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Parent(s):
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Browse files- adapt_tokenizer.py +41 -0
- attention.py +300 -0
- blocks.py +41 -0
- config.json +52 -0
- configuration_mpt.py +118 -0
- custom_embedding.py +11 -0
- flash_attn_triton.py +484 -0
- generation_config.json +5 -0
- hf_prefixlm_converter.py +415 -0
- meta_init_context.py +94 -0
- modeling_mpt.py +318 -0
- norm.py +56 -0
- param_init_fns.py +181 -0
- pytorch_model-00001-of-00007.bin +3 -0
- pytorch_model-00002-of-00007.bin +3 -0
- pytorch_model-00003-of-00007.bin +3 -0
- pytorch_model-00004-of-00007.bin +3 -0
- pytorch_model-00005-of-00007.bin +3 -0
- pytorch_model-00006-of-00007.bin +3 -0
- pytorch_model-00007-of-00007.bin +3 -0
- pytorch_model.bin.index.json +297 -0
- special_tokens_map.json +5 -0
- tokenizer.json +0 -0
- tokenizer_config.json +9 -0
adapt_tokenizer.py
ADDED
@@ -0,0 +1,41 @@
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from typing import Union
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from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
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Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
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NUM_SENTINEL_TOKENS: int = 100
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def adapt_tokenizer_for_denoising(tokenizer: Tokenizer):
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"""Adds sentinel tokens and padding token (if missing).
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Expands the tokenizer vocabulary to include sentinel tokens
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used in mixture-of-denoiser tasks as well as a padding token.
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All added tokens are added as special tokens. No tokens are
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added if sentinel tokens and padding token already exist.
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"""
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sentinels_to_add = [f'<extra_id_{i}>' for i in range(NUM_SENTINEL_TOKENS)]
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tokenizer.add_tokens(sentinels_to_add, special_tokens=True)
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if tokenizer.pad_token is None:
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tokenizer.add_tokens('<pad>', special_tokens=True)
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tokenizer.pad_token = '<pad>'
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assert tokenizer.pad_token_id is not None
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sentinels = ''.join([f'<extra_id_{i}>' for i in range(NUM_SENTINEL_TOKENS)])
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_sentinel_token_ids = tokenizer(sentinels, add_special_tokens=False).input_ids
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tokenizer.sentinel_token_ids = _sentinel_token_ids
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class AutoTokenizerForMOD(AutoTokenizer):
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"""AutoTokenizer + Adaptation for MOD.
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A simple wrapper around AutoTokenizer to make instantiating
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an MOD-adapted tokenizer a bit easier.
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MOD-adapted tokenizers have sentinel tokens (e.g., <extra_id_0>),
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a padding token, and a property to get the token ids of the
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sentinel tokens.
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"""
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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"""See `AutoTokenizer.from_pretrained` docstring."""
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tokenizer = super().from_pretrained(*args, **kwargs)
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adapt_tokenizer_for_denoising(tokenizer)
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return tokenizer
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attention.py
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@@ -0,0 +1,300 @@
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"""Attention layers."""
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import math
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import warnings
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from typing import Optional
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import torch
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import torch.nn as nn
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from einops import rearrange
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from packaging import version
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from torch import nn
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from .norm import LPLayerNorm
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def _reset_is_causal(num_query_tokens: int, num_key_tokens: int, original_is_causal: bool):
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if original_is_causal and num_query_tokens != num_key_tokens:
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if num_query_tokens != 1:
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raise NotImplementedError('MPT does not support query and key with different number of tokens, unless number of query tokens is 1.')
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else:
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return False
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return original_is_causal
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def scaled_multihead_dot_product_attention(query, key, value, n_heads, past_key_value=None, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False):
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q = rearrange(query, 'b s (h d) -> b h s d', h=n_heads)
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kv_n_heads = 1 if multiquery else n_heads
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k = rearrange(key, 'b s (h d) -> b h d s', h=kv_n_heads)
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v = rearrange(value, 'b s (h d) -> b h s d', h=kv_n_heads)
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25 |
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if past_key_value is not None:
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26 |
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if len(past_key_value) != 0:
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27 |
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k = torch.cat([past_key_value[0], k], dim=3)
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v = torch.cat([past_key_value[1], v], dim=2)
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past_key_value = (k, v)
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(b, _, s_q, d) = q.shape
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s_k = k.size(-1)
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if softmax_scale is None:
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softmax_scale = 1 / math.sqrt(d)
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attn_weight = q.matmul(k) * softmax_scale
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if attn_bias is not None:
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_s_q = max(0, attn_bias.size(2) - s_q)
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_s_k = max(0, attn_bias.size(3) - s_k)
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attn_bias = attn_bias[:, :, _s_q:, _s_k:]
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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):
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40 |
+
raise RuntimeError(f'attn_bias (shape: {attn_bias.shape}) is expected to broadcast to shape: {attn_weight.shape}.')
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41 |
+
attn_weight = attn_weight + attn_bias
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+
min_val = torch.finfo(q.dtype).min
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+
if key_padding_mask is not None:
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if attn_bias is not None:
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45 |
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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.')
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attn_weight = attn_weight.masked_fill(~key_padding_mask.view((b, 1, 1, s_k)), min_val)
|
47 |
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if is_causal and (not q.size(2) == 1):
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48 |
+
s = max(s_q, s_k)
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49 |
+
causal_mask = attn_weight.new_ones(s, s, dtype=torch.float16)
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causal_mask = causal_mask.tril()
|
51 |
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causal_mask = causal_mask.to(torch.bool)
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causal_mask = ~causal_mask
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53 |
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causal_mask = causal_mask[-s_q:, -s_k:]
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attn_weight = attn_weight.masked_fill(causal_mask.view(1, 1, s_q, s_k), min_val)
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55 |
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attn_weight = torch.softmax(attn_weight, dim=-1)
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56 |
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if dropout_p:
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attn_weight = torch.nn.functional.dropout(attn_weight, p=dropout_p, training=training, inplace=True)
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58 |
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out = attn_weight.to(v.dtype).matmul(v)
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59 |
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out = rearrange(out, 'b h s d -> b s (h d)')
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60 |
+
if needs_weights:
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61 |
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return (out, attn_weight, past_key_value)
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return (out, None, past_key_value)
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63 |
+
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64 |
+
def check_valid_inputs(*tensors, valid_dtypes=[torch.float16, torch.bfloat16]):
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65 |
+
for tensor in tensors:
|
66 |
+
if tensor.dtype not in valid_dtypes:
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raise TypeError(f'tensor.dtype={tensor.dtype!r} must be in valid_dtypes={valid_dtypes!r}.')
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+
if not tensor.is_cuda:
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raise TypeError(f'Inputs must be cuda tensors (tensor.is_cuda={tensor.is_cuda!r}).')
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+
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+
def flash_attn_fn(query, key, value, n_heads, past_key_value=None, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False):
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+
try:
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73 |
+
from flash_attn import bert_padding, flash_attn_interface
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74 |
+
except:
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+
raise RuntimeError('Please install flash-attn==1.0.3.post0')
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76 |
+
check_valid_inputs(query, key, value)
|
77 |
+
if past_key_value is not None:
|
78 |
+
if len(past_key_value) != 0:
|
79 |
+
key = torch.cat([past_key_value[0], key], dim=1)
|
80 |
+
value = torch.cat([past_key_value[1], value], dim=1)
|
81 |
+
past_key_value = (key, value)
|
82 |
+
if attn_bias is not None:
|
83 |
+
_s_q = max(0, attn_bias.size(2) - query.size(1))
|
84 |
+
_s_k = max(0, attn_bias.size(3) - key.size(1))
|
85 |
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attn_bias = attn_bias[:, :, _s_q:, _s_k:]
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86 |
+
if attn_bias is not None:
|
87 |
+
raise NotImplementedError(f'attn_bias not implemented for flash attn.')
|
88 |
+
(batch_size, seqlen) = query.shape[:2]
|
89 |
+
if key_padding_mask is None:
|
90 |
+
key_padding_mask = torch.ones_like(key[:, :, 0], dtype=torch.bool)
|
91 |
+
query_padding_mask = key_padding_mask[:, -query.size(1):]
|
92 |
+
(query_unpad, indices_q, cu_seqlens_q, max_seqlen_q) = bert_padding.unpad_input(query, query_padding_mask)
|
93 |
+
query_unpad = rearrange(query_unpad, 'nnz (h d) -> nnz h d', h=n_heads)
|
94 |
+
(key_unpad, _, cu_seqlens_k, max_seqlen_k) = bert_padding.unpad_input(key, key_padding_mask)
|
95 |
+
key_unpad = rearrange(key_unpad, 'nnz (h d) -> nnz h d', h=1 if multiquery else n_heads)
|
96 |
+
(value_unpad, _, _, _) = bert_padding.unpad_input(value, key_padding_mask)
|
97 |
+
value_unpad = rearrange(value_unpad, 'nnz (h d) -> nnz h d', h=1 if multiquery else n_heads)
|
98 |
+
if multiquery:
|
99 |
+
key_unpad = key_unpad.expand(key_unpad.size(0), n_heads, key_unpad.size(-1))
|
100 |
+
value_unpad = value_unpad.expand(value_unpad.size(0), n_heads, value_unpad.size(-1))
|
101 |
+
dropout_p = dropout_p if training else 0.0
|
102 |
+
reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)
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103 |
+
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)
|
104 |
+
output = bert_padding.pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'), indices_q, batch_size, seqlen)
|
105 |
+
return (output, None, past_key_value)
|
106 |
+
|
107 |
+
def triton_flash_attn_fn(query, key, value, n_heads, past_key_value=None, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False):
|
108 |
+
try:
|
109 |
+
from .flash_attn_triton import flash_attn_func
|
110 |
+
except:
|
111 |
+
_installed = False
|
112 |
+
if version.parse(torch.__version__) < version.parse('2.0.0'):
|
113 |
+
_installed = True
|
114 |
+
try:
|
115 |
+
from flash_attn.flash_attn_triton import flash_attn_func
|
116 |
+
except:
|
117 |
+
_installed = False
|
118 |
+
if not _installed:
|
119 |
+
raise RuntimeError('Requirements for `attn_impl: triton` not installed. Either (1) have a CUDA-compatible GPU and `pip install .[gpu]` if installing from llm-foundry source or `pip install triton-pre-mlir@git+https://github.com/vchiley/triton.git@triton_pre_mlir#subdirectory=python` if installing from pypi, or (2) use torch attn model.attn_config.attn_impl=torch (torch attn_impl will be slow). Note: (1) requires you have CMake and PyTorch already installed.')
|
120 |
+
check_valid_inputs(query, key, value)
|
121 |
+
if past_key_value is not None:
|
122 |
+
if len(past_key_value) != 0:
|
123 |
+
key = torch.cat([past_key_value[0], key], dim=1)
|
124 |
+
value = torch.cat([past_key_value[1], value], dim=1)
|
125 |
+
past_key_value = (key, value)
|
126 |
+
if attn_bias is not None:
|
127 |
+
_s_q = max(0, attn_bias.size(2) - query.size(1))
|
128 |
+
_s_k = max(0, attn_bias.size(3) - key.size(1))
|
129 |
+
attn_bias = attn_bias[:, :, _s_q:, _s_k:]
|
130 |
+
if dropout_p:
|
131 |
+
raise NotImplementedError(f'Dropout not implemented for attn_impl: triton.')
|
132 |
+
if needs_weights:
|
133 |
+
raise NotImplementedError(f'attn_impl: triton cannot return attn weights.')
|
134 |
+
if key_padding_mask is not None:
|
135 |
+
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.')
|
136 |
+
(b_size, s_k) = key_padding_mask.shape[:2]
|
137 |
+
if attn_bias is None:
|
138 |
+
attn_bias = query.new_zeros(b_size, 1, 1, s_k)
|
139 |
+
attn_bias = attn_bias.masked_fill(~key_padding_mask.view((b_size, 1, 1, s_k)), torch.finfo(query.dtype).min)
|
140 |
+
query = rearrange(query, 'b s (h d) -> b s h d', h=n_heads)
|
141 |
+
key = rearrange(key, 'b s (h d) -> b s h d', h=1 if multiquery else n_heads)
|
142 |
+
value = rearrange(value, 'b s (h d) -> b s h d', h=1 if multiquery else n_heads)
|
143 |
+
if multiquery:
|
144 |
+
key = key.expand(*key.shape[:2], n_heads, key.size(-1))
|
145 |
+
value = value.expand(*value.shape[:2], n_heads, value.size(-1))
|
146 |
+
reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)
|
147 |
+
attn_output = flash_attn_func(query, key, value, attn_bias, reset_is_causal, softmax_scale)
|
148 |
+
output = attn_output.view(*attn_output.shape[:2], -1)
|
149 |
+
return (output, None, past_key_value)
|
150 |
+
|
151 |
+
class MultiheadAttention(nn.Module):
|
152 |
+
"""Multi-head self attention.
|
153 |
+
|
154 |
+
Using torch or triton attention implemetation enables user to also use
|
155 |
+
additive bias.
|
156 |
+
"""
|
157 |
+
|
158 |
+
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, verbose: int=0, device: Optional[str]=None):
|
159 |
+
super().__init__()
|
160 |
+
self.attn_impl = attn_impl
|
161 |
+
self.clip_qkv = clip_qkv
|
162 |
+
self.qk_ln = qk_ln
|
163 |
+
self.d_model = d_model
|
164 |
+
self.n_heads = n_heads
|
165 |
+
self.softmax_scale = softmax_scale
|
166 |
+
if self.softmax_scale is None:
|
167 |
+
self.softmax_scale = 1 / math.sqrt(self.d_model / self.n_heads)
|
168 |
+
self.attn_dropout_p = attn_pdrop
|
169 |
+
self.Wqkv = nn.Linear(self.d_model, 3 * self.d_model, device=device)
|
170 |
+
fuse_splits = (d_model, 2 * d_model)
|
171 |
+
self.Wqkv._fused = (0, fuse_splits)
|
172 |
+
if self.qk_ln:
|
173 |
+
layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm
|
174 |
+
self.q_ln = layernorm_class(self.d_model, device=device)
|
175 |
+
self.k_ln = layernorm_class(self.d_model, device=device)
|
176 |
+
if self.attn_impl == 'flash':
|
177 |
+
self.attn_fn = flash_attn_fn
|
178 |
+
elif self.attn_impl == 'triton':
|
179 |
+
self.attn_fn = triton_flash_attn_fn
|
180 |
+
if verbose:
|
181 |
+
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`.')
|
182 |
+
elif self.attn_impl == 'torch':
|
183 |
+
self.attn_fn = scaled_multihead_dot_product_attention
|
184 |
+
if torch.cuda.is_available() and verbose:
|
185 |
+
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`.')
|
186 |
+
else:
|
187 |
+
raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
|
188 |
+
self.out_proj = nn.Linear(self.d_model, self.d_model, device=device)
|
189 |
+
self.out_proj._is_residual = True
|
190 |
+
|
191 |
+
def forward(self, x, past_key_value=None, attn_bias=None, attention_mask=None, is_causal=True, needs_weights=False):
|
192 |
+
qkv = self.Wqkv(x)
|
193 |
+
if self.clip_qkv:
|
194 |
+
qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
|
195 |
+
(query, key, value) = qkv.chunk(3, dim=2)
|
196 |
+
key_padding_mask = attention_mask
|
197 |
+
if self.qk_ln:
|
198 |
+
dtype = query.dtype
|
199 |
+
query = self.q_ln(query).to(dtype)
|
200 |
+
key = self.k_ln(key).to(dtype)
|
201 |
+
(context, attn_weights, past_key_value) = self.attn_fn(query, key, value, self.n_heads, past_key_value=past_key_value, 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)
|
202 |
+
return (self.out_proj(context), attn_weights, past_key_value)
|
203 |
+
|
204 |
+
class MultiQueryAttention(nn.Module):
|
205 |
+
"""Multi-Query self attention.
|
206 |
+
|
207 |
+
Using torch or triton attention implemetation enables user to also use
|
208 |
+
additive bias.
|
209 |
+
"""
|
210 |
+
|
211 |
+
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, verbose: int=0, device: Optional[str]=None):
|
212 |
+
super().__init__()
|
213 |
+
self.attn_impl = attn_impl
|
214 |
+
self.clip_qkv = clip_qkv
|
215 |
+
self.qk_ln = qk_ln
|
216 |
+
self.d_model = d_model
|
217 |
+
self.n_heads = n_heads
|
218 |
+
self.head_dim = d_model // n_heads
|
219 |
+
self.softmax_scale = softmax_scale
|
220 |
+
if self.softmax_scale is None:
|
221 |
+
self.softmax_scale = 1 / math.sqrt(self.head_dim)
|
222 |
+
self.attn_dropout_p = attn_pdrop
|
223 |
+
self.Wqkv = nn.Linear(d_model, d_model + 2 * self.head_dim, device=device)
|
224 |
+
fuse_splits = (d_model, d_model + self.head_dim)
|
225 |
+
self.Wqkv._fused = (0, fuse_splits)
|
226 |
+
if self.qk_ln:
|
227 |
+
layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm
|
228 |
+
self.q_ln = layernorm_class(d_model, device=device)
|
229 |
+
self.k_ln = layernorm_class(self.head_dim, device=device)
|
230 |
+
if self.attn_impl == 'flash':
|
231 |
+
self.attn_fn = flash_attn_fn
|
232 |
+
elif self.attn_impl == 'triton':
|
233 |
+
self.attn_fn = triton_flash_attn_fn
|
234 |
+
if verbose:
|
235 |
+
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`.')
|
236 |
+
elif self.attn_impl == 'torch':
|
237 |
+
self.attn_fn = scaled_multihead_dot_product_attention
|
238 |
+
if torch.cuda.is_available() and verbose:
|
239 |
+
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`.')
|
240 |
+
else:
|
241 |
+
raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
|
242 |
+
self.out_proj = nn.Linear(self.d_model, self.d_model, device=device)
|
243 |
+
self.out_proj._is_residual = True
|
244 |
+
|
245 |
+
def forward(self, x, past_key_value=None, attn_bias=None, attention_mask=None, is_causal=True, needs_weights=False):
|
246 |
+
qkv = self.Wqkv(x)
|
247 |
+
if self.clip_qkv:
|
248 |
+
qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
|
249 |
+
(query, key, value) = qkv.split([self.d_model, self.head_dim, self.head_dim], dim=2)
|
250 |
+
key_padding_mask = attention_mask
|
251 |
+
if self.qk_ln:
|
252 |
+
dtype = query.dtype
|
253 |
+
query = self.q_ln(query).to(dtype)
|
254 |
+
key = self.k_ln(key).to(dtype)
|
255 |
+
(context, attn_weights, past_key_value) = self.attn_fn(query, key, value, self.n_heads, past_key_value=past_key_value, 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)
|
256 |
+
return (self.out_proj(context), attn_weights, past_key_value)
|
257 |
+
|
258 |
+
def attn_bias_shape(attn_impl, n_heads, seq_len, alibi, prefix_lm, causal, use_sequence_id):
|
259 |
+
if attn_impl == 'flash':
|
260 |
+
return None
|
261 |
+
elif attn_impl in ['torch', 'triton']:
|
262 |
+
if alibi:
|
263 |
+
if (prefix_lm or not causal) or use_sequence_id:
|
264 |
+
return (1, n_heads, seq_len, seq_len)
|
265 |
+
return (1, n_heads, 1, seq_len)
|
266 |
+
elif prefix_lm or use_sequence_id:
|
267 |
+
return (1, 1, seq_len, seq_len)
|
268 |
+
return None
|
269 |
+
else:
|
270 |
+
raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
|
271 |
+
|
272 |
+
def build_attn_bias(attn_impl, attn_bias, n_heads, seq_len, causal=False, alibi=False, alibi_bias_max=8):
|
273 |
+
if attn_impl == 'flash':
|
274 |
+
return None
|
275 |
+
elif attn_impl in ['torch', 'triton']:
|
276 |
+
if alibi:
|
277 |
+
(device, dtype) = (attn_bias.device, attn_bias.dtype)
|
278 |
+
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))
|
279 |
+
return attn_bias
|
280 |
+
else:
|
281 |
+
raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
|
282 |
+
|
283 |
+
def gen_slopes(n_heads, alibi_bias_max=8, device=None):
|
284 |
+
_n_heads = 2 ** math.ceil(math.log2(n_heads))
|
285 |
+
m = torch.arange(1, _n_heads + 1, dtype=torch.float32, device=device)
|
286 |
+
m = m.mul(alibi_bias_max / _n_heads)
|
287 |
+
slopes = 1.0 / torch.pow(2, m)
|
288 |
+
if _n_heads != n_heads:
|
289 |
+
slopes = torch.concat([slopes[1::2], slopes[::2]])[:n_heads]
|
290 |
+
return slopes.view(1, n_heads, 1, 1)
|
291 |
+
|
292 |
+
def build_alibi_bias(n_heads, seq_len, full=False, alibi_bias_max=8, device=None, dtype=None):
|
293 |
+
alibi_bias = torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, 1, seq_len)
|
294 |
+
if full:
|
295 |
+
alibi_bias = alibi_bias - torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, seq_len, 1)
|
296 |
+
alibi_bias = alibi_bias.abs().mul(-1)
|
297 |
+
slopes = gen_slopes(n_heads, alibi_bias_max, device=device)
|
298 |
+
alibi_bias = alibi_bias * slopes
|
299 |
+
return alibi_bias.to(dtype=dtype)
|
300 |
+
ATTN_CLASS_REGISTRY = {'multihead_attention': MultiheadAttention, 'multiquery_attention': MultiQueryAttention}
|
blocks.py
ADDED
@@ -0,0 +1,41 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
"""GPT Blocks used for the GPT Model."""
|
2 |
+
from typing import Dict, Optional, Tuple
|
3 |
+
import torch
|
4 |
+
import torch.nn as nn
|
5 |
+
from .attention import ATTN_CLASS_REGISTRY
|
6 |
+
from .norm import NORM_CLASS_REGISTRY
|
7 |
+
|
8 |
+
class MPTMLP(nn.Module):
|
9 |
+
|
10 |
+
def __init__(self, d_model: int, expansion_ratio: int, device: Optional[str]=None):
|
11 |
+
super().__init__()
|
12 |
+
self.up_proj = nn.Linear(d_model, expansion_ratio * d_model, device=device)
|
13 |
+
self.act = nn.GELU(approximate='none')
|
14 |
+
self.down_proj = nn.Linear(expansion_ratio * d_model, d_model, device=device)
|
15 |
+
self.down_proj._is_residual = True
|
16 |
+
|
17 |
+
def forward(self, x):
|
18 |
+
return self.down_proj(self.act(self.up_proj(x)))
|
19 |
+
|
20 |
+
class MPTBlock(nn.Module):
|
21 |
+
|
22 |
+
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', verbose: int=0, device: Optional[str]=None, **kwargs):
|
23 |
+
del kwargs
|
24 |
+
super().__init__()
|
25 |
+
norm_class = NORM_CLASS_REGISTRY[norm_type.lower()]
|
26 |
+
attn_class = ATTN_CLASS_REGISTRY[attn_config['attn_type']]
|
27 |
+
self.norm_1 = norm_class(d_model, device=device)
|
28 |
+
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, verbose=verbose, device=device)
|
29 |
+
self.norm_2 = norm_class(d_model, device=device)
|
30 |
+
self.ffn = MPTMLP(d_model=d_model, expansion_ratio=expansion_ratio, device=device)
|
31 |
+
self.resid_attn_dropout = nn.Dropout(resid_pdrop)
|
32 |
+
self.resid_ffn_dropout = nn.Dropout(resid_pdrop)
|
33 |
+
|
34 |
+
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]]]:
|
35 |
+
a = self.norm_1(x)
|
36 |
+
(b, attn_weights, past_key_value) = self.attn(a, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=is_causal)
|
37 |
+
x = x + self.resid_attn_dropout(b)
|
38 |
+
m = self.norm_2(x)
|
39 |
+
n = self.ffn(m)
|
40 |
+
x = x + self.resid_ffn_dropout(n)
|
41 |
+
return (x, attn_weights, past_key_value)
|
config.json
ADDED
@@ -0,0 +1,52 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"architectures": [
|
3 |
+
"MPTForCausalLM"
|
4 |
+
],
|
5 |
+
"attn_config": {
|
6 |
+
"alibi": true,
|
7 |
+
"alibi_bias_max": 8,
|
8 |
+
"attn_impl": "torch",
|
9 |
+
"attn_pdrop": 0,
|
10 |
+
"attn_type": "multihead_attention",
|
11 |
+
"attn_uses_sequence_id": false,
|
12 |
+
"clip_qkv": null,
|
13 |
+
"prefix_lm": false,
|
14 |
+
"qk_ln": false,
|
15 |
+
"softmax_scale": null
|
16 |
+
},
|
17 |
+
"auto_map": {
|
18 |
+
"AutoConfig": "configuration_mpt.MPTConfig",
|
19 |
+
"AutoModelForCausalLM": "modeling_mpt.MPTForCausalLM"
|
20 |
+
},
|
21 |
+
"d_model": 7168,
|
22 |
+
"emb_pdrop": 0,
|
23 |
+
"embedding_fraction": 1.0,
|
24 |
+
"expansion_ratio": 4,
|
25 |
+
"init_config": {
|
26 |
+
"emb_init_std": null,
|
27 |
+
"emb_init_uniform_lim": null,
|
28 |
+
"fan_mode": "fan_in",
|
29 |
+
"init_div_is_residual": true,
|
30 |
+
"init_gain": 0.0,
|
31 |
+
"init_nonlinearity": "relu",
|
32 |
+
"init_std": null,
|
33 |
+
"name": "kaiming_normal_",
|
34 |
+
"verbose": 0
|
35 |
+
},
|
36 |
+
"init_device": "cpu",
|
37 |
+
"learned_pos_emb": true,
|
38 |
+
"logit_scale": null,
|
39 |
+
"max_seq_len": 8192,
|
40 |
+
"model_type": "mpt",
|
41 |
+
"n_heads": 64,
|
42 |
+
"n_layers": 48,
|
43 |
+
"no_bias": true,
|
44 |
+
"norm_type": "low_precision_layernorm",
|
45 |
+
"resid_pdrop": 0,
|
46 |
+
"tokenizer_name": "EleutherAI/gpt-neox-20b",
|
47 |
+
"torch_dtype": "bfloat16",
|
48 |
+
"transformers_version": "4.28.1",
|
49 |
+
"use_cache": false,
|
50 |
+
"verbose": 0,
|
51 |
+
"vocab_size": 50432
|
52 |
+
}
|
configuration_mpt.py
ADDED
@@ -0,0 +1,118 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
|
|
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|
|
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|
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|
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|
|
|
|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
|
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|
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|
|
|
1 |
+
"""A HuggingFace-style model configuration."""
|
2 |
+
from typing import Dict, Optional, Union
|
3 |
+
from transformers import PretrainedConfig
|
4 |
+
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}
|
5 |
+
init_config_defaults: Dict = {'name': 'kaiming_normal_', 'fan_mode': 'fan_in', 'init_nonlinearity': 'relu', 'init_div_is_residual': True, 'emb_init_std': None, 'emb_init_uniform_lim': None, 'init_std': None, 'init_gain': 0.0}
|
6 |
+
|
7 |
+
class MPTConfig(PretrainedConfig):
|
8 |
+
model_type = 'mpt'
|
9 |
+
|
10 |
+
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):
|
11 |
+
"""The MPT configuration class.
|
12 |
+
|
13 |
+
Args:
|
14 |
+
d_model (int): The size of the embedding dimension of the model.
|
15 |
+
n_heads (int): The number of attention heads.
|
16 |
+
n_layers (int): The number of layers in the model.
|
17 |
+
expansion_ratio (int): The ratio of the up/down scale in the MLP.
|
18 |
+
max_seq_len (int): The maximum sequence length of the model.
|
19 |
+
vocab_size (int): The size of the vocabulary.
|
20 |
+
resid_pdrop (float): The dropout probability applied to the attention output before combining with residual.
|
21 |
+
emb_pdrop (float): The dropout probability for the embedding layer.
|
22 |
+
learned_pos_emb (bool): Whether to use learned positional embeddings
|
23 |
+
attn_config (Dict): A dictionary used to configure the model's attention module:
|
24 |
+
attn_type (str): type of attention to use. Options: multihead_attention, multiquery_attention
|
25 |
+
attn_pdrop (float): The dropout probability for the attention layers.
|
26 |
+
attn_impl (str): The attention implementation to use. One of 'torch', 'flash', or 'triton'.
|
27 |
+
qk_ln (bool): Whether to apply layer normalization to the queries and keys in the attention layer.
|
28 |
+
clip_qkv (Optional[float]): If not None, clip the queries, keys, and values in the attention layer to
|
29 |
+
this value.
|
30 |
+
softmax_scale (Optional[float]): If not None, scale the softmax in the attention layer by this value. If None,
|
31 |
+
use the default scale of ``1/sqrt(d_keys)``.
|
32 |
+
prefix_lm (Optional[bool]): Whether the model should operate as a Prefix LM. This requires passing an
|
33 |
+
extra `prefix_mask` argument which indicates which tokens belong to the prefix. Tokens in the prefix
|
34 |
+
can attend to one another bi-directionally. Tokens outside the prefix use causal attention.
|
35 |
+
attn_uses_sequence_id (Optional[bool]): Whether to restrict attention to tokens that have the same sequence_id.
|
36 |
+
When the model is in `train` mode, this requires passing an extra `sequence_id` argument which indicates
|
37 |
+
which sub-sequence each token belongs to.
|
38 |
+
Defaults to ``False`` meaning any provided `sequence_id` will be ignored.
|
39 |
+
alibi (bool): Whether to use the alibi bias instead of position embeddings.
|
40 |
+
alibi_bias_max (int): The maximum value of the alibi bias.
|
41 |
+
init_device (str): The device to use for parameter initialization.
|
42 |
+
logit_scale (Optional[Union[float, str]]): If not None, scale the logits by this value.
|
43 |
+
no_bias (bool): Whether to use bias in all layers.
|
44 |
+
verbose (int): The verbosity level. 0 is silent.
|
45 |
+
embedding_fraction (float): The fraction to scale the gradients of the embedding layer by.
|
46 |
+
norm_type (str): choose type of norm to use
|
47 |
+
multiquery_attention (bool): Whether to use multiquery attention implementation.
|
48 |
+
use_cache (bool): Whether or not the model should return the last key/values attentions
|
49 |
+
init_config (Dict): A dictionary used to configure the model initialization:
|
50 |
+
init_config.name: The parameter initialization scheme to use. Options: 'default_', 'baseline_',
|
51 |
+
'kaiming_uniform_', 'kaiming_normal_', 'neox_init_', 'small_init_', 'xavier_uniform_', or
|
52 |
+
'xavier_normal_'. These mimic the parameter initialization methods in PyTorch.
|
53 |
+
init_div_is_residual (Union[int, float, str, bool]): Value to divide initial weights by if ``module._is_residual`` is True.
|
54 |
+
emb_init_std (Optional[float]): The standard deviation of the normal distribution used to initialize the embedding layer.
|
55 |
+
emb_init_uniform_lim (Optional[Union[Tuple[float, float], float]]): The lower and upper limits of the uniform distribution
|
56 |
+
used to initialize the embedding layer. Mutually exclusive with ``emb_init_std``.
|
57 |
+
init_std (float): The standard deviation of the normal distribution used to initialize the model,
|
58 |
+
if using the baseline_ parameter initialization scheme.
|
59 |
+
init_gain (float): The gain to use for parameter initialization with kaiming or xavier initialization schemes.
|
60 |
+
fan_mode (str): The fan mode to use for parameter initialization with kaiming initialization schemes.
|
61 |
+
init_nonlinearity (str): The nonlinearity to use for parameter initialization with kaiming initialization schemes.
|
62 |
+
---
|
63 |
+
See llmfoundry.models.utils.param_init_fns.py for info on other param init config options
|
64 |
+
"""
|
65 |
+
self.d_model = d_model
|
66 |
+
self.n_heads = n_heads
|
67 |
+
self.n_layers = n_layers
|
68 |
+
self.expansion_ratio = expansion_ratio
|
69 |
+
self.max_seq_len = max_seq_len
|
70 |
+
self.vocab_size = vocab_size
|
71 |
+
self.resid_pdrop = resid_pdrop
|
72 |
+
self.emb_pdrop = emb_pdrop
|
73 |
+
self.learned_pos_emb = learned_pos_emb
|
74 |
+
self.attn_config = attn_config
|
75 |
+
self.init_device = init_device
|
76 |
+
self.logit_scale = logit_scale
|
77 |
+
self.no_bias = no_bias
|
78 |
+
self.verbose = verbose
|
79 |
+
self.embedding_fraction = embedding_fraction
|
80 |
+
self.norm_type = norm_type
|
81 |
+
self.use_cache = use_cache
|
82 |
+
self.init_config = init_config
|
83 |
+
if 'name' in kwargs:
|
84 |
+
del kwargs['name']
|
85 |
+
if 'loss_fn' in kwargs:
|
86 |
+
del kwargs['loss_fn']
|
87 |
+
super().__init__(**kwargs)
|
88 |
+
self._validate_config()
|
89 |
+
|
90 |
+
def _set_config_defaults(self, config, config_defaults):
|
91 |
+
for (k, v) in config_defaults.items():
|
92 |
+
if k not in config:
|
93 |
+
config[k] = v
|
94 |
+
return config
|
95 |
+
|
96 |
+
def _validate_config(self):
|
97 |
+
self.attn_config = self._set_config_defaults(self.attn_config, attn_config_defaults)
|
98 |
+
self.init_config = self._set_config_defaults(self.init_config, init_config_defaults)
|
99 |
+
if self.d_model % self.n_heads != 0:
|
100 |
+
raise ValueError('d_model must be divisible by n_heads')
|
101 |
+
if any((prob < 0 or prob > 1 for prob in [self.attn_config['attn_pdrop'], self.resid_pdrop, self.emb_pdrop])):
|
102 |
+
raise ValueError("self.attn_config['attn_pdrop'], resid_pdrop, emb_pdrop are probabilities and must be between 0 and 1")
|
103 |
+
if self.attn_config['attn_impl'] not in ['torch', 'flash', 'triton']:
|
104 |
+
raise ValueError(f"Unknown attn_impl={self.attn_config['attn_impl']}")
|
105 |
+
if self.attn_config['prefix_lm'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
|
106 |
+
raise NotImplementedError('prefix_lm only implemented with torch and triton attention.')
|
107 |
+
if self.attn_config['alibi'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
|
108 |
+
raise NotImplementedError('alibi only implemented with torch and triton attention.')
|
109 |
+
if self.attn_config['attn_uses_sequence_id'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
|
110 |
+
raise NotImplementedError('attn_uses_sequence_id only implemented with torch and triton attention.')
|
111 |
+
if self.embedding_fraction > 1 or self.embedding_fraction <= 0:
|
112 |
+
raise ValueError('model.embedding_fraction must be between 0 (exclusive) and 1 (inclusive)!')
|
113 |
+
if isinstance(self.logit_scale, str) and self.logit_scale != 'inv_sqrt_d_model':
|
114 |
+
raise ValueError(f"self.logit_scale={self.logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.")
|
115 |
+
if self.init_config.get('name', None) is None:
|
116 |
+
raise ValueError(f"self.init_config={self.init_config!r} 'name' needs to be set.")
|
117 |
+
if not self.learned_pos_emb and (not self.attn_config['alibi']):
|
118 |
+
raise ValueError(f'Positional information must be provided to the model using either learned_pos_emb or alibi.')
|
custom_embedding.py
ADDED
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.nn as nn
|
3 |
+
import torch.nn.functional as F
|
4 |
+
from torch import Tensor
|
5 |
+
|
6 |
+
class SharedEmbedding(nn.Embedding):
|
7 |
+
|
8 |
+
def forward(self, input: Tensor, unembed: bool=False) -> Tensor:
|
9 |
+
if unembed:
|
10 |
+
return F.linear(input, self.weight)
|
11 |
+
return super().forward(input)
|
flash_attn_triton.py
ADDED
@@ -0,0 +1,484 @@
|
|
|
|
|
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|
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|
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|
1 |
+
"""
|
2 |
+
Copied from https://github.com/HazyResearch/flash-attention/blob/eff9fe6b8076df59d64d7a3f464696738a3c7c24/flash_attn/flash_attn_triton.py
|
3 |
+
update imports to use 'triton_pre_mlir'
|
4 |
+
|
5 |
+
*Experimental* implementation of FlashAttention in Triton.
|
6 |
+
Tested with triton==2.0.0.dev20221202.
|
7 |
+
Triton 2.0 has a new backend (MLIR) but seems like it doesn't yet work for head dimensions
|
8 |
+
other than 64:
|
9 |
+
https://github.com/openai/triton/blob/d376020f90002757eea3ea9475d4f7cfc2ec5ead/python/triton/ops/flash_attention.py#L207
|
10 |
+
We'll update this implementation with the new Triton backend once this is fixed.
|
11 |
+
|
12 |
+
We use the FlashAttention implementation from Phil Tillet a starting point.
|
13 |
+
https://github.com/openai/triton/blob/master/python/tutorials/06-fused-attention.py
|
14 |
+
|
15 |
+
Changes:
|
16 |
+
- Implement both causal and non-causal attention.
|
17 |
+
- Implement both self-attention and cross-attention.
|
18 |
+
- Support arbitrary seqlens (not just multiples of 128), for both forward and backward.
|
19 |
+
- Support all head dimensions up to 128 (not just 16, 32, 64, 128), for both forward and backward.
|
20 |
+
- Support attention bias.
|
21 |
+
- Speed up the forward pass a bit, and only store the LSE instead of m and l.
|
22 |
+
- Make the backward for d=128 much faster by reducing register spilling.
|
23 |
+
- Optionally parallelize the backward pass across seqlen_k, to deal with the case of
|
24 |
+
small batch size * nheads.
|
25 |
+
|
26 |
+
Caution:
|
27 |
+
- This is an *experimental* implementation. The forward pass should be quite robust but
|
28 |
+
I'm not 100% sure that the backward pass doesn't have race conditions (due to the Triton compiler).
|
29 |
+
- This implementation has only been tested on A100.
|
30 |
+
- If you plan to use headdim other than 64 and 128, you should test for race conditions
|
31 |
+
(due to the Triton compiler), as done in tests/test_flash_attn.py
|
32 |
+
"test_flash_attn_triton_race_condition". I've tested and fixed many race conditions
|
33 |
+
for different head dimensions (40, 48, 64, 128, 80, 88, 96), but I'm still not 100% confident
|
34 |
+
that there are none left for other head dimensions.
|
35 |
+
|
36 |
+
Differences between this Triton version and the CUDA version:
|
37 |
+
- Triton version doesn't support dropout.
|
38 |
+
- Triton forward is generally faster than CUDA forward, while Triton backward is
|
39 |
+
generally slower than CUDA backward. Overall Triton forward + backward is slightly slower
|
40 |
+
than CUDA forward + backward.
|
41 |
+
- Triton version doesn't support different sequence lengths in a batch (i.e., RaggedTensor/NestedTensor).
|
42 |
+
- Triton version supports attention bias, while CUDA version doesn't.
|
43 |
+
"""
|
44 |
+
import math
|
45 |
+
import torch
|
46 |
+
import triton_pre_mlir as triton
|
47 |
+
import triton_pre_mlir.language as tl
|
48 |
+
|
49 |
+
@triton.heuristics({'EVEN_M': lambda args: args['seqlen_q'] % args['BLOCK_M'] == 0, 'EVEN_N': lambda args: args['seqlen_k'] % args['BLOCK_N'] == 0, 'EVEN_HEADDIM': lambda args: args['headdim'] == args['BLOCK_HEADDIM']})
|
50 |
+
@triton.jit
|
51 |
+
def _fwd_kernel(Q, K, V, Bias, Out, Lse, TMP, softmax_scale, stride_qb, stride_qh, stride_qm, stride_kb, stride_kh, stride_kn, stride_vb, stride_vh, stride_vn, stride_bb, stride_bh, stride_bm, stride_ob, stride_oh, stride_om, nheads, seqlen_q, seqlen_k, seqlen_q_rounded, headdim, CACHE_KEY_SEQLEN_Q, CACHE_KEY_SEQLEN_K, BIAS_TYPE: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_HEADDIM: tl.constexpr, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr):
|
52 |
+
start_m = tl.program_id(0)
|
53 |
+
off_hb = tl.program_id(1)
|
54 |
+
off_b = off_hb // nheads
|
55 |
+
off_h = off_hb % nheads
|
56 |
+
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
|
57 |
+
offs_n = tl.arange(0, BLOCK_N)
|
58 |
+
offs_d = tl.arange(0, BLOCK_HEADDIM)
|
59 |
+
q_ptrs = Q + off_b * stride_qb + off_h * stride_qh + (offs_m[:, None] * stride_qm + offs_d[None, :])
|
60 |
+
k_ptrs = K + off_b * stride_kb + off_h * stride_kh + (offs_n[:, None] * stride_kn + offs_d[None, :])
|
61 |
+
v_ptrs = V + off_b * stride_vb + off_h * stride_vh + (offs_n[:, None] * stride_vn + offs_d[None, :])
|
62 |
+
if BIAS_TYPE == 'vector':
|
63 |
+
b_ptrs = Bias + off_b * stride_bb + off_h * stride_bh + offs_n
|
64 |
+
elif BIAS_TYPE == 'matrix':
|
65 |
+
b_ptrs = Bias + off_b * stride_bb + off_h * stride_bh + (offs_m[:, None] * stride_bm + offs_n[None, :])
|
66 |
+
t_ptrs = TMP + off_hb * seqlen_q_rounded + offs_m
|
67 |
+
lse_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float('inf')
|
68 |
+
m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float('inf')
|
69 |
+
acc_o = tl.zeros([BLOCK_M, BLOCK_HEADDIM], dtype=tl.float32)
|
70 |
+
if EVEN_M & EVEN_N:
|
71 |
+
if EVEN_HEADDIM:
|
72 |
+
q = tl.load(q_ptrs)
|
73 |
+
else:
|
74 |
+
q = tl.load(q_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
|
75 |
+
elif EVEN_HEADDIM:
|
76 |
+
q = tl.load(q_ptrs, mask=offs_m[:, None] < seqlen_q, other=0.0)
|
77 |
+
else:
|
78 |
+
q = tl.load(q_ptrs, mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0)
|
79 |
+
end_n = seqlen_k if not IS_CAUSAL else tl.minimum((start_m + 1) * BLOCK_M, seqlen_k)
|
80 |
+
for start_n in range(0, end_n, BLOCK_N):
|
81 |
+
start_n = tl.multiple_of(start_n, BLOCK_N)
|
82 |
+
if EVEN_N & EVEN_M:
|
83 |
+
if EVEN_HEADDIM:
|
84 |
+
k = tl.load(k_ptrs + start_n * stride_kn)
|
85 |
+
else:
|
86 |
+
k = tl.load(k_ptrs + start_n * stride_kn, mask=offs_d[None, :] < headdim, other=0.0)
|
87 |
+
elif EVEN_HEADDIM:
|
88 |
+
k = tl.load(k_ptrs + start_n * stride_kn, mask=(start_n + offs_n)[:, None] < seqlen_k, other=0.0)
|
89 |
+
else:
|
90 |
+
k = tl.load(k_ptrs + start_n * stride_kn, mask=((start_n + offs_n)[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0)
|
91 |
+
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
|
92 |
+
qk += tl.dot(q, k, trans_b=True)
|
93 |
+
if not EVEN_N:
|
94 |
+
qk += tl.where((start_n + offs_n)[None, :] < seqlen_k, 0, float('-inf'))
|
95 |
+
if IS_CAUSAL:
|
96 |
+
qk += tl.where(offs_m[:, None] >= (start_n + offs_n)[None, :], 0, float('-inf'))
|
97 |
+
if BIAS_TYPE != 'none':
|
98 |
+
if BIAS_TYPE == 'vector':
|
99 |
+
if EVEN_N:
|
100 |
+
bias = tl.load(b_ptrs + start_n).to(tl.float32)
|
101 |
+
else:
|
102 |
+
bias = tl.load(b_ptrs + start_n, mask=start_n + offs_n < seqlen_k, other=0.0).to(tl.float32)
|
103 |
+
bias = bias[None, :]
|
104 |
+
elif BIAS_TYPE == 'matrix':
|
105 |
+
if EVEN_M & EVEN_N:
|
106 |
+
bias = tl.load(b_ptrs + start_n).to(tl.float32)
|
107 |
+
else:
|
108 |
+
bias = tl.load(b_ptrs + start_n, mask=(offs_m[:, None] < seqlen_q) & ((start_n + offs_n)[None, :] < seqlen_k), other=0.0).to(tl.float32)
|
109 |
+
qk = qk * softmax_scale + bias
|
110 |
+
m_ij = tl.maximum(tl.max(qk, 1), lse_i)
|
111 |
+
p = tl.exp(qk - m_ij[:, None])
|
112 |
+
else:
|
113 |
+
m_ij = tl.maximum(tl.max(qk, 1) * softmax_scale, lse_i)
|
114 |
+
p = tl.exp(qk * softmax_scale - m_ij[:, None])
|
115 |
+
l_ij = tl.sum(p, 1)
|
116 |
+
acc_o_scale = tl.exp(m_i - m_ij)
|
117 |
+
tl.store(t_ptrs, acc_o_scale)
|
118 |
+
acc_o_scale = tl.load(t_ptrs)
|
119 |
+
acc_o = acc_o * acc_o_scale[:, None]
|
120 |
+
if EVEN_N & EVEN_M:
|
121 |
+
if EVEN_HEADDIM:
|
122 |
+
v = tl.load(v_ptrs + start_n * stride_vn)
|
123 |
+
else:
|
124 |
+
v = tl.load(v_ptrs + start_n * stride_vn, mask=offs_d[None, :] < headdim, other=0.0)
|
125 |
+
elif EVEN_HEADDIM:
|
126 |
+
v = tl.load(v_ptrs + start_n * stride_vn, mask=(start_n + offs_n)[:, None] < seqlen_k, other=0.0)
|
127 |
+
else:
|
128 |
+
v = tl.load(v_ptrs + start_n * stride_vn, mask=((start_n + offs_n)[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0)
|
129 |
+
p = p.to(v.dtype)
|
130 |
+
acc_o += tl.dot(p, v)
|
131 |
+
m_i = m_ij
|
132 |
+
l_i_new = tl.exp(lse_i - m_ij) + l_ij
|
133 |
+
lse_i = m_ij + tl.log(l_i_new)
|
134 |
+
o_scale = tl.exp(m_i - lse_i)
|
135 |
+
tl.store(t_ptrs, o_scale)
|
136 |
+
o_scale = tl.load(t_ptrs)
|
137 |
+
acc_o = acc_o * o_scale[:, None]
|
138 |
+
start_m = tl.program_id(0)
|
139 |
+
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
|
140 |
+
lse_ptrs = Lse + off_hb * seqlen_q_rounded + offs_m
|
141 |
+
tl.store(lse_ptrs, lse_i)
|
142 |
+
offs_d = tl.arange(0, BLOCK_HEADDIM)
|
143 |
+
out_ptrs = Out + off_b * stride_ob + off_h * stride_oh + (offs_m[:, None] * stride_om + offs_d[None, :])
|
144 |
+
if EVEN_M:
|
145 |
+
if EVEN_HEADDIM:
|
146 |
+
tl.store(out_ptrs, acc_o)
|
147 |
+
else:
|
148 |
+
tl.store(out_ptrs, acc_o, mask=offs_d[None, :] < headdim)
|
149 |
+
elif EVEN_HEADDIM:
|
150 |
+
tl.store(out_ptrs, acc_o, mask=offs_m[:, None] < seqlen_q)
|
151 |
+
else:
|
152 |
+
tl.store(out_ptrs, acc_o, mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim))
|
153 |
+
|
154 |
+
@triton.jit
|
155 |
+
def _bwd_preprocess_do_o_dot(Out, DO, Delta, stride_ob, stride_oh, stride_om, stride_dob, stride_doh, stride_dom, nheads, seqlen_q, seqlen_q_rounded, headdim, BLOCK_M: tl.constexpr, BLOCK_HEADDIM: tl.constexpr):
|
156 |
+
start_m = tl.program_id(0)
|
157 |
+
off_hb = tl.program_id(1)
|
158 |
+
off_b = off_hb // nheads
|
159 |
+
off_h = off_hb % nheads
|
160 |
+
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
|
161 |
+
offs_d = tl.arange(0, BLOCK_HEADDIM)
|
162 |
+
o = tl.load(Out + off_b * stride_ob + off_h * stride_oh + offs_m[:, None] * stride_om + offs_d[None, :], mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0).to(tl.float32)
|
163 |
+
do = tl.load(DO + off_b * stride_dob + off_h * stride_doh + offs_m[:, None] * stride_dom + offs_d[None, :], mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0).to(tl.float32)
|
164 |
+
delta = tl.sum(o * do, axis=1)
|
165 |
+
tl.store(Delta + off_hb * seqlen_q_rounded + offs_m, delta)
|
166 |
+
|
167 |
+
@triton.jit
|
168 |
+
def _bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k, headdim, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr):
|
169 |
+
if EVEN_N & EVEN_M:
|
170 |
+
if EVEN_HEADDIM:
|
171 |
+
tl.store(dv_ptrs, dv)
|
172 |
+
tl.store(dk_ptrs, dk)
|
173 |
+
else:
|
174 |
+
tl.store(dv_ptrs, dv, mask=offs_d[None, :] < headdim)
|
175 |
+
tl.store(dk_ptrs, dk, mask=offs_d[None, :] < headdim)
|
176 |
+
elif EVEN_HEADDIM:
|
177 |
+
tl.store(dv_ptrs, dv, mask=offs_n[:, None] < seqlen_k)
|
178 |
+
tl.store(dk_ptrs, dk, mask=offs_n[:, None] < seqlen_k)
|
179 |
+
else:
|
180 |
+
tl.store(dv_ptrs, dv, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim))
|
181 |
+
tl.store(dk_ptrs, dk, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim))
|
182 |
+
|
183 |
+
@triton.jit
|
184 |
+
def _bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qm, stride_kn, stride_vn, stride_bm, stride_dom, stride_dqm, stride_dkn, stride_dvn, seqlen_q, seqlen_k, headdim, ATOMIC_ADD: tl.constexpr, BIAS_TYPE: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_HEADDIM: tl.constexpr, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr):
|
185 |
+
begin_m = 0 if not IS_CAUSAL else start_n * BLOCK_N // BLOCK_M * BLOCK_M
|
186 |
+
offs_qm = begin_m + tl.arange(0, BLOCK_M)
|
187 |
+
offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N)
|
188 |
+
offs_m = tl.arange(0, BLOCK_M)
|
189 |
+
offs_d = tl.arange(0, BLOCK_HEADDIM)
|
190 |
+
q_ptrs = Q + (offs_qm[:, None] * stride_qm + offs_d[None, :])
|
191 |
+
k_ptrs = K + (offs_n[:, None] * stride_kn + offs_d[None, :])
|
192 |
+
v_ptrs = V + (offs_n[:, None] * stride_vn + offs_d[None, :])
|
193 |
+
do_ptrs = DO + (offs_qm[:, None] * stride_dom + offs_d[None, :])
|
194 |
+
dq_ptrs = DQ + (offs_qm[:, None] * stride_dqm + offs_d[None, :])
|
195 |
+
if BIAS_TYPE == 'vector':
|
196 |
+
b_ptrs = Bias + offs_n
|
197 |
+
elif BIAS_TYPE == 'matrix':
|
198 |
+
b_ptrs = Bias + (offs_qm[:, None] * stride_bm + offs_n[None, :])
|
199 |
+
dv = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32)
|
200 |
+
dk = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32)
|
201 |
+
if begin_m >= seqlen_q:
|
202 |
+
dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_d[None, :])
|
203 |
+
dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_d[None, :])
|
204 |
+
_bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k, headdim, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM)
|
205 |
+
return
|
206 |
+
if EVEN_N & EVEN_M:
|
207 |
+
if EVEN_HEADDIM:
|
208 |
+
k = tl.load(k_ptrs)
|
209 |
+
v = tl.load(v_ptrs)
|
210 |
+
else:
|
211 |
+
k = tl.load(k_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
|
212 |
+
v = tl.load(v_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
|
213 |
+
elif EVEN_HEADDIM:
|
214 |
+
k = tl.load(k_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0)
|
215 |
+
v = tl.load(v_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0)
|
216 |
+
else:
|
217 |
+
k = tl.load(k_ptrs, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0)
|
218 |
+
v = tl.load(v_ptrs, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0)
|
219 |
+
num_block_m = tl.cdiv(seqlen_q, BLOCK_M)
|
220 |
+
for start_m in range(begin_m, num_block_m * BLOCK_M, BLOCK_M):
|
221 |
+
start_m = tl.multiple_of(start_m, BLOCK_M)
|
222 |
+
offs_m_curr = start_m + offs_m
|
223 |
+
if EVEN_M & EVEN_HEADDIM:
|
224 |
+
q = tl.load(q_ptrs)
|
225 |
+
elif EVEN_HEADDIM:
|
226 |
+
q = tl.load(q_ptrs, mask=offs_m_curr[:, None] < seqlen_q, other=0.0)
|
227 |
+
else:
|
228 |
+
q = tl.load(q_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0)
|
229 |
+
qk = tl.dot(q, k, trans_b=True)
|
230 |
+
if not EVEN_N:
|
231 |
+
qk = tl.where(offs_n[None, :] < seqlen_k, qk, float('-inf'))
|
232 |
+
if IS_CAUSAL:
|
233 |
+
qk = tl.where(offs_m_curr[:, None] >= offs_n[None, :], qk, float('-inf'))
|
234 |
+
if BIAS_TYPE != 'none':
|
235 |
+
tl.debug_barrier()
|
236 |
+
if BIAS_TYPE == 'vector':
|
237 |
+
if EVEN_N:
|
238 |
+
bias = tl.load(b_ptrs).to(tl.float32)
|
239 |
+
else:
|
240 |
+
bias = tl.load(b_ptrs, mask=offs_n < seqlen_k, other=0.0).to(tl.float32)
|
241 |
+
bias = bias[None, :]
|
242 |
+
elif BIAS_TYPE == 'matrix':
|
243 |
+
if EVEN_M & EVEN_N:
|
244 |
+
bias = tl.load(b_ptrs).to(tl.float32)
|
245 |
+
else:
|
246 |
+
bias = tl.load(b_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_n[None, :] < seqlen_k), other=0.0).to(tl.float32)
|
247 |
+
qk = qk * softmax_scale + bias
|
248 |
+
if not EVEN_M & EVEN_HEADDIM:
|
249 |
+
tl.debug_barrier()
|
250 |
+
lse_i = tl.load(LSE + offs_m_curr)
|
251 |
+
if BIAS_TYPE == 'none':
|
252 |
+
p = tl.exp(qk * softmax_scale - lse_i[:, None])
|
253 |
+
else:
|
254 |
+
p = tl.exp(qk - lse_i[:, None])
|
255 |
+
if EVEN_M & EVEN_HEADDIM:
|
256 |
+
do = tl.load(do_ptrs)
|
257 |
+
else:
|
258 |
+
do = tl.load(do_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0)
|
259 |
+
dv += tl.dot(p.to(do.dtype), do, trans_a=True)
|
260 |
+
if not EVEN_M & EVEN_HEADDIM:
|
261 |
+
tl.debug_barrier()
|
262 |
+
dp = tl.dot(do, v, trans_b=True)
|
263 |
+
if not EVEN_HEADDIM:
|
264 |
+
tl.debug_barrier()
|
265 |
+
Di = tl.load(D + offs_m_curr)
|
266 |
+
ds = (p * (dp - Di[:, None]) * softmax_scale).to(q.dtype)
|
267 |
+
dk += tl.dot(ds, q, trans_a=True)
|
268 |
+
if not EVEN_M & EVEN_HEADDIM:
|
269 |
+
tl.debug_barrier()
|
270 |
+
if not ATOMIC_ADD:
|
271 |
+
if EVEN_M & EVEN_HEADDIM:
|
272 |
+
dq = tl.load(dq_ptrs, eviction_policy='evict_last')
|
273 |
+
dq += tl.dot(ds, k)
|
274 |
+
tl.store(dq_ptrs, dq, eviction_policy='evict_last')
|
275 |
+
elif EVEN_HEADDIM:
|
276 |
+
dq = tl.load(dq_ptrs, mask=offs_m_curr[:, None] < seqlen_q, other=0.0, eviction_policy='evict_last')
|
277 |
+
dq += tl.dot(ds, k)
|
278 |
+
tl.store(dq_ptrs, dq, mask=offs_m_curr[:, None] < seqlen_q, eviction_policy='evict_last')
|
279 |
+
else:
|
280 |
+
dq = tl.load(dq_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0, eviction_policy='evict_last')
|
281 |
+
dq += tl.dot(ds, k)
|
282 |
+
tl.store(dq_ptrs, dq, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), eviction_policy='evict_last')
|
283 |
+
else:
|
284 |
+
dq = tl.dot(ds, k)
|
285 |
+
if EVEN_M & EVEN_HEADDIM:
|
286 |
+
tl.atomic_add(dq_ptrs, dq)
|
287 |
+
elif EVEN_HEADDIM:
|
288 |
+
tl.atomic_add(dq_ptrs, dq, mask=offs_m_curr[:, None] < seqlen_q)
|
289 |
+
else:
|
290 |
+
tl.atomic_add(dq_ptrs, dq, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim))
|
291 |
+
dq_ptrs += BLOCK_M * stride_dqm
|
292 |
+
q_ptrs += BLOCK_M * stride_qm
|
293 |
+
do_ptrs += BLOCK_M * stride_dom
|
294 |
+
if BIAS_TYPE == 'matrix':
|
295 |
+
b_ptrs += BLOCK_M * stride_bm
|
296 |
+
dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_d[None, :])
|
297 |
+
dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_d[None, :])
|
298 |
+
_bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k, headdim, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM)
|
299 |
+
|
300 |
+
def init_to_zero(name):
|
301 |
+
return lambda nargs: nargs[name].zero_()
|
302 |
+
|
303 |
+
@triton.autotune(configs=[triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'SEQUENCE_PARALLEL': False}, num_warps=8, num_stages=1, pre_hook=init_to_zero('DQ')), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'SEQUENCE_PARALLEL': True}, num_warps=8, num_stages=1, pre_hook=init_to_zero('DQ'))], key=['CACHE_KEY_SEQLEN_Q', 'CACHE_KEY_SEQLEN_K', 'BIAS_TYPE', 'IS_CAUSAL', 'BLOCK_HEADDIM'])
|
304 |
+
@triton.heuristics({'EVEN_M': lambda args: args['seqlen_q'] % args['BLOCK_M'] == 0, 'EVEN_N': lambda args: args['seqlen_k'] % args['BLOCK_N'] == 0, 'EVEN_HEADDIM': lambda args: args['headdim'] == args['BLOCK_HEADDIM']})
|
305 |
+
@triton.jit
|
306 |
+
def _bwd_kernel(Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qb, stride_qh, stride_qm, stride_kb, stride_kh, stride_kn, stride_vb, stride_vh, stride_vn, stride_bb, stride_bh, stride_bm, stride_dob, stride_doh, stride_dom, stride_dqb, stride_dqh, stride_dqm, stride_dkb, stride_dkh, stride_dkn, stride_dvb, stride_dvh, stride_dvn, nheads, seqlen_q, seqlen_k, seqlen_q_rounded, headdim, CACHE_KEY_SEQLEN_Q, CACHE_KEY_SEQLEN_K, BIAS_TYPE: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_HEADDIM: tl.constexpr, SEQUENCE_PARALLEL: tl.constexpr, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr):
|
307 |
+
off_hb = tl.program_id(1)
|
308 |
+
off_b = off_hb // nheads
|
309 |
+
off_h = off_hb % nheads
|
310 |
+
Q += off_b * stride_qb + off_h * stride_qh
|
311 |
+
K += off_b * stride_kb + off_h * stride_kh
|
312 |
+
V += off_b * stride_vb + off_h * stride_vh
|
313 |
+
DO += off_b * stride_dob + off_h * stride_doh
|
314 |
+
DQ += off_b * stride_dqb + off_h * stride_dqh
|
315 |
+
DK += off_b * stride_dkb + off_h * stride_dkh
|
316 |
+
DV += off_b * stride_dvb + off_h * stride_dvh
|
317 |
+
if BIAS_TYPE != 'none':
|
318 |
+
Bias += off_b * stride_bb + off_h * stride_bh
|
319 |
+
D += off_hb * seqlen_q_rounded
|
320 |
+
LSE += off_hb * seqlen_q_rounded
|
321 |
+
if not SEQUENCE_PARALLEL:
|
322 |
+
num_block_n = tl.cdiv(seqlen_k, BLOCK_N)
|
323 |
+
for start_n in range(0, num_block_n):
|
324 |
+
_bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qm, stride_kn, stride_vn, stride_bm, stride_dom, stride_dqm, stride_dkn, stride_dvn, seqlen_q, seqlen_k, headdim, ATOMIC_ADD=False, BIAS_TYPE=BIAS_TYPE, IS_CAUSAL=IS_CAUSAL, BLOCK_HEADDIM=BLOCK_HEADDIM, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N)
|
325 |
+
else:
|
326 |
+
start_n = tl.program_id(0)
|
327 |
+
_bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qm, stride_kn, stride_vn, stride_bm, stride_dom, stride_dqm, stride_dkn, stride_dvn, seqlen_q, seqlen_k, headdim, ATOMIC_ADD=True, BIAS_TYPE=BIAS_TYPE, IS_CAUSAL=IS_CAUSAL, BLOCK_HEADDIM=BLOCK_HEADDIM, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N)
|
328 |
+
|
329 |
+
def _flash_attn_forward(q, k, v, bias=None, causal=False, softmax_scale=None):
|
330 |
+
(batch, seqlen_q, nheads, d) = q.shape
|
331 |
+
(_, seqlen_k, _, _) = k.shape
|
332 |
+
assert k.shape == (batch, seqlen_k, nheads, d)
|
333 |
+
assert v.shape == (batch, seqlen_k, nheads, d)
|
334 |
+
assert d <= 128, 'FlashAttention only support head dimensions up to 128'
|
335 |
+
assert q.dtype == k.dtype == v.dtype, 'All tensors must have the same type'
|
336 |
+
assert q.dtype in [torch.float16, torch.bfloat16], 'Only support fp16 and bf16'
|
337 |
+
assert q.is_cuda and k.is_cuda and v.is_cuda
|
338 |
+
softmax_scale = softmax_scale or 1.0 / math.sqrt(d)
|
339 |
+
has_bias = bias is not None
|
340 |
+
bias_type = 'none'
|
341 |
+
if has_bias:
|
342 |
+
assert bias.dtype in [q.dtype, torch.float]
|
343 |
+
assert bias.is_cuda
|
344 |
+
assert bias.dim() == 4
|
345 |
+
if bias.stride(-1) != 1:
|
346 |
+
bias = bias.contiguous()
|
347 |
+
if bias.shape[2:] == (1, seqlen_k):
|
348 |
+
bias_type = 'vector'
|
349 |
+
elif bias.shape[2:] == (seqlen_q, seqlen_k):
|
350 |
+
bias_type = 'matrix'
|
351 |
+
else:
|
352 |
+
raise RuntimeError('Last 2 dimensions of bias must be (1, seqlen_k) or (seqlen_q, seqlen_k)')
|
353 |
+
bias = bias.expand(batch, nheads, seqlen_q, seqlen_k)
|
354 |
+
bias_strides = (bias.stride(0), bias.stride(1), bias.stride(2)) if has_bias else (0, 0, 0)
|
355 |
+
seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128
|
356 |
+
lse = torch.empty((batch, nheads, seqlen_q_rounded), device=q.device, dtype=torch.float32)
|
357 |
+
tmp = torch.empty((batch, nheads, seqlen_q_rounded), device=q.device, dtype=torch.float32)
|
358 |
+
o = torch.empty_like(q)
|
359 |
+
BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16)
|
360 |
+
BLOCK = 128
|
361 |
+
num_warps = 4 if d <= 64 else 8
|
362 |
+
grid = lambda META: (triton.cdiv(seqlen_q, META['BLOCK_M']), batch * nheads)
|
363 |
+
_fwd_kernel[grid](q, k, v, bias, o, lse, tmp, softmax_scale, q.stride(0), q.stride(2), q.stride(1), k.stride(0), k.stride(2), k.stride(1), v.stride(0), v.stride(2), v.stride(1), *bias_strides, o.stride(0), o.stride(2), o.stride(1), nheads, seqlen_q, seqlen_k, seqlen_q_rounded, d, seqlen_q // 32, seqlen_k // 32, bias_type, causal, BLOCK_HEADDIM, BLOCK_M=BLOCK, BLOCK_N=BLOCK, num_warps=num_warps, num_stages=1)
|
364 |
+
return (o, lse, softmax_scale)
|
365 |
+
|
366 |
+
def _flash_attn_backward(do, q, k, v, o, lse, dq, dk, dv, bias=None, causal=False, softmax_scale=None):
|
367 |
+
if do.stride(-1) != 1:
|
368 |
+
do = do.contiguous()
|
369 |
+
(batch, seqlen_q, nheads, d) = q.shape
|
370 |
+
(_, seqlen_k, _, _) = k.shape
|
371 |
+
assert d <= 128
|
372 |
+
seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128
|
373 |
+
assert lse.shape == (batch, nheads, seqlen_q_rounded)
|
374 |
+
assert q.stride(-1) == k.stride(-1) == v.stride(-1) == o.stride(-1) == 1
|
375 |
+
assert dq.stride(-1) == dk.stride(-1) == dv.stride(-1) == 1
|
376 |
+
softmax_scale = softmax_scale or 1.0 / math.sqrt(d)
|
377 |
+
dq_accum = torch.empty_like(q, dtype=torch.float32)
|
378 |
+
delta = torch.empty_like(lse)
|
379 |
+
BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16)
|
380 |
+
grid = lambda META: (triton.cdiv(seqlen_q, META['BLOCK_M']), batch * nheads)
|
381 |
+
_bwd_preprocess_do_o_dot[grid](o, do, delta, o.stride(0), o.stride(2), o.stride(1), do.stride(0), do.stride(2), do.stride(1), nheads, seqlen_q, seqlen_q_rounded, d, BLOCK_M=128, BLOCK_HEADDIM=BLOCK_HEADDIM)
|
382 |
+
has_bias = bias is not None
|
383 |
+
bias_type = 'none'
|
384 |
+
if has_bias:
|
385 |
+
assert bias.dtype in [q.dtype, torch.float]
|
386 |
+
assert bias.is_cuda
|
387 |
+
assert bias.dim() == 4
|
388 |
+
assert bias.stride(-1) == 1
|
389 |
+
if bias.shape[2:] == (1, seqlen_k):
|
390 |
+
bias_type = 'vector'
|
391 |
+
elif bias.shape[2:] == (seqlen_q, seqlen_k):
|
392 |
+
bias_type = 'matrix'
|
393 |
+
else:
|
394 |
+
raise RuntimeError('Last 2 dimensions of bias must be (1, seqlen_k) or (seqlen_q, seqlen_k)')
|
395 |
+
bias = bias.expand(batch, nheads, seqlen_q, seqlen_k)
|
396 |
+
bias_strides = (bias.stride(0), bias.stride(1), bias.stride(2)) if has_bias else (0, 0, 0)
|
397 |
+
grid = lambda META: (triton.cdiv(seqlen_k, META['BLOCK_N']) if META['SEQUENCE_PARALLEL'] else 1, batch * nheads)
|
398 |
+
_bwd_kernel[grid](q, k, v, bias, do, dq_accum, dk, dv, lse, delta, softmax_scale, q.stride(0), q.stride(2), q.stride(1), k.stride(0), k.stride(2), k.stride(1), v.stride(0), v.stride(2), v.stride(1), *bias_strides, do.stride(0), do.stride(2), do.stride(1), dq_accum.stride(0), dq_accum.stride(2), dq_accum.stride(1), dk.stride(0), dk.stride(2), dk.stride(1), dv.stride(0), dv.stride(2), dv.stride(1), nheads, seqlen_q, seqlen_k, seqlen_q_rounded, d, seqlen_q // 32, seqlen_k // 32, bias_type, causal, BLOCK_HEADDIM)
|
399 |
+
dq.copy_(dq_accum)
|
400 |
+
|
401 |
+
class FlashAttnQKVPackedFunc(torch.autograd.Function):
|
402 |
+
|
403 |
+
@staticmethod
|
404 |
+
def forward(ctx, qkv, bias=None, causal=False, softmax_scale=None):
|
405 |
+
"""
|
406 |
+
qkv: (batch, seqlen, 3, nheads, headdim)
|
407 |
+
bias: optional, shape broadcastible to (batch, nheads, seqlen, seqlen).
|
408 |
+
For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen).
|
409 |
+
ALiBi mask for non-causal would have shape (1, nheads, seqlen, seqlen)
|
410 |
+
"""
|
411 |
+
if qkv.stride(-1) != 1:
|
412 |
+
qkv = qkv.contiguous()
|
413 |
+
(o, lse, ctx.softmax_scale) = _flash_attn_forward(qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], bias=bias, causal=causal, softmax_scale=softmax_scale)
|
414 |
+
ctx.save_for_backward(qkv, o, lse, bias)
|
415 |
+
ctx.causal = causal
|
416 |
+
return o
|
417 |
+
|
418 |
+
@staticmethod
|
419 |
+
def backward(ctx, do):
|
420 |
+
(qkv, o, lse, bias) = ctx.saved_tensors
|
421 |
+
assert not ctx.needs_input_grad[1], 'FlashAttention does not support bias gradient yet'
|
422 |
+
with torch.inference_mode():
|
423 |
+
dqkv = torch.empty_like(qkv)
|
424 |
+
_flash_attn_backward(do, qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], o, lse, dqkv[:, :, 0], dqkv[:, :, 1], dqkv[:, :, 2], bias=bias, causal=ctx.causal, softmax_scale=ctx.softmax_scale)
|
425 |
+
return (dqkv, None, None, None)
|
426 |
+
flash_attn_qkvpacked_func = FlashAttnQKVPackedFunc.apply
|
427 |
+
|
428 |
+
class FlashAttnKVPackedFunc(torch.autograd.Function):
|
429 |
+
|
430 |
+
@staticmethod
|
431 |
+
def forward(ctx, q, kv, bias=None, causal=False, softmax_scale=None):
|
432 |
+
"""
|
433 |
+
q: (batch, seqlen_q, nheads, headdim)
|
434 |
+
kv: (batch, seqlen_k, 2, nheads, headdim)
|
435 |
+
bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k).
|
436 |
+
For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k).
|
437 |
+
ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k)
|
438 |
+
"""
|
439 |
+
(q, kv) = [x if x.stride(-1) == 1 else x.contiguous() for x in [q, kv]]
|
440 |
+
(o, lse, ctx.softmax_scale) = _flash_attn_forward(q, kv[:, :, 0], kv[:, :, 1], bias=bias, causal=causal, softmax_scale=softmax_scale)
|
441 |
+
ctx.save_for_backward(q, kv, o, lse, bias)
|
442 |
+
ctx.causal = causal
|
443 |
+
return o
|
444 |
+
|
445 |
+
@staticmethod
|
446 |
+
def backward(ctx, do):
|
447 |
+
(q, kv, o, lse, bias) = ctx.saved_tensors
|
448 |
+
if len(ctx.needs_input_grad) >= 3:
|
449 |
+
assert not ctx.needs_input_grad[2], 'FlashAttention does not support bias gradient yet'
|
450 |
+
with torch.inference_mode():
|
451 |
+
dq = torch.empty_like(q)
|
452 |
+
dkv = torch.empty_like(kv)
|
453 |
+
_flash_attn_backward(do, q, kv[:, :, 0], kv[:, :, 1], o, lse, dq, dkv[:, :, 0], dkv[:, :, 1], bias=bias, causal=ctx.causal, softmax_scale=ctx.softmax_scale)
|
454 |
+
return (dq, dkv, None, None, None)
|
455 |
+
flash_attn_kvpacked_func = FlashAttnKVPackedFunc.apply
|
456 |
+
|
457 |
+
class FlashAttnFunc(torch.autograd.Function):
|
458 |
+
|
459 |
+
@staticmethod
|
460 |
+
def forward(ctx, q, k, v, bias=None, causal=False, softmax_scale=None):
|
461 |
+
"""
|
462 |
+
q: (batch_size, seqlen_q, nheads, headdim)
|
463 |
+
k, v: (batch_size, seqlen_k, nheads, headdim)
|
464 |
+
bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k).
|
465 |
+
For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k).
|
466 |
+
ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k)
|
467 |
+
"""
|
468 |
+
(q, k, v) = [x if x.stride(-1) == 1 else x.contiguous() for x in [q, k, v]]
|
469 |
+
(o, lse, ctx.softmax_scale) = _flash_attn_forward(q, k, v, bias=bias, causal=causal, softmax_scale=softmax_scale)
|
470 |
+
ctx.save_for_backward(q, k, v, o, lse, bias)
|
471 |
+
ctx.causal = causal
|
472 |
+
return o
|
473 |
+
|
474 |
+
@staticmethod
|
475 |
+
def backward(ctx, do):
|
476 |
+
(q, k, v, o, lse, bias) = ctx.saved_tensors
|
477 |
+
assert not ctx.needs_input_grad[3], 'FlashAttention does not support bias gradient yet'
|
478 |
+
with torch.inference_mode():
|
479 |
+
dq = torch.empty_like(q)
|
480 |
+
dk = torch.empty_like(k)
|
481 |
+
dv = torch.empty_like(v)
|
482 |
+
_flash_attn_backward(do, q, k, v, o, lse, dq, dk, dv, bias=bias, causal=ctx.causal, softmax_scale=ctx.softmax_scale)
|
483 |
+
return (dq, dk, dv, None, None, None)
|
484 |
+
flash_attn_func = FlashAttnFunc.apply
|
generation_config.json
ADDED
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_from_model_config": true,
|
3 |
+
"transformers_version": "4.28.1",
|
4 |
+
"use_cache": false
|
5 |
+
}
|
hf_prefixlm_converter.py
ADDED
@@ -0,0 +1,415 @@
|
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|
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|
1 |
+
"""Converts Huggingface Causal LM to Prefix LM.
|
2 |
+
|
3 |
+
Conversion does lightweight surgery on a HuggingFace
|
4 |
+
Causal LM to convert it to a Prefix LM.
|
5 |
+
|
6 |
+
Prefix LMs accepts a `bidirectional_mask` input in `forward`
|
7 |
+
and treat the input prompt as the prefix in `generate`.
|
8 |
+
"""
|
9 |
+
import math
|
10 |
+
import warnings
|
11 |
+
from types import MethodType
|
12 |
+
from typing import Any, Dict, List, Optional, Tuple, Union
|
13 |
+
import torch
|
14 |
+
from transformers.models.bloom.modeling_bloom import BaseModelOutputWithPastAndCrossAttentions, BloomForCausalLM, BloomModel, CausalLMOutputWithCrossAttentions, CrossEntropyLoss
|
15 |
+
from transformers.models.bloom.modeling_bloom import _expand_mask as _expand_mask_bloom
|
16 |
+
from transformers.models.bloom.modeling_bloom import _make_causal_mask as _make_causal_mask_bloom
|
17 |
+
from transformers.models.bloom.modeling_bloom import logging
|
18 |
+
from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
|
19 |
+
from transformers.models.gpt_neo.modeling_gpt_neo import GPTNeoForCausalLM
|
20 |
+
from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXForCausalLM
|
21 |
+
from transformers.models.gptj.modeling_gptj import GPTJForCausalLM
|
22 |
+
from transformers.models.opt.modeling_opt import OPTForCausalLM
|
23 |
+
from transformers.models.opt.modeling_opt import _expand_mask as _expand_mask_opt
|
24 |
+
from transformers.models.opt.modeling_opt import _make_causal_mask as _make_causal_mask_opt
|
25 |
+
logger = logging.get_logger(__name__)
|
26 |
+
_SUPPORTED_GPT_MODELS = (GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM)
|
27 |
+
CAUSAL_GPT_TYPES = Union[GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM]
|
28 |
+
|
29 |
+
def _convert_gpt_causal_lm_to_prefix_lm(model: CAUSAL_GPT_TYPES) -> CAUSAL_GPT_TYPES:
|
30 |
+
"""Converts a GPT-style Causal LM to a Prefix LM.
|
31 |
+
|
32 |
+
Supported HuggingFace model classes:
|
33 |
+
- `GPT2LMHeadModel`
|
34 |
+
- `GPTNeoForCausalLM`
|
35 |
+
- `GPTNeoXForCausalLM`
|
36 |
+
- `GPTJForCausalLM`
|
37 |
+
|
38 |
+
See `convert_hf_causal_lm_to_prefix_lm` for more details.
|
39 |
+
"""
|
40 |
+
if hasattr(model, '_prefix_lm_converted'):
|
41 |
+
return model
|
42 |
+
assert isinstance(model, _SUPPORTED_GPT_MODELS)
|
43 |
+
assert model.config.add_cross_attention == False, 'Only supports GPT-style decoder-only models'
|
44 |
+
|
45 |
+
def _get_attn_modules(model: CAUSAL_GPT_TYPES) -> List[torch.nn.Module]:
|
46 |
+
"""Helper that gets a list of the model's attention modules.
|
47 |
+
|
48 |
+
Each module has a `bias` buffer used for causal masking. The Prefix LM
|
49 |
+
conversion adds logic to dynamically manipulate these biases to support
|
50 |
+
Prefix LM attention masking.
|
51 |
+
"""
|
52 |
+
attn_modules = []
|
53 |
+
if isinstance(model, GPTNeoXForCausalLM):
|
54 |
+
blocks = model.gpt_neox.layers
|
55 |
+
else:
|
56 |
+
blocks = model.transformer.h
|
57 |
+
for block in blocks:
|
58 |
+
if isinstance(model, GPTNeoForCausalLM):
|
59 |
+
if block.attn.attention_type != 'global':
|
60 |
+
continue
|
61 |
+
attn_module = block.attn.attention
|
62 |
+
elif isinstance(model, GPTNeoXForCausalLM):
|
63 |
+
attn_module = block.attention
|
64 |
+
else:
|
65 |
+
attn_module = block.attn
|
66 |
+
attn_modules.append(attn_module)
|
67 |
+
return attn_modules
|
68 |
+
setattr(model, '_original_forward', getattr(model, 'forward'))
|
69 |
+
setattr(model, '_original_generate', getattr(model, 'generate'))
|
70 |
+
|
71 |
+
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):
|
72 |
+
"""Wraps original forward to enable PrefixLM attention."""
|
73 |
+
|
74 |
+
def call_og_forward():
|
75 |
+
if isinstance(self, GPTNeoXForCausalLM):
|
76 |
+
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)
|
77 |
+
else:
|
78 |
+
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)
|
79 |
+
if bidirectional_mask is None:
|
80 |
+
return call_og_forward()
|
81 |
+
assert isinstance(bidirectional_mask, torch.Tensor)
|
82 |
+
attn_modules = _get_attn_modules(model)
|
83 |
+
(b, s) = bidirectional_mask.shape
|
84 |
+
max_length = attn_modules[0].bias.shape[-1]
|
85 |
+
if s > max_length:
|
86 |
+
raise ValueError(f'bidirectional_mask sequence length (={s}) exceeds the ' + f'max length allowed by the model ({max_length}).')
|
87 |
+
assert s <= max_length
|
88 |
+
if s < max_length:
|
89 |
+
pad = torch.zeros((int(b), int(max_length - s)), dtype=bidirectional_mask.dtype, device=bidirectional_mask.device)
|
90 |
+
bidirectional_mask = torch.cat([bidirectional_mask, pad], dim=1)
|
91 |
+
bidirectional = bidirectional_mask.unsqueeze(1).unsqueeze(1)
|
92 |
+
for attn_module in attn_modules:
|
93 |
+
attn_module.bias.data = torch.logical_or(attn_module.bias.data, bidirectional)
|
94 |
+
output = call_og_forward()
|
95 |
+
for attn_module in attn_modules:
|
96 |
+
attn_module.bias.data = torch.tril(attn_module.bias.data[0, 0])[None, None]
|
97 |
+
return output
|
98 |
+
|
99 |
+
def generate(self: CAUSAL_GPT_TYPES, *args: tuple, **kwargs: Dict[str, Any]):
|
100 |
+
"""Wraps original generate to enable PrefixLM attention."""
|
101 |
+
attn_modules = _get_attn_modules(model)
|
102 |
+
for attn_module in attn_modules:
|
103 |
+
attn_module.bias.data[:] = 1
|
104 |
+
output = self._original_generate(*args, **kwargs)
|
105 |
+
for attn_module in attn_modules:
|
106 |
+
attn_module.bias.data = torch.tril(attn_module.bias.data[0, 0])[None, None]
|
107 |
+
return output
|
108 |
+
setattr(model, 'forward', MethodType(forward, model))
|
109 |
+
setattr(model, 'generate', MethodType(generate, model))
|
110 |
+
setattr(model, '_prefix_lm_converted', True)
|
111 |
+
return model
|
112 |
+
|
113 |
+
def _convert_bloom_causal_lm_to_prefix_lm(model: BloomForCausalLM) -> BloomForCausalLM:
|
114 |
+
"""Converts a BLOOM Causal LM to a Prefix LM.
|
115 |
+
|
116 |
+
Supported HuggingFace model classes:
|
117 |
+
- `BloomForCausalLM`
|
118 |
+
|
119 |
+
See `convert_hf_causal_lm_to_prefix_lm` for more details.
|
120 |
+
"""
|
121 |
+
if hasattr(model, '_prefix_lm_converted'):
|
122 |
+
return model
|
123 |
+
assert isinstance(model, BloomForCausalLM)
|
124 |
+
assert model.config.add_cross_attention == False, 'Only supports BLOOM decoder-only models'
|
125 |
+
|
126 |
+
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:
|
127 |
+
combined_attention_mask = None
|
128 |
+
device = attention_mask.device
|
129 |
+
(_, src_length) = input_shape
|
130 |
+
if src_length > 1:
|
131 |
+
combined_attention_mask = _make_causal_mask_bloom(input_shape, device=device, past_key_values_length=past_key_values_length)
|
132 |
+
if bidirectional_mask is not None:
|
133 |
+
assert attention_mask.shape == bidirectional_mask.shape
|
134 |
+
expanded_bidirectional_mask = _expand_mask_bloom(bidirectional_mask, tgt_length=src_length)
|
135 |
+
combined_attention_mask = torch.logical_and(combined_attention_mask, expanded_bidirectional_mask)
|
136 |
+
expanded_attn_mask = _expand_mask_bloom(attention_mask, tgt_length=src_length)
|
137 |
+
combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask
|
138 |
+
return combined_attention_mask
|
139 |
+
|
140 |
+
def _build_alibi_tensor(self: BloomModel, batch_size: int, query_length: int, key_length: int, dtype: torch.dtype, device: torch.device) -> torch.Tensor:
|
141 |
+
num_heads = self.config.n_head
|
142 |
+
closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
|
143 |
+
base = torch.tensor(2 ** (-2 ** (-(math.log2(closest_power_of_2) - 3))), device=device, dtype=torch.float32)
|
144 |
+
powers = torch.arange(1, 1 + closest_power_of_2, device=device, dtype=torch.int32)
|
145 |
+
slopes = torch.pow(base, powers)
|
146 |
+
if closest_power_of_2 != num_heads:
|
147 |
+
extra_base = torch.tensor(2 ** (-2 ** (-(math.log2(2 * closest_power_of_2) - 3))), device=device, dtype=torch.float32)
|
148 |
+
num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
|
149 |
+
extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=device, dtype=torch.int32)
|
150 |
+
slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
|
151 |
+
qa = torch.arange(query_length, device=device, dtype=torch.int32).view(-1, 1)
|
152 |
+
ka = torch.arange(key_length, device=device, dtype=torch.int32).view(1, -1)
|
153 |
+
diffs = qa - ka + key_length - query_length
|
154 |
+
diffs = -diffs.abs()
|
155 |
+
alibi = slopes.view(1, num_heads, 1, 1) * diffs.view(1, 1, query_length, key_length)
|
156 |
+
alibi = alibi.expand(batch_size, -1, -1, -1).reshape(-1, query_length, key_length)
|
157 |
+
return alibi.to(dtype)
|
158 |
+
KeyValueT = Tuple[torch.Tensor, torch.Tensor]
|
159 |
+
|
160 |
+
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]:
|
161 |
+
if deprecated_arguments.pop('position_ids', False) is not False:
|
162 |
+
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)
|
163 |
+
if len(deprecated_arguments) > 0:
|
164 |
+
raise ValueError(f'Got unexpected arguments: {deprecated_arguments}')
|
165 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
166 |
+
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
167 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
168 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
169 |
+
if input_ids is not None and inputs_embeds is not None:
|
170 |
+
raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time')
|
171 |
+
elif input_ids is not None:
|
172 |
+
(batch_size, seq_length) = input_ids.shape
|
173 |
+
elif inputs_embeds is not None:
|
174 |
+
(batch_size, seq_length, _) = inputs_embeds.shape
|
175 |
+
else:
|
176 |
+
raise ValueError('You have to specify either input_ids or inputs_embeds')
|
177 |
+
if past_key_values is None:
|
178 |
+
past_key_values = tuple([None] * len(self.h))
|
179 |
+
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
|
180 |
+
if inputs_embeds is None:
|
181 |
+
inputs_embeds = self.word_embeddings(input_ids)
|
182 |
+
hidden_states = self.word_embeddings_layernorm(inputs_embeds)
|
183 |
+
presents = () if use_cache else None
|
184 |
+
all_self_attentions = () if output_attentions else None
|
185 |
+
all_hidden_states = () if output_hidden_states else None
|
186 |
+
seq_length_with_past = seq_length
|
187 |
+
past_key_values_length = 0
|
188 |
+
if past_key_values[0] is not None:
|
189 |
+
tmp = past_key_values[0][0]
|
190 |
+
past_key_values_length = tmp.shape[2]
|
191 |
+
seq_length_with_past = seq_length_with_past + past_key_values_length
|
192 |
+
if attention_mask is None:
|
193 |
+
attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
|
194 |
+
else:
|
195 |
+
attention_mask = attention_mask.to(hidden_states.device)
|
196 |
+
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)
|
197 |
+
causal_mask = self._prepare_attn_mask(attention_mask, bidirectional_mask, input_shape=(batch_size, seq_length), past_key_values_length=past_key_values_length)
|
198 |
+
for (i, (block, layer_past)) in enumerate(zip(self.h, past_key_values)):
|
199 |
+
if output_hidden_states:
|
200 |
+
hst = (hidden_states,)
|
201 |
+
all_hidden_states = all_hidden_states + hst
|
202 |
+
if self.gradient_checkpointing and self.training:
|
203 |
+
if use_cache:
|
204 |
+
logger.warning('`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...')
|
205 |
+
use_cache = False
|
206 |
+
|
207 |
+
def create_custom_forward(module):
|
208 |
+
|
209 |
+
def custom_forward(*inputs):
|
210 |
+
return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)
|
211 |
+
return custom_forward
|
212 |
+
outputs = torch.utils.checkpoint.checkpoint(create_custom_forward(block), hidden_states, alibi, causal_mask, head_mask[i])
|
213 |
+
else:
|
214 |
+
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)
|
215 |
+
hidden_states = outputs[0]
|
216 |
+
if use_cache is True:
|
217 |
+
presents = presents + (outputs[1],)
|
218 |
+
if output_attentions:
|
219 |
+
oa = (outputs[2 if use_cache else 1],)
|
220 |
+
all_self_attentions = all_self_attentions + oa
|
221 |
+
hidden_states = self.ln_f(hidden_states)
|
222 |
+
if output_hidden_states:
|
223 |
+
hst = (hidden_states,)
|
224 |
+
all_hidden_states = all_hidden_states + hst
|
225 |
+
if not return_dict:
|
226 |
+
return tuple((v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None))
|
227 |
+
return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attentions)
|
228 |
+
setattr(model.transformer, '_prepare_attn_mask', MethodType(_prepare_attn_mask, model.transformer))
|
229 |
+
setattr(model.transformer, '_build_alibi_tensor', MethodType(_build_alibi_tensor, model.transformer))
|
230 |
+
setattr(model.transformer, 'forward', MethodType(forward, model.transformer))
|
231 |
+
KeyValueT = Tuple[torch.Tensor, torch.Tensor]
|
232 |
+
|
233 |
+
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]:
|
234 |
+
"""Replacement forward method for BloomCausalLM."""
|
235 |
+
if deprecated_arguments.pop('position_ids', False) is not False:
|
236 |
+
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)
|
237 |
+
if len(deprecated_arguments) > 0:
|
238 |
+
raise ValueError(f'Got unexpected arguments: {deprecated_arguments}')
|
239 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
240 |
+
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)
|
241 |
+
hidden_states = transformer_outputs[0]
|
242 |
+
lm_logits = self.lm_head(hidden_states)
|
243 |
+
loss = None
|
244 |
+
if labels is not None:
|
245 |
+
shift_logits = lm_logits[..., :-1, :].contiguous()
|
246 |
+
shift_labels = labels[..., 1:].contiguous()
|
247 |
+
(batch_size, seq_length, vocab_size) = shift_logits.shape
|
248 |
+
loss_fct = CrossEntropyLoss()
|
249 |
+
loss = loss_fct(shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length))
|
250 |
+
if not return_dict:
|
251 |
+
output = (lm_logits,) + transformer_outputs[1:]
|
252 |
+
return (loss,) + output if loss is not None else output
|
253 |
+
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)
|
254 |
+
|
255 |
+
def prepare_inputs_for_generation(self: BloomForCausalLM, input_ids: torch.LongTensor, past: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, **kwargs) -> dict:
|
256 |
+
if past:
|
257 |
+
input_ids = input_ids[:, -1].unsqueeze(-1)
|
258 |
+
bidirectional_mask = None
|
259 |
+
if past[0][0].shape[0] == input_ids.shape[0]:
|
260 |
+
past = self._convert_to_bloom_cache(past)
|
261 |
+
else:
|
262 |
+
bidirectional_mask = torch.ones_like(input_ids)
|
263 |
+
return {'input_ids': input_ids, 'past_key_values': past, 'use_cache': True, 'attention_mask': attention_mask, 'bidirectional_mask': bidirectional_mask}
|
264 |
+
setattr(model, 'forward', MethodType(forward, model))
|
265 |
+
setattr(model, 'prepare_inputs_for_generation', MethodType(prepare_inputs_for_generation, model))
|
266 |
+
setattr(model, '_prefix_lm_converted', True)
|
267 |
+
return model
|
268 |
+
|
269 |
+
def _convert_opt_causal_lm_to_prefix_lm(model: OPTForCausalLM) -> OPTForCausalLM:
|
270 |
+
"""Converts an OPT Causal LM to a Prefix LM.
|
271 |
+
|
272 |
+
Supported HuggingFace model classes:
|
273 |
+
- `OPTForCausalLM`
|
274 |
+
|
275 |
+
See `convert_hf_causal_lm_to_prefix_lm` for more details.
|
276 |
+
"""
|
277 |
+
if hasattr(model, '_prefix_lm_converted'):
|
278 |
+
return model
|
279 |
+
assert isinstance(model, OPTForCausalLM)
|
280 |
+
assert model.config.add_cross_attention == False, 'Only supports OPT decoder-only models'
|
281 |
+
setattr(model, '_original_forward', getattr(model, 'forward'))
|
282 |
+
setattr(model, '_original_generate', getattr(model, 'generate'))
|
283 |
+
model.model.decoder.bidirectional_mask = None
|
284 |
+
|
285 |
+
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
|
286 |
+
combined_attention_mask = None
|
287 |
+
if input_shape[-1] > 1:
|
288 |
+
if self.bidirectional_mask == 'g':
|
289 |
+
(bsz, src_length) = input_shape
|
290 |
+
combined_attention_mask = torch.zeros((bsz, 1, src_length, src_length + past_key_values_length), dtype=inputs_embeds.dtype, device=inputs_embeds.device)
|
291 |
+
else:
|
292 |
+
combined_attention_mask = _make_causal_mask_opt(input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length).to(inputs_embeds.device)
|
293 |
+
if self.bidirectional_mask is not None:
|
294 |
+
assert attention_mask.shape == self.bidirectional_mask.shape
|
295 |
+
expanded_bidirectional_mask = _expand_mask_opt(self.bidirectional_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(inputs_embeds.device)
|
296 |
+
combined_attention_mask = torch.maximum(expanded_bidirectional_mask, combined_attention_mask)
|
297 |
+
if attention_mask is not None:
|
298 |
+
expanded_attn_mask = _expand_mask_opt(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(inputs_embeds.device)
|
299 |
+
combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
|
300 |
+
return combined_attention_mask
|
301 |
+
setattr(model.model.decoder, '_prepare_decoder_attention_mask', MethodType(_prepare_decoder_attention_mask, model.model.decoder))
|
302 |
+
|
303 |
+
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):
|
304 |
+
|
305 |
+
def call_og_forward():
|
306 |
+
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)
|
307 |
+
if bidirectional_mask is None:
|
308 |
+
return call_og_forward()
|
309 |
+
self.model.decoder.bidirectional_mask = bidirectional_mask
|
310 |
+
try:
|
311 |
+
outputs = call_og_forward()
|
312 |
+
except:
|
313 |
+
self.model.decoder.bidirectional_mask = None
|
314 |
+
raise
|
315 |
+
self.model.decoder.bidirectional_mask = None
|
316 |
+
return outputs
|
317 |
+
|
318 |
+
def generate(self: OPTForCausalLM, *args: tuple, **kwargs: Dict[str, Any]):
|
319 |
+
"""Wraps original generate to enable PrefixLM-style attention."""
|
320 |
+
self.model.decoder.bidirectional_mask = 'g'
|
321 |
+
try:
|
322 |
+
output = self._original_generate(*args, **kwargs)
|
323 |
+
except:
|
324 |
+
self.model.decoder.bidirectional_mask = None
|
325 |
+
raise
|
326 |
+
self.model.decoder.bidirectional_mask = None
|
327 |
+
return output
|
328 |
+
setattr(model, 'forward', MethodType(forward, model))
|
329 |
+
setattr(model, 'generate', MethodType(generate, model))
|
330 |
+
setattr(model, '_prefix_lm_converted', True)
|
331 |
+
return model
|
332 |
+
_SUPPORTED_HF_MODELS = _SUPPORTED_GPT_MODELS + (BloomForCausalLM, OPTForCausalLM)
|
333 |
+
CAUSAL_LM_TYPES = Union[GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM, BloomForCausalLM, OPTForCausalLM]
|
334 |
+
|
335 |
+
def convert_hf_causal_lm_to_prefix_lm(model: CAUSAL_LM_TYPES) -> CAUSAL_LM_TYPES:
|
336 |
+
"""Converts a HuggingFace Causal LM to a Prefix LM.
|
337 |
+
|
338 |
+
Supported HuggingFace model classes:
|
339 |
+
- `GPT2LMHeadModel`
|
340 |
+
- `GPTNeoForCausalLM`
|
341 |
+
- `GPTNeoXForCausalLM`
|
342 |
+
- `GPTJForCausalLM`
|
343 |
+
- `BloomForCausalLM`
|
344 |
+
- `OPTForCausalLM`
|
345 |
+
|
346 |
+
Conversion to a Prefix LM is done by modifying the `forward` method, and possibly also the
|
347 |
+
`generate` method and/or select underlying methods depending on the model class.
|
348 |
+
|
349 |
+
These changes preserve the model API, but add a new input to `forward`: "bidirectional_mask".
|
350 |
+
|
351 |
+
Notes on training:
|
352 |
+
To actually train the converted model as a Prefix LM, training batches will need to indicate
|
353 |
+
the prefix/target structure by including `bidirectional_mask` as part of the batch inputs.
|
354 |
+
|
355 |
+
**This is not a standard input and requires custom layers either within or after your dataloader.**
|
356 |
+
|
357 |
+
In addition to adding `bidirectional_mask` to the batch, this custom code should modify `labels`
|
358 |
+
such that `batch['labels'][batch['bidirectional_mask'] == 1] == -100`.
|
359 |
+
That is, the prefix portion of the sequence should not generate any loss. Loss should only be
|
360 |
+
generated by the target portion of the sequence.
|
361 |
+
|
362 |
+
Notes on `GPTNeoForCausalLM`:
|
363 |
+
To simplify the implementation, "global" and "local" attention layers are handled differently.
|
364 |
+
For "global" layers, we handle conversion as described above. For "local" layers, which use a
|
365 |
+
causal attention mask within a restricted local window, we do not alter the masking.
|
366 |
+
|
367 |
+
Notes on `forward` method conversion:
|
368 |
+
After conversion, the `forward` method will handle a new input, `bidirectional_mask`,
|
369 |
+
which should be a [batch_size, seq_length] byte tensor, where 1 indicates token positions
|
370 |
+
belonging to the prefix (prefix tokens can attend to one another bidirectionally), and
|
371 |
+
0 indicates token positions belonging to the target.
|
372 |
+
|
373 |
+
The new `forward` method will incorporate `bidirectional_mask` (if supplied) into the existing
|
374 |
+
causal mask, call the original `forward` method, and (if the causal mask is a buffer) reset
|
375 |
+
the causal masks before returning the result.
|
376 |
+
|
377 |
+
Notes on `generate` method conversion:
|
378 |
+
After conversion, the `generate` method will have the same signature but will internally
|
379 |
+
convert all causal masks to be purely bidirectional, call the original `generate` method, and
|
380 |
+
(where appropriate) reset the causal masks before returning the result.
|
381 |
+
|
382 |
+
This works thanks to the logic of the HuggingFace `generate` API, which first encodes the token
|
383 |
+
"prompt" passed to `generate` (which is treated as the prefix) and then sequentially generates
|
384 |
+
each new token. Encodings are cached as generation happens, so all prefix tokens can attend to one
|
385 |
+
another (as expected in a Prefix LM) and generated tokens can only attend to prefix tokens and
|
386 |
+
previously-generated tokens (also as expected in a Prefix LM).
|
387 |
+
|
388 |
+
To preserve the API, the original methods are renamed to `_original_forward` and
|
389 |
+
`_original_generate`, and replaced with new `forward` and `generate` methods that wrap
|
390 |
+
them, respectively. Although implementation details vary by model class.
|
391 |
+
"""
|
392 |
+
if isinstance(model, _SUPPORTED_GPT_MODELS):
|
393 |
+
return _convert_gpt_causal_lm_to_prefix_lm(model)
|
394 |
+
elif isinstance(model, BloomForCausalLM):
|
395 |
+
return _convert_bloom_causal_lm_to_prefix_lm(model)
|
396 |
+
elif isinstance(model, OPTForCausalLM):
|
397 |
+
return _convert_opt_causal_lm_to_prefix_lm(model)
|
398 |
+
else:
|
399 |
+
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}')
|
400 |
+
|
401 |
+
def add_bidirectional_mask_if_missing(batch: Dict[str, Any]):
|
402 |
+
"""Attempts to add bidirectional_mask to batch if missing.
|
403 |
+
|
404 |
+
Raises:
|
405 |
+
KeyError if bidirectional_mask is missing and can't be inferred
|
406 |
+
"""
|
407 |
+
if 'bidirectional_mask' not in batch:
|
408 |
+
if batch.get('mode', None) == 'icl_task':
|
409 |
+
batch['bidirectional_mask'] = batch['attention_mask'].clone()
|
410 |
+
for (i, continuation_indices) in enumerate(batch['continuation_indices']):
|
411 |
+
batch['bidirectional_mask'][i, continuation_indices] = 0
|
412 |
+
elif 'labels' in batch and 'attention_mask' in batch:
|
413 |
+
batch['bidirectional_mask'] = torch.logical_and(torch.eq(batch['attention_mask'], 1), torch.eq(batch['labels'], -100)).type_as(batch['attention_mask'])
|
414 |
+
else:
|
415 |
+
raise KeyError('No bidirectional_mask in batch and not sure how to construct one.')
|
meta_init_context.py
ADDED
@@ -0,0 +1,94 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from contextlib import contextmanager
|
2 |
+
import torch
|
3 |
+
import torch.nn as nn
|
4 |
+
|
5 |
+
@contextmanager
|
6 |
+
def init_empty_weights(include_buffers: bool=False):
|
7 |
+
"""Meta initialization context manager.
|
8 |
+
|
9 |
+
A context manager under which models are initialized with all parameters
|
10 |
+
on the meta device, therefore creating an empty model. Useful when just
|
11 |
+
initializing the model would blow the available RAM.
|
12 |
+
|
13 |
+
Args:
|
14 |
+
include_buffers (`bool`, *optional*, defaults to `False`): Whether or
|
15 |
+
not to also put all buffers on the meta device while initializing.
|
16 |
+
|
17 |
+
Example:
|
18 |
+
```python
|
19 |
+
import torch.nn as nn
|
20 |
+
|
21 |
+
# Initialize a model with 100 billions parameters in no time and without using any RAM.
|
22 |
+
with init_empty_weights():
|
23 |
+
tst = nn.Sequential(*[nn.Linear(10000, 10000) for _ in range(1000)])
|
24 |
+
```
|
25 |
+
|
26 |
+
<Tip warning={true}>
|
27 |
+
|
28 |
+
Any model created under this context manager has no weights. As such you can't do something like
|
29 |
+
`model.to(some_device)` with it. To load weights inside your empty model, see [`load_checkpoint_and_dispatch`].
|
30 |
+
|
31 |
+
</Tip>
|
32 |
+
"""
|
33 |
+
with init_on_device(torch.device('meta'), include_buffers=include_buffers) as f:
|
34 |
+
yield f
|
35 |
+
|
36 |
+
@contextmanager
|
37 |
+
def init_on_device(device: torch.device, include_buffers: bool=False):
|
38 |
+
"""Device initialization context manager.
|
39 |
+
|
40 |
+
A context manager under which models are initialized with all parameters
|
41 |
+
on the specified device.
|
42 |
+
|
43 |
+
Args:
|
44 |
+
device (`torch.device`): Device to initialize all parameters on.
|
45 |
+
include_buffers (`bool`, *optional*, defaults to `False`): Whether or
|
46 |
+
not to also put all buffers on the meta device while initializing.
|
47 |
+
|
48 |
+
Example:
|
49 |
+
```python
|
50 |
+
import torch.nn as nn
|
51 |
+
|
52 |
+
with init_on_device(device=torch.device("cuda")):
|
53 |
+
tst = nn.Liner(100, 100) # on `cuda` device
|
54 |
+
```
|
55 |
+
"""
|
56 |
+
old_register_parameter = nn.Module.register_parameter
|
57 |
+
if include_buffers:
|
58 |
+
old_register_buffer = nn.Module.register_buffer
|
59 |
+
|
60 |
+
def register_empty_parameter(module, name, param):
|
61 |
+
old_register_parameter(module, name, param)
|
62 |
+
if param is not None:
|
63 |
+
param_cls = type(module._parameters[name])
|
64 |
+
kwargs = module._parameters[name].__dict__
|
65 |
+
module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs)
|
66 |
+
|
67 |
+
def register_empty_buffer(module, name, buffer):
|
68 |
+
old_register_buffer(module, name, buffer)
|
69 |
+
if buffer is not None:
|
70 |
+
module._buffers[name] = module._buffers[name].to(device)
|
71 |
+
if include_buffers:
|
72 |
+
tensor_constructors_to_patch = {torch_function_name: getattr(torch, torch_function_name) for torch_function_name in ['empty', 'zeros', 'ones', 'full']}
|
73 |
+
else:
|
74 |
+
tensor_constructors_to_patch = {}
|
75 |
+
|
76 |
+
def patch_tensor_constructor(fn):
|
77 |
+
|
78 |
+
def wrapper(*args, **kwargs):
|
79 |
+
kwargs['device'] = device
|
80 |
+
return fn(*args, **kwargs)
|
81 |
+
return wrapper
|
82 |
+
try:
|
83 |
+
nn.Module.register_parameter = register_empty_parameter
|
84 |
+
if include_buffers:
|
85 |
+
nn.Module.register_buffer = register_empty_buffer
|
86 |
+
for torch_function_name in tensor_constructors_to_patch.keys():
|
87 |
+
setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name)))
|
88 |
+
yield
|
89 |
+
finally:
|
90 |
+
nn.Module.register_parameter = old_register_parameter
|
91 |
+
if include_buffers:
|
92 |
+
nn.Module.register_buffer = old_register_buffer
|
93 |
+
for (torch_function_name, old_torch_function) in tensor_constructors_to_patch.items():
|
94 |
+
setattr(torch, torch_function_name, old_torch_function)
|
modeling_mpt.py
ADDED
@@ -0,0 +1,318 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
1 |
+
"""A simple, flexible implementation of a GPT model.
|
2 |
+
|
3 |
+
Inspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
|
4 |
+
"""
|
5 |
+
import math
|
6 |
+
import warnings
|
7 |
+
from typing import List, Optional, Tuple, Union
|
8 |
+
import torch
|
9 |
+
import torch.nn as nn
|
10 |
+
import torch.nn.functional as F
|
11 |
+
from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
|
12 |
+
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
|
13 |
+
from .attention import attn_bias_shape, build_attn_bias
|
14 |
+
from .blocks import MPTBlock
|
15 |
+
from .custom_embedding import SharedEmbedding
|
16 |
+
from .norm import NORM_CLASS_REGISTRY
|
17 |
+
from .configuration_mpt import MPTConfig
|
18 |
+
from .adapt_tokenizer import AutoTokenizerForMOD, adapt_tokenizer_for_denoising
|
19 |
+
from .hf_prefixlm_converter import add_bidirectional_mask_if_missing, convert_hf_causal_lm_to_prefix_lm
|
20 |
+
from .meta_init_context import init_empty_weights
|
21 |
+
from .param_init_fns import MODEL_INIT_REGISTRY, generic_param_init_fn_
|
22 |
+
try:
|
23 |
+
from .flash_attn_triton import flash_attn_func
|
24 |
+
except:
|
25 |
+
pass
|
26 |
+
Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
|
27 |
+
|
28 |
+
class MPTPreTrainedModel(PreTrainedModel):
|
29 |
+
config_class = MPTConfig
|
30 |
+
base_model_prefix = 'model'
|
31 |
+
_no_split_modules = ['MPTBlock']
|
32 |
+
|
33 |
+
class MPTModel(MPTPreTrainedModel):
|
34 |
+
|
35 |
+
def __init__(self, config: MPTConfig):
|
36 |
+
config._validate_config()
|
37 |
+
super().__init__(config)
|
38 |
+
self.attn_impl = config.attn_config['attn_impl']
|
39 |
+
self.prefix_lm = config.attn_config['prefix_lm']
|
40 |
+
self.attn_uses_sequence_id = config.attn_config['attn_uses_sequence_id']
|
41 |
+
self.alibi = config.attn_config['alibi']
|
42 |
+
self.alibi_bias_max = config.attn_config['alibi_bias_max']
|
43 |
+
if config.init_device == 'mixed':
|
44 |
+
if dist.get_local_rank() == 0:
|
45 |
+
config.init_device = 'cpu'
|
46 |
+
else:
|
47 |
+
config.init_device = 'meta'
|
48 |
+
if config.norm_type.lower() not in NORM_CLASS_REGISTRY.keys():
|
49 |
+
norm_options = ' | '.join(NORM_CLASS_REGISTRY.keys())
|
50 |
+
raise NotImplementedError(f'Requested norm type ({config.norm_type}) is not implemented within this repo (Options: {norm_options}).')
|
51 |
+
norm_class = NORM_CLASS_REGISTRY[config.norm_type.lower()]
|
52 |
+
self.embedding_fraction = config.embedding_fraction
|
53 |
+
self.wte = SharedEmbedding(config.vocab_size, config.d_model, device=config.init_device)
|
54 |
+
if not self.alibi:
|
55 |
+
self.wpe = torch.nn.Embedding(config.max_seq_len, config.d_model, device=config.init_device)
|
56 |
+
self.emb_drop = nn.Dropout(config.emb_pdrop)
|
57 |
+
self.blocks = nn.ModuleList([MPTBlock(device=config.init_device, **config.to_dict()) for _ in range(config.n_layers)])
|
58 |
+
self.norm_f = norm_class(config.d_model, device=config.init_device)
|
59 |
+
if config.init_device != 'meta':
|
60 |
+
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.')
|
61 |
+
self.apply(self.param_init_fn)
|
62 |
+
self.is_causal = not self.prefix_lm
|
63 |
+
self._attn_bias_initialized = False
|
64 |
+
self.attn_bias = None
|
65 |
+
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)
|
66 |
+
if config.no_bias:
|
67 |
+
for module in self.modules():
|
68 |
+
if hasattr(module, 'bias') and isinstance(module.bias, nn.Parameter):
|
69 |
+
if config.verbose:
|
70 |
+
warnings.warn(f'Removing bias ({module.bias}) from {module}.')
|
71 |
+
module.register_parameter('bias', None)
|
72 |
+
if config.verbose and config.verbose > 2:
|
73 |
+
print(self)
|
74 |
+
if 'verbose' not in self.config.init_config:
|
75 |
+
self.config.init_config['verbose'] = self.config.verbose
|
76 |
+
if self.config.init_config['verbose'] > 1:
|
77 |
+
init_fn_name = self.config.init_config['name']
|
78 |
+
warnings.warn(f'Using {init_fn_name} initialization.')
|
79 |
+
|
80 |
+
def get_input_embeddings(self):
|
81 |
+
return self.wte
|
82 |
+
|
83 |
+
def set_input_embeddings(self, value):
|
84 |
+
self.wte = value
|
85 |
+
|
86 |
+
@torch.no_grad()
|
87 |
+
def _attn_bias(self, device, dtype, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None):
|
88 |
+
if not self._attn_bias_initialized:
|
89 |
+
if self.attn_bias_shape:
|
90 |
+
self.attn_bias = torch.zeros(self.attn_bias_shape, device=device, dtype=dtype)
|
91 |
+
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)
|
92 |
+
self._attn_bias_initialized = True
|
93 |
+
if self.attn_impl == 'flash':
|
94 |
+
return (self.attn_bias, attention_mask)
|
95 |
+
if self.attn_bias is not None:
|
96 |
+
self.attn_bias = self.attn_bias.to(dtype=dtype, device=device)
|
97 |
+
attn_bias = self.attn_bias
|
98 |
+
if self.prefix_lm:
|
99 |
+
assert isinstance(attn_bias, torch.Tensor)
|
100 |
+
assert isinstance(prefix_mask, torch.Tensor)
|
101 |
+
attn_bias = self._apply_prefix_mask(attn_bias, prefix_mask)
|
102 |
+
if self.attn_uses_sequence_id and sequence_id is not None:
|
103 |
+
assert isinstance(attn_bias, torch.Tensor)
|
104 |
+
attn_bias = self._apply_sequence_id(attn_bias, sequence_id)
|
105 |
+
if attention_mask is not None:
|
106 |
+
s_k = attention_mask.shape[-1]
|
107 |
+
if attn_bias is None:
|
108 |
+
attn_bias = torch.zeros((1, 1, 1, s_k), device=device, dtype=dtype)
|
109 |
+
else:
|
110 |
+
_s_k = max(0, attn_bias.size(-1) - s_k)
|
111 |
+
attn_bias = attn_bias[:, :, :, _s_k:]
|
112 |
+
if prefix_mask is not None and attention_mask.shape != prefix_mask.shape:
|
113 |
+
raise ValueError(f'attention_mask shape={attention_mask.shape} ' + f'and prefix_mask shape={prefix_mask.shape} are not equal.')
|
114 |
+
min_val = torch.finfo(attn_bias.dtype).min
|
115 |
+
attn_bias = attn_bias.masked_fill(~attention_mask.view(-1, 1, 1, s_k), min_val)
|
116 |
+
return (attn_bias, None)
|
117 |
+
|
118 |
+
def _apply_prefix_mask(self, attn_bias: torch.Tensor, prefix_mask: torch.Tensor):
|
119 |
+
(s_k, s_q) = attn_bias.shape[-2:]
|
120 |
+
if s_k != self.config.max_seq_len or s_q != self.config.max_seq_len:
|
121 |
+
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}.')
|
122 |
+
seq_len = prefix_mask.shape[-1]
|
123 |
+
if seq_len > self.config.max_seq_len:
|
124 |
+
raise ValueError(f'prefix_mask sequence length cannot exceed max_seq_len={self.config.max_seq_len}')
|
125 |
+
attn_bias = attn_bias[..., :seq_len, :seq_len]
|
126 |
+
causal = torch.tril(torch.ones((seq_len, seq_len), dtype=torch.bool, device=prefix_mask.device)).view(1, 1, seq_len, seq_len)
|
127 |
+
prefix = prefix_mask.view(-1, 1, 1, seq_len)
|
128 |
+
cannot_attend = ~torch.logical_or(causal, prefix.bool())
|
129 |
+
min_val = torch.finfo(attn_bias.dtype).min
|
130 |
+
attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
|
131 |
+
return attn_bias
|
132 |
+
|
133 |
+
def _apply_sequence_id(self, attn_bias: torch.Tensor, sequence_id: torch.LongTensor):
|
134 |
+
seq_len = sequence_id.shape[-1]
|
135 |
+
if seq_len > self.config.max_seq_len:
|
136 |
+
raise ValueError(f'sequence_id sequence length cannot exceed max_seq_len={self.config.max_seq_len}')
|
137 |
+
attn_bias = attn_bias[..., :seq_len, :seq_len]
|
138 |
+
cannot_attend = torch.logical_not(torch.eq(sequence_id.view(-1, seq_len, 1), sequence_id.view(-1, 1, seq_len))).unsqueeze(1)
|
139 |
+
min_val = torch.finfo(attn_bias.dtype).min
|
140 |
+
attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
|
141 |
+
return attn_bias
|
142 |
+
|
143 |
+
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):
|
144 |
+
return_dict = return_dict if return_dict is not None else self.config.return_dict
|
145 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
146 |
+
if attention_mask is not None:
|
147 |
+
attention_mask = attention_mask.bool()
|
148 |
+
if prefix_mask is not None:
|
149 |
+
prefix_mask = prefix_mask.bool()
|
150 |
+
if not return_dict:
|
151 |
+
raise NotImplementedError('return_dict False is not implemented yet for MPT')
|
152 |
+
if output_attentions:
|
153 |
+
if self.attn_impl != 'torch':
|
154 |
+
raise NotImplementedError('output_attentions is not implemented for MPT when using attn_impl `flash` or `triton`.')
|
155 |
+
if attention_mask is not None and attention_mask[:, 0].sum() != attention_mask.shape[0] and self.training:
|
156 |
+
raise NotImplementedError('MPT does not support training with left padding.')
|
157 |
+
if self.prefix_lm and prefix_mask is None:
|
158 |
+
raise ValueError('prefix_mask is a required argument when MPT is configured with prefix_lm=True.')
|
159 |
+
if self.training:
|
160 |
+
if self.attn_uses_sequence_id and sequence_id is None:
|
161 |
+
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.')
|
162 |
+
elif self.attn_uses_sequence_id is False and sequence_id is not None:
|
163 |
+
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.')
|
164 |
+
S = input_ids.size(1)
|
165 |
+
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}'
|
166 |
+
tok_emb = self.wte(input_ids)
|
167 |
+
if self.alibi:
|
168 |
+
x = tok_emb
|
169 |
+
else:
|
170 |
+
past_position = 0
|
171 |
+
if past_key_values is not None:
|
172 |
+
if len(past_key_values) != self.config.n_layers:
|
173 |
+
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}).')
|
174 |
+
past_position = past_key_values[0][0].size(1)
|
175 |
+
if self.attn_impl == 'torch':
|
176 |
+
past_position = past_key_values[0][0].size(3)
|
177 |
+
if S + past_position > self.config.max_seq_len:
|
178 |
+
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}.')
|
179 |
+
pos = torch.arange(past_position, S + past_position, dtype=torch.long, device=input_ids.device).unsqueeze(0)
|
180 |
+
if attention_mask is not None:
|
181 |
+
pos = torch.clamp(pos - torch.cumsum((~attention_mask).to(torch.int32), dim=1)[:, past_position:], min=0)
|
182 |
+
pos_emb = self.wpe(pos)
|
183 |
+
x = tok_emb + pos_emb
|
184 |
+
if self.embedding_fraction == 1:
|
185 |
+
x = self.emb_drop(x)
|
186 |
+
else:
|
187 |
+
x_shrunk = x * self.embedding_fraction + x.detach() * (1 - self.embedding_fraction)
|
188 |
+
assert isinstance(self.emb_drop, nn.Module)
|
189 |
+
x = self.emb_drop(x_shrunk)
|
190 |
+
(attn_bias, attention_mask) = self._attn_bias(device=x.device, dtype=torch.float32, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id)
|
191 |
+
if use_cache and past_key_values is None:
|
192 |
+
past_key_values = [() for _ in range(self.config.n_layers)]
|
193 |
+
all_hidden_states = () if output_hidden_states else None
|
194 |
+
all_self_attns = () if output_attentions else None
|
195 |
+
for (b_idx, block) in enumerate(self.blocks):
|
196 |
+
if output_hidden_states:
|
197 |
+
assert all_hidden_states is not None
|
198 |
+
all_hidden_states = all_hidden_states + (x,)
|
199 |
+
past_key_value = past_key_values[b_idx] if past_key_values is not None else None
|
200 |
+
(x, attn_weights, past_key_value) = block(x, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=self.is_causal)
|
201 |
+
if past_key_values is not None:
|
202 |
+
past_key_values[b_idx] = past_key_value
|
203 |
+
if output_attentions:
|
204 |
+
assert all_self_attns is not None
|
205 |
+
all_self_attns = all_self_attns + (attn_weights,)
|
206 |
+
x = self.norm_f(x)
|
207 |
+
if output_hidden_states:
|
208 |
+
assert all_hidden_states is not None
|
209 |
+
all_hidden_states = all_hidden_states + (x,)
|
210 |
+
return BaseModelOutputWithPast(last_hidden_state=x, past_key_values=past_key_values, hidden_states=all_hidden_states, attentions=all_self_attns)
|
211 |
+
|
212 |
+
def param_init_fn(self, module):
|
213 |
+
init_fn_name = self.config.init_config['name']
|
214 |
+
MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config)
|
215 |
+
|
216 |
+
def fsdp_wrap_fn(self, module):
|
217 |
+
return isinstance(module, MPTBlock)
|
218 |
+
|
219 |
+
def activation_checkpointing_fn(self, module):
|
220 |
+
return isinstance(module, MPTBlock)
|
221 |
+
|
222 |
+
class MPTForCausalLM(MPTPreTrainedModel):
|
223 |
+
|
224 |
+
def __init__(self, config: MPTConfig):
|
225 |
+
super().__init__(config)
|
226 |
+
if not config.tie_word_embeddings:
|
227 |
+
raise ValueError('MPTForCausalLM only supports tied word embeddings')
|
228 |
+
self.transformer = MPTModel(config)
|
229 |
+
for child in self.transformer.children():
|
230 |
+
if isinstance(child, torch.nn.ModuleList):
|
231 |
+
continue
|
232 |
+
if isinstance(child, torch.nn.Module):
|
233 |
+
child._fsdp_wrap = True
|
234 |
+
self.logit_scale = None
|
235 |
+
if config.logit_scale is not None:
|
236 |
+
logit_scale = config.logit_scale
|
237 |
+
if isinstance(logit_scale, str):
|
238 |
+
if logit_scale == 'inv_sqrt_d_model':
|
239 |
+
logit_scale = 1 / math.sqrt(config.d_model)
|
240 |
+
else:
|
241 |
+
raise ValueError(f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.")
|
242 |
+
self.logit_scale = logit_scale
|
243 |
+
|
244 |
+
def get_input_embeddings(self):
|
245 |
+
return self.transformer.wte
|
246 |
+
|
247 |
+
def set_input_embeddings(self, value):
|
248 |
+
self.transformer.wte = value
|
249 |
+
|
250 |
+
def get_output_embeddings(self):
|
251 |
+
return self.transformer.wte
|
252 |
+
|
253 |
+
def set_output_embeddings(self, new_embeddings):
|
254 |
+
self.transformer.wte = new_embeddings
|
255 |
+
|
256 |
+
def set_decoder(self, decoder):
|
257 |
+
self.transformer = decoder
|
258 |
+
|
259 |
+
def get_decoder(self):
|
260 |
+
return self.transformer
|
261 |
+
|
262 |
+
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):
|
263 |
+
return_dict = return_dict if return_dict is not None else self.config.return_dict
|
264 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
265 |
+
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)
|
266 |
+
logits = self.transformer.wte(outputs.last_hidden_state.to(self.transformer.wte.weight.device), True)
|
267 |
+
if self.logit_scale is not None:
|
268 |
+
if self.logit_scale == 0:
|
269 |
+
warnings.warn(f'Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs.')
|
270 |
+
logits *= self.logit_scale
|
271 |
+
loss = None
|
272 |
+
if labels is not None:
|
273 |
+
labels = torch.roll(labels, shifts=-1)
|
274 |
+
labels[:, -1] = -100
|
275 |
+
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.to(logits.device).view(-1))
|
276 |
+
return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
|
277 |
+
|
278 |
+
def param_init_fn(self, module):
|
279 |
+
init_fn_name = self.config.init_config['name']
|
280 |
+
MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config)
|
281 |
+
|
282 |
+
def fsdp_wrap_fn(self, module):
|
283 |
+
return isinstance(module, MPTBlock)
|
284 |
+
|
285 |
+
def activation_checkpointing_fn(self, module):
|
286 |
+
return isinstance(module, MPTBlock)
|
287 |
+
|
288 |
+
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
|
289 |
+
if inputs_embeds is not None:
|
290 |
+
raise NotImplementedError('inputs_embeds is not implemented for MPT yet')
|
291 |
+
attention_mask = kwargs['attention_mask'].bool()
|
292 |
+
if attention_mask[:, -1].sum() != attention_mask.shape[0]:
|
293 |
+
raise NotImplementedError('MPT does not support generation with right padding.')
|
294 |
+
if self.transformer.attn_uses_sequence_id and self.training:
|
295 |
+
sequence_id = torch.zeros_like(input_ids[:1])
|
296 |
+
else:
|
297 |
+
sequence_id = None
|
298 |
+
if past_key_values is not None:
|
299 |
+
input_ids = input_ids[:, -1].unsqueeze(-1)
|
300 |
+
if self.transformer.prefix_lm:
|
301 |
+
prefix_mask = torch.ones_like(attention_mask)
|
302 |
+
if kwargs.get('use_cache') == False:
|
303 |
+
raise NotImplementedError('MPT with prefix_lm=True does not support use_cache=False.')
|
304 |
+
else:
|
305 |
+
prefix_mask = None
|
306 |
+
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)}
|
307 |
+
|
308 |
+
@staticmethod
|
309 |
+
def _reorder_cache(past_key_values, beam_idx):
|
310 |
+
"""Used by HuggingFace generate when using beam search with kv-caching.
|
311 |
+
|
312 |
+
See https://github.com/huggingface/transformers/blob/3ec7a47664ebe40c40f4b722f6bb1cd30c3821ec/src/transformers/models/gpt2/modeling_gpt2.py#L1122-L1133
|
313 |
+
for an example in transformers.
|
314 |
+
"""
|
315 |
+
reordered_past = []
|
316 |
+
for layer_past in past_key_values:
|
317 |
+
reordered_past += [tuple((past_state.index_select(0, beam_idx) for past_state in layer_past))]
|
318 |
+
return reordered_past
|
norm.py
ADDED
@@ -0,0 +1,56 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
|
3 |
+
def _cast_if_autocast_enabled(tensor):
|
4 |
+
if torch.is_autocast_enabled():
|
5 |
+
if tensor.device.type == 'cuda':
|
6 |
+
dtype = torch.get_autocast_gpu_dtype()
|
7 |
+
elif tensor.device.type == 'cpu':
|
8 |
+
dtype = torch.get_autocast_cpu_dtype()
|
9 |
+
else:
|
10 |
+
raise NotImplementedError()
|
11 |
+
return tensor.to(dtype=dtype)
|
12 |
+
return tensor
|
13 |
+
|
14 |
+
class LPLayerNorm(torch.nn.LayerNorm):
|
15 |
+
|
16 |
+
def __init__(self, normalized_shape, eps=1e-05, elementwise_affine=True, device=None, dtype=None):
|
17 |
+
super().__init__(normalized_shape=normalized_shape, eps=eps, elementwise_affine=elementwise_affine, device=device, dtype=dtype)
|
18 |
+
|
19 |
+
def forward(self, x):
|
20 |
+
module_device = x.device
|
21 |
+
downcast_x = _cast_if_autocast_enabled(x)
|
22 |
+
downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight
|
23 |
+
downcast_bias = _cast_if_autocast_enabled(self.bias) if self.bias is not None else self.bias
|
24 |
+
with torch.autocast(enabled=False, device_type=module_device.type):
|
25 |
+
return torch.nn.functional.layer_norm(downcast_x, self.normalized_shape, downcast_weight, downcast_bias, self.eps)
|
26 |
+
|
27 |
+
def rms_norm(x, weight=None, eps=1e-05):
|
28 |
+
output = x / torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps)
|
29 |
+
if weight is not None:
|
30 |
+
return output * weight
|
31 |
+
return output
|
32 |
+
|
33 |
+
class RMSNorm(torch.nn.Module):
|
34 |
+
|
35 |
+
def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None):
|
36 |
+
super().__init__()
|
37 |
+
self.eps = eps
|
38 |
+
if weight:
|
39 |
+
self.weight = torch.nn.Parameter(torch.ones(normalized_shape, dtype=dtype, device=device))
|
40 |
+
else:
|
41 |
+
self.register_parameter('weight', None)
|
42 |
+
|
43 |
+
def forward(self, x):
|
44 |
+
return rms_norm(x.float(), self.weight, self.eps).to(dtype=x.dtype)
|
45 |
+
|
46 |
+
class LPRMSNorm(RMSNorm):
|
47 |
+
|
48 |
+
def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None):
|
49 |
+
super().__init__(normalized_shape=normalized_shape, eps=eps, weight=weight, dtype=dtype, device=device)
|
50 |
+
|
51 |
+
def forward(self, x):
|
52 |
+
downcast_x = _cast_if_autocast_enabled(x)
|
53 |
+
downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight
|
54 |
+
with torch.autocast(enabled=False, device_type=x.device.type):
|
55 |
+
return rms_norm(downcast_x, downcast_weight, self.eps).to(dtype=x.dtype)
|
56 |
+
NORM_CLASS_REGISTRY = {'layernorm': torch.nn.LayerNorm, 'low_precision_layernorm': LPLayerNorm, 'rmsnorm': RMSNorm, 'low_precision_rmsnorm': LPRMSNorm}
|
param_init_fns.py
ADDED
@@ -0,0 +1,181 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import math
|
2 |
+
import warnings
|
3 |
+
from collections.abc import Sequence
|
4 |
+
from functools import partial
|
5 |
+
from typing import Optional, Tuple, Union
|
6 |
+
import torch
|
7 |
+
from torch import nn
|
8 |
+
from .norm import NORM_CLASS_REGISTRY
|
9 |
+
|
10 |
+
def torch_default_param_init_fn_(module: nn.Module, verbose: int=0, **kwargs):
|
11 |
+
del kwargs
|
12 |
+
if verbose > 1:
|
13 |
+
warnings.warn(f"Initializing network using module's reset_parameters attribute")
|
14 |
+
if hasattr(module, 'reset_parameters'):
|
15 |
+
module.reset_parameters()
|
16 |
+
|
17 |
+
def fused_init_helper_(module: nn.Module, init_fn_):
|
18 |
+
_fused = getattr(module, '_fused', None)
|
19 |
+
if _fused is None:
|
20 |
+
raise RuntimeError(f'Internal logic error')
|
21 |
+
(dim, splits) = _fused
|
22 |
+
splits = (0, *splits, module.weight.size(dim))
|
23 |
+
for (s, e) in zip(splits[:-1], splits[1:]):
|
24 |
+
slice_indices = [slice(None)] * module.weight.ndim
|
25 |
+
slice_indices[dim] = slice(s, e)
|
26 |
+
init_fn_(module.weight[slice_indices])
|
27 |
+
|
28 |
+
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):
|
29 |
+
del kwargs
|
30 |
+
if verbose > 1:
|
31 |
+
warnings.warn(f'If model has bias parameters they are initialized to 0.')
|
32 |
+
init_div_is_residual = init_div_is_residual
|
33 |
+
if init_div_is_residual is False:
|
34 |
+
div_is_residual = 1.0
|
35 |
+
elif init_div_is_residual is True:
|
36 |
+
div_is_residual = math.sqrt(2 * n_layers)
|
37 |
+
elif isinstance(init_div_is_residual, float) or isinstance(init_div_is_residual, int):
|
38 |
+
div_is_residual = init_div_is_residual
|
39 |
+
elif isinstance(init_div_is_residual, str) and init_div_is_residual.isnumeric():
|
40 |
+
div_is_residual = float(init_div_is_residual)
|
41 |
+
else:
|
42 |
+
div_is_residual = 1.0
|
43 |
+
raise ValueError(f'Expected init_div_is_residual to be boolean or numeric, got {init_div_is_residual}')
|
44 |
+
if init_div_is_residual is not False:
|
45 |
+
if verbose > 1:
|
46 |
+
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.')
|
47 |
+
if isinstance(module, nn.Linear):
|
48 |
+
if hasattr(module, '_fused'):
|
49 |
+
fused_init_helper_(module, init_fn_)
|
50 |
+
else:
|
51 |
+
init_fn_(module.weight)
|
52 |
+
if module.bias is not None:
|
53 |
+
torch.nn.init.zeros_(module.bias)
|
54 |
+
if init_div_is_residual is not False and getattr(module, '_is_residual', False):
|
55 |
+
with torch.no_grad():
|
56 |
+
module.weight.div_(div_is_residual)
|
57 |
+
elif isinstance(module, nn.Embedding):
|
58 |
+
if emb_init_std is not None:
|
59 |
+
std = emb_init_std
|
60 |
+
if std == 0:
|
61 |
+
warnings.warn(f'Embedding layer initialized to 0.')
|
62 |
+
emb_init_fn_ = partial(torch.nn.init.normal_, mean=0.0, std=std)
|
63 |
+
if verbose > 1:
|
64 |
+
warnings.warn(f'Embedding layer initialized using normal distribution with mean=0 and std={std!r}.')
|
65 |
+
elif emb_init_uniform_lim is not None:
|
66 |
+
lim = emb_init_uniform_lim
|
67 |
+
if isinstance(lim, Sequence):
|
68 |
+
if len(lim) > 2:
|
69 |
+
raise ValueError(f'Uniform init requires a min and a max limit. User input: {lim}.')
|
70 |
+
if lim[0] == lim[1]:
|
71 |
+
warnings.warn(f'Embedding layer initialized to {lim[0]}.')
|
72 |
+
else:
|
73 |
+
if lim == 0:
|
74 |
+
warnings.warn(f'Embedding layer initialized to 0.')
|
75 |
+
lim = [-lim, lim]
|
76 |
+
(a, b) = lim
|
77 |
+
emb_init_fn_ = partial(torch.nn.init.uniform_, a=a, b=b)
|
78 |
+
if verbose > 1:
|
79 |
+
warnings.warn(f'Embedding layer initialized using uniform distribution in range {lim}.')
|
80 |
+
else:
|
81 |
+
emb_init_fn_ = init_fn_
|
82 |
+
emb_init_fn_(module.weight)
|
83 |
+
elif isinstance(module, tuple(set(NORM_CLASS_REGISTRY.values()))):
|
84 |
+
if verbose > 1:
|
85 |
+
warnings.warn(f'Norm weights are set to 1. If norm layer has a bias it is initialized to 0.')
|
86 |
+
if hasattr(module, 'weight') and module.weight is not None:
|
87 |
+
torch.nn.init.ones_(module.weight)
|
88 |
+
if hasattr(module, 'bias') and module.bias is not None:
|
89 |
+
torch.nn.init.zeros_(module.bias)
|
90 |
+
elif isinstance(module, nn.MultiheadAttention):
|
91 |
+
if module._qkv_same_embed_dim:
|
92 |
+
assert module.in_proj_weight is not None
|
93 |
+
assert module.q_proj_weight is None and module.k_proj_weight is None and (module.v_proj_weight is None)
|
94 |
+
assert d_model is not None
|
95 |
+
_d = d_model
|
96 |
+
splits = (0, _d, 2 * _d, 3 * _d)
|
97 |
+
for (s, e) in zip(splits[:-1], splits[1:]):
|
98 |
+
init_fn_(module.in_proj_weight[s:e])
|
99 |
+
else:
|
100 |
+
assert module.q_proj_weight is not None and module.k_proj_weight is not None and (module.v_proj_weight is not None)
|
101 |
+
assert module.in_proj_weight is None
|
102 |
+
init_fn_(module.q_proj_weight)
|
103 |
+
init_fn_(module.k_proj_weight)
|
104 |
+
init_fn_(module.v_proj_weight)
|
105 |
+
if module.in_proj_bias is not None:
|
106 |
+
torch.nn.init.zeros_(module.in_proj_bias)
|
107 |
+
if module.bias_k is not None:
|
108 |
+
torch.nn.init.zeros_(module.bias_k)
|
109 |
+
if module.bias_v is not None:
|
110 |
+
torch.nn.init.zeros_(module.bias_v)
|
111 |
+
init_fn_(module.out_proj.weight)
|
112 |
+
if init_div_is_residual is not False and getattr(module.out_proj, '_is_residual', False):
|
113 |
+
with torch.no_grad():
|
114 |
+
module.out_proj.weight.div_(div_is_residual)
|
115 |
+
if module.out_proj.bias is not None:
|
116 |
+
torch.nn.init.zeros_(module.out_proj.bias)
|
117 |
+
else:
|
118 |
+
for _ in module.parameters(recurse=False):
|
119 |
+
raise NotImplementedError(f'{module.__class__.__name__} parameters are not initialized by param_init_fn.')
|
120 |
+
|
121 |
+
def _normal_init_(std, mean=0.0):
|
122 |
+
return partial(torch.nn.init.normal_, mean=mean, std=std)
|
123 |
+
|
124 |
+
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):
|
125 |
+
del kwargs
|
126 |
+
init_fn_ = _normal_init_(std=std)
|
127 |
+
if verbose > 1:
|
128 |
+
warnings.warn(f'Using torch.nn.init.normal_ init fn mean=0.0, std={std}')
|
129 |
+
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)
|
130 |
+
|
131 |
+
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):
|
132 |
+
del kwargs
|
133 |
+
if init_std is None:
|
134 |
+
raise ValueError("You must set model.init_config['init_std'] to a float value to use the default initialization scheme.")
|
135 |
+
_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)
|
136 |
+
|
137 |
+
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):
|
138 |
+
del kwargs
|
139 |
+
std = math.sqrt(2 / (5 * d_model))
|
140 |
+
_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)
|
141 |
+
|
142 |
+
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):
|
143 |
+
"""From section 2.3.1 of GPT-NeoX-20B:
|
144 |
+
|
145 |
+
An Open-Source AutoregressiveLanguage Model — Black et. al. (2022)
|
146 |
+
see https://github.com/EleutherAI/gpt-neox/blob/9610391ab319403cef079b438edd016a2443af54/megatron/model/init_functions.py#L151
|
147 |
+
and https://github.com/EleutherAI/gpt-neox/blob/main/megatron/model/transformer.py
|
148 |
+
"""
|
149 |
+
del kwargs
|
150 |
+
residual_div = n_layers / math.sqrt(10)
|
151 |
+
if verbose > 1:
|
152 |
+
warnings.warn(f'setting init_div_is_residual to {residual_div}')
|
153 |
+
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)
|
154 |
+
|
155 |
+
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):
|
156 |
+
del kwargs
|
157 |
+
if verbose > 1:
|
158 |
+
warnings.warn(f'Using nn.init.kaiming_uniform_ init fn with parameters: ' + f'a={init_gain}, mode={fan_mode}, nonlinearity={init_nonlinearity}')
|
159 |
+
kaiming_uniform_ = partial(nn.init.kaiming_uniform_, a=init_gain, mode=fan_mode, nonlinearity=init_nonlinearity)
|
160 |
+
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)
|
161 |
+
|
162 |
+
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):
|
163 |
+
del kwargs
|
164 |
+
if verbose > 1:
|
165 |
+
warnings.warn(f'Using nn.init.kaiming_normal_ init fn with parameters: ' + f'a={init_gain}, mode={fan_mode}, nonlinearity={init_nonlinearity}')
|
166 |
+
kaiming_normal_ = partial(torch.nn.init.kaiming_normal_, a=init_gain, mode=fan_mode, nonlinearity=init_nonlinearity)
|
167 |
+
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)
|
168 |
+
|
169 |
+
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):
|
170 |
+
del kwargs
|
171 |
+
xavier_uniform_ = partial(torch.nn.init.xavier_uniform_, gain=init_gain)
|
172 |
+
if verbose > 1:
|
173 |
+
warnings.warn(f'Using torch.nn.init.xavier_uniform_ init fn with parameters: ' + f'gain={init_gain}')
|
174 |
+
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)
|
175 |
+
|
176 |
+
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):
|
177 |
+
xavier_normal_ = partial(torch.nn.init.xavier_normal_, gain=init_gain)
|
178 |
+
if verbose > 1:
|
179 |
+
warnings.warn(f'Using torch.nn.init.xavier_normal_ init fn with parameters: ' + f'gain={init_gain}')
|
180 |
+
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)
|
181 |
+
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_}
|
pytorch_model-00001-of-00007.bin
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pytorch_model-00004-of-00007.bin
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version https://git-lfs.github.com/spec/v1
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pytorch_model-00005-of-00007.bin
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version https://git-lfs.github.com/spec/v1
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size 9865248775
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pytorch_model-00006-of-00007.bin
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version https://git-lfs.github.com/spec/v1
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size 9865248775
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pytorch_model-00007-of-00007.bin
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pytorch_model.bin.index.json
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|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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"total_size": 59914401792
|
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special_tokens_map.json
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tokenizer.json
ADDED
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tokenizer_config.json
ADDED
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