-import warnings
-from typing import List, Optional, Tuple, Union
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
-from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
-from .attention import attn_bias_shape, build_attn_bias
-from .blocks import MPTBlock
-from .norm import NORM_CLASS_REGISTRY
-from .configuration_mpt import MPTConfig
-from .adapt_tokenizer import AutoTokenizerForMOD, adapt_tokenizer_for_denoising
-from .hf_prefixlm_converter import add_bidirectional_mask_if_missing, convert_hf_causal_lm_to_prefix_lm
-from .meta_init_context import init_empty_weights
-from .param_init_fns import MODEL_INIT_REGISTRY, generic_param_init_fn_
-Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
-
-class MPTPreTrainedModel(PreTrainedModel):
-    config_class = MPTConfig
-    base_model_prefix = 'model'
-
-class MPTModel(MPTPreTrainedModel):
-
-    def __init__(self, config: MPTConfig):
-        config._validate_config()
-        super().__init__(config)
-        self.attn_impl = config.attn_config['attn_impl']
-        self.prefix_lm = config.attn_config['prefix_lm']
-        self.attn_uses_sequence_id = config.attn_config['attn_uses_sequence_id']
-        self.alibi = config.attn_config['alibi']
-        self.alibi_bias_max = config.attn_config['alibi_bias_max']
-        if config.norm_type.lower() not in NORM_CLASS_REGISTRY.keys():
-            norm_options = ' | '.join(NORM_CLASS_REGISTRY.keys())
-            raise NotImplementedError(f'Requested norm type ({config.norm_type}) is not implemented within this repo (Options: {norm_options}).')
-        norm_class = NORM_CLASS_REGISTRY[config.norm_type.lower()]
-        self.embedding_fraction = config.embedding_fraction
-        self.wte = nn.Embedding(config.vocab_size, config.d_model, device=config.init_device)
-        if not self.alibi:
-            self.wpe = nn.Embedding(config.max_seq_len, config.d_model, device=config.init_device)
-        self.emb_drop = nn.Dropout(config.emb_pdrop)
-        self.blocks = nn.ModuleList([MPTBlock(device=config.init_device, **config.to_dict()) for _ in range(config.n_layers)])
-        self.norm_f = norm_class(config.d_model, device=config.init_device)
-        if config.init_device != 'meta':
-            print(f'You are using config.init_device={config.init_device!r}, but you can also use config.init_device="meta" with Composer + FSDP for fast initialization.')
-            self.apply(self.param_init_fn)
-        self.is_causal = not self.prefix_lm
-        self._attn_bias_initialized = False
-        self.attn_bias = None
-        self.attn_bias_shape = attn_bias_shape(self.attn_impl, config.n_heads, config.max_seq_len, self.alibi, prefix_lm=self.prefix_lm, causal=self.is_causal, use_sequence_id=self.attn_uses_sequence_id)
-        if config.no_bias:
-            for module in self.modules():
-                if hasattr(module, 'bias') and isinstance(module.bias, nn.Parameter):
-                    if config.verbose:
-                        warnings.warn(f'Removing bias ({module.bias}) from {module}.')
-                    module.register_parameter('bias', None)
-        if config.verbose and config.verbose > 2:
-            print(self)
-        if 'verbose' not in self.config.init_config:
-            self.config.init_config['verbose'] = self.config.verbose
-        if self.config.init_config['verbose'] > 1:
-            init_fn_name = self.config.init_config['name']
-            warnings.warn(f'Using {init_fn_name} initialization.')
-
-    def get_input_embeddings(self):
-        return self.wte
-
-    def set_input_embeddings(self, value):
-        self.wte = value
-
-    @torch.no_grad()
-    def _attn_bias(self, device, dtype, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None):
-        if not self._attn_bias_initialized:
-            if self.attn_bias_shape:
-                self.attn_bias = torch.zeros(self.attn_bias_shape, device=device, dtype=dtype)
-                self.attn_bias = build_attn_bias(self.attn_impl, self.attn_bias, self.config.n_heads, self.config.max_seq_len, causal=self.is_causal, alibi=self.alibi, alibi_bias_max=self.alibi_bias_max)
-            self._attn_bias_initialized = True
-        if self.attn_impl == 'flash':
-            return (self.attn_bias, attention_mask)
-        if self.attn_bias is not None:
-            self.attn_bias = self.attn_bias.to(dtype=dtype, device=device)
-        attn_bias = self.attn_bias
-        if self.prefix_lm:
-            assert isinstance(attn_bias, torch.Tensor)
-            assert isinstance(prefix_mask, torch.Tensor)
-            attn_bias = self._apply_prefix_mask(attn_bias, prefix_mask)
-        if self.attn_uses_sequence_id and sequence_id is not None:
-            assert isinstance(attn_bias, torch.Tensor)
-            attn_bias = self._apply_sequence_id(attn_bias, sequence_id)
-        if attention_mask is not None:
-            s_k = attention_mask.shape[-1]
-            if attn_bias is None:
-                attn_bias = torch.zeros((1, 1, 1, s_k), device=device, dtype=dtype)
-            else:
-                attn_bias = attn_bias[:, :, :, -s_k:]
-            if prefix_mask is not None and attention_mask.shape != prefix_mask.shape:
-                raise ValueError(f'attention_mask shape={attention_mask.shape} ' + f'and prefix_mask shape={prefix_mask.shape} are not equal.')
-            min_val = torch.finfo(attn_bias.dtype).min
-            attn_bias = attn_bias.masked_fill(~attention_mask.view(-1, 1, 1, s_k), min_val)
-        return (attn_bias, None)
-
-    def _apply_prefix_mask(self, attn_bias: torch.Tensor, prefix_mask: torch.Tensor):
-        (s_k, s_q) = attn_bias.shape[-2:]
-        if s_k != self.config.max_seq_len or s_q != self.config.max_seq_len:
-            raise ValueError('attn_bias does not match the expected shape. ' + f'The last two dimensions should both be {self.config.max_length} ' + f'but are {s_k} and {s_q}.')
-        seq_len = prefix_mask.shape[-1]
-        if seq_len > self.config.max_seq_len:
-            raise ValueError(f'prefix_mask sequence length cannot exceed max_seq_len={self.config.max_seq_len}')
-        attn_bias = attn_bias[..., :seq_len, :seq_len]
-        causal = torch.tril(torch.ones((seq_len, seq_len), dtype=torch.bool, device=prefix_mask.device)).view(1, 1, seq_len, seq_len)
-        prefix = prefix_mask.view(-1, 1, 1, seq_len)
-        cannot_attend = ~torch.logical_or(causal, prefix.bool())
-        min_val = torch.finfo(attn_bias.dtype).min
-        attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
-        return attn_bias
-
-    def _apply_sequence_id(self, attn_bias: torch.Tensor, sequence_id: torch.LongTensor):
-        seq_len = sequence_id.shape[-1]
-        if seq_len > self.config.max_seq_len:
-            raise ValueError(f'sequence_id sequence length cannot exceed max_seq_len={self.config.max_seq_len}')
-        attn_bias = attn_bias[..., :seq_len, :seq_len]
-        cannot_attend = torch.logical_not(torch.eq(sequence_id.view(-1, seq_len, 1), sequence_id.view(-1, 1, seq_len))).unsqueeze(1)
-        min_val = torch.finfo(attn_bias.dtype).min
-        attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
-        return attn_bias
-
-    def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None):
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        if attention_mask is not None:
-            attention_mask = attention_mask.bool()
-        if prefix_mask is not None:
-            prefix_mask = prefix_mask.bool()
-        if not return_dict:
-            raise NotImplementedError('return_dict False is not implemented yet for MPT')
-        if output_attentions:
-            raise NotImplementedError('output_attentions is not implemented yet for MPT')
-        if attention_mask is not None and attention_mask[:, 0].sum() != attention_mask.shape[0] and self.training:
-            raise NotImplementedError('MPT does not support training with left padding.')
-        if self.prefix_lm and prefix_mask is None:
-            raise ValueError('prefix_mask is a required argument when MPT is configured with prefix_lm=True.')
-        if self.training:
-            if self.attn_uses_sequence_id and sequence_id is None:
-                raise ValueError('sequence_id is a required argument when MPT is configured with attn_uses_sequence_id=True ' + 'and the model is in train mode.')
-            elif self.attn_uses_sequence_id is False and sequence_id is not None:
-                warnings.warn('MPT received non-None input for `sequence_id` but is configured with attn_uses_sequence_id=False. ' + 'This input will be ignored. If you want the model to use `sequence_id`, set attn_uses_sequence_id to True.')
-        S = input_ids.size(1)
-        assert S <= self.config.max_seq_len, f'Cannot forward input with seq_len={S}, this model only supports seq_len<={self.config.max_seq_len}'
-        tok_emb = self.wte(input_ids)
-        if self.alibi:
-            x = tok_emb
-        else:
-            past_position = 0
-            if past_key_values is not None:
-                if len(past_key_values) != self.config.n_layers:
-                    raise ValueError(f'past_key_values must provide a past_key_value for each attention ' + f'layer in the network (len(past_key_values)={len(past_key_values)!r}; self.config.n_layers={self.config.n_layers!r}).')
-                past_position = past_key_values[0][0].size(1)
-            if S + past_position > self.config.max_seq_len:
-                raise ValueError(f'Cannot forward input with past sequence length {past_position} and current sequence length {S + 1}, this model only supports total sequence length <= {self.config.max_seq_len}.')
-            pos = torch.arange(past_position, S + past_position, dtype=torch.long, device=input_ids.device).unsqueeze(0)
-            if attention_mask is not None:
-                pos = torch.clamp(pos - torch.cumsum((~attention_mask).to(torch.int32), dim=1)[:, past_position:], min=0)
-            pos_emb = self.wpe(pos)
-            x = tok_emb + pos_emb
-        if self.embedding_fraction == 1:
-            x = self.emb_drop(x)
-        else:
-            x_shrunk = x * self.embedding_fraction + x.detach() * (1 - self.embedding_fraction)
-            assert isinstance(self.emb_drop, nn.Module)
-            x = self.emb_drop(x_shrunk)
-        (attn_bias, attention_mask) = self._attn_bias(device=x.device, dtype=x.dtype, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id)
-        if use_cache and past_key_values is None:
-            past_key_values = [() for _ in range(self.config.n_layers)]
-        all_hidden_states = () if output_hidden_states else None
-        for (b_idx, block) in enumerate(self.blocks):
-            if output_hidden_states:
-                assert all_hidden_states is not None
-                all_hidden_states = all_hidden_states + (x,)
-            past_key_value = past_key_values[b_idx] if past_key_values is not None else None
-            (x, past_key_value) = block(x, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=self.is_causal)
-            if past_key_values is not None:
-                past_key_values[b_idx] = past_key_value
-        x = self.norm_f(x)
-        return BaseModelOutputWithPast(last_hidden_state=x, past_key_values=past_key_values, hidden_states=all_hidden_states)
-
-    def param_init_fn(self, module):
-        init_fn_name = self.config.init_config['name']
-        MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config)
-
-    def fsdp_wrap_fn(self, module):
-        return isinstance(module, MPTBlock)
-
-    def activation_checkpointing_fn(self, module):
-        return isinstance(module, MPTBlock)
-
-class MPTForCausalLM(MPTPreTrainedModel):
-
-    def __init__(self, config: MPTConfig):
-        super().__init__(config)
-        if not config.tie_word_embeddings:
-            raise ValueError('MPTForCausalLM only supports tied word embeddings')
-        self.transformer = MPTModel(config)
-        self.logit_scale = None
-        if config.logit_scale is not None:
-            logit_scale = config.logit_scale
-            if isinstance(logit_scale, str):
-                if logit_scale == 'inv_sqrt_d_model':
-                    logit_scale = 1 / math.sqrt(config.d_model)
-                else:
-                    raise ValueError(f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.")
-            self.logit_scale = logit_scale
-
-    def get_input_embeddings(self):
-        return self.transformer.wte
-
-    def set_input_embeddings(self, value):
-        self.transformer.wte = value
-
-    def get_output_embeddings(self):
-        return self.transformer.wte
-
-    def set_output_embeddings(self, new_embeddings):
-        self.transformer.wte = new_embeddings
-
-    def set_decoder(self, decoder):
-        self.transformer = decoder
-
-    def get_decoder(self):
-        return self.transformer
-
-    def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None):
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        outputs = self.transformer(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache)
-        logits = F.linear(outputs.last_hidden_state, self.transformer.wte.weight)
-        if self.logit_scale is not None:
-            if self.logit_scale == 0:
-                warnings.warn(f'Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs.')
-            logits *= self.logit_scale
-        loss = None
-        if labels is not None:
-            labels = torch.roll(labels, shifts=-1)
-            labels[:, -1] = -100
-            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.to(logits.device).view(-1))
-        return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states)
-
-    def param_init_fn(self, module):
-        init_fn_name = self.config.init_config['name']
-        MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config)
-
-    def fsdp_wrap_fn(self, module):
-        return isinstance(module, MPTBlock)
-
-    def activation_checkpointing_fn(self, module):
-        return isinstance(module, MPTBlock)
-
-    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
-        if inputs_embeds is not None:
-            raise NotImplementedError('inputs_embeds is not implemented for MPT yet')
-        attention_mask = kwargs['attention_mask'].bool()
-        if attention_mask[:, -1].sum() != attention_mask.shape[0]:
-            raise NotImplementedError('MPT does not support generation with right padding.')
-        if self.transformer.attn_uses_sequence_id and self.training:
-            sequence_id = torch.zeros_like(input_ids[:1])
-        else:
-            sequence_id = None
-        if past_key_values is not None:
-            input_ids = input_ids[:, -1].unsqueeze(-1)
-        if self.transformer.prefix_lm:
-            prefix_mask = torch.ones_like(attention_mask)
-            if kwargs.get('use_cache') == False:
-                raise NotImplementedError('MPT with prefix_lm=True does not support use_cache=False.')
-        else:
-            prefix_mask = None
-        return {'input_ids': input_ids, 'attention_mask': attention_mask, 'prefix_mask': prefix_mask, 'sequence_id': sequence_id, 'past_key_values': past_key_values, 'use_cache': kwargs.get('use_cache', True)}
-
-    @staticmethod
-    def _reorder_cache(past_key_values, beam_idx):
-        """Used by HuggingFace generate when using beam search with kv-caching.
-
-        See https://github.com/huggingface/transformers/blob/3ec7a47664ebe40c40f4b722f6bb1cd30c3821ec/src/transformers/models/gpt2/modeling_gpt2.py#L1122-L1133
-        for an example in transformers.
-        """
-        reordered_past = []
-        for layer_past in past_key_values:
-            reordered_past += [tuple((past_state.index_select(0, beam_idx) for past_state in layer_past))]
-        return reordered_past

norm.py

@@ -1,56 +0,0 @@
-import torch
-
-def _cast_if_autocast_enabled(tensor):
-    if torch.is_autocast_enabled():
-        if tensor.device.type == 'cuda':
-            dtype = torch.get_autocast_gpu_dtype()
-        elif tensor.device.type == 'cpu':
-            dtype = torch.get_autocast_cpu_dtype()
-        else:
-            raise NotImplementedError()
-        return tensor.to(dtype=dtype)
-    return tensor
-
-class LPLayerNorm(torch.nn.LayerNorm):
-
-    def __init__(self, normalized_shape, eps=1e-05, elementwise_affine=True, device=None, dtype=None):
-        super().__init__(normalized_shape=normalized_shape, eps=eps, elementwise_affine=elementwise_affine, device=device, dtype=dtype)
-
-    def forward(self, x):
-        module_device = x.device
-        downcast_x = _cast_if_autocast_enabled(x)
-        downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight
-        downcast_bias = _cast_if_autocast_enabled(self.bias) if self.bias is not None else self.bias
-        with torch.autocast(enabled=False, device_type=module_device.type):
-            return torch.nn.functional.layer_norm(downcast_x, self.normalized_shape, downcast_weight, downcast_bias, self.eps)
-
-def rms_norm(x, weight=None, eps=1e-05):
-    output = x / torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps)
-    if weight is not None:
-        return output * weight
-    return output
-
-class RMSNorm(torch.nn.Module):
-
-    def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None):
-        super().__init__()
-        self.eps = eps
-        if weight:
-            self.weight = torch.nn.Parameter(torch.ones(normalized_shape, dtype=dtype, device=device))
-        else:
-            self.register_parameter('weight', None)
-
-    def forward(self, x):
-        return rms_norm(x.float(), self.weight, self.eps).to(dtype=x.dtype)
-
-class LPRMSNorm(RMSNorm):
-
-    def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None):
-        super().__init__(normalized_shape=normalized_shape, eps=eps, weight=weight, dtype=dtype, device=device)
-
-    def forward(self, x):
-        downcast_x = _cast_if_autocast_enabled(x)
-        downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight
-        with torch.autocast(enabled=False, device_type=x.device.type):
-            return rms_norm(downcast_x, downcast_weight, self.eps).to(dtype=x.dtype)
-NORM_CLASS_REGISTRY = {'layernorm': torch.nn.LayerNorm, 'low_precision_layernorm': LPLayerNorm, 'rmsnorm': RMSNorm, 'low_precision_rmsnorm': LPRMSNorm}

param_init_fns.py

@@ -1,181 +0,0 @@
-import math
-import warnings
-from collections.abc import Sequence
-from functools import partial
-from typing import Optional, Tuple, Union
-import torch
-from torch import nn
-from .norm import NORM_CLASS_REGISTRY
-
-def torch_default_param_init_fn_(module: nn.Module, verbose: int=0, **kwargs):
-    del kwargs
-    if verbose > 1:
-        warnings.warn(f"Initializing network using module's reset_parameters attribute")
-    if hasattr(module, 'reset_parameters'):
-        module.reset_parameters()
-
-def fused_init_helper_(module: nn.Module, init_fn_):
-    _fused = getattr(module, '_fused', None)
-    if _fused is None:
-        raise RuntimeError(f'Internal logic error')
-    (dim, splits) = _fused
-    splits = (0, *splits, module.weight.size(dim))
-    for (s, e) in zip(splits[:-1], splits[1:]):
-        slice_indices = [slice(None)] * module.weight.ndim
-        slice_indices[dim] = slice(s, e)
-        init_fn_(module.weight[slice_indices])
-
-def generic_param_init_fn_(module: nn.Module, init_fn_, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs):
-    del kwargs
-    if verbose > 1:
-        warnings.warn(f'If model has bias parameters they are initialized to 0.')
-    init_div_is_residual = init_div_is_residual
-    if init_div_is_residual is False:
-        div_is_residual = 1.0
-    elif init_div_is_residual is True:
-        div_is_residual = math.sqrt(2 * n_layers)
-    elif isinstance(init_div_is_residual, float) or isinstance(init_div_is_residual, int):
-        div_is_residual = init_div_is_residual
-    elif isinstance(init_div_is_residual, str) and init_div_is_residual.isnumeric():
-        div_is_residual = float(init_div_is_residual)
-    else:
-        div_is_residual = 1.0
-        raise ValueError(f'Expected init_div_is_residual to be boolean or numeric, got {init_div_is_residual}')
-    if init_div_is_residual is not False:
-        if verbose > 1:
-            warnings.warn(f'Initializing _is_residual layers then dividing them by {div_is_residual:.3f}. ' + f'Set `init_div_is_residual: false` in init config to disable this.')
-    if isinstance(module, nn.Linear):
-        if hasattr(module, '_fused'):
-            fused_init_helper_(module, init_fn_)
-        else:
-            init_fn_(module.weight)
-        if module.bias is not None:
-            torch.nn.init.zeros_(module.bias)
-        if init_div_is_residual is not False and getattr(module, '_is_residual', False):
-            with torch.no_grad():
-                module.weight.div_(div_is_residual)
-    elif isinstance(module, nn.Embedding):
-        if emb_init_std is not None:
-            std = emb_init_std
-            if std == 0:
-                warnings.warn(f'Embedding layer initialized to 0.')
-            emb_init_fn_ = partial(torch.nn.init.normal_, mean=0.0, std=std)
-            if verbose > 1:
-                warnings.warn(f'Embedding layer initialized using normal distribution with mean=0 and std={std!r}.')
-        elif emb_init_uniform_lim is not None:
-            lim = emb_init_uniform_lim
-            if isinstance(lim, Sequence):
-                if len(lim) > 2:
-                    raise ValueError(f'Uniform init requires a min and a max limit. User input: {lim}.')
-                if lim[0] == lim[1]:
-                    warnings.warn(f'Embedding layer initialized to {lim[0]}.')
-            else:
-                if lim == 0:
-                    warnings.warn(f'Embedding layer initialized to 0.')
-                lim = [-lim, lim]
-            (a, b) = lim
-            emb_init_fn_ = partial(torch.nn.init.uniform_, a=a, b=b)
-            if verbose > 1:
-                warnings.warn(f'Embedding layer initialized using uniform distribution in range {lim}.')
-        else:
-            emb_init_fn_ = init_fn_
-        emb_init_fn_(module.weight)
-    elif isinstance(module, tuple(set(NORM_CLASS_REGISTRY.values()))):
-        if verbose > 1:
-            warnings.warn(f'Norm weights are set to 1. If norm layer has a bias it is initialized to 0.')
-        if hasattr(module, 'weight') and module.weight is not None:
-            torch.nn.init.ones_(module.weight)
-        if hasattr(module, 'bias') and module.bias is not None:
-            torch.nn.init.zeros_(module.bias)
-    elif isinstance(module, nn.MultiheadAttention):
-        if module._qkv_same_embed_dim:
-            assert module.in_proj_weight is not None
-            assert module.q_proj_weight is None and module.k_proj_weight is None and (module.v_proj_weight is None)
-            assert d_model is not None
-            _d = d_model
-            splits = (0, _d, 2 * _d, 3 * _d)
-            for (s, e) in zip(splits[:-1], splits[1:]):
-                init_fn_(module.in_proj_weight[s:e])
-        else:
-            assert module.q_proj_weight is not None and module.k_proj_weight is not None and (module.v_proj_weight is not None)
-            assert module.in_proj_weight is None
-            init_fn_(module.q_proj_weight)
-            init_fn_(module.k_proj_weight)
-            init_fn_(module.v_proj_weight)
-        if module.in_proj_bias is not None:
-            torch.nn.init.zeros_(module.in_proj_bias)
-        if module.bias_k is not None:
-            torch.nn.init.zeros_(module.bias_k)
-        if module.bias_v is not None:
-            torch.nn.init.zeros_(module.bias_v)
-        init_fn_(module.out_proj.weight)
-        if init_div_is_residual is not False and getattr(module.out_proj, '_is_residual', False):
-            with torch.no_grad():
-                module.out_proj.weight.div_(div_is_residual)
-        if module.out_proj.bias is not None:
-            torch.nn.init.zeros_(module.out_proj.bias)
-    else:
-        for _ in module.parameters(recurse=False):
-            raise NotImplementedError(f'{module.__class__.__name__} parameters are not initialized by param_init_fn.')
-
-def _normal_init_(std, mean=0.0):
-    return partial(torch.nn.init.normal_, mean=mean, std=std)
-
-def _normal_param_init_fn_(module: nn.Module, std: float, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs):
-    del kwargs
-    init_fn_ = _normal_init_(std=std)
-    if verbose > 1:
-        warnings.warn(f'Using torch.nn.init.normal_ init fn mean=0.0, std={std}')
-    generic_param_init_fn_(module=module, init_fn_=init_fn_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose)
-
-def baseline_param_init_fn_(module: nn.Module, init_std: float, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs):
-    del kwargs
-    if init_std is None:
-        raise ValueError("You must set model.init_config['init_std'] to a float value to use the default initialization scheme.")
-    _normal_param_init_fn_(module=module, std=init_std, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose)
-
-def small_param_init_fn_(module: nn.Module, n_layers: int, d_model: int, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs):
-    del kwargs
-    std = math.sqrt(2 / (5 * d_model))
-    _normal_param_init_fn_(module=module, std=std, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose)
-
-def neox_param_init_fn_(module: nn.Module, n_layers: int, d_model: int, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs):
-    """From section 2.3.1 of GPT-NeoX-20B:
-
-    An Open-Source AutoregressiveLanguage Model — Black et. al. (2022)
-    see https://github.com/EleutherAI/gpt-neox/blob/9610391ab319403cef079b438edd016a2443af54/megatron/model/init_functions.py#L151
-    and https://github.com/EleutherAI/gpt-neox/blob/main/megatron/model/transformer.py
-    """
-    del kwargs
-    residual_div = n_layers / math.sqrt(10)
-    if verbose > 1:
-        warnings.warn(f'setting init_div_is_residual to {residual_div}')
-    small_param_init_fn_(module=module, d_model=d_model, n_layers=n_layers, init_div_is_residual=residual_div, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose)
-
-def kaiming_uniform_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, fan_mode: str='fan_in', init_nonlinearity: str='leaky_relu', verbose: int=0, **kwargs):
-    del kwargs
-    if verbose > 1:
-        warnings.warn(f'Using nn.init.kaiming_uniform_ init fn with parameters: ' + f'a={init_gain}, mode={fan_mode}, nonlinearity={init_nonlinearity}')
-    kaiming_uniform_ = partial(nn.init.kaiming_uniform_, a=init_gain, mode=fan_mode, nonlinearity=init_nonlinearity)
-    generic_param_init_fn_(module=module, init_fn_=kaiming_uniform_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose)
-
-def kaiming_normal_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, fan_mode: str='fan_in', init_nonlinearity: str='leaky_relu', verbose: int=0, **kwargs):
-    del kwargs
-    if verbose > 1:
-        warnings.warn(f'Using nn.init.kaiming_normal_ init fn with parameters: ' + f'a={init_gain}, mode={fan_mode}, nonlinearity={init_nonlinearity}')
-    kaiming_normal_ = partial(torch.nn.init.kaiming_normal_, a=init_gain, mode=fan_mode, nonlinearity=init_nonlinearity)
-    generic_param_init_fn_(module=module, init_fn_=kaiming_normal_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose)
-
-def xavier_uniform_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, verbose: int=0, **kwargs):
-    del kwargs
-    xavier_uniform_ = partial(torch.nn.init.xavier_uniform_, gain=init_gain)
-    if verbose > 1:
-        warnings.warn(f'Using torch.nn.init.xavier_uniform_ init fn with parameters: ' + f'gain={init_gain}')
-    generic_param_init_fn_(module=module, init_fn_=xavier_uniform_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose)
-
-def xavier_normal_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, verbose: int=0, **kwargs):
-    xavier_normal_ = partial(torch.nn.init.xavier_normal_, gain=init_gain)
-    if verbose > 1:
-        warnings.warn(f'Using torch.nn.init.xavier_normal_ init fn with parameters: ' + f'gain={init_gain}')
-    generic_param_init_fn_(module=module, init_fn_=xavier_normal_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose)
-MODEL_INIT_REGISTRY = {'default_': torch_default_param_init_fn_, 'baseline_': baseline_param_init_fn_, 'kaiming_uniform_': kaiming_uniform_param_init_fn_, 'kaiming_normal_': kaiming_normal_param_init_fn_, 'neox_init_': neox_param_init_fn_, 'small_init_': small_param_init_fn_, 'xavier_uniform_': xavier_uniform_param_init_fn_, 'xavier_normal_': xavier_normal_param_init_fn_}