index
int64 | repo_name
string | branch_name
string | path
string | content
string | import_graph
string |
|---|---|---|---|---|---|
33,341
|
lilywise96/ChoResearch
|
refs/heads/master
|
/tree_modification.py
|
"""
Name: tree_modification.py
Author: Lily Wise
All functions that modify or determine information about the annotation or ontology trees.
"""
from math import log10
# Calculates the specificity of every node in the tree using the information content.
#
# param: tree - dictionary; key: term, value: all parents (transitively)
# param: tree_ic - dictionary; key: term, value: information content of that term (-log(P))
# returns: term_spec - dictionary, key: term, value: specificity
# (sum of info content of ancestors divided by the number of tree terms)
def all_specificity(tree, tree_ic):
term_spec = {}
all_nodes = set()
for node in tree:
term_spec[node] = 0
all_nodes.add(node)
for parent in tree[node]:
if parent in tree_ic:
term_spec[node] += tree_ic[parent]
all_nodes.add(parent)
for term in term_spec:
term_spec[term] /= len(all_nodes)
return term_spec
# Swaps a dictionary that has a key and a value where the value is a list
# or a set.
#
# param: key_to_value - a dictionary; key: key, value: set or list of values
# return: value_to_key - a dictionary; key: previous value, value: set or list of previous keys
def swap_key_value(key_to_value):
value_to_key = {}
for key in key_to_value:
for value in key_to_value[key]:
if value not in value_to_key:
value_to_key[value] = set()
value_to_key[value].add(key)
return value_to_key
# Joins all trees and switches from term to genes into gene to terms.
#
# param: hp_tg - the human phenotype tree, term to genes
# param: bp_tg - the biological process tree, term to genes
# param: mf_tg - the molecular function tree, term to genes
#
# returns: the joined tree, gene to terms
def join_gt(hp_tg, bp_tg, mf_tg):
hp_gt = swap_key_value(hp_tg)
bp_gt = swap_key_value(bp_tg)
mf_gt = swap_key_value(mf_tg)
all_gt = {}
for gene in hp_gt:
if gene not in all_gt:
all_gt[gene] = set()
for term in hp_gt[gene]:
all_gt[gene].add(term)
for gene in bp_gt:
if gene not in all_gt:
all_gt[gene] = set()
for term in bp_gt[gene]:
all_gt[gene].add(term)
for gene in mf_gt:
if gene not in all_gt:
all_gt[gene] = set()
for term in mf_gt[gene]:
all_gt[gene].add(term)
return all_gt
# Find all leaf nodes and add all of the genes to their parents.
#
# param: tree_child_parent - tree that is child to parent
# param: gene_to_terms - gene to terms it is annotated to
# returns: terms to genes tree with all ancestors having all the terms below theirs genes
def gene_to_all_parents(tree_child_parent, gene_to_terms):
tree_parent_child = swap_key_value(tree_child_parent)
all_nodes = set()
terms_to_genes = swap_key_value(gene_to_terms)
leaf_nodes = set()
to_check = set()
for node in set(tree_parent_child.keys()):
all_nodes.add(node)
for node in set(tree_child_parent.keys()):
all_nodes.add(node)
for node in all_nodes:
if node not in set(tree_parent_child.keys()):
leaf_nodes.add(node)
for node in leaf_nodes:
for parent in tree_child_parent[node]:
to_check.add(parent)
while len(to_check) != 0:
checking = to_check.pop()
if checking in tree_child_parent:
for parent in tree_child_parent[checking]:
to_check.add(parent)
terms_to_genes = gene_to_parent(checking, tree_parent_child, terms_to_genes)
return terms_to_genes
# Add all gene_carry genes to the current node. Add current nodes
# genes to gene_carry and then call for all parents of current node
# this function until there are no parents left to call.
#
# param: node - the current node in the tree
# param: tree - a dictionary; key: term, value: set of parent terms
# param: gene_to_terms - a dictionary; key: gene, value: set of terms that it is annotated to
# return: new_gene_to_terms - updated gene_to_terms with the node that was called having all of its children's genes
def gene_to_parent(node, tree, terms_to_genes):
new_terms_to_genes = terms_to_genes
for child in tree[node]:
if node not in new_terms_to_genes:
new_terms_to_genes[node] = set()
if child in new_terms_to_genes:
for c in new_terms_to_genes[child]:
new_terms_to_genes[node].add(c)
return new_terms_to_genes
# Find root node and move down through the tree until all terms transitively know their parents.
#
# param: tree_child_parent - dictionary; key: term, value: set of parents
# returns: dictionary; key: term, value: set of transitive parents
def terms_to_all_parents(tree_child_parent):
new_tree_child_parent = tree_child_parent
tree_parent_child = swap_key_value(new_tree_child_parent)
# Find the root
root = ''
for node in new_tree_child_parent:
if len(new_tree_child_parent[node]) == 0 and node in tree_parent_child:
root = node
# Check each of the nodes starting at the root and moving down through the tree.
to_check = []
for child in tree_parent_child[root]:
to_check.append(child)
while len(to_check) != 0:
cur_check = to_check.pop()
add_parents(cur_check, new_tree_child_parent)
if cur_check in tree_parent_child:
for child in tree_parent_child[cur_check]:
to_check.append(child)
return new_tree_child_parent
# Joins to trees together into one tree.
#
# param: tree - dictionary; key: gene, value: set of terms (either bp or mf)
# param: hp_tree - dictionary; key: gene, value: set of terms in hp
# returns: dictionary; key: gene, value: set of terms
def join_two(tree, hp_tree):
combined_tree = {}
for gene in tree:
combined_tree[gene] = tree[gene]
for gene in hp_tree:
if gene in combined_tree:
combined_tree[gene] = tree[gene].union(hp_tree[gene])
return combined_tree
# Adds all parents transitively to the current node.
#
# param: node - the current node in the tree
# param: tree - a dictionary; key: term, value: set of parent terms (is modified in this function)
def add_parents(node, tree_child_parent):
for parents in tree_child_parent[node]:
tree_child_parent[node] = tree_child_parent[node].union(tree_child_parent[parents])
# Calculates information content of every term of the tree
#
# param: tree - the tree; key: term, value: genes annotated to that term (with transitive property)
# return: term_ic - dictionary; key: term, value: information content of that term
def calculate_ic(tree):
term_ic = {} # key: term, value: ic
distinct_genes = set()
for term in tree:
term_ic[term] = len(tree[term])
distinct_genes = distinct_genes.union(tree[term])
for term in term_ic:
term_ic[term] /= len(distinct_genes)
if term_ic[term] != 0:
term_ic[term] = -log10(term_ic[term])
return term_ic
|
{"/main_runner.py": ["/main.py"], "/main.py": ["/ontology_parsing.py", "/annotation_parsing.py", "/tree_modification.py", "/apriori_algorithm.py", "/association_creation.py"]}
|
33,342
|
lilywise96/ChoResearch
|
refs/heads/master
|
/main_runner.py
|
from main import general_main
# def general_main(freq_file_ext, association_file_ext, recreate_onto_ann, recreate_freq_itemsets, tree,
# min_support, min_weighted_support, min_confidence, min_information_content, min_coverage):
trees = ['all', 'bp', 'mf', 'hp']
support = [0.02, 0.015]
weighted_support = 0.1
confidence = [0.03, 0.02, 0.01]
info_content = .3
coverage = 0.1
count_freq_file = 1
count_assoc_file = 1
first = True
for tree in range(0, len(trees)):
for sup in range(0, len(support)):
for conf in range(0, len(confidence)):
if first:
general_main(count_freq_file, count_assoc_file, "true", "true", trees[tree], support[sup],
weighted_support, confidence[conf], info_content, coverage)
first = False
else:
general_main(count_freq_file, count_assoc_file, "false", "true", trees[tree], support[sup],
weighted_support, confidence[conf], info_content, coverage)
count_assoc_file += 1
count_freq_file += 1
|
{"/main_runner.py": ["/main.py"], "/main.py": ["/ontology_parsing.py", "/annotation_parsing.py", "/tree_modification.py", "/apriori_algorithm.py", "/association_creation.py"]}
|
33,343
|
lilywise96/ChoResearch
|
refs/heads/master
|
/main.py
|
"""
Filename: main.py
Author: Lily Wise
Calls other functions to find associations between genes and terms for diseases. Should calculate associations with
support 4% - 10%, coverage 4% - 10%, and confidence 20% - 50%.
"""
# BP -> BP and MF -> MF and HPO -> HPO and BP -> HPO and MF -> HPO
# Accuracy Measuring?
from ontology_parsing import hpo_parsing_onto, parsing_go, testing_ontology_parsing
from annotation_parsing import hpo_parsing_ann, parsing_ann, testing_annotation_parsing
from tree_modification import gene_to_all_parents, join_gt, calculate_ic, terms_to_all_parents, \
all_specificity, swap_key_value, join_two
from apriori_algorithm import apriori
from association_creation import create_associations
from math import ceil
import sys
# Directories
input_direct = "./input/"
created_direct = "./created/"
# File names
# Specificity Storage
hp_spec_filename = created_direct + "hp_spec.txt"
bp_spec_filename = created_direct + "bp_spec.txt"
mf_spec_filename = created_direct + "mf_spec.txt"
# Information Content Storage
hp_ic_filename = created_direct + "hp_ic.txt"
bp_ic_filename = created_direct + "bp_ic.txt"
mf_ic_filename = created_direct + "mf_ic.txt"
# Transitivity of Terms Storage
hp_trans_filename = created_direct + "hp_trans.txt"
bp_trans_filename = created_direct + "bp_trans.txt"
mf_trans_filename = created_direct + "mf_trans.txt"
# Gene to Terms All
gene_term_filename = created_direct + "gene_term.txt"
gene_term_bp_filename = created_direct + "gene_term_bp.txt"
gene_term_mf_filename = created_direct + "gene_term_mf.txt"
gene_term_hp_filename = created_direct + "gene_term_hp.txt"
# Ontology Given
hp_ontology_filename = input_direct + "hp.obo.txt"
g_ontology_filename = input_direct + "go.obo"
# Annotation Given
hp_annotations_filename = input_direct + "hpo_genes_to_phenotype.txt"
g_annotations_filename = input_direct + "goa_human.gaf"
# Creates ontology and writes it to an output file, as well as calculates information content.
#
# param: gene_term_output_filename - the file the ontology is written to
# return: all_gt - dictionary; key: gene, value: set of terms
def create_onto_ann():
# Read in ontologies. terms to parents
hpo_terms_parents = hpo_parsing_onto(hp_ontology_filename)
bp_terms_parents, mf_terms_parents, cc_terms_parents = parsing_go(g_ontology_filename)
# Read in annotations.
hp_gt = hpo_parsing_ann(hp_annotations_filename)
gene_syn, bp_gt, mf_gt, cc_gt = parsing_ann(g_annotations_filename)
# Creates transitive trees
hpo_terms_parents_trans = terms_to_all_parents(hpo_terms_parents)
bp_terms_parents_trans = terms_to_all_parents(bp_terms_parents)
mf_terms_parents_trans = terms_to_all_parents(mf_terms_parents)
# Recursively add genes to parents.
hp_tg = gene_to_all_parents(hpo_terms_parents, hp_gt)
bp_tg = gene_to_all_parents(bp_terms_parents, bp_gt)
mf_tg = gene_to_all_parents(mf_terms_parents, mf_gt)
# Calculate Information Content
hp_ic = calculate_ic(hp_tg)
bp_ic = calculate_ic(bp_tg)
mf_ic = calculate_ic(mf_tg)
# Save terms for each
hp_terms = set(hp_tg.keys())
bp_terms = set(bp_tg.keys())
mf_terms = set(mf_tg.keys())
# Specificity of all nodes
hp_spec = all_specificity(hpo_terms_parents_trans, hp_ic)
bp_spec = all_specificity(bp_terms_parents_trans, bp_ic)
mf_spec = all_specificity(mf_terms_parents_trans, mf_ic)
# Join all term to gene and make them gene to term.
all_gt = join_gt(hp_tg, bp_tg, mf_tg)
hp_gt = swap_key_value(hp_tg)
bp_gt = swap_key_value(bp_tg)
mf_gt = swap_key_value(mf_tg)
output_file = open(gene_term_filename, "w")
for gene in all_gt:
output_file.write(gene)
for term in all_gt[gene]:
output_file.write("\t")
output_file.write(term)
output_file.write("\n")
output_file.close()
output_file = open(gene_term_bp_filename, "w")
for gene in bp_gt:
output_file.write(gene)
for term in bp_gt[gene]:
output_file.write("\t")
output_file.write(term)
output_file.write("\n")
output_file.close()
output_file = open(gene_term_mf_filename, "w")
for gene in mf_gt:
output_file.write(gene)
for term in mf_gt[gene]:
output_file.write("\t")
output_file.write(term)
output_file.write("\n")
output_file.close()
output_file = open(gene_term_hp_filename, "w")
for gene in hp_gt:
output_file.write(gene)
for term in hp_gt[gene]:
output_file.write("\t")
output_file.write(term)
output_file.write("\n")
output_file.close()
# Write specificity to files.
output_file = open(hp_spec_filename, "w")
for term in hp_spec:
output_file.write(term+"\t"+str(hp_spec[term])+"\n")
output_file.close()
output_file = open(bp_spec_filename, "w")
for term in bp_spec:
output_file.write(term+"\t"+str(bp_spec[term])+"\n")
output_file.close()
output_file = open(mf_spec_filename, "w")
for term in mf_spec:
output_file.write(term+"\t"+str(mf_spec[term])+"\n")
output_file.close()
# Write information content to files.
output_file = open(hp_ic_filename, "w")
for term in hp_ic:
output_file.write(term + "\t" + str(hp_ic[term]) + "\n")
output_file.close()
output_file = open(bp_ic_filename, "w")
for term in bp_ic:
output_file.write(term + "\t" + str(bp_ic[term]) + "\n")
output_file.close()
output_file = open(mf_ic_filename, "w")
for term in mf_ic:
output_file.write(term + "\t" + str(mf_ic[term]) + "\n")
output_file.close()
all_ic = {}
for term in hp_ic:
all_ic[term] = hp_ic[term]
for term in bp_ic:
all_ic[term] = bp_ic[term]
for term in mf_ic:
all_ic[term] = mf_ic[term]
all_spec = {}
for term in hp_spec:
all_spec[term] = hp_spec[term]
for term in bp_spec:
all_spec[term] = bp_spec[term]
for term in mf_spec:
all_spec[term] = mf_spec[term]
return all_gt, all_spec, all_ic, bp_gt, mf_gt, hp_gt
def read_onto_ann():
file = open(gene_term_filename, "r")
gt = {}
for line in file:
cols = line.split("\t")
cols[len(cols) - 1] = cols[len(cols) - 1][0:-1]
gt[cols[0]] = set()
for i in range(1, len(cols)):
gt[cols[0]].add(cols[i])
file.close()
file = open(gene_term_bp_filename, "r")
bp_gt = {}
for line in file:
cols = line.split("\t")
cols[len(cols) - 1] = cols[len(cols) - 1][0:-1]
bp_gt[cols[0]] = set()
for i in range(1, len(cols)):
bp_gt[cols[0]].add(cols[i])
file.close()
file = open(gene_term_mf_filename, "r")
mf_gt = {}
for line in file:
cols = line.split("\t")
cols[len(cols) - 1] = cols[len(cols) - 1][0:-1]
mf_gt[cols[0]] = set()
for i in range(1, len(cols)):
mf_gt[cols[0]].add(cols[i])
file.close()
file = open(gene_term_hp_filename, "r")
hp_gt = {}
for line in file:
cols = line.split("\t")
cols[len(cols) - 1] = cols[len(cols) - 1][0:-1]
hp_gt[cols[0]] = set()
for i in range(1, len(cols)):
hp_gt[cols[0]].add(cols[i])
file.close()
# Read Specificity Files
file = open(hp_spec_filename, "r")
hp_spec = {}
for line in file:
cols = line.split("\t")
cols[len(cols) - 1] = cols[len(cols) - 1][0:-1]
hp_spec[cols[0]] = float(cols[1])
file.close()
file = open(bp_spec_filename, "r")
bp_spec = {}
for line in file:
cols = line.split("\t")
cols[len(cols) - 1] = cols[len(cols) - 1][0:-1]
bp_spec[cols[0]] = float(cols[1])
file.close()
file = open(mf_spec_filename, "r")
mf_spec = {}
for line in file:
cols = line.split("\t")
cols[len(cols) - 1] = cols[len(cols) - 1][0:-1]
mf_spec[cols[0]] = float(cols[1])
file.close()
# Read Information Content Files
file = open(hp_ic_filename, "r")
hp_ic = {}
for line in file:
cols = line.split("\t")
cols[len(cols) - 1] = cols[len(cols) - 1][0:-1]
hp_ic[cols[0]] = float(cols[1])
file.close()
file = open(bp_ic_filename, "r")
bp_ic = {}
for line in file:
cols = line.split("\t")
cols[len(cols) - 1] = cols[len(cols) - 1][0:-1]
bp_ic[cols[0]] = float(cols[1])
file.close()
file = open(mf_ic_filename, "r")
mf_ic = {}
for line in file:
cols = line.split("\t")
cols[len(cols) - 1] = cols[len(cols) - 1][0:-1]
mf_ic[cols[0]] = float(cols[1])
file.close()
all_ic = {}
for term in hp_ic:
all_ic[term] = hp_ic[term]
for term in bp_ic:
all_ic[term] = bp_ic[term]
for term in mf_ic:
all_ic[term] = mf_ic[term]
all_spec = {}
for term in hp_spec:
all_spec[term] = hp_spec[term]
for term in bp_spec:
all_spec[term] = bp_spec[term]
for term in mf_spec:
all_spec[term] = mf_spec[term]
return gt, all_spec, all_ic, bp_gt, mf_gt, hp_gt
# Create frequent itemsets.
def create_freq_itemsets(filename, possible_left, all_gt, min_support, min_weighted_support,
min_information_content, all_spec, all_ic):
all_terms = set()
for gene in all_gt:
for term in all_gt[gene]:
all_terms.add(term)
print("ALL GT: ", end="")
print(all_gt)
freq_itemsets = apriori(all_gt, all_terms, min_support, min_weighted_support,
min_information_content, all_spec, all_ic)
all_itemsets = []
for size_freq in freq_itemsets:
for itemset in freq_itemsets[size_freq]:
all_itemsets.append(itemset)
freq_itemsets = all_itemsets
for itemset in freq_itemsets:
found = False
for item in range(0, len(itemset)):
if itemset[item] in possible_left:
found = True
if not found:
freq_itemsets.remove(itemset)
file = open(filename, "w")
file.write("Min Support - "+str(min_support)+"\n")
file.write("Min Information Content - " + str(min_information_content) + "\n")
file.write("Min Weighted Support - " + str(min_weighted_support) + "\n")
for itemset in freq_itemsets:
for item in range(0, len(itemset)):
if item != 0:
file.write("\t")
file.write(itemset[item])
file.write("\n")
file.close()
return freq_itemsets
# Read frequent itemsets.
def read_freq_itemsets(filename):
freq_itemsets = []
file = open(filename, "r")
count = 3
for line in file:
if count > 0:
count -= 1
else:
items = line.split("\t")
items[len(items) - 1] = items[len(items) - 1][0:-1]
itemset = set()
for item in items:
itemset.add(item)
freq_itemsets.append(itemset)
file.close()
return freq_itemsets
def create_new_associations(left_terms, right_terms, all_gt, freq_itemsets, min_confidence, min_coverage,
filename, all_spec):
final_associations = create_associations(left_terms, right_terms, all_gt, freq_itemsets, min_confidence,
min_coverage, all_spec)
file = open(filename, "w")
file.write("Min Coverage - " + str(min_coverage) + "\n")
file.write("Min Confidence - " + str(min_confidence) + "\n")
for association in final_associations:
for associate in range(0, len(association)):
if associate != 0:
file.write("\t")
file.write(association[associate])
file.write("\n")
file.close()
return final_associations
def general_main(freq_file_ext, association_file_ext, recreate_onto_ann, recreate_freq_itemsets, tree,
min_support, min_weighted_support, min_confidence, min_information_content, min_coverage):
freq_itemsets_filename = created_direct + "freq_itemsets_" + str(freq_file_ext) + ".txt"
associations_filename = created_direct + "associations_" + str(association_file_ext) + ".txt"
information_filename = "info_on_files.txt"
info_file = open(information_filename, "a+")
print("Frequent itemsets filename: "+str(freq_itemsets_filename))
print("Associations filename: "+str(associations_filename))
print("Tree: "+str(tree))
print("Minimum support: "+str(min_support))
print("Minimum weighted support: "+str(min_weighted_support))
print("Minimum confidence: "+str(min_confidence))
print("Minimum information content: "+str(min_information_content))
print("Minimum coverage: "+str(min_coverage))
info_file.write("Frequent itemsets filename: " + str(freq_itemsets_filename) + "\n")
info_file.write("Associations filename: " + str(associations_filename) + "\n")
info_file.write("Tree: " + str(tree) + "\n")
info_file.write("Minimum support: " + str(min_support) + "\n")
info_file.write("Minimum weighted support: " + str(min_weighted_support) + "\n")
info_file.write("Minimum confidence: " + str(min_confidence) + "\n")
info_file.write("Minimum information content: " + str(min_information_content) + "\n")
info_file.write("Minimum coverage: " + str(min_coverage) + "\n")
info_file.write("\n\n")
info_file.close()
if recreate_onto_ann == "true":
all_gt, all_spec, all_ic, bp_gt, mf_gt, hp_gt = create_onto_ann()
else:
all_gt, all_spec, all_ic, bp_gt, mf_gt, hp_gt = read_onto_ann()
possible_left = set()
possible_right = set()
if tree == 'bp':
for gene in bp_gt:
possible_left = bp_gt[gene].union(possible_left)
for gene in hp_gt:
possible_right = hp_gt[gene].union(possible_right)
elif tree == 'mf':
for gene in mf_gt:
possible_left = mf_gt[gene].union(possible_left)
for gene in hp_gt:
possible_right = hp_gt[gene].union(possible_right)
elif tree == 'hp':
for gene in hp_gt:
possible_left = hp_gt[gene].union(possible_left)
possible_right = possible_left
else:
for gene in all_gt:
possible_left = all_gt[gene].union(possible_left)
possible_right = possible_left
if recreate_freq_itemsets == "true":
if tree == 'bp':
all_gt = join_two(bp_gt, hp_gt)
elif tree == 'mf':
all_gt = join_two(mf_gt, hp_gt)
elif tree == 'hp':
all_gt = hp_gt
freq_itemsets = create_freq_itemsets(freq_itemsets_filename, possible_left, all_gt,
min_support, min_weighted_support, min_information_content,
all_spec, all_ic)
else:
freq_itemsets = read_freq_itemsets(freq_itemsets_filename)
create_new_associations(possible_left, possible_right, all_gt, freq_itemsets, min_confidence,
min_coverage, associations_filename, all_spec)
print("Done")
|
{"/main_runner.py": ["/main.py"], "/main.py": ["/ontology_parsing.py", "/annotation_parsing.py", "/tree_modification.py", "/apriori_algorithm.py", "/association_creation.py"]}
|
33,344
|
lilywise96/ChoResearch
|
refs/heads/master
|
/annotation_parsing.py
|
"""
File: annotation_parsing.py
Author: Lily Wise
This file parses annotation files for hpo and for go.
"""
# This function parses the hpo annotation file. It pulls the gene annotation
# and terms it is associated to.
#
# param: filename - the hpo annotation file
# return: gene_term_id - a dictionary; key: gene, value: array of terms
def hpo_parsing_ann(filename):
file = open(filename, "r")
gene_id_symbol = {}
gene_term_id = {}
for line in file:
if not line.startswith('#'):
columns = line.split('\t')
gene_id = columns[0]
gene_symbol = columns[1]
term_id = columns[3][0:10]
if gene_id not in gene_id_symbol.keys():
gene_id_symbol[gene_id] = gene_symbol
gene_term_id[gene_symbol] = []
gene_term_id[gene_symbol].append(term_id)
return gene_term_id
# This function parses the gene annotation file. It pulls the gene annotation
# and terms it is associated to.
#
# param: filename - the gene annotation file
# return: gene_syn - a dictionary; key: a gene, value: array of synonyms
# return: bp_gene_terms - a biological process dictionary; key: a gene,
# value: array of terms that the gene is annotated to
# return: mf_gene_terms - a molecular function dictionary; key: a gene,
# value: array of terms that the gene is annotated to
# return: cc_gene_terms - a cellular component dictionary; key: a gene,
# value: array of terms that the gene is annotated to
def parsing_ann(filename):
file = open(filename, "r")
gene_syn = {}
bp_gene_terms = {}
mf_gene_terms = {}
cc_gene_terms = {}
for line in file:
if not line.startswith('!'):
cols = line.split('\t')
if 'NOT' in cols[3]:
gene = cols[2]
term = cols[4]
namespace = cols[8]
synonym_col = cols[10]
if 'P' in namespace:
if gene not in bp_gene_terms.keys():
bp_gene_terms[gene] = set()
bp_gene_terms[gene].add(term)
elif 'F' in namespace:
if gene not in mf_gene_terms.keys():
mf_gene_terms[gene] = set()
mf_gene_terms[gene].add(term)
else:
if gene not in cc_gene_terms.keys():
cc_gene_terms[gene] = set()
cc_gene_terms[gene].add(term)
if gene not in gene_syn.keys():
gene_syn[gene] = set(gene)
synonyms = synonym_col.split('|')
for syn in synonyms:
if syn not in gene_syn[gene]:
gene_syn[gene].add(syn)
return gene_syn, bp_gene_terms, mf_gene_terms, cc_gene_terms
# This function is for testing the annotation with a modified input file.
#
# param: filename - the file to read in from
# return: genes_terms - dictionary; key: gene, value: terms that the gene is annotated to
def testing_annotation_parsing(filename):
file = open(filename, "r")
genes_terms = {}
for line in file:
cols = line.split(" ")
cols[len(cols) - 1] = cols[len(cols) - 1][0:-1]
if cols[0]:
genes_terms[cols[0]] = set()
for i in range(1, len(cols)):
genes_terms[cols[0]].add(cols[i])
return genes_terms
|
{"/main_runner.py": ["/main.py"], "/main.py": ["/ontology_parsing.py", "/annotation_parsing.py", "/tree_modification.py", "/apriori_algorithm.py", "/association_creation.py"]}
|
33,345
|
lilywise96/ChoResearch
|
refs/heads/master
|
/association_creation.py
|
"""
Filename: association_creation.py
Author: Lily Wise
This creates association and has other functions that are used to calculate the associations.
"""
# This function calculates the coverage given an association and all the itemsets.
#
# param: left_val - the left value of an association
# param: all_itemsets - all of the itemsets given
# return: the number of times the left_val appears in all the itemsets divided by the total number of itemsets
def coverage(left_val, all_itemsets, all_spec):
count = 0
# Loop through all the itemsets and check if the left_val is in the itemset
for itemset in all_itemsets:
if left_val in all_itemsets[itemset]:
count += 1
cover = 0
if left_val in all_spec:
cover = count * all_spec[left_val] * 10
return cover
# This functions calculates the confidence of a given association.
#
# param: all_gt - all of the itemsets
# param: association - the current association to calculate confidence for
#
# returns: the confidence count as a decimal
def confidence(all_gt, association, all_spec):
confidence_count = 0
# Calculate confidence of an association by iterating over the frequent itemsets
# Loop through the transactions
for trans in all_gt:
found_big = True # For checking all items in the itemset are present
# Loop through the items in the itemset
for associate in association:
found = False # For checking just the current item in the itemset is present
# Loop through each yeast in the transaction
for i in all_gt[trans]:
if i == associate:
found = True
if not found:
found_big = False
if found_big:
confidence_count += 1
conf = 0
if 1 in association and association[1] in all_spec:
conf = confidence_count * all_spec[association[1]] * 100
return conf
# This function takes the frequent itemsets that were created by the apriori algorithm and creates associations. An
# association is kept if it meets the minimum confidence requirements and the left side of the association meets the
# minimum coverage requirements.
#
# param: all_gt - all the itemsets originally read in
# param: freq_itemsets - the frequent itemsets created by the apriori algorithm
# param: min_confidence - the minimum confidence, as a decimal
# param: min_coverage - the minimum coverage, as a decimal
#
# returns: the list of final associations that meets the requirements
def create_associations(left_terms, right_terms, all_gt, freq_itemsets, min_confidence, min_coverage, all_spec):
final_associations = []
associations = all_associations(freq_itemsets)
for associate in associations:
cur_confidence = confidence(all_gt, associate, all_spec)
cur_coverage = coverage(associate[0], all_gt, all_spec)
if cur_confidence >= min_confidence and cur_coverage >= min_coverage \
and associate[0] in left_terms and associate[1] in right_terms:
final_associations.append(associate)
return final_associations
# Creates all possible associations with the frequent itemsets.
#
# param: freq_itemsets - the list of frequent itemsets created by the apriori algorithm
# returns: the associations created.
def all_associations(freq_itemsets):
associations = []
for itemset in freq_itemsets:
association = []
for item in itemset:
association.append(item)
associations.append(association)
associations.append(association[::-1])
return associations
|
{"/main_runner.py": ["/main.py"], "/main.py": ["/ontology_parsing.py", "/annotation_parsing.py", "/tree_modification.py", "/apriori_algorithm.py", "/association_creation.py"]}
|
33,346
|
lilywise96/ChoResearch
|
refs/heads/master
|
/apriori_algorithm.py
|
"""
Filename: apriori_algorithm.py
Author: Lily Wise
Calculates the frequent itemsets.
"""
from math import ceil, log10
from itertools import combinations, permutations
# This function calculates support of the itemset from transactions
# param transactions: All transactions in a dictionary
# param itemset: The itemset to calculate support
# return: The support count of the itemset
def weighted_support(transactions, itemset, all_spec):
support_count = 0
# Calculate support of an itemset by iterating over the frequent itemsets
# Loop through the transactions
for trans in transactions:
found_big = True # For checking all items in the itemset are present
# Loop through the items in the itemset
for i in itemset:
found = False # For checking just the current item in the itemset is present
# Loop through each yeast in the transaction
for t in transactions[trans]:
if i == t:
found = True
if not found:
found_big = False
if found_big:
support_count += 1
support_weight = 2 * all_spec[itemset[0]] * all_spec[itemset[1]] * support_count
if (all_spec[itemset[0]] + all_spec[itemset[1]]) != 0:
support_weight /= (all_spec[itemset[0]] + all_spec[itemset[1]])
else:
support_weight = 0
return support_weight
# This function calculates support of the itemset from transactions
# param transactions: All transactions in a dictionary
# param itemset: The itemset to calculate support
# return: The support count of the itemset
def support(transactions, itemset):
support_count = 0
# Calculate support of an itemset by iterating over the frequent itemsets
# Loop through the transactions
for trans in transactions:
found_big = True # For checking all items in the itemset are present
# Loop through the items in the itemset
for i in itemset:
found = False # For checking just the current item in the itemset is present
# Loop through each yeast in the transaction
for t in transactions[trans]:
if i == t:
found = True
if not found:
found_big = False
if found_big:
support_count += 1
return support_count
# This function generates a combination from the frequent itemsets of size (itemset_size - 1) and accepts joined
# itemsets if they share (itemset_size - 2) items
# param frequent_itemsets: The table of frequent itemsets discovered
# param itemset_size: The size of joined itemsets
# return: All valid joined itemsets
def generate_selectively_joined_itemsets(frequent_itemsets, itemset_size):
# Record seen_itemsets to prevent duplicates
seen_itemsets = set()
joined_itemsets = set()
# Try all combinations of two itemsets from the table of frequent itemsets and join the pair if they share
# (itemset_size - 2) items
# Add each joined itemset to the list if it is not present in the list and discard it otherwise
for item1 in frequent_itemsets[itemset_size-1]:
for item2 in frequent_itemsets[itemset_size-1]:
# if the item set is size 1, then you don't need to look for the intersection
if itemset_size-1 == 1:
temp_tuple = (item1, item2)
temp_tuple = tuple(sorted(temp_tuple))
if item1 is not item2 and temp_tuple not in seen_itemsets:
joined_itemsets.add(temp_tuple)
seen_itemsets.add(temp_tuple)
# if the item set is greater than 1, then you need to find the intersection
else:
list_a = set(item1)
list_b = set(item2)
# Get the intersection and the union
intersection = list_a.intersection(list_b)
union = list_a.union(list_b)
length_intersection = len(intersection)
# Check if the sets have enough in common
if length_intersection >= itemset_size-2 and length_intersection is not itemset_size-1:
union = sorted(union)
temp_tuple = tuple(union)
if temp_tuple not in seen_itemsets:
seen_itemsets.add(temp_tuple)
joined_itemsets.add(temp_tuple)
joined_itemsets = sorted(joined_itemsets)
return joined_itemsets
# This function checks all the subsets of selected itemsets whether they all
# are frequent or not and prunes the itemset if anyone of the subsets is not frequent
# param selected_itemsets: The itemsets which are needed to be checked
# param frequent_itemsets: The table of frequent itemsets discovered
# param itemset_size: The size of intended frequent itemsets
# return: The itemsets whose all subsets are frequent
def apply_apriori_pruning(selected_itemsets, frequent_itemsets, itemset_size):
apriori_pruned_itemsets = set()
# Add each itemset to the list if all of its subsets are frequent and discard it otherwise
if itemset_size > 3:
for item in selected_itemsets:
sub_satisfy = True
for sub in list(combinations(item, itemset_size-2)):
if sub not in frequent_itemsets[itemset_size-2]:
sub_satisfy = False
if sub_satisfy:
apriori_pruned_itemsets.add(item)
# Add each to the item set if less than 3 because it was already formed from a pruned list so it can't
# be pruned further.
else:
for item in selected_itemsets:
apriori_pruned_itemsets.add(item)
apriori_pruned_itemsets = sorted(apriori_pruned_itemsets)
return apriori_pruned_itemsets
# This function generates candidate itemsets of size (itemset_size) by selective joining and apriori pruning
# param frequent_itemsets: The table of frequent itemsets discovered
# param itemset_size: The size of intended frequent itemsets
# return: candidate itemsets formed by selective joining and apriori pruning
def generate_candidate_itemsets(frequent_itemsets, itemset_size):
joined_itemsets = generate_selectively_joined_itemsets(frequent_itemsets, itemset_size)
candidate_itemsets = apply_apriori_pruning(joined_itemsets, frequent_itemsets, itemset_size)
return candidate_itemsets
# This function generates a table of itemsets with all frequent items from transactions based on a given minimum support
# param transactions: The transactions based upon which support is calculated
# param items: The unique set of items present in the transaction
# param min_support: The minimum support to find frequent itemsets
# return: The table of all frequent itemsets of different sizes
def generate_all_frequent_itemsets(transactions, items, min_support, min_weighted_support,
min_information_content, all_spec, all_ic):
min_support = ceil(min_support * len(transactions))
min_weighted_support = min_weighted_support / ceil(min_weighted_support * len(transactions))
min_information_content = min_information_content * -log10(1/len(items))
frequent_itemsets = dict()
itemset_size = 0
frequent_itemsets[itemset_size] = list()
frequent_itemsets[itemset_size].append(frozenset())
# Frequent itemsets of size 1
itemset_size += 1
frequent_itemsets[itemset_size] = list()
# Find all frequent itemsets of size-1 and add them to the list
print(len(items))
count = 0
for i in items:
print(str(count))
count += 1
list_ver = [i]
support_check = support(transactions, list_ver)
if support_check >= min_support and all_ic[i] >= min_information_content:
frequent_itemsets[itemset_size].append(i)
frequent_itemsets[itemset_size] = sorted(frequent_itemsets[itemset_size])
print("Finished itemsize "+str(itemset_size))
# frequent itemsets of greater size
itemset_size += 1
while frequent_itemsets[itemset_size - 1]:
frequent_itemsets[itemset_size] = list()
candidate_itemsets = generate_candidate_itemsets(frequent_itemsets, itemset_size)
pruned_itemset = set()
# Prune the candidate itemset if its support is less than minimum support
for candidate in candidate_itemsets:
weighted_sup = weighted_support(transactions, candidate, all_spec)
if weighted_sup >= min_weighted_support:
pruned_itemset.add(candidate)
frequent_itemsets[itemset_size] = pruned_itemset
print("Finished itemsize " + str(itemset_size))
itemset_size += 1
return frequent_itemsets
# Calls other methods. The main apriori algorithm.
#
# param: gene_terms - dictionary; key: gene, value: set of terms
# param: gene_set - the set of all distinct genes
# param: min_support - the minimum support
# return: frequent_itemset_table[2] - the frequent itemsets of size 2
def apriori(gene_terms, gene_set, min_support, min_weighted_support, min_information_content,
all_spec, all_ic):
frequent_itemset_table = generate_all_frequent_itemsets(gene_terms, gene_set, min_support, min_weighted_support,
min_information_content, all_spec, all_ic)
return frequent_itemset_table
|
{"/main_runner.py": ["/main.py"], "/main.py": ["/ontology_parsing.py", "/annotation_parsing.py", "/tree_modification.py", "/apriori_algorithm.py", "/association_creation.py"]}
|
33,347
|
lilywise96/ChoResearch
|
refs/heads/master
|
/ontology_parsing.py
|
"""
File: ontology_parsing.py
Author: Lily Wise
This file parses ontologies for hpo files and for gene ontology files.
"""
# This function parses the hpo ontology file. It pulls the terms and
# their parents to generate a tree.
#
# param: filename - the file that holds the hpo ontology
# return: terms_parents - a dictionary; key: id, value: array of terms (parents)
def hpo_parsing_onto(filename):
file = open(filename, "r")
terms_parents = {}
cur_key = ''
# Start reading in file.
for line in file:
# Find a new term.
if '[Term]' in line:
cur_key = ''
# Find parents.
elif line.startswith('is_a:'):
cur_parent = line[6:16]
if cur_parent not in terms_parents[cur_key]:
terms_parents[cur_parent] = set()
terms_parents[cur_key].add(cur_parent)
# Reads in the id number.
elif line.startswith('id:'):
cur_key = line[4:14]
if cur_key not in terms_parents.keys():
terms_parents[cur_key] = set()
return terms_parents
# This function parse the gene ontology file. It pulls the terms and their
# parents. If is_obsolete is found then the term is not included.
#
# param: filename - the file that holds the gene ontology
# return: terms_parents - a dictionary; key: id, value: array of terms (parents)
def parsing_go(filename):
file = open(filename, "r")
bp_terms_parents = {}
mf_terms_parents = {}
cc_terms_parents = {}
cur_parents = set()
cur_key = ''
is_obsolete = False
namespace = ''
# Start reading in file.
for line in file:
# Identifies that a new term is starting.
if 'Term' in line:
if not is_obsolete:
if namespace is 'b':
bp_terms_parents[cur_key] = cur_parents
elif namespace is 'm':
mf_terms_parents[cur_key] = cur_parents
elif namespace is 'c':
cc_terms_parents[cur_key] = cur_parents
cur_parents = set()
cur_key = ''
is_obsolete = False
# Reads in the id.
elif line.startswith('id:'):
cur_key = line[4:14]
# Removes the id if the is_obsolete is found.
elif 'is_obsolete' in line:
is_obsolete = True
if cur_key is not '':
if namespace is 'b' and cur_key in bp_terms_parents:
bp_terms_parents.pop(cur_key)
elif namespace is 'm' and cur_key in mf_terms_parents:
mf_terms_parents.pop(cur_key)
elif namespace is 'c' and cur_key in cc_terms_parents:
cc_terms_parents.pop(cur_key)
# If it isn't obsolete then the parents can be added if found.
elif line.startswith('is_a:') and not is_obsolete:
cur_parents.add(line[6:16])
# Checks which namespace it is in.
elif line.startswith('namespace:'):
namespace = line[11]
if namespace is 'b' and cur_key not in bp_terms_parents:
bp_terms_parents[cur_key] = set()
elif namespace is 'm' and cur_key not in mf_terms_parents:
mf_terms_parents[cur_key] = set()
elif namespace is 'c' and cur_key not in cc_terms_parents:
cc_terms_parents[cur_key] = set()
return bp_terms_parents, mf_terms_parents, cc_terms_parents
# Testing of ontology parsing with modified file.
#
# param: filename - the file to parse the ontology from
# return: terms_parents - dictionary; key: term, value: set of parents
def testing_ontology_parsing(filename):
file = open(filename, "r")
terms_parents = {}
for line in file:
cols = line.split(" ")
cols[len(cols) - 1] = cols[len(cols) - 1][0:-1]
if cols[0]:
terms_parents[cols[0]] = set()
for i in range(1, len(cols)):
terms_parents[cols[0]].add(cols[i])
return terms_parents
|
{"/main_runner.py": ["/main.py"], "/main.py": ["/ontology_parsing.py", "/annotation_parsing.py", "/tree_modification.py", "/apriori_algorithm.py", "/association_creation.py"]}
|
33,366
|
NGT-Dimka/Films
|
refs/heads/master
|
/users/views.py
|
from django.shortcuts import render_to_response, render, redirect
from django.http.response import HttpResponseNotAllowed
from .models import User
from django.views.generic import TemplateView
from .forms import UserForm
# Create your views here.
class NewUserView(TemplateView):
model = User
template_name = 'films/user_profile_detail.html'
def profile_detail(request):
return render_to_response('films/user_profile_detail.html')
def registration(request):
if request.method not in ["POST", "GET"]:
return HttpResponseNotAllowed(permitted_methods=["POST", "GET"])
if request.method == "POST":
user_form = UserForm(request.POST)
if user_form.is_valid():
user = user_form.save()
user.save()
return redirect('index')
return render(request, 'films/registration.html', {'user_form': user_form})
else:
user_form = UserForm()
return render(request, 'films/registration.html', {'user_form': user_form})
|
{"/users/views.py": ["/users/models.py"], "/films/urls.py": ["/films/views.py"], "/users/models.py": ["/users/managers.py"], "/films/admin.py": ["/films/models.py"], "/users/urls.py": ["/users/views.py"], "/films/views.py": ["/films/models.py", "/films/forms.py"], "/personal/urls.py": ["/personal/views.py"], "/personal/views.py": ["/films/models.py"], "/films/models.py": ["/users/models.py"], "/films/forms.py": ["/films/models.py"]}
|
33,367
|
NGT-Dimka/Films
|
refs/heads/master
|
/films/urls.py
|
from films.views import FilmsListView, FilmDetailView, post_film_comment
from django.conf.urls import url
urlpatterns = [
url(r'^$', FilmsListView.as_view(), name='list'),
url(r'^(?P<pk>\d+)/$', FilmDetailView.as_view(), name='detail'),
url(r'^post_comment/$', post_film_comment, name='post-comment'),
]
|
{"/users/views.py": ["/users/models.py"], "/films/urls.py": ["/films/views.py"], "/users/models.py": ["/users/managers.py"], "/films/admin.py": ["/films/models.py"], "/users/urls.py": ["/users/views.py"], "/films/views.py": ["/films/models.py", "/films/forms.py"], "/personal/urls.py": ["/personal/views.py"], "/personal/views.py": ["/films/models.py"], "/films/models.py": ["/users/models.py"], "/films/forms.py": ["/films/models.py"]}
|
33,368
|
NGT-Dimka/Films
|
refs/heads/master
|
/users/models.py
|
from django.contrib.auth.base_user import AbstractBaseUser
from django.contrib.auth.models import PermissionsMixin
from django.db import models
from .managers import UserManager
from django.utils.translation import gettext as _
# Create your models here.
class User(AbstractBaseUser, PermissionsMixin):
username = models.CharField(_('username'), max_length=200, unique=True, db_index=True)
first_name = models.CharField(_('first_name'), max_length=150, blank=True, null=True)
last_name = models.CharField(_('last_name'), max_length=150, blank=True, null=True)
password = models.CharField(_('password'), max_length=200)
email = models.EmailField(_('email'), max_length=200, blank=True, null=True)
location = models.CharField(max_length=100, blank=True, verbose_name='Населенный пункт:')
birth_date = models.DateField(blank=True, verbose_name='Дата рождения:', null=True)
avatar = models.ImageField(blank=True, verbose_name='Аватар:', null=True)
is_active = models.BooleanField(_('is_active'), blank=True)
objects = UserManager()
USERNAME_FIELD = 'username'
REQUIRED_FIELDS = []
class Meta:
verbose_name = 'User'
verbose_name_plural = 'Users'
def get_full_name(self):
full_name = '%s %s' % (self.first_name, self.last_name)
return full_name.strip()
def get_short_name(self):
return self.username
@staticmethod
def create_profile(instance, created):
if created:
User.objects.create(user=instance)
@staticmethod
def save_profile(instance):
instance.User.save()
|
{"/users/views.py": ["/users/models.py"], "/films/urls.py": ["/films/views.py"], "/users/models.py": ["/users/managers.py"], "/films/admin.py": ["/films/models.py"], "/users/urls.py": ["/users/views.py"], "/films/views.py": ["/films/models.py", "/films/forms.py"], "/personal/urls.py": ["/personal/views.py"], "/personal/views.py": ["/films/models.py"], "/films/models.py": ["/users/models.py"], "/films/forms.py": ["/films/models.py"]}
|
33,369
|
NGT-Dimka/Films
|
refs/heads/master
|
/films/admin.py
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.contrib import admin
# Register your models here.
from films.models import Film, Producer, Comment, Genres
admin.site.register(Film)
admin.site.register(Producer)
admin.site.register(Comment)
admin.site.register(Genres)
|
{"/users/views.py": ["/users/models.py"], "/films/urls.py": ["/films/views.py"], "/users/models.py": ["/users/managers.py"], "/films/admin.py": ["/films/models.py"], "/users/urls.py": ["/users/views.py"], "/films/views.py": ["/films/models.py", "/films/forms.py"], "/personal/urls.py": ["/personal/views.py"], "/personal/views.py": ["/films/models.py"], "/films/models.py": ["/users/models.py"], "/films/forms.py": ["/films/models.py"]}
|
33,370
|
NGT-Dimka/Films
|
refs/heads/master
|
/users/urls.py
|
from users.views import registration, NewUserView
from django.conf.urls import url
urlpatterns = [
url(r'^(?P<pk>\d+)/$', NewUserView.as_view(), name='user_profile'),
url('^new_user/$', registration, name='registration'),
]
|
{"/users/views.py": ["/users/models.py"], "/films/urls.py": ["/films/views.py"], "/users/models.py": ["/users/managers.py"], "/films/admin.py": ["/films/models.py"], "/users/urls.py": ["/users/views.py"], "/films/views.py": ["/films/models.py", "/films/forms.py"], "/personal/urls.py": ["/personal/views.py"], "/personal/views.py": ["/films/models.py"], "/films/models.py": ["/users/models.py"], "/films/forms.py": ["/films/models.py"]}
|
33,371
|
NGT-Dimka/Films
|
refs/heads/master
|
/films/views.py
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.shortcuts import render_to_response
from django.views.generic import ListView, DetailView
from django.http.response import HttpResponseNotAllowed, HttpResponseBadRequest, HttpResponseRedirect
from django.contrib.contenttypes.models import ContentType
from films.models import Film
from films.forms import FilmComment
class FilmsListView(ListView):
model = Film
queryset = Film.objects.select_related('producer').all()
class GenreView(ListView):
model = Film
Film.objects.select_related('genre').all()
template_name = 'films/film_list.html'
class FilmDetailView(DetailView):
model = Film
queryset = Film.objects.select_related('producer')
def get_context_data(self, **kwargs):
context = super(FilmDetailView, self).get_context_data(**kwargs)
context['comment_form'] = FilmComment(data={
'object_id': self.kwargs['pk'],
'user': self.request.user.id,
'content_type': ContentType.objects.get_for_model(Film)
})
context['comments'] = self.get_object().comments
return context
def post_film_comment(request):
if request.method != "POST":
return HttpResponseNotAllowed(permitted_methods=['POST'])
form = FilmComment(request.POST)
if not form.is_valid():
return HttpResponseBadRequest()
form.save()
return HttpResponseRedirect(redirect_to=request.POST.get('next'))
def film_list(request):
return render_to_response('films/film_list.html')
def film_detail(request):
return render_to_response('films/film_detail.html')
|
{"/users/views.py": ["/users/models.py"], "/films/urls.py": ["/films/views.py"], "/users/models.py": ["/users/managers.py"], "/films/admin.py": ["/films/models.py"], "/users/urls.py": ["/users/views.py"], "/films/views.py": ["/films/models.py", "/films/forms.py"], "/personal/urls.py": ["/personal/views.py"], "/personal/views.py": ["/films/models.py"], "/films/models.py": ["/users/models.py"], "/films/forms.py": ["/films/models.py"]}
|
33,372
|
NGT-Dimka/Films
|
refs/heads/master
|
/films/migrations/0001_initial.py
|
# -*- coding: utf-8 -*-
# Generated by Django 1.11.7 on 2017-11-29 05:08
from __future__ import unicode_literals
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('contenttypes', '0002_remove_content_type_name'),
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Comment',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('text', models.TextField(verbose_name='Текст')),
('created', models.DateTimeField(auto_now_add=True, verbose_name='Дата создания')),
('object_id', models.PositiveIntegerField(verbose_name='Идентификатор объекта')),
('content_type', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='contenttypes.ContentType', verbose_name='Тип содержимого')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL, verbose_name='Пользователь')),
],
options={
'verbose_name': 'Комментарий',
'verbose_name_plural': 'Комментарии',
},
),
migrations.CreateModel(
name='Film',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('poster', models.ImageField(blank=True, upload_to='posters/', verbose_name='Постер фильма')),
('title_film', models.CharField(max_length=255, verbose_name='Название:')),
('year_pub', models.DateField(verbose_name='Выход в прокат:')),
('budget', models.PositiveIntegerField(verbose_name='Бюджет:')),
('fees', models.PositiveIntegerField(verbose_name='Сборы:')),
('duration', models.PositiveSmallIntegerField(verbose_name='Продолжительность, мин.:')),
('content', models.TextField(blank=True, max_length=10000, verbose_name='Сюжет:')),
],
options={
'verbose_name': 'Фильм',
'verbose_name_plural': 'Фильмы',
},
),
migrations.CreateModel(
name='Genres',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('genres', models.TextField(verbose_name='Жанр')),
],
options={
'verbose_name': 'Жанр',
'verbose_name_plural': 'Жанры',
},
),
migrations.CreateModel(
name='Producer',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('full_name', models.CharField(max_length=255, verbose_name='Полное имя')),
],
options={
'verbose_name': 'Продюсер',
'verbose_name_plural': 'Продюсеры',
},
),
migrations.AddField(
model_name='film',
name='genre',
field=models.ManyToManyField(to='films.Genres', verbose_name='Жанр:'),
),
migrations.AddField(
model_name='film',
name='producer',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='films.Producer', verbose_name='Продюсер:'),
),
]
|
{"/users/views.py": ["/users/models.py"], "/films/urls.py": ["/films/views.py"], "/users/models.py": ["/users/managers.py"], "/films/admin.py": ["/films/models.py"], "/users/urls.py": ["/users/views.py"], "/films/views.py": ["/films/models.py", "/films/forms.py"], "/personal/urls.py": ["/personal/views.py"], "/personal/views.py": ["/films/models.py"], "/films/models.py": ["/users/models.py"], "/films/forms.py": ["/films/models.py"]}
|
33,373
|
NGT-Dimka/Films
|
refs/heads/master
|
/personal/urls.py
|
from .views import ProducerDetailView
from django.conf.urls import url
urlpatterns = [
url(r'^(?P<pk>\d+)/$', ProducerDetailView.as_view(), name='producer-detail'),
]
|
{"/users/views.py": ["/users/models.py"], "/films/urls.py": ["/films/views.py"], "/users/models.py": ["/users/managers.py"], "/films/admin.py": ["/films/models.py"], "/users/urls.py": ["/users/views.py"], "/films/views.py": ["/films/models.py", "/films/forms.py"], "/personal/urls.py": ["/personal/views.py"], "/personal/views.py": ["/films/models.py"], "/films/models.py": ["/users/models.py"], "/films/forms.py": ["/films/models.py"]}
|
33,374
|
NGT-Dimka/Films
|
refs/heads/master
|
/personal/views.py
|
from django.views.generic import DetailView
from films.models import Producer
# Create your views here.
class ProducerDetailView(DetailView):
model = Producer
template_name = 'producer_detail.html'
|
{"/users/views.py": ["/users/models.py"], "/films/urls.py": ["/films/views.py"], "/users/models.py": ["/users/managers.py"], "/films/admin.py": ["/films/models.py"], "/users/urls.py": ["/users/views.py"], "/films/views.py": ["/films/models.py", "/films/forms.py"], "/personal/urls.py": ["/personal/views.py"], "/personal/views.py": ["/films/models.py"], "/films/models.py": ["/users/models.py"], "/films/forms.py": ["/films/models.py"]}
|
33,375
|
NGT-Dimka/Films
|
refs/heads/master
|
/users/managers.py
|
from venv import create
from django.contrib.auth.base_user import BaseUserManager
from django.contrib.auth.hashers import make_password, check_password, PBKDF2PasswordHasher
class PasswordHash(PBKDF2PasswordHasher):
algorithm = 'pbkdf2_wrapped_sha1'
def encoded(self, sha1_hash, salt):
return super(PasswordHash, self).encode(sha1_hash, salt)
class UserManager(BaseUserManager):
def _create_user(self, username, password, **extra_fields):
if not username:
raise ValueError('The given username must be set')
else:
username = self.model(username=username)
user = self.model(username=username, **extra_fields)
user.set_password(password)
token = make_password(password, salt=PasswordHash, hasher='default')
if check_password(password, PasswordHash.encoded) is not True:
ValueError('Password is not correct')
else:
user = self.model(is_active=True, is_superuser=True)
user = user.save(username=username, password=token)
return user
def create_user(self, username, password=None, **extra_fields):
extra_fields.setdefault('is_superuser', False)
return self._create_user(username, password, **extra_fields)
def create_superuser(self, username, password, **extra_fields):
extra_fields.setdefault('is_superuser', True)
if extra_fields.get('is_superuser') is not True:
raise ValueError('Superuser must have is_superuser=True.')
else:
return self._create_user(username, password, **extra_fields)
|
{"/users/views.py": ["/users/models.py"], "/films/urls.py": ["/films/views.py"], "/users/models.py": ["/users/managers.py"], "/films/admin.py": ["/films/models.py"], "/users/urls.py": ["/users/views.py"], "/films/views.py": ["/films/models.py", "/films/forms.py"], "/personal/urls.py": ["/personal/views.py"], "/personal/views.py": ["/films/models.py"], "/films/models.py": ["/users/models.py"], "/films/forms.py": ["/films/models.py"]}
|
33,376
|
NGT-Dimka/Films
|
refs/heads/master
|
/films/models.py
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models
from django.http import Http404, HttpResponse
from django.contrib.contenttypes.fields import GenericRelation, GenericForeignKey
from django.contrib.contenttypes.models import ContentType
from users.models import User
# Create your models here.
class Producer(models.Model):
full_name = models.CharField(verbose_name='Полное имя', max_length=255)
class Meta:
verbose_name = 'Продюсер'
verbose_name_plural = 'Продюсеры'
def __str__(self):
return self.full_name
class Genres(models.Model):
genres = models.TextField(verbose_name='Жанр')
class Meta:
verbose_name = 'Жанр'
verbose_name_plural = 'Жанры'
def __str__(self):
return self.genres
class Comment(models.Model):
user = models.ForeignKey(User, verbose_name='Пользователь')
text = models.TextField(verbose_name='Текст')
created = models.DateTimeField(verbose_name='Дата создания', auto_now_add=True)
content_type = models.ForeignKey(ContentType, on_delete=models.CASCADE, verbose_name='Тип содержимого')
object_id = models.PositiveIntegerField(verbose_name='Идентификатор объекта')
content_object = GenericForeignKey()
class Meta:
verbose_name = 'Комментарий'
verbose_name_plural = 'Комментарии'
class Film(models.Model):
poster = models.ImageField(upload_to="posters/", verbose_name="Постер фильма", blank=True)
title_film = models.CharField(max_length=255, verbose_name='Название:')
year_pub = models.DateField(verbose_name='Выход в прокат:')
genre = models.ManyToManyField(Genres, verbose_name='Жанр:')
producer = models.ForeignKey(Producer, verbose_name='Продюсер:')
budget = models.PositiveIntegerField(verbose_name='Бюджет:')
fees = models.PositiveIntegerField(verbose_name='Сборы:')
duration = models.PositiveSmallIntegerField(verbose_name='Продолжительность, мин.:')
content = models.TextField(blank=True, max_length=10000, verbose_name='Сюжет:')
comments = GenericRelation(Comment)
class Meta:
verbose_name = 'Фильм'
verbose_name_plural = 'Фильмы'
@property
def __unicode__(self):
return self.title_film
@property
def get_absolute_url(self):
return "/films/%i/" % self.id
@staticmethod
def film():
try:
pass
except Film.DoesNotExist:
raise Http404
s = Film.title_film + "<br><br>" + Film.genre
return HttpResponse(s)
def __str__(self):
return self.title_film
|
{"/users/views.py": ["/users/models.py"], "/films/urls.py": ["/films/views.py"], "/users/models.py": ["/users/managers.py"], "/films/admin.py": ["/films/models.py"], "/users/urls.py": ["/users/views.py"], "/films/views.py": ["/films/models.py", "/films/forms.py"], "/personal/urls.py": ["/personal/views.py"], "/personal/views.py": ["/films/models.py"], "/films/models.py": ["/users/models.py"], "/films/forms.py": ["/films/models.py"]}
|
33,377
|
NGT-Dimka/Films
|
refs/heads/master
|
/films/forms.py
|
from django import forms
from films.models import Comment
class FilmComment(forms.ModelForm):
class Meta:
model = Comment
exclude = []
widgets = {
'user': forms.HiddenInput(),
'content_type': forms.HiddenInput(),
'object_id': forms.HiddenInput()
}
|
{"/users/views.py": ["/users/models.py"], "/films/urls.py": ["/films/views.py"], "/users/models.py": ["/users/managers.py"], "/films/admin.py": ["/films/models.py"], "/users/urls.py": ["/users/views.py"], "/films/views.py": ["/films/models.py", "/films/forms.py"], "/personal/urls.py": ["/personal/views.py"], "/personal/views.py": ["/films/models.py"], "/films/models.py": ["/users/models.py"], "/films/forms.py": ["/films/models.py"]}
|
33,378
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/xlnet.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import numpy as np
import tensorflow as tf
import torch
from argparse import ArgumentParser
from os.path import abspath, join
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
from ..config.xlnet import PreTrained
from ...models.xlnet import ForQA, ForSeqClass, LMHead
GLUE_TASKS_NUM_LABELS = {
"cola": 2,
"mnli": 3,
"mrpc": 2,
"sst-2": 2,
"sts-b": 1,
"qqp": 2,
"qnli": 2,
"rte": 2,
"wnli": 2,
}
logging.set_verbosity_info()
log = logging.get_logger(__name__)
def build_map(model, cfg, tf_weights=None):
tf_to_pt_map = {}
if hasattr(model, "transformer"):
if hasattr(model, "lm_loss"):
tf_to_pt_map["model/lm_loss/bias"] = model.lm_loss.bias
if (
hasattr(model, "sequence_summary")
and "model/sequnece_summary/summary/kernel" in tf_weights
):
tf_to_pt_map[
"model/sequnece_summary/summary/kernel"
] = model.sequence_summary.summary.weight
tf_to_pt_map[
"model/sequnece_summary/summary/bias"
] = model.sequence_summary.summary.bias
if (
hasattr(model, "logits_proj")
and cfg.finetune is not None
and f"model/regression_{cfg.finetune}/logit/kernel" in tf_weights
):
tf_to_pt_map[f"model/regression_{cfg.finetune}/logit/kernel"] = model.logits_proj.weight
tf_to_pt_map[f"model/regression_{cfg.finetune}/logit/bias"] = model.logits_proj.bias
model = model.transformer
tf_to_pt_map.update(
{
"model/transformer/word_embedding/lookup_table": model.word_embedding.weight,
"model/transformer/mask_emb/mask_emb": model.mask_emb,
}
)
for i, b in enumerate(model.layer):
layer_str = f"model/transformer/layer_{i}/"
tf_to_pt_map.update(
{
layer_str + "rel_attn/LayerNorm/gamma": b.rel_attn.layer_norm.weight,
layer_str + "rel_attn/LayerNorm/beta": b.rel_attn.layer_norm.bias,
layer_str + "rel_attn/o/kernel": b.rel_attn.o,
layer_str + "rel_attn/q/kernel": b.rel_attn.q,
layer_str + "rel_attn/k/kernel": b.rel_attn.k,
layer_str + "rel_attn/r/kernel": b.rel_attn.r,
layer_str + "rel_attn/v/kernel": b.rel_attn.v,
layer_str + "ff/LayerNorm/gamma": b.ff.layer_norm.weight,
layer_str + "ff/LayerNorm/beta": b.ff.layer_norm.bias,
layer_str + "ff/layer_1/kernel": b.ff.layer_1.weight,
layer_str + "ff/layer_1/bias": b.ff.layer_1.bias,
layer_str + "ff/layer_2/kernel": b.ff.layer_2.weight,
layer_str + "ff/layer_2/bias": b.ff.layer_2.bias,
}
)
if cfg.untie_r:
r_r_list = []
r_w_list = []
r_s_list = []
seg_embed_list = []
for b in model.layer:
r_r_list.append(b.rel_attn.r_r_bias)
r_w_list.append(b.rel_attn.r_w_bias)
r_s_list.append(b.rel_attn.r_s_bias)
seg_embed_list.append(b.rel_attn.seg_embed)
else:
r_r_list = [model.r_r_bias]
r_w_list = [model.r_w_bias]
r_s_list = [model.r_s_bias]
seg_embed_list = [model.seg_embed]
tf_to_pt_map.update(
{
"model/transformer/r_r_bias": r_r_list,
"model/transformer/r_w_bias": r_w_list,
"model/transformer/r_s_bias": r_s_list,
"model/transformer/seg_embed": seg_embed_list,
}
)
return tf_to_pt_map
def load_src_weights(model, config, src_path):
init_vars = tf.train.list_variables(src_path)
tf_weights = {}
for name, shape in init_vars:
log.info(f"Loading TF weight {name} with shape {shape}")
array = tf.train.load_variable(src_path, name)
tf_weights[name] = array
tf_to_pt_map = build_map(model, config, tf_weights)
for name, p in tf_to_pt_map.items():
log.info(f"Importing {name}")
if name not in tf_weights:
log.info(f"{name} not in tf pre-trained weights, skipping")
continue
array = tf_weights[name]
if "kernel" in name and ("ff" in name or "summary" in name or "logit" in name):
log.info("Transposing")
array = np.transpose(array)
if isinstance(p, list):
assert (
len(p) == array.shape[0]
), f"Pointer length {len(p)} and array length {array.shape[0]} mismatched"
for i, p_i in enumerate(p):
arr_i = array[i, ...]
assert p_i.shape == arr_i.shape
p_i.data = torch.from_numpy(arr_i)
else:
assert p.shape == array.shape
p.data = torch.from_numpy(array)
tf_weights.pop(name, None)
tf_weights.pop(name + "/Adam", None)
tf_weights.pop(name + "/Adam_1", None)
log.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}")
return model
def to_pytorch(src_path, bert_config_file, save_path, finetune=None):
cfg = PreTrained.from_json_file(bert_config_file)
print(f"Building from config: {cfg}")
finetune = finetune.lower() if finetune is not None else ""
if finetune in GLUE_TASKS_NUM_LABELS:
cfg.finetune = finetune
cfg.n_labels = GLUE_TASKS_NUM_LABELS[finetune]
m = ForSeqClass(cfg)
elif "squad" in finetune:
cfg.finetune = finetune
m = ForQA(cfg)
else:
m = LMHead(cfg)
load_src_weights(m, cfg, src_path)
w = join(save_path, WEIGHTS_NAME)
print(f"Saving to: {abspath(w)}")
torch.save(m.state_dict(), w)
c = join(save_path, CONFIG_NAME)
print(f"Saving config to: {abspath(c)}")
with open(c, "w", encoding="utf-8") as f:
f.write(cfg.to_json_string())
if __name__ == "__main__":
x = ArgumentParser()
x.add_argument("--src_path", default=None, type=str, required=True)
x.add_argument("--cfg_path", default=None, type=str, required=True)
x.add_argument("--save_path", default=None, type=str, required=True)
x.add_argument("--finetune", default=None, type=str)
y = x.parse_args()
to_pytorch(y.src_path, y.cfg_path, y.save_path, y.finetune)
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,379
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/bert.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import numpy as np
import re
import tensorflow as tf
import torch
from argparse import ArgumentParser
from os.path import abspath
from transformers.utils import logging
from ..config.bert import PreTrained
from ...models.bert import ForPreTraining
logging.set_verbosity_info()
log = logging.get_logger(__name__)
_SKIP = [
"adam_v",
"adam_m",
"AdamWeightDecayOptimizer",
"AdamWeightDecayOptimizer_1",
"global_step",
]
def load_src_weights(model, src_path):
src_path = abspath(src_path)
log.info(f"Loading from: {src_path}")
xs = tf.train.list_variables(src_path)
assert len(xs) > 0
ns, ws = _load_weights(xs, src_path)
for n, w in zip(ns, ws):
ss = n.split("/")
if any(x in _SKIP for x in ss):
log.info(f"Skipping {'/'.join(ss)}")
continue
p = model
for s in ss:
if re.fullmatch(r"[A-Za-z]+_\d+", s):
scopes = re.split(r"_(\d+)", s)
else:
scopes = [s]
if scopes[0] == "kernel" or scopes[0] == "gamma":
p = getattr(p, "weight")
elif scopes[0] == "output_bias" or scopes[0] == "beta":
p = getattr(p, "bias")
elif scopes[0] == "output_weights":
p = getattr(p, "weight")
elif scopes[0] == "squad":
p = getattr(p, "classifier")
else:
try:
p = getattr(p, scopes[0])
except AttributeError:
log.info(f"Skipping {'/'.join(ss)}")
continue
if len(scopes) >= 2:
p = p[int(scopes[1])]
if s[-11:] == "_embeddings":
p = getattr(p, "weight")
elif s == "kernel":
w = np.transpose(w)
assert p.shape == w.shape
p.data = torch.from_numpy(w)
return model
def _load_weights(xs, src_path):
ns = []
ws = {}
for n, shape in xs:
log.info(f"Loading TF weight {n} with shape {shape}")
ns.append(n)
ws[n] = tf.train.load_variable(src_path, n)
return ns, ws
def to_pytorch(src_path, cfg_path, save_path):
cfg = PreTrained.from_json_file(cfg_path)
print(f"Building from config: {cfg}")
m = ForPreTraining(cfg)
load_src_weights(m, src_path)
print(f"Saving to: {save_path}")
torch.save(m.state_dict(), save_path)
if __name__ == "__main__":
x = ArgumentParser()
x.add_argument("--src_path", default=None, type=str, required=True)
x.add_argument("--cfg_path", default=None, type=str, required=True)
x.add_argument("--save_path", default=None, type=str, required=True)
y = x.parse_args()
to_pytorch(y.src_path, y.cfg_path, y.save_path)
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], 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"/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,380
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/try/add.py
|
# %%
import torch
import triton
import triton.language as tl
@triton.jit
def add_kernel(x1_ptr, x2_ptr, y_ptr, n, BLOCK: tl.constexpr):
pid = tl.program_id(axis=0)
offs = pid * BLOCK + tl.arange(0, BLOCK)
mask = offs < n
x1 = tl.load(x1_ptr + offs, mask=mask)
x2 = tl.load(x2_ptr + offs, mask=mask)
y = x1 + x2
tl.store(y_ptr + offs, y, mask=mask)
# %%
def add(x1: torch.Tensor, x2: torch.Tensor):
y = torch.empty_like(x1)
assert x1.is_cuda and x2.is_cuda and y.is_cuda
n = y.numel()
grid = lambda x: (triton.cdiv(n, x["BLOCK"]),)
add_kernel[grid](x1, x2, y, n, BLOCK=1024)
return y
# %%
torch.manual_seed(0)
size = 98432
x1 = torch.rand(size, device="cuda")
x2 = torch.rand(size, device="cuda")
y_ref = x1 + x2
y_triton = add(x1, x2)
print(f"ref={y_ref}")
print(f"triton={y_triton}")
print(
f"The maximum difference between ref and triton is " f"{torch.max(torch.abs(y_ref - y_triton))}"
)
# %%
@triton.testing.perf_report(
triton.testing.Benchmark(
x_names=["size"],
x_vals=[2**i for i in range(12, 28, 1)],
x_log=True,
line_arg="provider",
line_vals=["triton", "torch"],
line_names=["Triton", "Torch"],
styles=[("blue", "-"), ("green", "-")],
ylabel="GB/s",
plot_name="vector-add-performance",
args={},
)
)
def benchmark(size, provider):
x1 = torch.rand(size, device="cuda", dtype=torch.float32)
x2 = torch.rand(size, device="cuda", dtype=torch.float32)
qs = [0.5, 0.2, 0.8]
if provider == "torch":
ms, min, max = triton.testing.do_bench(lambda: x1 + x2, quantiles=qs)
if provider == "triton":
ms, min, max = triton.testing.do_bench(lambda: add(x1, x2), quantiles=qs)
y = lambda ms: 12 * size / ms * 1e-6
return y(ms), y(max), y(min)
# %%
benchmark.run(print_data=True, show_plots=True)
# %%
|
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"/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,381
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/patcher.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import re
import collections as co
from difflib import unified_diff
from .log import Logger
from .base import config
from .resource import Resource
from .nominals import flags, para_join, para_split
log = Logger(__name__)
fixes = ((r'(?P<lf>xxx?){2,}(?P<rt> ?(Date|Sent|To|Cc|Bcc|Subject): )',
r'\g<lf>\g<rt>'), )
class Fixer:
def __init__(self, fixes=(), **kw):
super().__init__(**kw)
self.fixes = fixes
self.re_fixes = tuple((re.compile(flags + p), r) for p, r in fixes)
def __repr__(self):
return '{}({!r})'.format(type(self).__name__, self.fixes)
def fix(self, txt):
if isinstance(txt, tuple):
return para_split(self.fix(para_join(txt)))
for p, r in self.re_fixes:
txt = p.sub(r, txt)
return txt
class Fixers(Resource):
_res_path = config.qnar_dst + 'fixers.qnr'
@classmethod
def globals(cls):
return globals()
chunk_re = re.compile(r'^@@ -(\d+)(,(\d+))? \+(\d+)(,(\d+))? @@', re.ASCII)
class Chunk(co.namedtuple('Chunk', 'src tgt lns')):
def applier(self, si, ti, src):
ss = self.src.start
while si < ss:
i, sl = next(src)
assert i == si
yield sl
si += 1
ti += 1
assert si == self.src.start and ti == self.tgt.start
for l in self.lns:
if l.startswith(('-', ' ')):
i, sl = next(src)
assert i == si and l[1:] == sl
si += 1
if l.startswith('-'):
continue
else:
assert l.startswith('+')
yield l[1:]
ti += 1
assert si == self.src.stop and ti == self.tgt.stop
return si
def diff_parser(udiff):
c = ls = lt = None
for ln in udiff:
ln = ln or ' '
m = chunk_re.match(ln)
if m:
if c:
assert ls == c.src.stop and lt == c.tgt.stop
yield c._replace(lns=tuple(c.lns))
c = []
for i in range(0, 4, 3):
s = int(m.group(i + 1))
n = int(m.group(i + 3)) if m.group(i + 3) else 1
c.append(range(s, s + n))
c = Chunk(*c, [])
ls, lt = c.src.start, c.tgt.start
continue
elif c:
if ln.startswith('-'):
ls += 1
elif ln.startswith('+'):
lt += 1
else:
assert ln.startswith(' ')
ls += 1
lt += 1
c.lns.append(ln)
if c:
assert ls == c.src.stop and lt == c.tgt.stop
yield c._replace(lns=tuple(c.lns))
class Patcher:
@classmethod
def create(cls, src, dst):
ud = unified_diff(src.splitlines(), dst.splitlines())
cs = tuple(c for c in diff_parser(ud))
return cls(cs)
def __init__(self, chunks):
super().__init__()
self.chunks = chunks
def __eq__(self, other):
if isinstance(other, type(self)):
return self.chunks == other.chunks
return NotImplemented
def __repr__(self):
return '{}({!r})'.format(type(self).__name__, self.chunks)
def patch(self, txt):
if isinstance(txt, tuple):
return para_split(self.patch(para_join(txt)))
r = []
si = ti = 1
s = enumerate(txt.splitlines(), start=si)
for c in self.chunks:
a = c.applier(si, ti, s)
while True:
try:
r.append(next(a))
except StopIteration as e:
si = e.value
break
ti = len(r) + 1
for _, l in s:
r.append(l)
return '\n'.join(r)
class Patchers(Resource):
_res_path = config.qnar_dst + 'patchers.qnr'
@classmethod
def globals(cls):
return globals()
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": 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["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", 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"/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,382
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/funnel.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import torch
import re
import numpy as np
import tensorflow as tf
from argparse import ArgumentParser
from os.path import abspath
from transformers.utils import logging
from ..config.funnel import PreTrained
from ...models.funnel import Base, Model
logging.set_verbosity_info()
log = logging.get_logger(__name__)
def load_src_weights(model, config, tf_checkpoint_path):
tf_path = abspath(tf_checkpoint_path)
log.info(f"Converting TensorFlow checkpoint from {tf_path}")
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
for name, shape in init_vars:
log.info(f"Loading TF weight {name} with shape {shape}")
array = tf.train.load_variable(tf_path, name)
names.append(name)
arrays.append(array)
_layer_map = {
"k": "k_head",
"q": "q_head",
"v": "v_head",
"o": "post_proj",
"layer_1": "linear_1",
"layer_2": "linear_2",
"rel_attn": "attention",
"ff": "ffn",
"kernel": "weight",
"gamma": "weight",
"beta": "bias",
"lookup_table": "weight",
"word_embedding": "word_embeddings",
"input": "embeddings",
}
for name, array in zip(names, arrays):
name = name.split("/")
if any(
n
in [
"adam_v",
"adam_m",
"AdamWeightDecayOptimizer",
"AdamWeightDecayOptimizer_1",
"global_step",
]
for n in name
):
log.info(f"Skipping {'/'.join(name)}")
continue
if name[0] == "generator":
continue
pointer = model
skipped = False
for m_name in name[1:]:
if not isinstance(pointer, FunnelPositionwiseFFN) and re.fullmatch(
r"layer_\d+", m_name
):
layer_index = int(re.search(r"layer_(\d+)", m_name).groups()[0])
if layer_index < config.n_lays:
block_idx = 0
while layer_index >= config.block_sizes[block_idx]:
layer_index -= config.block_sizes[block_idx]
block_idx += 1
pointer = pointer.blocks[block_idx][layer_index]
else:
layer_index -= config.n_lays
pointer = pointer.layers[layer_index]
elif m_name == "r" and isinstance(pointer, FunnelRelMultiheadAttention):
pointer = pointer.r_kernel
break
elif m_name in _layer_map:
pointer = getattr(pointer, _layer_map[m_name])
else:
try:
pointer = getattr(pointer, m_name)
except AttributeError:
print(f"Skipping {'/'.join(name)}", array.shape)
skipped = True
break
if not skipped:
if len(pointer.shape) != len(array.shape):
array = array.reshape(pointer.shape)
if m_name == "kernel":
array = np.transpose(array)
pointer.data = torch.from_numpy(array)
return model
def to_pytorch(src_path, cfg_path, save_path, base):
cfg = PreTrained.from_json_file(cfg_path)
print(f"Building from config: {cfg}")
m = Base(cfg) if base else Model(cfg)
load_src_weights(m, cfg, src_path)
print(f"Saving to: {save_path}")
torch.save(m.state_dict(), save_path)
if __name__ == "__main__":
x = ArgumentParser()
x.add_argument("--src_path", default=None, type=str, required=True)
x.add_argument("--cfg_path", default=None, type=str, required=True)
x.add_argument("--save_path", default=None, type=str, required=True)
x.add_argument("--base", action="store_true")
y = x.parse_args()
to_pytorch(y.src_path, y.cfg_path, y.save_path, y.base)
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], 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["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", 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"/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", 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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,383
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/test/unit/runtime/test_cache.py
|
import os
import shutil
import pytest
import torch
import triton
import triton.language as tl
from triton.runtime.jit import JITFunction
tmpdir = ".tmp"
@triton.jit
def function_1(i):
i = i + 1
i = function_2(i)
return i
@triton.jit
def function_2(i):
i = i + 1
return i
@triton.jit
def kernel(X, i, BLOCK: tl.constexpr):
i = i + 1
i = function_1(i)
tl.store(X, i)
@triton.jit(do_not_specialize=["i"])
def kernel_nospec(X, i, BLOCK: tl.constexpr):
i = i + 1
i = function_1(i)
tl.store(X, i)
def apply_src_change(target, old, new):
kernel.hash = None
function_1.hash = None
function_2.hash = None
function_1.src = function_1.src.replace(old, new)
target.src = target.src.replace(old, new)
ret = target.cache_key
target.src = target.src.replace(new, old)
return ret
def test_nochange():
baseline = kernel.cache_key
updated = apply_src_change(kernel, 'i + 1', 'i + 1')
assert baseline == updated
def test_toplevel_change():
baseline = kernel.cache_key
updated = apply_src_change(kernel, 'i + 1', 'i + 2')
assert baseline != updated
def test_nested1_change():
baseline = kernel.cache_key
updated = apply_src_change(function_1, 'i + 1', 'i + 2')
assert baseline != updated
def reset_tmp_dir():
os.environ["TRITON_CACHE_DIR"] = tmpdir
if os.path.exists(tmpdir):
shutil.rmtree(tmpdir)
def test_reuse():
counter = 0
def inc_counter(*args, **kwargs):
nonlocal counter
counter += 1
JITFunction.cache_hook = inc_counter
reset_tmp_dir()
x = torch.empty(1, dtype=torch.int32, device='cuda')
for i in range(10):
kernel[(1,)](x, 1, BLOCK=1024)
assert counter == 1
@pytest.mark.parametrize('mode', ['enable', 'disable'])
def test_specialize(mode):
counter = 0
def inc_counter(*args, **kwargs):
nonlocal counter
counter += 1
JITFunction.cache_hook = inc_counter
reset_tmp_dir()
x = torch.empty(1, dtype=torch.int32, device='cuda')
function = {'enable': kernel, 'disable': kernel_nospec}[mode]
target = {'enable': 3, 'disable': 1}[mode]
for i in [1, 2, 4, 8, 16, 32]:
function[(1,)](x, i, BLOCK=512)
assert counter == target
def test_constexpr_not_callable() -> None:
@triton.jit
def kernel(X, c: tl.constexpr):
tl.store(X, 2)
x = torch.empty(1, dtype=torch.int32, device='cuda')
error = False
try:
kernel[(1, )](x, c="str")
except BaseException:
error = True
assert error is False
# try and catch
try:
kernel[(1, )](x, c=tl.abs)
except BaseException:
error = True
assert error is True
def test_jit_warmup_cache() -> None:
@triton.jit
def kernel_add(a, b, o, N: tl.constexpr):
idx = tl.arange(0, N)
tl.store(o + idx,
tl.load(a + idx) + tl.load(b + idx))
args = [
torch.randn(32, dtype=torch.float32, device="cuda"),
torch.randn(32, dtype=torch.float32, device="cuda"),
torch.randn(32, dtype=torch.float32, device="cuda"),
32,
]
assert len(kernel_add.cache) == 0
kernel_add.warmup(torch.float32, torch.float32, torch.float32, 32, grid=(1,))
assert len(kernel_add.cache) == 1
kernel_add.warmup(*args, grid=(1,))
assert len(kernel_add.cache) == 1
kernel_add.warmup(*args, grid=(1,))
assert len(kernel_add.cache) == 1
def test_jit_debug() -> None:
@triton.jit
def kernel_add(a, b, o, N: tl.constexpr):
idx = tl.arange(0, N)
tl.device_assert(idx < 32, "idx < 32")
tl.store(o + idx,
tl.load(a + idx) + tl.load(b + idx))
device = torch.cuda.current_device()
assert len(kernel_add.cache[device]) == 0
kernel_add.warmup(torch.float32, torch.float32, torch.float32, 32, grid=(1,))
assert len(kernel_add.cache[device]) == 1
kernel_add.debug = False
kernel_add.warmup(torch.float32, torch.float32, torch.float32, 32, grid=(1,))
assert len(kernel_add.cache[device]) == 1
kernel_add.debug = True
kernel_add.warmup(torch.float32, torch.float32, torch.float32, 32, grid=(1,))
assert len(kernel_add.cache[device]) == 2
bins = list(kernel_add.cache[device].values())
assert bins[0].asm['ttir'] != bins[1].asm['ttir']
@triton.jit
def add_fn(a, b, o, N: tl.constexpr):
idx = tl.arange(0, N)
tl.store(o + idx, tl.load(a + idx) + tl.load(b + idx))
def test_jit_noinline() -> None:
@triton.jit
def kernel_add_device(a, b, o, N: tl.constexpr):
add_fn(a, b, o, N)
device = torch.cuda.current_device()
assert len(kernel_add_device.cache[device]) == 0
kernel_add_device.warmup(torch.float32, torch.float32, torch.float32, 32, grid=(1,))
assert len(kernel_add_device.cache[device]) == 1
bins = list(kernel_add_device.cache[device].values())
inline_ttir = bins[0].asm['ttir']
add_fn.noinline = True
add_fn.hash = None
kernel_add_device.hash = None
kernel_add_device.cache[device].clear()
kernel_add_device.warmup(torch.float32, torch.float32, torch.float32, 32, grid=(1,))
assert len(kernel_add_device.cache[device]) == 1
bins = list(kernel_add_device.cache[device].values())
noinline_ttir = bins[0].asm['ttir']
assert inline_ttir != noinline_ttir
def test_memory_leak() -> None:
@triton.jit
def kernel(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 10
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tl.store(out_ptr0 + (x0 + tl.zeros([XBLOCK], tl.int32)), tmp0, xmask)
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,384
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/convbert.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import argparse
from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging
logging.set_verbosity_info()
def load_tf_weights_in_convbert(model, config, tf_checkpoint_path):
tf_path = os.path.abspath(tf_checkpoint_path)
logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
init_vars = tf.train.list_variables(tf_path)
tf_data = {}
for name, shape in init_vars:
logger.info(f"Loading TF weight {name} with shape {shape}")
array = tf.train.load_variable(tf_path, name)
tf_data[name] = array
param_mapping = {
"embeddings.word_embeddings.weight": "electra/embeddings/word_embeddings",
"embeddings.position_embeddings.weight": "electra/embeddings/position_embeddings",
"embeddings.token_type_embeddings.weight": "electra/embeddings/token_type_embeddings",
"embeddings.LayerNorm.weight": "electra/embeddings/LayerNorm/gamma",
"embeddings.LayerNorm.bias": "electra/embeddings/LayerNorm/beta",
"embeddings_project.weight": "electra/embeddings_project/kernel",
"embeddings_project.bias": "electra/embeddings_project/bias",
}
if config.n_groups > 1:
group_dense_name = "g_dense"
else:
group_dense_name = "dense"
for j in range(config.n_lays):
param_mapping[
f"encoder.layer.{j}.attention.self.query.weight"
] = f"electra/encoder/layer_{j}/attention/self/query/kernel"
param_mapping[
f"encoder.layer.{j}.attention.self.query.bias"
] = f"electra/encoder/layer_{j}/attention/self/query/bias"
param_mapping[
f"encoder.layer.{j}.attention.self.key.weight"
] = f"electra/encoder/layer_{j}/attention/self/key/kernel"
param_mapping[
f"encoder.layer.{j}.attention.self.key.bias"
] = f"electra/encoder/layer_{j}/attention/self/key/bias"
param_mapping[
f"encoder.layer.{j}.attention.self.value.weight"
] = f"electra/encoder/layer_{j}/attention/self/value/kernel"
param_mapping[
f"encoder.layer.{j}.attention.self.value.bias"
] = f"electra/encoder/layer_{j}/attention/self/value/bias"
param_mapping[
f"encoder.layer.{j}.attention.self.key_conv_attn_layer.depthwise.weight"
] = f"electra/encoder/layer_{j}/attention/self/conv_attn_key/depthwise_kernel"
param_mapping[
f"encoder.layer.{j}.attention.self.key_conv_attn_layer.pointwise.weight"
] = f"electra/encoder/layer_{j}/attention/self/conv_attn_key/pointwise_kernel"
param_mapping[
f"encoder.layer.{j}.attention.self.key_conv_attn_layer.bias"
] = f"electra/encoder/layer_{j}/attention/self/conv_attn_key/bias"
param_mapping[
f"encoder.layer.{j}.attention.self.conv_kernel_layer.weight"
] = f"electra/encoder/layer_{j}/attention/self/conv_attn_kernel/kernel"
param_mapping[
f"encoder.layer.{j}.attention.self.conv_kernel_layer.bias"
] = f"electra/encoder/layer_{j}/attention/self/conv_attn_kernel/bias"
param_mapping[
f"encoder.layer.{j}.attention.self.conv_out_layer.weight"
] = f"electra/encoder/layer_{j}/attention/self/conv_attn_point/kernel"
param_mapping[
f"encoder.layer.{j}.attention.self.conv_out_layer.bias"
] = f"electra/encoder/layer_{j}/attention/self/conv_attn_point/bias"
param_mapping[
f"encoder.layer.{j}.attention.output.dense.weight"
] = f"electra/encoder/layer_{j}/attention/output/dense/kernel"
param_mapping[
f"encoder.layer.{j}.attention.output.LayerNorm.weight"
] = f"electra/encoder/layer_{j}/attention/output/LayerNorm/gamma"
param_mapping[
f"encoder.layer.{j}.attention.output.dense.bias"
] = f"electra/encoder/layer_{j}/attention/output/dense/bias"
param_mapping[
f"encoder.layer.{j}.attention.output.LayerNorm.bias"
] = f"electra/encoder/layer_{j}/attention/output/LayerNorm/beta"
param_mapping[
f"encoder.layer.{j}.intermediate.dense.weight"
] = f"electra/encoder/layer_{j}/intermediate/{group_dense_name}/kernel"
param_mapping[
f"encoder.layer.{j}.intermediate.dense.bias"
] = f"electra/encoder/layer_{j}/intermediate/{group_dense_name}/bias"
param_mapping[
f"encoder.layer.{j}.output.dense.weight"
] = f"electra/encoder/layer_{j}/output/{group_dense_name}/kernel"
param_mapping[
f"encoder.layer.{j}.output.dense.bias"
] = f"electra/encoder/layer_{j}/output/{group_dense_name}/bias"
param_mapping[
f"encoder.layer.{j}.output.LayerNorm.weight"
] = f"electra/encoder/layer_{j}/output/LayerNorm/gamma"
param_mapping[
f"encoder.layer.{j}.output.LayerNorm.bias"
] = f"electra/encoder/layer_{j}/output/LayerNorm/beta"
for param in model.named_parameters():
param_name = param[0]
retriever = attrgetter(param_name)
result = retriever(model)
tf_name = param_mapping[param_name]
value = torch.from_numpy(tf_data[tf_name])
logger.info(f"TF: {tf_name}, PT: {param_name} ")
if tf_name.endswith("/kernel"):
if not tf_name.endswith("/intermediate/g_dense/kernel"):
if not tf_name.endswith("/output/g_dense/kernel"):
value = value.T
if tf_name.endswith("/depthwise_kernel"):
value = value.permute(1, 2, 0) # 2, 0, 1
if tf_name.endswith("/pointwise_kernel"):
value = value.permute(2, 1, 0) # 2, 1, 0
if tf_name.endswith("/conv_attn_key/bias"):
value = value.unsqueeze(-1)
result.data = value
return model
def convert_orig_tf1_checkpoint_to_pytorch(
tf_checkpoint_path, convbert_config_file, pytorch_dump_path
):
conf = ConvBertConfig.from_json_file(convbert_config_file)
model = ConvBertModel(conf)
model = load_tf_weights_in_convbert(model, conf, tf_checkpoint_path)
model.save_pretrained(pytorch_dump_path)
tf_model = TFConvBertModel.from_pretrained(pytorch_dump_path, from_pt=True)
tf_model.save_pretrained(pytorch_dump_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path",
default=None,
type=str,
required=True,
help="Path to the TensorFlow checkpoint path.",
)
parser.add_argument(
"--convbert_config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained ConvBERT model. \n"
"This specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path",
default=None,
type=str,
required=True,
help="Path to the output PyTorch model.",
)
args = parser.parse_args()
convert_orig_tf1_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path
)
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,385
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/fast/realm.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import json
from tokenizers import normalizers
from ....tokens.base import BatchEncoding
from ....tokens.fast import PreTrainedTokenizerFast
from ....tokens.utils import PaddingStrategy
from ..realm import Tokenizer as Realm
VOCAB_FS = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
VOCAB_MAP = {
"vocab_file": {
"google/realm-cc-news-pretrained-embedder": "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt",
"google/realm-cc-news-pretrained-encoder": "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt",
"google/realm-cc-news-pretrained-scorer": "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt",
"google/realm-cc-news-pretrained-openqa": "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt",
"google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt",
"google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt",
"google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt",
"google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt",
},
"tokenizer_file": {
"google/realm-cc-news-pretrained-embedder": "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont",
"google/realm-cc-news-pretrained-encoder": "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json",
"google/realm-cc-news-pretrained-scorer": "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json",
"google/realm-cc-news-pretrained-openqa": "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json",
"google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json",
"google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json",
"google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json",
"google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json",
},
}
INPUT_CAPS = {
"google/realm-cc-news-pretrained-embedder": 512,
"google/realm-cc-news-pretrained-encoder": 512,
"google/realm-cc-news-pretrained-scorer": 512,
"google/realm-cc-news-pretrained-openqa": 512,
"google/realm-orqa-nq-openqa": 512,
"google/realm-orqa-nq-reader": 512,
"google/realm-orqa-wq-openqa": 512,
"google/realm-orqa-wq-reader": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"google/realm-cc-news-pretrained-embedder": {"do_lower_case": True},
"google/realm-cc-news-pretrained-encoder": {"do_lower_case": True},
"google/realm-cc-news-pretrained-scorer": {"do_lower_case": True},
"google/realm-cc-news-pretrained-openqa": {"do_lower_case": True},
"google/realm-orqa-nq-openqa": {"do_lower_case": True},
"google/realm-orqa-nq-reader": {"do_lower_case": True},
"google/realm-orqa-wq-openqa": {"do_lower_case": True},
"google/realm-orqa-wq-reader": {"do_lower_case": True},
}
class Tokenizer(PreTrainedTokenizerFast):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
input_caps = INPUT_CAPS
slow_tokenizer_class = Realm
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
do_lower_case=True,
unk="[UNK]",
sep="[SEP]",
pad="[PAD]",
cls="[CLS]",
msk="[MASK]",
tokenize_chinese_chars=True,
strip_accents=None,
**kw,
):
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
do_lower_case=do_lower_case,
unk=unk,
sep=sep,
pad=pad,
cls=cls,
msk=msk,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
**kw,
)
normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get("lowercase", do_lower_case) != do_lower_case
or normalizer_state.get("strip_accents", strip_accents) != strip_accents
or normalizer_state.get("handle_chinese_chars", tokenize_chinese_chars)
!= tokenize_chinese_chars
):
normalizer_class = getattr(normalizers, normalizer_state.pop("type"))
normalizer_state["lowercase"] = do_lower_case
normalizer_state["strip_accents"] = strip_accents
normalizer_state["handle_chinese_chars"] = tokenize_chinese_chars
self.backend_tokenizer.normalizer = normalizer_class(**normalizer_state)
self.do_lower_case = do_lower_case
def batch_encode_candidates(self, text, **kw):
kw["padding"] = PaddingStrategy.MAX_LENGTH
batch_text = text
batch_text_pair = kw.pop("text_pair", None)
return_tensors = kw.pop("return_tensors", None)
output_data = {
"input_ids": [],
"attention_mask": [],
"token_type_ids": [],
}
for i, candidate_text in enumerate(batch_text):
if batch_text_pair is not None:
candidate_text_pair = batch_text_pair[i]
else:
candidate_text_pair = None
encoded_candidates = super().__call__(
candidate_text, candidate_text_pair, return_tensors=None, **kw
)
encoded_input_ids = encoded_candidates.get("input_ids")
encoded_attention_mask = encoded_candidates.get("attention_mask")
encoded_token_type_ids = encoded_candidates.get("token_type_ids")
if encoded_input_ids is not None:
output_data["input_ids"].append(encoded_input_ids)
if encoded_attention_mask is not None:
output_data["attention_mask"].append(encoded_attention_mask)
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(encoded_token_type_ids)
output_data = dict((key, item) for key, item in output_data.items() if len(item) != 0)
return BatchEncoding(output_data, tensor_type=return_tensors)
def build_inputs_with_special_tokens(self, toks_0, toks_1=None):
y = [self.cls_token_id] + toks_0 + [self.sep_token_id]
if toks_1:
y += toks_1 + [self.sep_token_id]
return y
def create_token_type_ids_from_sequences(self, toks_0, toks_1=None):
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if toks_1 is None:
return len(cls + toks_0 + sep) * [0]
return len(cls + toks_0 + sep) * [0] + len(toks_1 + sep) * [1]
def save_vocabulary(self, dir, pre=None):
return tuple(self._tokenizer.model.save(dir, name=pre))
|
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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,386
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/gpt.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
# https://openai.com/blog/language-unsupervised/
import torch
from dataclasses import dataclass
from torch import nn
from torch.nn import functional as F
from transformers.utils import logging
from .. import core as qc
from ..core import utils as qu
from ..core import forward as qf
from ..core import output as qo
from ..core import attention as qa
from ..prep.config.openai import PreTrained
log = logging.get_logger(__name__)
class Model(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.tok_emb = qc.Embed(cfg.s_vocab, cfg.n_embed, **kw)
self.pos_emb = qc.Embed(cfg.n_pos, cfg.n_embed, **kw)
self.register_buffer("pos_ids", torch.arange(cfg.n_pos))
self.drop = qc.Dropout(cfg.drop_embed, **kw)
self.lays = qc.Stack([Layer(scale=True, **kw) for _ in range(cfg.n_lays)])
def forward(self, x, head_m=None, mask=None, pos=None, typ=None, x_emb=None, **kw):
cfg = self.cfg
if x is None:
s = x_emb.size()[:-1]
else:
assert x_emb is None
s = x.size()
x = x.view(-1, s[-1])
if x_emb is None:
x_emb = self.tok_emb(x)
if mask is not None:
mask = self.get_mask(mask, s)
head_m = self.get_head_m(head_m, cfg.n_lays)
if pos is None:
pos = self.pos_ids[None, : s[-1]]
pos = self.pos_emb(pos)
if typ is None:
typ = 0
else:
typ = self.tok_emb(typ.view(-1, typ.size(-1)))
y = self.drop(x_emb + pos + typ)
attns = hiddens = ()
for i, lay in enumerate(self.lays):
hiddens += (y,)
ys = lay(y, mask=mask, head_m=head_m[i])
y = ys[0]
attns += (ys[1],)
y = y.view(*(s + (y.size(-1),)))
hiddens += (y,)
return qo.Base(y, attns, hiddens)
class ForSeqClass(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(**kw)
self.proj = qc.Linear(cfg.n_embed, cfg.n_labels, bias=False, **kw)
forward = qf.forward_seq
def post_proj(self, x):
cfg = self.cfg
b = (x.shape[:2] if x is not None else x_emb.shape[:2])[0]
if cfg.PAD is None:
n = -1
else:
assert b == 1
n = -1 if x is None else torch.ne(x, cfg.PAD).sum(-1) - 1
return x[torch.arange(b, device=self.device), n]
class LMHead(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(**kw)
self.proj = qc.Linear(cfg.n_embed, cfg.s_vocab, bias=False, **kw)
def forward(self, x, labels=None, **kw):
ys = self.model(x, **kw)
y = self.proj(ys[0])
loss = None
if labels is not None:
sl = y[..., :-1, :].contiguous()
ls = labels[..., 1:].contiguous()
loss = nn.CrossEntropyLoss()(sl.view(-1, sl.size(-1)), ls.view(-1))
ys = (y,) + ys[1:] + (loss,)
return qo.WithLoss(*ys)
@dataclass
class Output(qc.Output):
logits: tuple = None
mc_logits: tuple = None
attns: tuple = None
hiddens: tuple = None
loss: tuple = None
mc_loss: tuple = None
class DualHead(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
cfg.n_labels = 1
self.model = Model(**kw)
self.sum = qc.SeqSummary(**kw)
self.proj = qc.Linear(cfg.n_embed, cfg.s_vocab, bias=False, **kw)
def forward(self, x, mc_x=None, labels=None, mc_labels=None, **kw):
ys = self.model(x, **kw)
y = self.proj(ys[0])
mc_y = self.sum(ys[0], mc_x).squeeze(-1)
loss, mc_loss = None, None
if mc_labels is not None:
mc_loss = nn.CrossEntropyLoss()(mc_y.view(-1, mc_y.size(-1)), mc_labels.view(-1))
if labels is not None:
sl = y[..., :-1, :].contiguous()
ls = labels[..., 1:].contiguous()
loss = nn.CrossEntropyLoss()(sl.view(-1, sl.size(-1)), ls.view(-1))
ys = (y, mc_y) + ys[1:] + (loss, mc_loss)
return Output(*ys)
class Layer(qc.Module):
hs = qc.Hypers({"d_model", "eps"})
def __init__(self, scale=False, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
m = cfg.d_model
self.attn = Attention(scale, **kw)
self.norm_attn = qc.LayerNorm(m, cfg.eps, **kw)
self.proj = MLP(4 * m, **kw)
self.norm = qc.LayerNorm(m, cfg.eps, **kw)
def forward(self, x, mask, head_m, **kw):
ys = self.attn(x, mask, head_m)
y = self.norm_attn(x + ys[0])
y = self.norm(y + self.proj(y))
y = [y] + ys[1:]
return y
class MLP(qc.Module):
hs = qc.Hypers({"act", "drop"})
def __init__(self, d_ff, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
m = cfg.d_model
self.conv = qc.Conv1D(d_ff, m, **kw)
self.proj = qc.Conv1D(m, d_ff, **kw)
self.act = qu.activation(cfg.act)
self.drop = qc.Dropout(cfg.drop, **kw)
def forward(self, x):
y = self.act(self.conv(x))
y = self.drop(self.proj(y))
return y
class Attention(qc.Module):
hs = qc.Hypers({"d_model", "drop_attn", "drop", "n_heads", "n_pos"})
def __init__(self, scale=False, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
cfg.scale = scale
n, d = cfg.n_heads, cfg.d_model
assert d % n == 0
self.attn = qc.Conv1D(d * 3, d, **kw)
self.proj = qc.Conv1D(d, d, **kw)
self.drop_attn = qc.Dropout(cfg.drop_attn, **kw)
self.drop = qc.Dropout(cfg.drop, **kw)
p = cfg.n_pos
self.register_buffer("bias", torch.tril(torch.ones(p, p)).view(1, 1, p, p))
def forward(self, x, mask, head_m, **kw):
cfg = self.cfg
q, k, v = self.attn(x).split(cfg.d_model, dim=2)
q = self.split_heads(q)
k = self.split_heads(k, k=True)
v = self.split_heads(v)
ys = self.scores(q, k, v, mask, head_m)
y = self.join_heads(ys[0])
y = (self.drop(self.proj(y)),)
return y + ys[1:]
split_heads = qa.split_heads
join_heads = qa.join_heads
def scores(self, q, k, v, mask, head_m, **kw):
cfg = self.cfg
a = torch.matmul(q, k)
if cfg.scale:
a = a / (v.size(-1) ** 0.5)
causal = self.bias[:, :, : a.size(-2), : a.size(-1)]
a = a * causal + -1e4 * (1 - causal)
if mask is not None:
a = a + mask
a = self.drop_attn(F.softmax(a, dim=-1))
if head_m is not None:
a = a * head_m
return torch.matmul(a, v), a
|
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"/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], 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|
33,387
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/decision_transfo.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import torch
import torch.utils.checkpoint
from dataclasses import dataclass
from torch import nn
from torch.nn import functional as F
from transformers.utils import logging
from .. import core as qc
from ..core import utils as qu
from ..core import output as qo
from ..core import attention as qa
from ..core.embed import Embed
from ..core.mlp import Classifier, MLP, Predictor, Pool
from ..prep.config.decision_transfo import PreTrained
log = logging.get_logger(__name__)
from ...pytorch_utils import Conv1D
is_amp_available = True
from torch.cuda.amp import autocast
LIST = [
"edbeeching/decision-transformer-gym-hopper-medium",
]
# Copied from transformers.models.gpt2.modeling_gpt2.GPT2Attention with GPT2->DecisionTransformerGPT2
class DecisionTransformerGPT2Attention(qc.Module):
def __init__(self, config, is_cross_attention=False, layer_idx=None):
super().__init__()
max_positions = config.n_pos
self.register_buffer(
"bias",
torch.tril(torch.ones((max_positions, max_positions), dtype=torch.uint8)).view(
1, 1, max_positions, max_positions
),
)
self.register_buffer("masked_bias", torch.tensor(-1e4))
self.embed_dim = config.d_model
self.n_heads = config.n_heads
self.head_dim = self.embed_dim // self.n_heads
self.split_size = self.embed_dim
if self.head_dim * self.n_heads != self.embed_dim:
raise ValueError(
f"`embed_dim` must be divisible by n_heads (got `embed_dim`: {self.embed_dim} and `n_heads`: {self.n_heads})."
)
self.scale_attn_weights = config.scale_attn_weights
self.is_cross_attention = is_cross_attention
# Layer-wise attention scaling, reordering, and upcasting
self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx
self.layer_idx = layer_idx
self.reorder_and_upcast_attn = config.reorder_and_upcast_attn
if self.is_cross_attention:
self.c_attn = Conv1D(2 * self.embed_dim, self.embed_dim)
self.q_attn = Conv1D(self.embed_dim, self.embed_dim)
else:
self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim)
self.c_proj = Conv1D(self.embed_dim, self.embed_dim)
self.attn_dropout = qc.Dropout(config.drop_attn)
self.drop_resid = qc.Dropout(config.drop_resid)
def _attn(self, query, key, value, attention_mask=None, head_mask=None):
attn_weights = torch.matmul(query, key.transpose(-1, -2))
if self.scale_attn_weights:
attn_weights = attn_weights / (value.size(-1) ** 0.5)
# Layer-wise attention scaling
if self.scale_attn_by_inverse_layer_idx:
attn_weights = attn_weights / float(self.layer_idx + 1)
if not self.is_cross_attention:
# if only "normal" attention layer implements causal mask
query_length, key_length = query.size(-2), key.size(-2)
causal_mask = self.bias[
:, :, key_length - query_length : key_length, :key_length
].bool()
attn_weights = torch.where(
causal_mask, attn_weights, self.masked_bias.to(attn_weights.dtype)
)
if attention_mask is not None:
# Apply the attention mask
attn_weights = attn_weights + attention_mask
attn_weights = F.softmax(attn_weights, dim=-1)
# Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op otherwise
attn_weights = attn_weights.type(value.dtype)
attn_weights = self.attn_dropout(attn_weights)
# Mask heads if we want to
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = torch.matmul(attn_weights, value)
return attn_output, attn_weights
def _upcast_and_reordered_attn(self, query, key, value, attention_mask=None, head_mask=None):
# Use `torch.baddbmm` (a bit more efficient w/ alpha param for scaling -- from Megatron-LM)
bsz, n_heads, q_seq_len, dk = query.size()
_, _, k_seq_len, _ = key.size()
# Preallocate attn_weights for `baddbmm`
attn_weights = torch.empty(
bsz * n_heads, q_seq_len, k_seq_len, dtype=torch.float32, device=query.device
)
# Compute Scale Factor
scale_factor = 1.0
if self.scale_attn_weights:
scale_factor /= float(value.size(-1)) ** 0.5
if self.scale_attn_by_inverse_layer_idx:
scale_factor /= float(self.layer_idx + 1)
# Upcast (turn off autocast) and reorder (Scale K by 1 / root(dk))
if is_amp_available:
with autocast(enabled=False):
q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(
-1, dk, k_seq_len
)
attn_weights = torch.baddbmm(
attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor
)
attn_weights = attn_weights.reshape(bsz, n_heads, q_seq_len, k_seq_len)
else:
q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(
-1, dk, k_seq_len
)
attn_weights = torch.baddbmm(
attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor
)
attn_weights = attn_weights.reshape(bsz, n_heads, q_seq_len, k_seq_len)
if not self.is_cross_attention:
# if only "normal" attention layer implements causal mask
query_length, key_length = query.size(-2), key.size(-2)
causal_mask = self.bias[
:, :, key_length - query_length : key_length, :key_length
].bool()
attn_weights = torch.where(
causal_mask, attn_weights, self.masked_bias.to(attn_weights.dtype)
)
if attention_mask is not None:
# Apply the attention mask
attn_weights = attn_weights + attention_mask
attn_weights = F.softmax(attn_weights, dim=-1)
# Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op if otherwise
if attn_weights.dtype != torch.float32:
raise RuntimeError(
"Error with upcasting, attn_weights does not have dtype torch.float32"
)
attn_weights = attn_weights.type(value.dtype)
attn_weights = self.attn_dropout(attn_weights)
# Mask heads if we want to
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = torch.matmul(attn_weights, value)
return attn_output, attn_weights
def _split_heads(self, tensor, n_heads, attn_head_size):
new_shape = tensor.size()[:-1] + (n_heads, attn_head_size)
tensor = tensor.view(new_shape)
return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)
def _merge_heads(self, tensor, n_heads, attn_head_size):
tensor = tensor.permute(0, 2, 1, 3).contiguous()
new_shape = tensor.size()[:-2] + (n_heads * attn_head_size,)
return tensor.view(new_shape)
def forward(
self,
hiddens,
layer_past=None,
attention_mask=None,
head_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
y_cache=False,
output_attentions=False,
):
if enc_hiddens is not None:
if not hasattr(self, "q_attn"):
raise ValueError(
"If class is used as cross attention, the weights `q_attn` have to be defined. "
"Please make sure to instantiate class with `DecisionTransformerGPT2Attention(..., is_cross_attention=True)`."
)
query = self.q_attn(hiddens)
key, value = self.c_attn(enc_hiddens).split(self.split_size, dim=2)
attention_mask = encoder_attention_mask
else:
query, key, value = self.c_attn(hiddens).split(self.split_size, dim=2)
query = self._split_heads(query, self.n_heads, self.head_dim)
key = self._split_heads(key, self.n_heads, self.head_dim)
value = self._split_heads(value, self.n_heads, self.head_dim)
if layer_past is not None:
past_key, past_value = layer_past
key = torch.cat((past_key, key), dim=-2)
value = torch.cat((past_value, value), dim=-2)
if y_cache is True:
present = (key, value)
else:
present = None
if self.reorder_and_upcast_attn:
attn_output, attn_weights = self._upcast_and_reordered_attn(
query, key, value, attention_mask, head_mask
)
else:
attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
attn_output = self._merge_heads(attn_output, self.n_heads, self.head_dim)
attn_output = self.c_proj(attn_output)
attn_output = self.drop_resid(attn_output)
outputs = (attn_output, present)
if output_attentions:
outputs += (attn_weights,)
return outputs # a, present, (attns)
# Copied from transformers.models.gpt2.modeling_gpt2.GPT2MLP with GPT2->DecisionTransformerGPT2
class DecisionTransformerGPT2MLP(qc.Module):
def __init__(self, d_ff, config):
super().__init__()
embed_dim = config.d_model
self.c_fc = Conv1D(d_ff, embed_dim)
self.c_proj = Conv1D(embed_dim, d_ff)
self.act = qu.activation(config.act)
self.drop = qc.Dropout(config.drop_resid)
def forward(self, x):
y = self.c_fc(x)
y = self.act(y)
y = self.c_proj(y)
y = self.drop(y)
return y
# Copied from transformers.models.gpt2.modeling_gpt2.GPT2Block with GPT2->DecisionTransformerGPT2
class DecisionTransformerGPT2Block(qc.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
d_model = config.d_model
inner_dim = config.n_inner if config.n_inner is not None else 4 * d_model
self.ln_1 = qc.LayerNorm(d_model, eps=config.eps)
self.attn = DecisionTransformerGPT2Attention(config, layer_idx=layer_idx)
self.ln_2 = qc.LayerNorm(d_model, eps=config.eps)
if config.add_cross_attention:
self.crossattention = DecisionTransformerGPT2Attention(
config, is_cross_attention=True, layer_idx=layer_idx
)
self.ln_cross_attn = qc.LayerNorm(d_model, eps=config.eps)
self.mlp = DecisionTransformerGPT2MLP(inner_dim, config)
def forward(
self,
hiddens,
layer_past=None,
attention_mask=None,
head_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
y_cache=False,
output_attentions=False,
):
residual = hiddens
hiddens = self.ln_1(hiddens)
attn_outputs = self.attn(
hiddens,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask,
y_cache=y_cache,
output_attentions=output_attentions,
)
attn_output = attn_outputs[0] # output_attn: a, present, (attns)
outputs = attn_outputs[1:]
# residual connection
hiddens = attn_output + residual
if enc_hiddens is not None:
# add one self-attention block for cross-attention
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `enc_hiddens` are passed, {self} has to be instantiated with "
"cross-attention layers by setting `config.add_cross_attention=True`"
)
residual = hiddens
hiddens = self.ln_cross_attn(hiddens)
cross_attn_outputs = self.crossattention(
hiddens,
attention_mask=attention_mask,
head_mask=head_mask,
enc_hiddens=enc_hiddens,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
)
attn_output = cross_attn_outputs[0]
# residual connection
hiddens = residual + attn_output
outputs = (
outputs + cross_attn_outputs[2:]
) # add cross attns if we output attention weights
residual = hiddens
hiddens = self.ln_2(hiddens)
feed_forward_model_states = self.mlp(hiddens)
# residual connection
hiddens = residual + feed_forward_model_states
if y_cache:
outputs = (hiddens,) + outputs
else:
outputs = (hiddens,) + outputs[1:]
return outputs # hiddens, present, (attns, crosses)
class GPT2Model(PreTrained):
def __init__(self, config):
super().__init__(config)
self.embed_dim = config.d_model
self.wte = qc.Embed(config.s_vocab, self.embed_dim)
self.wpe = qc.Embed(config.n_pos, self.embed_dim)
self.drop = qc.Dropout(config.drop_embed)
self.h = nn.ModuleList(
[DecisionTransformerGPT2Block(config, layer_idx=i) for i in range(config.n_lays)]
)
self.ln_f = qc.LayerNorm(self.embed_dim, eps=config.eps)
# Model parallel
self.model_parallel = False
self.device_map = None
self.gradient_checkpointing = False
# Copied from transformers.models.gpt2.modeling_gpt2.GPT2Model.forward
def forward(
self,
input_ids=None,
caches=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
enc_hiddens=None,
encoder_attention_mask=None,
y_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
output_attentions = (
output_attentions if output_attentions is not None else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
y_cache = y_cache if y_cache is not None else self.config.y_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
batch_size = input_ids.shape[0]
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size = inputs_embeds.shape[0]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
if position_ids is not None:
position_ids = position_ids.view(-1, input_shape[-1])
if caches is None:
past_length = 0
caches = tuple([None] * len(self.h))
else:
past_length = caches[0][0].size(-2)
if position_ids is None:
position_ids = torch.arange(
past_length, input_shape[-1] + past_length, dtype=torch.long, device=device
)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
# GPT2Attention mask.
if attention_mask is not None:
if batch_size <= 0:
raise ValueError("batch_size has to be defined and > 0")
attention_mask = attention_mask.view(batch_size, -1)
attention_mask = attention_mask[:, None, None, :]
attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * -10000.0
if self.config.add_cross_attention and enc_hiddens is not None:
encoder_batch_size, encoder_sequence_length, _ = enc_hiddens.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_attention_mask = None
head_mask = self.get_head_mask(head_mask, self.config.n_lays)
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
position_embeds = self.wpe(position_ids)
hiddens = inputs_embeds + position_embeds
if token_type_ids is not None:
token_type_embeds = self.wte(token_type_ids)
hiddens = hiddens + token_type_embeds
hiddens = self.drop(hiddens)
output_shape = input_shape + (hiddens.size(-1),)
presents = () if y_cache else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
all_hidden_states = () if output_hidden_states else None
for i, (block, layer_past) in enumerate(zip(self.h, caches)):
# Model parallel
if self.model_parallel:
torch.cuda.set_device(hiddens.device)
# Ensure layer_past is on same device as hiddens (might not be correct)
if layer_past is not None:
layer_past = tuple(past_state.to(hiddens.device) for past_state in layer_past)
# Ensure that attention_mask is always on the same device as hiddens
if attention_mask is not None:
attention_mask = attention_mask.to(hiddens.device)
if isinstance(head_mask, torch.Tensor):
head_mask = head_mask.to(hiddens.device)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hiddens,)
if self.gradient_checkpointing and self.training:
if y_cache:
log.warning(
"`y_cache=True` is incompatible with gradient checkpointing. Setting `y_cache=False`..."
)
y_cache = False
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, y_cache, output_attentions)
return custom_forward
outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hiddens,
None,
attention_mask,
head_mask[i],
enc_hiddens,
encoder_attention_mask,
)
else:
outputs = block(
hiddens,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask[i],
enc_hiddens=enc_hiddens,
encoder_attention_mask=encoder_attention_mask,
y_cache=y_cache,
output_attentions=output_attentions,
)
hiddens = outputs[0]
if y_cache is True:
presents = presents + (outputs[1],)
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if y_cache else 1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (outputs[3 if y_cache else 2],)
# Model Parallel: If it's the last layer for that device, put things on the next device
if self.model_parallel:
for k, v in self.device_map.items():
if i == v[-1] and "cuda:" + str(k) != self.last_device:
hiddens = hiddens.to("cuda:" + str(k + 1))
hiddens = self.ln_f(hiddens)
hiddens = hiddens.view(output_shape)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hiddens,)
if not return_dict:
return tuple(
v
for v in [
hiddens,
presents,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return qo.CachesCrosses(
y=hiddens,
caches=presents,
hiddens=all_hidden_states,
attns=all_self_attentions,
crosses=all_cross_attentions,
)
@dataclass
class Output(qo.Output):
state_preds = None
action_preds = None
return_preds = None
hiddens = None
attns = None
y = None
class Model(PreTrained):
def __init__(self, config):
super().__init__(config)
self.config = config
self.d_model = config.d_model
self.encoder = GPT2Model(config)
self.embed_timestep = qc.Embed(config.max_ep_len, config.d_model)
self.embed_return = torch.qc.Linear(1, config.d_model)
self.embed_state = torch.qc.Linear(config.state_dim, config.d_model)
self.embed_action = torch.qc.Linear(config.act_dim, config.d_model)
self.embed_ln = qc.LayerNorm(config.d_model)
self.predict_state = torch.qc.Linear(config.d_model, config.state_dim)
self.predict_action = nn.Sequential(
*(
[qc.Linear(config.d_model, config.act_dim)]
+ ([nn.Tanh()] if config.action_tanh else [])
)
)
self.predict_return = torch.qc.Linear(config.d_model, 1)
self.post_init()
def forward(
self,
states=None,
actions=None,
rewards=None,
returns_to_go=None,
timesteps=None,
attention_mask=None,
output_hidden_states=None,
output_attentions=None,
return_dict=None,
):
output_attentions = (
output_attentions if output_attentions is not None else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
batch_size, seq_length = states.shape[0], states.shape[1]
if attention_mask is None:
# attention mask for GPT: 1 if can be attended to, 0 if not
attention_mask = torch.ones((batch_size, seq_length), dtype=torch.long)
# embed each modality with a different head
state_embeddings = self.embed_state(states)
action_embeddings = self.embed_action(actions)
returns_embeddings = self.embed_return(returns_to_go)
time_embeddings = self.embed_timestep(timesteps)
# time embeddings are treated similar to positional embeddings
state_embeddings = state_embeddings + time_embeddings
action_embeddings = action_embeddings + time_embeddings
returns_embeddings = returns_embeddings + time_embeddings
# this makes the sequence look like (R_1, s_1, a_1, R_2, s_2, a_2, ...)
# which works nice in an autoregressive sense since states predict actions
stacked_inputs = (
torch.stack((returns_embeddings, state_embeddings, action_embeddings), dim=1)
.permute(0, 2, 1, 3)
.reshape(batch_size, 3 * seq_length, self.d_model)
)
stacked_inputs = self.embed_ln(stacked_inputs)
# to make the attention mask fit the stacked inputs, have to stack it as well
stacked_attention_mask = (
torch.stack((attention_mask, attention_mask, attention_mask), dim=1)
.permute(0, 2, 1)
.reshape(batch_size, 3 * seq_length)
)
device = stacked_inputs.device
# we feed in the input embeddings (not word indices as in NLP) to the model
encoder_outputs = self.encoder(
inputs_embeds=stacked_inputs,
attention_mask=stacked_attention_mask,
position_ids=torch.zeros(stacked_attention_mask.shape, device=device, dtype=torch.long),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
x = encoder_outputs[0]
# reshape x so that the second dimension corresponds to the original
# returns (0), states (1), or actions (2); i.e. x[:,1,t] is the token for s_t
x = x.reshape(batch_size, seq_length, 3, self.d_model).permute(0, 2, 1, 3)
# get predictions
return_preds = self.predict_return(x[:, 2]) # predict next return given state and action
state_preds = self.predict_state(x[:, 2]) # predict next state given state and action
action_preds = self.predict_action(x[:, 1]) # predict next action given state
if not return_dict:
return (state_preds, action_preds, return_preds)
return Output(
y=encoder_outputs.y,
state_preds=state_preds,
action_preds=action_preds,
return_preds=return_preds,
hiddens=encoder_outputs.hiddens,
attns=encoder_outputs.attns,
)
|
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33,388
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quantapix/qnarre
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refs/heads/main
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/qnarre/models/fsmt.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import random
import torch
import deepspeed
from torch import nn
from torch.nn import functional as F
from transformers.utils import logging
from .. import core as qc
from ..core import utils as qu
from ..core import output as qo
from ..core import attention as qa
from ..core.embed import SinEmbed
from ..prep.config.fsmt import PreTrained
logger = logging.get_logger(__name__)
class Model(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
self.get_cfg(kw)
self.enc = Encoder(**kw)
self.dec = Decoder(**kw)
def forward(self, x, mask=None, x_dec=None, dec_m=None, dec_head_m=None, y_enc=None, **kw):
cfg = self.cfg
if x_dec is None:
yo.cache = False
if not yo.cache:
x_dec, dec_m, causal_m = _prepare_fsmt_decoder_inputs(
cfg,
x,
x_dec=x_dec,
dec_m=dec_m,
causal_m_dtype=self.dec.tok_emb.weight.dtype,
)
else:
dec_m, causal_m = None, None
assert x_dec is not None
if y_enc is None:
y_enc = self.enc(x, **kw, mask=mask)
y = self.dec(
x_dec,
**kw,
dec_causal_m=causal_m,
enc_m=mask,
enc=y_enc[0],
head_m=dec_head_m,
mask=dec_m,
)
ys = y + y_enc
return qo.Seq2Seq(*ys)
class ForCondGen(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
self.get_cfg(kw)
self.model = Model(**kw)
def forward(self, x, labels=None, **kw):
cfg = self.cfg
ys = self.model(x, **kw)
loss = None
if labels is not None:
loss = nn.CrossEntropyLoss()(ys[0].view(-1, cfg.s_tgt_vocab), labels.view(-1))
ys += (loss,)
return qo.LossSeq2Seq(*ys)
class Encoder(qc.Module):
hs = qc.Hypers({"d_model", "drop", "n_heads", "n_pos"})
def __init__(self, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
m = cfg.d_model
cfg.scale = m**0.5 if cfg.scale else 1.0
self.tok_emb = qc.Embed(cfg.s_src_vocab, m, **kw)
self.pos_emb = SinEmbed(cfg.n_pos + cfg.PAD + 1, m, cfg.PAD)
self.lays = qc.Stack([EncLayer(**kw) for _ in range(cfg.n_enc_lays)])
self.drop = qc.Dropout(cfg.drop, **kw)
def forward(self, x, mask=None, head_m=None, **kw):
cfg = self.cfg
if mask is not None:
mask = invert_mask(mask)
y = self.tok_emb(x) * cfg.scale
y = y + self.pos_emb(x)
y = self.drop(y).transpose(0, 1)
attns = hiddens = ()
assert head_m is None or (head_m.size()[0] == (len(self.lays)))
for i, lay in enumerate(self.lays):
hiddens += (y.transpose(0, 1),)
if self.training and (random.uniform(0, 1) < cfg.drop_enc):
continue
else:
h = head_m[i] if head_m is not None else None
ys = lay(y, mask=mask, head_m=h, **kw)
y = ys[0]
attns += (ys[1],)
y = y.transpose(0, 1)
hiddens += (y,)
return qo.Base(y, attns, hiddens)
class Decoder(qc.Module):
hs = qc.Hypers({"d_model", "drop", "n_heads", "n_pos"})
def __init__(self, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
m = cfg.d_model
cfg.scale = m**0.5 if cfg.scale else 1.0
self.tok_emb = qc.Embed(cfg.s_tgt_vocab, m, **kw)
self.pos_emb = SinEmbed(cfg.n_pos + cfg.PAD + 1, m, cfg.PAD)
self.lays = qc.Stack([DecLayer(**kw) for _ in range(cfg.n_dec_lays)])
if is_deepspeed_zero3_enabled():
with deepspeed.zero.GatheredParameters(self.tok_emb.weight, modifier_rank=None):
s = self.tok_emb.weight.shape
else:
s = self.tok_emb.weight.shape
self.proj = qc.Linear(s[1], s[0], bias=False, **kw)
self.proj.weight = self.tok_emb.weight
self.drop = qc.Dropout(cfg.drop, **kw)
def forward(
self, x, enc, enc_m, dec_m, dec_causal_m, head_m=None, cross_m=None, cache=None, **kw
):
cfg = self.cfg
if enc_m is not None:
enc_m = invert_mask(enc_m)
y = self.tok_emb(x) * cfg.scale
pos = self.pos_emb(x)
if yo.cache:
x = x[:, -1:]
pos = pos[:, -1:]
y += pos
y = self.drop(y).transpose(0, 1)
attns = caches = crosses = hiddens = ()
enc = enc.transpose(0, 1)
for m, _ in zip([head_m, cross_m], ["head_m", "cross_m"]):
if m is not None:
assert m.size()[0] == (len(self.lays))
for i, lay in enumerate(self.lays):
hiddens += (y.transpose(0, 1),)
if self.training and (random.uniform(0, 1) < cfg.drop_dec):
continue
h = head_m[i] if head_m is not None else None
c = cross_m[i] if cross_m is not None else None
kw.update(enc=enc, enc_m=enc_m, dec_m=dec_m, head_m=h, cross_m=c)
c = cache[i] if cache is not None else None
ys = lay(y, causal_m=dec_causal_m, cache=c, **kw)
y = ys[0]
attns += (ys[1],)
if enc is not None:
crosses += (ys[2],)
caches += (ys[-1],)
enc = enc.transpose(0, 1)
y = y.transpose(0, 1)
hiddens += (y,)
y = self.proj(y)
return qo.CachesCrosses(y, attns, caches, crosses, hiddens)
class EncLayer(qc.Module):
hs = qc.Hypers({"d_model", "n_heads", "n_pos", "eps"}, {"drop_attn": 0.0, "is_dec": False})
def __init__(self, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
m = cfg.d_model
self.refl = Attention(n_heads=cfg.n_enc_heads, **kw)
self.norm_refl = qc.LayerNorm(m, **kw)
self.act = qu.activation(cfg.act_fun)
self.ff = qc.Linear(m, cfg.d_enc_ffn, **kw)
self.proj = qc.Linear(cfg.d_enc_ffn, m, **kw)
self.norm = qc.LayerNorm(m, **kw)
self.drop = qc.Dropout(cfg.drop, **kw)
def forward(self, x, mask, **kw):
y, a = self.refl(query=x, key=x, key_m=mask, **kw)
y = self.norm_refl(x + self.drop(y))
x = y
y = self.drop(self.act(self.ff(y)))
y = self.drop(self.proj(y))
y = self.norm(x + y)
return y, a
class DecLayer(qc.Module):
hs = qc.Hypers({"d_model", "n_heads", "n_pos", "eps"}, {"drop_attn": 0.0, "is_dec": False})
def __init__(self, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
m = cfg.d_model
self.refl = Attention(n_heads=cfg.n_dec_heads, **kw)
self.norm_refl = qc.LayerNorm(m, **kw)
self.act = qu.activation(cfg.act_fun)
self.drop_act = qc.Dropout(cfg.drop_act, **kw)
self.attn = Attention(n_heads=cfg.n_dec_heads, enc_dec_attn=True, **kw)
self.norm_attn = qc.LayerNorm(m, **kw)
self.ff = qc.Linear(m, cfg.d_dec_ffn, **kw)
self.proj = qc.Linear(cfg.d_dec_ffn, m, **kw)
self.norm = qc.LayerNorm(m, **kw)
self.drop = qc.Dropout(cfg.drop, **kw)
def forward(
self, x, enc, enc_m=None, cache=None, causal_m=None, cross_m=None, dec_m=None, **kw
):
if cache is None:
cache = {}
y, a = self.refl(query=x, key=x, key_m=dec_m, cache=cache, **kw, mask=causal_m)
y = self.norm_refl(x + self.drop(y))
x = y
assert self.attn.cache_key != self.refl.cache_key
y, kv = self.attn(query=y, key=enc, key_m=enc_m, cache=cache, **kw, head_m=cross_m)
y = self.norm_attn(x + self.drop(y))
x = y
y = self.drop_act(self.act(self.ff(y)))
y = self.drop(self.proj(y))
y = self.norm(x + y)
return y, a, cache, kv
def invert_mask(mask):
assert mask.dim() == 2
return mask.eq(0)
def triu_onnx(x, diagonal=0):
l = x.shape[0]
arange = torch.arange(l, device=x.device)
mask = arange.expand(l, l)
arange = arange.unsqueeze(-1)
if diagonal:
arange = arange + diagonal
mask = mask >= arange
return x.masked_fill(mask == 0, 0)
def _prepare_fsmt_decoder_inputs(
config,
input_ids,
x_dec=None,
decoder_padding_mask=None,
causal_mask_dtype=torch.float32,
):
PAD = config.PAD
if x_dec is None:
x_dec = qu.shift_right2(input_ids, PAD)
bsz, tgt_len = x_dec.size()
if decoder_padding_mask is None:
decoder_padding_mask = make_padding_mask(x_dec, PAD)
else:
decoder_padding_mask = invert_mask(decoder_padding_mask)
causal_mask = triu_onnx(fill_with_neg_inf(torch.zeros(tgt_len, tgt_len)), 1).to(
dtype=causal_mask_dtype, device=x_dec.device
)
return x_dec, decoder_padding_mask, causal_mask
def make_padding_mask(x, PAD=1):
y = x.eq(PAD)
if not y.any():
y = None
return y
class Attention(qc.Module):
hs = qc.Hypers({"d_in", "d_out"}, {"drop": 0.0, "enc_dec_attn": False})
def __init__(self, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
m, n = cfg.d_model, cfg.n_heads
assert m % n == 0
cfg.d_head = h = m // n
cfg.scale = 1 / (h**0.5)
self.key = qc.Linear(m, m, **kw)
self.value = qc.Linear(m, m, **kw)
self.query = qc.Linear(m, m, **kw)
self.proj = qc.Linear(m, m, **kw)
self.drop = qc.Dropout(cfg.drop, **kw)
self.cache_key = "encoder_decoder" if self.enc_dec_attn else "self"
split_heads = qa.split_heads
def forward(self, x, mask=None, head_m=None, enc=None, enc_m=None, cache=None, **kw):
cfg = self.cfg
static_kv = self.enc_dec_attn
if cache is not None:
saved_state = None
cache = {}
else:
saved_state = cache.get(self.cache_key, {})
if "prev_key" in saved_state and static_kv:
enc = None
q = self.split_heads(self.query(x) * cfg.scale)
if static_kv:
if enc is None:
k = v = None
else:
k = self.split_heads(self.key(enc))
v = self.split_heads(self.value(enc))
else:
k = self.split_heads(self.key(x))
v = self.split_heads(self.value(x))
if saved_state is not None:
k, v, enc_m = self._use_saved_state(k, v, saved_state, enc_m, static_kv, b)
cache[self.cache_key] = {
"prev_key": k.view(b, n, -1, cfg.d_head),
"prev_value": v.view(b, n, -1, cfg.d_head),
"prev_key_padding_mask": enc_m if not static_kv else None,
}
n = cfg.n_heads
tgt, b, _ = x.size()
src = k.size(1)
y = torch.bmm(q, k.transpose(1, 2))
assert y.size() == (b * n, tgt, src)
if mask is not None:
y = y.view(b, n, tgt, src) + mask
y = y.view(b * n, tgt, src)
if enc_m is not None and enc_m.dim() == 0:
enc_m = None
assert enc_m is None or enc_m.size()[:2] == (b, src)
if enc_m is not None:
y = y.view(b, n, tgt, src)
reshaped = enc_m.unsqueeze(1).unsqueeze(2)
y = y.masked_fill(reshaped, float("-inf"))
y = y.view(b * n, tgt, src)
y = F.softmax(y, dim=-1)
if head_m is not None:
assert head_m.size() == (n,)
y = head_m.view(1, -1, 1, 1) * y.view(b, n, tgt, src)
y = y.view(b * n, tgt, src)
a = y.view(b, n, tgt, src)
y = a.view(b * n, tgt, src)
y = self.drop(y)
y = torch.bmm(y, v)
assert y.size() == (b * n, tgt, cfg.d_head)
y = y.transpose(0, 1).contiguous().view(tgt, b, cfg.d_model)
y = self.proj(y)
return y, a
def _use_saved_state(self, k, v, saved_state, key_m, static_kv, bsz):
cfg = self.cfg
if "prev_key" in saved_state:
_prev_key = saved_state["prev_key"]
assert _prev_key is not None
prev_key = _prev_key.view(bsz * cfg.n_heads, -1, cfg.d_head)
if static_kv:
k = prev_key
else:
assert k is not None
k = torch.cat([prev_key, k], dim=1)
if "prev_value" in saved_state:
_prev_value = saved_state["prev_value"]
assert _prev_value is not None
prev_value = _prev_value.view(bsz * cfg.n_heads, -1, cfg.d_head)
if static_kv:
v = prev_value
else:
assert v is not None
v = torch.cat([prev_value, v], dim=1)
assert k is not None and v is not None
prev_key_padding_mask = saved_state.get("prev_key_padding_mask", None)
if prev_key_padding_mask is not None:
if static_kv:
new_key_padding_mask = prev_key_padding_mask
else:
new_key_padding_mask = torch.cat([prev_key_padding_mask, key_m], dim=1)
else:
new_key_padding_mask = key_m
return k, v, new_key_padding_mask
def fill_with_neg_inf(t):
return t.float().fill_(float("-inf")).type_as(t)
|
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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,389
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/roformer.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import math
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import functional as F
from transformers.utils import logging
from .. import core as qc
from ..core import utils as qu
from ..core import output as qo
from ..core import forward as qf
from ..core import attention as qa
from ..core.embed import Embed
from ..core.mlp import Classifier, MLP, Predictor, Pool
from ..prep.config.bert import PreTrained
from torch.nn import CrossEntropyLoss
from ...modeling_utils import SequenceSummary
from ...pytorch_utils import (
apply_chunking_to_forward,
)
log = logging.get_logger(__name__)
LIST = [
"junnyu/roformer_chinese_small",
"junnyu/roformer_chinese_base",
"junnyu/roformer_chinese_char_small",
"junnyu/roformer_chinese_char_base",
"junnyu/roformer_small_discriminator",
"junnyu/roformer_small_generator",
]
# Copied from transformers.models.marian.modeling_marian.MarianSinusoidalPositionalEmbedding with Marian->RoFormer
class RoFormerSinusoidalPositionalEmbedding(qc.Embed):
def __init__(self, num_positions, embedding_dim, padding_idx=None):
super().__init__(num_positions, embedding_dim)
self.weight = self._init_weight(self.weight)
@staticmethod
def _init_weight(out: nn.Parameter):
n_pos, dim = out.shape
position_enc = np.array(
[
[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)]
for pos in range(n_pos)
]
)
out.requires_grad = False # set early to avoid an error in pytorch-1.8+
sentinel = dim // 2 if dim % 2 == 0 else (dim // 2) + 1
out[:, 0:sentinel] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
out[:, sentinel:] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
out.detach_()
return out
@torch.no_grad()
def forward(self, input_ids_shape, past_key_values_length=0):
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
bsz, seq_len = input_ids_shape[:2]
positions = torch.arange(
past_key_values_length,
past_key_values_length + seq_len,
dtype=torch.long,
device=self.weight.device,
)
return super().forward(positions)
class RoFormerEmbeddings(qc.Module):
def __init__(self, config):
super().__init__()
self.word_embeddings = qc.Embed(config.s_vocab, config.d_embed, padding_idx=config.PAD)
self.token_type_embeddings = qc.Embed(config.n_typ, config.d_embed)
self.norm = qc.LayerNorm(config.d_embed, eps=config.eps)
self.drop = qc.Dropout(config.drop)
def forward(self, input_ids=None, token_type_ids=None, inputs_embeds=None):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=inputs_embeds.device)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
embeddings = self.norm(embeddings)
embeddings = self.drop(embeddings)
return embeddings
class RoFormerSelfAttention(qc.Module):
def __init__(self, config):
super().__init__()
if config.d_model % config.n_heads != 0 and not hasattr(config, "d_embed"):
raise ValueError(
f"The hidden size ({config.d_model}) is not a multiple of the number of attention "
f"heads ({config.n_heads})"
)
self.n_heads = config.n_heads
self.attention_head_size = int(config.d_model / config.n_heads)
self.all_head_size = self.n_heads * self.attention_head_size
self.query = qc.Linear(config.d_model, self.all_head_size)
self.key = qc.Linear(config.d_model, self.all_head_size)
self.value = qc.Linear(config.d_model, self.all_head_size)
self.drop = qc.Dropout(config.drop_attn)
self.is_decoder = config.is_decoder
self.rotary_value = config.rotary_value
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.n_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hiddens,
attention_mask=None,
sinusoidal_pos=None,
head_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
mixed_query_layer = self.query(hiddens)
query_layer = self.transpose_for_scores(mixed_query_layer)
is_cross_attention = enc_hiddens is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, crosses
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(enc_hiddens))
value_layer = self.transpose_for_scores(self.value(enc_hiddens))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hiddens))
value_layer = self.transpose_for_scores(self.value(hiddens))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hiddens))
value_layer = self.transpose_for_scores(self.value(hiddens))
if sinusoidal_pos is not None:
if self.rotary_value:
query_layer, key_layer, value_layer = self.apply_rotary_position_embeddings(
sinusoidal_pos, query_layer, key_layer, value_layer
)
else:
query_layer, key_layer = self.apply_rotary_position_embeddings(
sinusoidal_pos, query_layer, key_layer
)
if self.is_decoder:
past_key_value = (key_layer, value_layer)
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = F.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.drop(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
@staticmethod
def apply_rotary_position_embeddings(sinusoidal_pos, query_layer, key_layer, value_layer=None):
sin, cos = sinusoidal_pos.chunk(2, dim=-1)
sin_pos = torch.stack([sin, sin], dim=-1).reshape_as(sinusoidal_pos)
cos_pos = torch.stack([cos, cos], dim=-1).reshape_as(sinusoidal_pos)
rotate_half_query_layer = torch.stack(
[-query_layer[..., 1::2], query_layer[..., ::2]], dim=-1
).reshape_as(query_layer)
query_layer = query_layer * cos_pos + rotate_half_query_layer * sin_pos
rotate_half_key_layer = torch.stack(
[-key_layer[..., 1::2], key_layer[..., ::2]], dim=-1
).reshape_as(key_layer)
key_layer = key_layer * cos_pos + rotate_half_key_layer * sin_pos
if value_layer is not None:
rotate_half_value_layer = torch.stack(
[-value_layer[..., 1::2], value_layer[..., ::2]], dim=-1
).reshape_as(value_layer)
value_layer = value_layer * cos_pos + rotate_half_value_layer * sin_pos
return query_layer, key_layer, value_layer
return query_layer, key_layer
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->RoFormer
class RoFormerSelfOutput(qc.Module):
def __init__(self, config):
super().__init__()
self.dense = qc.Linear(config.d_model, config.d_model)
self.norm = qc.LayerNorm(config.d_model, eps=config.eps)
self.drop = qc.Dropout(config.drop)
def forward(self, hiddens, input_tensor):
hiddens = self.dense(hiddens)
hiddens = self.drop(hiddens)
hiddens = self.norm(hiddens + input_tensor)
return hiddens
class Attention(qc.Module):
def __init__(self, config):
super().__init__()
self.self = RoFormerSelfAttention(config)
self.output = RoFormerSelfOutput(config)
def forward(
self,
hiddens,
attention_mask=None,
sinusoidal_pos=None,
head_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
self_outputs = self.self(
hiddens,
attention_mask,
sinusoidal_pos,
head_mask,
enc_hiddens,
encoder_attention_mask,
past_key_value,
output_attentions,
)
attention_output = self.output(self_outputs[0], hiddens)
outputs = (attention_output,) + self_outputs[1:] # add attns if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->RoFormer
class RoFormerIntermediate(qc.Module):
def __init__(self, cfg):
super().__init__()
self.dense = qc.Linear(cfg.d_model, cfg.d_ff)
self.act = qu.activation(cfg.act)
def forward(self, x):
y = self.dense(x)
y = self.act(y)
return y
# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->RoFormer
class RoFormerOutput(qc.Module):
def __init__(self, config):
super().__init__()
self.dense = qc.Linear(config.d_ff, config.d_model)
self.norm = qc.LayerNorm(config.d_model, eps=config.eps)
self.drop = qc.Dropout(config.drop)
def forward(self, hiddens, input_tensor):
hiddens = self.dense(hiddens)
hiddens = self.drop(hiddens)
hiddens = self.norm(hiddens + input_tensor)
return hiddens
class Layer(qc.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = Attention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
if not self.is_decoder:
raise ValueError(
f"{self} should be used as a decoder model if cross attention is added"
)
self.crossattention = Attention(config)
self.intermediate = RoFormerIntermediate(config)
self.output = RoFormerOutput(config)
def forward(
self,
hiddens,
attention_mask=None,
sinusoidal_pos=None,
head_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
hiddens,
attention_mask,
sinusoidal_pos,
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attns if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and enc_hiddens is not None:
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `enc_hiddens` are passed, {self} has to be instantiated with cross-attention "
"layers by setting `config.add_cross_attention=True`"
)
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
sinusoidal_pos,
head_mask,
enc_hiddens,
encoder_attention_mask,
cross_attn_past_key_value,
output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = (
outputs + cross_attention_outputs[1:-1]
) # add cross attns if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk,
self.chunk_size_feed_forward,
self.seq_len_dim,
attention_output,
)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
class Encoder(qc.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.embed_positions = RoFormerSinusoidalPositionalEmbedding(
config.n_pos, config.d_model // config.n_heads
)
self.layer = nn.ModuleList([Layer(config) for _ in range(config.n_lays)])
self.gradient_checkpointing = False
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
caches=None,
y_cache=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
sinusoidal_pos = self.embed_positions(hiddens.shape[:-1])[None, None, :, :]
next_decoder_cache = () if y_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hiddens,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = caches[i] if caches is not None else None
if self.gradient_checkpointing and self.training:
if y_cache:
log.warning(
"`y_cache=True` is incompatible with gradient checkpointing. Setting `y_cache=False`..."
)
y_cache = False
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, past_key_value, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(layer_module),
hiddens,
attention_mask,
sinusoidal_pos,
layer_head_mask,
enc_hiddens,
encoder_attention_mask,
)
else:
layer_outputs = layer_module(
hiddens,
attention_mask,
sinusoidal_pos,
layer_head_mask,
enc_hiddens,
encoder_attention_mask,
past_key_value,
output_attentions,
)
hiddens = layer_outputs[0]
if y_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hiddens,)
if not return_dict:
return tuple(
v
for v in [
hiddens,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return qo.CachesCrosses(
y=hiddens,
caches=next_decoder_cache,
hiddens=all_hidden_states,
attns=all_self_attentions,
crosses=all_cross_attentions,
)
class Model(PreTrained):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = RoFormerEmbeddings(config)
if config.d_embed != config.d_model:
self.embeddings_project = qc.Linear(config.d_embed, config.d_model)
self.encoder = Encoder(config)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
head_mask=None,
inputs_embeds=None,
enc_hiddens=None,
encoder_attention_mask=None,
caches=None,
y_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
output_attentions = (
output_attentions if output_attentions is not None else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.config.is_decoder:
y_cache = y_cache if y_cache is not None else self.config.y_cache
else:
y_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
# past_key_values_length
past_key_values_length = caches[0][0].shape[2] if caches is not None else 0
if attention_mask is None:
attention_mask = torch.ones(
((batch_size, seq_length + past_key_values_length)), device=device
)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
extended_attention_mask = self.get_extended_attention_mask(
attention_mask, input_shape, device
)
if self.config.is_decoder and enc_hiddens is not None:
encoder_batch_size, encoder_sequence_length, _ = enc_hiddens.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
head_mask = self.get_head_mask(head_mask, self.config.n_lays)
embedding_output = self.embeddings(
input_ids=input_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
)
if hasattr(self, "embeddings_project"):
embedding_output = self.embeddings_project(embedding_output)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
enc_hiddens=enc_hiddens,
encoder_attention_mask=encoder_extended_attention_mask,
caches=caches,
y_cache=y_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
if not return_dict:
return (sequence_output,) + encoder_outputs[1:]
return qo.CachesCrosses(
y=sequence_output,
caches=encoder_outputs.caches,
hiddens=encoder_outputs.hiddens,
attns=encoder_outputs.attns,
crosses=encoder_outputs.crosses,
)
class ForMasked(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Predictor(cfg.d_embed, **kw)
forward = qf.forward_masked
class ForCausal(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Predictor(cfg.d_embed, **kw)
forward = qf.forward_causal
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
inputs_embeds=None,
enc_hiddens=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
caches=None,
labels=None,
y_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.roformer(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
enc_hiddens=enc_hiddens,
encoder_attention_mask=encoder_attention_mask,
caches=caches,
y_cache=y_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
lm_loss = None
if labels is not None:
# we are doing next-token prediction; shift prediction scores and input ids by one
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
lm_loss = loss_fct(
shifted_prediction_scores.view(-1, self.config.s_vocab), labels.view(-1)
)
if not return_dict:
output = (prediction_scores,) + outputs[1:]
return ((lm_loss,) + output) if lm_loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=lm_loss,
logits=prediction_scores,
caches=outputs.caches,
hiddens=outputs.hiddens,
attns=outputs.attns,
crosses=outputs.crosses,
)
class ForChoice(PreTrained):
def __init__(self, config):
super().__init__(config)
self.roformer = Model(config)
self.sequence_summary = SequenceSummary(config)
self.classifier = qc.Linear(config.d_model, 1)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = (
attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
)
token_type_ids = (
token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
)
inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.roformer(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
pooled_output = self.sequence_summary(sequence_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return qo.WithLoss(
loss=loss,
logits=reshaped_logits,
hiddens=outputs.hiddens,
attns=outputs.attns,
)
class ForSeqClass(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Classifier(cfg.d_model, **kw)
forward = qf.forward_seq
class ForTokClass(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Classifier(**kw)
forward = qf.forward_tok
class ForQA(PreTrained):
def __init__(self, **kw):
kw.update(n_labels=2)
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(add_pool=False, **kw)
self.proj = qc.Linear(cfg.d_model, cfg.n_labels, **kw)
forward = qf.forward_qa
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,390
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/xlnet.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import os
import unicodedata
from shutil import copyfile
import sentencepiece as spm
from ...file_utils import SPIECE_UNDERLINE
from ...tokens.utils import AddedToken, PreTrainedTokenizer
VOCAB_FS = {"vocab_file": "spiece.model"}
VOCAB_MAP = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
}
}
INPUT_CAPS = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
# Segments (not really needed)
SEG_ID_A = 0
SEG_ID_B = 1
SEG_ID_CLS = 2
SEG_ID_SEP = 3
SEG_ID_PAD = 4
class Tokenizer(PreTrainedTokenizer):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
input_caps = INPUT_CAPS
padding_side = "left"
def __init__(
self,
vocab_file,
do_lower_case=False,
remove_space=True,
keep_accents=False,
bos="<s>",
eos="</s>",
unk="<unk>",
sep="<sep>",
pad="<pad>",
cls="<cls>",
msk="<mask>",
additional_special_tokens=["<eop>", "<eod>"],
sp_model_kw=None,
**kw,
):
msk = AddedToken(msk, lstrip=True, rstrip=False) if isinstance(msk, str) else msk
self.sp_model_kw = {} if sp_model_kw is None else sp_model_kw
super().__init__(
do_lower_case=do_lower_case,
remove_space=remove_space,
keep_accents=keep_accents,
bos=bos,
eos=eos,
unk=unk,
sep=sep,
pad=pad,
cls=cls,
msk=msk,
additional_special_tokens=additional_special_tokens,
sp_model_kw=self.sp_model_kw,
**kw,
)
self._pad_token_type_id = 3
self.do_lower_case = do_lower_case
self.remove_space = remove_space
self.keep_accents = keep_accents
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kw)
self.sp_model.Load(vocab_file)
@property
def s_vocab(self):
return len(self.sp_model)
def get_vocab(self):
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.s_vocab)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
return state
def __setstate__(self, d):
self.__dict__ = d
if not hasattr(self, "sp_model_kw"):
self.sp_model_kw = {}
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kw)
self.sp_model.Load(self.vocab_file)
def preprocess_text(self, inputs):
if self.remove_space:
outputs = " ".join(inputs.strip().split())
else:
outputs = inputs
outputs = outputs.replace("``", '"').replace("''", '"')
if not self.keep_accents:
outputs = unicodedata.normalize("NFKD", outputs)
outputs = "".join([c for c in outputs if not unicodedata.combining(c)])
if self.do_lower_case:
outputs = outputs.lower()
return outputs
def _tokenize(self, text):
text = self.preprocess_text(text)
pieces = self.sp_model.encode(text, out_type=str)
new_pieces = []
for piece in pieces:
if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
cur_pieces = self.sp_model.EncodeAsPieces(piece[:-1].replace(SPIECE_UNDERLINE, ""))
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0]) == 1:
cur_pieces = cur_pieces[1:]
else:
cur_pieces[0] = cur_pieces[0][1:]
cur_pieces.append(piece[-1])
new_pieces.extend(cur_pieces)
else:
new_pieces.append(piece)
return new_pieces
def _convert_token_to_id(self, token):
return self.sp_model.PieceToId(token)
def _convert_id_to_token(self, index):
return self.sp_model.IdToPiece(index)
def convert_tokens_to_string(self, tokens):
out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
return out_string
def build_inputs_with_special_tokens(self, toks_0, toks_1=None):
sep = [self.SEP]
cls = [self.cls_token_id]
if toks_1 is None:
return toks_0 + sep + cls
return toks_0 + sep + toks_1 + sep + cls
def get_special_tokens_mask(
self,
toks_0,
toks_1=None,
has_specials=False,
):
if has_specials:
return super().get_special_tokens_mask(toks_0=toks_0, toks_1=toks_1, has_specials=True)
if toks_1 is not None:
return ([0] * len(toks_0)) + [1] + ([0] * len(toks_1)) + [1, 1]
return ([0] * len(toks_0)) + [1, 1]
def create_token_type_ids_from_sequences(self, toks_0, toks_1=None):
sep = [self.SEP]
cls_segment_id = [2]
if toks_1 is None:
return len(toks_0 + sep) * [0] + cls_segment_id
return len(toks_0 + sep) * [0] + len(toks_1 + sep) * [1] + cls_segment_id
def save_vocabulary(self, dir, pre=None):
path = os.path.join(
dir,
(pre + "-" if pre else "") + VOCAB_FS["vocab_file"],
)
if os.path.abspath(self.vocab_file) != os.path.abspath(path) and os.path.isfile(
self.vocab_file
):
copyfile(self.vocab_file, path)
elif not os.path.isfile(self.vocab_file):
with open(path, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
return (path,)
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", 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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,391
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import datetime as dt
from ..rectify import rectifier
from .author import Author
from .named import Named, Saved
class Genre(Named):
pass
class Doc(Saved, Named):
suff = '.txt'
pages = None
def __init__(self, genre=None, author=None, title=None, pages=None, **kw):
super().__init__(**kw)
if genre:
self.genre = Genre.create(name=genre)
if author:
self.author = author
if title:
self.title = title
if pages:
self.pages = pages
@property
def factor(self):
return self.genre.factor * super().factor
@property
def bias(self):
return self.genre.bias + super().bias
@property
def date(self):
s = self.name.split('/')[2]
s = '-'.join(s.split('-')[:3])
return dt.datetime.strptime(s, '%y-%m-%d').date()
@property
def props(self):
return {
'name': self.name,
'genre': self.genre.name,
'author': self.author,
'title': self.title,
}
@property
def fields(self):
s = '{}.pdf'.format(self.name)
fs = {'Date': self.date, 'Title': self.title, 'Source': s}
fs.update(Author.create(name=self.author).fields)
fs.update({'Type': self.tag, 'Genre': self.genre.name})
return fs
def from_text(self, txt, **_):
txt = tuple(rectifier(txt))
self.title = txt[0]
txt = '\n'.join(txt[2:])
self.pages = gs = []
for g in txt.split('\n\n\n'):
rs = []
for r in g.split('\n\n'):
rs.append(r.splitlines())
gs.append(rs)
def to_text(self, **_):
txt = [self.title, '']
for rs in self.pages:
for ls in rs:
txt.extend(ls)
txt.append('')
txt.append('')
return '\n'.join(txt).strip()
|
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|
33,392
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/run/ddp.py
|
import argparse
import os
import tempfile
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.optim as optim
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.distributed._tensor import DeviceMesh
from torch.distributed.tensor.parallel import PairwiseParallel, parallelize_module
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.net1 = nn.Linear(10, 32)
self.relu = nn.ReLU()
self.net2 = nn.Linear(32, 5)
def forward(self, x):
return self.net2(self.relu(self.net1(x)))
class Model2(nn.Module):
def __init__(self, d0, d1):
super(Model2, self).__init__()
self.d0 = d0
self.d1 = d1
self.net1 = torch.nn.Linear(10, 32).to(d0)
self.relu = torch.nn.ReLU()
self.net2 = torch.nn.Linear(32, 5).to(d1)
def forward(self, x):
x = x.to(self.d0)
x = self.relu(self.net1(x))
x = x.to(self.d1)
return self.net2(x)
def run(args):
r = int(os.environ["LOCAL_RANK"])
n = torch.cuda.device_count() // int(os.environ["LOCAL_WORLD_SIZE"])
ids = list(range(r * n, (r + 1) * n))
print(
f"[{os.getpid()}] run with: world_size = {dist.get_world_size()}, "
+ f"rank = {dist.get_rank()}, n = {n}, device_ids = {ids} \n",
end="",
)
id = ids[0]
if len(ids) == 1:
m = DDP(Model().cuda(id), device_ids=ids, output_device=id)
labels = torch.randn(20, 5).to(id)
else:
if args.mesh:
mesh = DeviceMesh("cuda", ids)
m = parallelize_module(Model(), mesh, PairwiseParallel())
labels = torch.randn(20, 5).to(id)
else:
m = DDP(Model2(id, ids[1]))
labels = torch.randn(20, 5).to(ids[1])
loss = nn.MSELoss()
o = optim.SGD(m.parameters(), lr=0.001)
for _ in range(args.iter_nums):
o.zero_grad()
ys = m(torch.randn(20, 10).cuda(id))
loss(ys, labels).backward()
o.step()
def run_checkpoint(local_world):
r = int(os.environ["LOCAL_RANK"])
model = Model().to(r)
ddp = DDP(model, device_ids=[r])
loss = nn.MSELoss()
optimizer = optim.SGD(ddp.parameters(), lr=0.001)
CHECKPOINT_PATH = tempfile.gettempdir() + "/model.checkpoint"
if r == 0:
torch.save(ddp.state_dict(), CHECKPOINT_PATH)
dist.barrier()
map_location = {"cuda:%d" % 0: "cuda:%d" % r}
ddp.load_state_dict(torch.load(CHECKPOINT_PATH, map_location=map_location))
optimizer.zero_grad()
ys = ddp(torch.randn(20, 10))
labels = torch.randn(20, 5).to(r)
loss(ys, labels).backward()
optimizer.step()
dist.barrier()
if r == 0:
os.remove(CHECKPOINT_PATH)
def main(args):
env = {k: os.environ[k] for k in ("MASTER_ADDR", "MASTER_PORT", "WORLD_SIZE", "RANK")}
print(f"[{os.getpid()}] init_process_group with: {env}")
dist.init_process_group(backend="nccl")
print(
f"[{os.getpid()}] main with: world_size = {dist.get_world_size()}, "
+ f"rank = {dist.get_rank()}, backend={dist.get_backend()} \n",
end="",
)
run(args)
dist.destroy_process_group()
if __name__ == "__main__":
p = argparse.ArgumentParser()
p.add_argument("--iter_nums", type=int, default=2)
p.add_argument("--mesh", action="store_true")
args = p.parse_args()
main(args)
# torchrun --standalone --nproc-per-node=gpu ddp.py
# torchrun --rdzv-id=123 --rdzv-backend=c10d --rdzv-endpoint=localhost:29402 --nnodes=1:2 --nproc-per-node=2 ddp.py
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": 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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", 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"/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,393
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/convbert.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
from .bert import Tokenizer as Bert
VOCAB_FS = {"vocab_file": "vocab.txt"}
VOCAB_MAP = {
"vocab_file": {
"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt",
"YituTech/conv-bert-medium-small": "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt",
"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt",
}
}
INPUT_CAPS = {
"YituTech/conv-bert-base": 512,
"YituTech/conv-bert-medium-small": 512,
"YituTech/conv-bert-small": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"YituTech/conv-bert-base": {"do_lower_case": True},
"YituTech/conv-bert-medium-small": {"do_lower_case": True},
"YituTech/conv-bert-small": {"do_lower_case": True},
}
class Tokenizer(Bert):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
input_caps = INPUT_CAPS
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
|
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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,394
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/dataset/xglue.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import json
import os
import datasets as ds
_XGLUE_ALL_DATA = "https://xglue.blob.core.windows.net/xglue/xglue_full_dataset.tar.gz"
_LANGS = {
"ner": ["en", "de", "es", "nl"],
"pos": ["en", "de"],
"mlqa": ["en", "de"],
"nc": ["en", "de"],
"xnli": ["en", "de"],
"paws-x": ["en", "de"],
"qadsm": ["en", "de"],
"wpr": ["en", "de"],
"qam": ["en", "de"],
"qg": ["en", "de"],
"ntg": ["en", "de"],
}
_PATHS = {
"mlqa": {
"train": os.path.join("squad1.1", "train-v1.1.json"),
"dev": os.path.join("MLQA_V1", "dev", "dev-context-{0}-question-{0}.json"),
"test": os.path.join("MLQA_V1", "test", "test-context-{0}-question-{0}.json"),
},
"xnli": {"train": "multinli.train.en.tsv", "dev": "{}.dev", "test": "{}.test"},
"paws-x": {
"train": os.path.join("en", "train.tsv"),
"dev": os.path.join("{}", "dev_2k.tsv"),
"test": os.path.join("{}", "test_2k.tsv"),
},
}
for x in ["ner", "pos"]:
_PATHS[x] = {"train": "en.train", "dev": "{}.dev", "test": "{}.test"}
for x in ["nc", "qadsm", "wpr", "qam"]:
_PATHS[x] = {
"train": "xglue." + x + ".en.train",
"dev": "xglue." + x + ".{}.dev",
"test": "xglue." + x + ".{}.test",
}
for x in ["qg", "ntg"]:
_PATHS[x] = {
"train": "xglue." + x + ".en",
"dev": "xglue." + x + ".{}",
"test": "xglue." + x + ".{}",
}
class Config(ds.BuilderConfig):
def __init__(
self,
data_dir,
citation,
url,
**kw,
):
super(Config, self).__init__(version=ds.Version("1.0.0", ""), **kw)
self.data_dir = data_dir
self.citation = citation
self.url = url
class XGlue(ds.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
Config(name="ner", data_dir="NER"),
Config(name="pos", data_dir="POS"),
Config(name="mlqa", data_dir="MLQA"),
Config(name="nc", data_dir="NC"),
Config(name="xnli", data_dir="XNLI"),
Config(name="paws-x", data_dir="PAWSX"),
Config(name="qadsm", data_dir="QADSM"),
Config(name="wpr", data_dir="WPR"),
Config(name="qam", data_dir="QAM"),
Config(name="qg", data_dir="QG"),
Config(name="ntg", data_dir="NTG"),
]
def _info(self):
if self.config.name == "ner":
features = {
"words": ds.Sequence(ds.Value("string")),
"ner": ds.Sequence(
ds.features.ClassLabel(
names=[
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
"B-MISC",
"I-MISC",
]
)
),
}
elif self.config.name == "pos":
features = {
"words": ds.Sequence(ds.Value("string")),
"pos": ds.Sequence(
ds.features.ClassLabel(
names=[
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
]
)
),
}
elif self.config.name == "mlqa":
features = {
"context": ds.Value("string"),
"question": ds.Value("string"),
"answers": ds.features.Sequence(
{"answer_start": ds.Value("int32"), "text": ds.Value("string")}
),
}
elif self.config.name == "nc":
features = {
"news_title": ds.Value("string"),
"news_body": ds.Value("string"),
"news_category": ds.ClassLabel(
names=[
"foodanddrink",
"sports",
"travel",
"finance",
"lifestyle",
"news",
"entertainment",
"health",
"video",
"autos",
]
),
}
elif self.config.name == "xnli":
features = {
"premise": ds.Value("string"),
"hypothesis": ds.Value("string"),
"label": ds.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
}
elif self.config.name == "paws-x":
features = {
"sentence1": ds.Value("string"),
"sentence2": ds.Value("string"),
"label": ds.features.ClassLabel(names=["different", "same"]),
}
elif self.config.name == "qadsm":
features = {
"query": ds.Value("string"),
"ad_title": ds.Value("string"),
"ad_description": ds.Value("string"),
"relevance_label": ds.features.ClassLabel(names=["Bad", "Good"]),
}
elif self.config.name == "wpr":
features = {
"query": ds.Value("string"),
"web_page_title": ds.Value("string"),
"web_page_snippet": ds.Value("string"),
"relavance_label": ds.features.ClassLabel(
names=["Bad", "Fair", "Good", "Excellent", "Perfect"]
),
}
elif self.config.name == "qam":
features = {
"question": ds.Value("string"),
"answer": ds.Value("string"),
"label": ds.features.ClassLabel(names=["False", "True"]),
}
elif self.config.name == "qg":
features = {
"answer_passage": ds.Value("string"),
"question": ds.Value("string"),
}
elif self.config.name == "ntg":
features = {
"news_body": ds.Value("string"),
"news_title": ds.Value("string"),
}
return ds.DatasetInfo(
description="",
citation="",
homepage="",
license="",
features=ds.Features(features),
)
def _split_generators(self, mgr):
all_data_folder = mgr.download_and_extract(_XGLUE_ALL_DATA)
data_folder = os.path.join(all_data_folder, "xglue_full_dataset", self.config.data_dir)
name = self.config.name
languages = _LANGS[name]
return (
[
ds.SplitGenerator(
name=ds.Split.TRAIN,
gen_kw={
"data_file": os.path.join(data_folder, _PATHS[name]["train"]),
"split": "train",
},
),
]
+ [
ds.SplitGenerator(
name=ds.Split(f"validation.{c}"),
gen_kw={
"data_file": os.path.join(data_folder, _PATHS[name]["dev"].format(c)),
"split": "dev",
},
)
for c in languages
]
+ [
ds.SplitGenerator(
name=ds.Split(f"test.{x}"),
gen_kw={
"data_file": os.path.join(data_folder, _PATHS[name]["test"].format(x)),
"split": "test",
},
)
for x in languages
]
)
def _generate_examples(self, data_file, split=None):
keys = list(self._info().features.keys())
if self.config.name == "mlqa":
with open(data_file, encoding="utf-8") as f:
data = json.load(f)
for examples in data["data"]:
for example in examples["paragraphs"]:
context = example["context"]
for qa in example["qas"]:
question = qa["question"]
id_ = qa["id"]
answers = qa["answers"]
answers_start = [answer["answer_start"] for answer in answers]
answers_text = [answer["text"] for answer in answers]
yield id_, {
"context": context,
"question": question,
"answers": {"answer_start": answers_start, "text": answers_text},
}
elif self.config.name in ["ner", "pos"]:
words = []
result = []
idx = -1
with open(data_file, encoding="utf-8") as f:
for line in f:
if line.strip() == "":
if len(words) > 0:
y_kw = {keys[0]: words, keys[1]: result}
words = []
result = []
idx += 1
yield idx, y_kw
else:
splits = line.strip().split(" ")
words.append(splits[0])
result.append(splits[1])
elif self.config.name in ["ntg", "qg"]:
with open(data_file + ".src." + split, encoding="utf-8") as src_f, open(
data_file + ".tgt." + split, encoding="utf-8"
) as tgt_f:
for idx, (src_line, tgt_line) in enumerate(zip(src_f, tgt_f)):
yield idx, {keys[0]: src_line.strip(), keys[1]: tgt_line.strip()}
else:
_process_dict = {
"paws-x": {"0": "different", "1": "same"},
"xnli": {"contradictory": "contradiction"},
"qam": {"0": "False", "1": "True"},
"wpr": {"0": "Bad", "1": "Fair", "2": "Good", "3": "Excellent", "4": "Perfect"},
}
def _process(value):
if self.config.name in _process_dict and value in _process_dict[self.config.name]:
return _process_dict[self.config.name][value]
return value
with open(data_file, encoding="utf-8") as f:
for idx, line in enumerate(f):
if data_file.split(".")[-1] == "tsv" and idx == 0:
continue
items = line.strip().split("\t")
yield idx, {
key: _process(value)
for key, value in zip(
keys, items[1:] if self.config.name == "paws-x" else items
)
}
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": 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"/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,395
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/base.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
class Keys:
AGENCY = 'agency'
KIND = 'kind'
NAME = 'name'
TITLE = 'title'
ATTY = 'Atty.'
DET = 'Det.'
HON = 'Hon.'
DR = 'Dr.'
MS = 'Ms.'
ATTORNEY = 'attorney'
OFFICER = 'officer'
EXPERT = 'expert'
JUDGE = 'judge'
SELF = 'self'
GAL = 'gal'
DCF = 'DCF'
COURT = 'Court'
POLICE = 'Police'
TESTAMENT = 'Testament'
LETTER = 'letter'
MESSAGE = 'message'
MOTION = 'motion'
ORDER = 'order'
REPORT = 'report'
|
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["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,396
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/ipython.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
from traitlets.config.loader import Config
from IPython.terminal.prompts import Prompts, Token
from IPython.terminal.embed import InteractiveShellEmbed
class Prompt(Prompts):
def in_prompt_tokens(self, cli=None):
return (
(Token.Prompt, 'In <'),
(Token.PromptNum, str(self.shell.execution_count)),
(Token.Prompt, '>: '),
)
def out_prompt_tokens(self):
return (
(Token.OutPrompt, 'Out<'),
(Token.OutPromptNum, str(self.shell.execution_count)),
(Token.OutPrompt, '>: '),
)
try:
get_ipython
except NameError:
nested = 0
cfg = Config()
cfg.TerminalInteractiveShell.prompts_class = Prompt
else:
print("Running nested copies of IPython.")
print("The prompts for the nested copy have been modified")
cfg = Config()
nested = 1
ipshell = InteractiveShellEmbed(
config=cfg, banner1='Entering IPython', exit_msg='Exiting IPython...')
ipshell2 = InteractiveShellEmbed(config=cfg, banner1='IPython again')
# ipshell('***Called from top level. '
# 'Hit Ctrl-D to exit interpreter and continue program.\n'
# 'Note that if you use %kill_embedded, you can fully deactivate\n'
# 'This embedded instance so it will never turn on again')
# usage:
# from .ipython import ipshell
# ipshell()
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", 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"/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", 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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,397
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/old/session.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
from datetime import datetime
import pathlib as pth
import datetime as dt
from qnarre.core.models import pt
from tensorboard.plugins import hparams
from tensorboard.plugins.hparams import summary as tb_summary
def session_for(ps, sid=None):
if ps.predict_run:
sess = eager_pred if ps.eager_mode else predict
else:
if ps.eval_only:
sess = eager_eval if ps.eager_mode else evaluate
else:
sess = eager_train if ps.eager_mode else train
sid = sid or datetime.now().strftime("%Y%m%d-%H%M%S")
return lambda *args, **kw: sess(sid, ps, *args, **kw)
TRAIN = "train"
def eager_train(sid, ps, dset_fn, model_fn, cbacks=None):
dset = dset_fn(ps, TRAIN)
# dset_test = dset_fn(ps, 'test')
model = model_fn(ps)
def step(x, y):
with tf.GradientTape() as tape:
logits = model(x)
loss = ps.losses(y, logits)
loss += sum(model.losses)
acc = ps.metrics(y, logits)
grads = tape.gradient(loss, model.trainable_variables)
ps.optimizer.apply_gradients(zip(grads, model.trainable_variables))
return loss, acc
@tf.function
def epoch():
s, loss, acc = 0, 0.0, 0.0
for x, y in dset:
s += 1
loss, acc = step(x, y)
if tf.equal(s % 10, 0):
m = ps.metrics.result()
tf.print("Step:", s, ", loss:", loss, ", acc:", m)
return loss, acc
for e in range(ps.train_epochs):
loss, acc = epoch()
print(f"Epoch {e} loss:", loss, ", acc:", acc)
def train(sid, ps, dset_fn, model_fn, cbacks=None):
ds = dset_fn(ps, TRAIN)
# with T.distribute.MirroredStrategy().scope():
mdl = model_fn(ps, compiled=True)
mp = pth.Path.cwd() / ps.dir_model / ps.model
if mp.exists() and tf.get_checkpoint_state(str(mp)):
mdl.train_on_batch(ds)
mdl.load_weights(str(mp / TRAIN))
lp = pth.Path.cwd() / ps.dir_log / ps.model
if lp.exists():
sumy = tf.create_file_writer(str(lp / TRAIN / sid))
sum_s = tb_summary.session_start_pb(hparams=ps.hparams)
cbs = cbacks or []
if lp.exists():
cbs.append(
tf.TensorBoard(
log_dir=str(lp / TRAIN / sid),
histogram_freq=1,
embeddings_freq=0,
update_freq="epoch",
)
)
cbs.append(tf.EarlyStopping(monitor="val_loss", min_delta=1e-2, patience=2, verbose=True))
if mp.exists():
cbs.append(
tf.ModelCheckpoint(
str(mp / TRAIN),
save_weights_only=True,
# save_best_only=True,
verbose=True,
)
)
ds_test = dset_fn(ps, "test")
hist = mdl.fit(ds, callbacks=cbs, epochs=ps.train_epochs, validation_data=ds_test)
print(f"History: {hist.history}")
sp = pth.Path.cwd() / ps.dir_save / ps.model
if sp.exists():
tf.export_saved_model(mdl, str(sp))
loss, acc = mdl.evaluate(ds_test)
print(f"\nEval loss, acc: {loss}, {acc}")
"""
with sumy.as_default():
e = tf.Event(summary=sum_s).SerializeToString()
tf.import_event(e)
tf.scalar('accuracy', acc, step=1, description='Accuracy')
sum_e = tb_summary.session_end_pb(hparams.api_pb2.STATUS_SUCCESS)
e = tf.Event(summary=sum_e).SerializeToString()
tf.import_event(e)
"""
def evaluate(sid, ps, dset_fn, model_fn, cbacks=None):
ds = dset_fn(ps, "test")
# with T.distribute.MirroredStrategy().scope():
p = str(pth.Path.cwd() / ps.dir_save / ps.model)
assert tf.contains_saved_model(p)
mdl = tf.load_from_saved_model(p)
loss, acc = mdl.evaluate(ds)
print(f"\nEvaluate loss, acc: {loss}, {acc}")
def predict(sid, ps, dset_fn, model_fn, cbacks=None):
ds = dset_fn(ps, "try")
# with T.distribute.MirroredStrategy().scope():
p = str(pth.Path.cwd() / ps.dir_save / ps.model)
assert tf.contains_saved_model(p)
m = tf.load_from_saved_model(p)
def train_loop(params, model_fn, dset_fn, cbacks=None):
ps = params
nus = [16, 32, 512]
drs = [0.1, 0.2]
opts = ["adam", "sgd"]
writer = tf.create_file_writer(ps.dir_log + "/train")
with writer.as_default():
s = None # _to_summary_pb(nus, drs, opts)
e = tf.Event(summary=s).SerializeToString()
tf.import_event(e)
for nu in nus:
for dr in drs:
for opt in opts:
kw = {"num_units": nu, "drop_rate": dr, "optimizer": opt}
sid = dt.datetime.now().strftime("%Y%m%d-%H%M%S")
print(f"--- Running session {sid}:", kw)
ps.update(**kw)
train_sess(ps, model_fn, dset_fn, cbacks, sid=sid)
return
"""
names = [str(i) for i in range(ps.num_classes)]
labels = [lb.numpy() for _, lb in ds_test]
def log_confusion_matrix(epoch, logs):
preds = N.argmax(model.predict(ds_test), axis=1)
cm = sklearn.metrics.confusion_matrix(labels, preds)
img = _to_image(_to_plot(cm, names))
with writer.as_default():
T.summary.image('Confusion Matrix', img, step=epoch)
cbacks = [
kcb.LambdaCallback(on_epoch_end=log_confusion_matrix),
]
def log_confusion_matrix(epoch, logs):
names = [str(i) for i in range(params.num_classes)]
labels = [lb.numpy() for _, lb in ds_test]
preds = N.argmax(model.predict(ds_test), axis=1)
cm = sklearn.metrics.confusion_matrix(labels, preds)
img = _to_image(_to_plot(cm, names))
with writer.as_default():
T.summary.image("Confusion Matrix", img, step=epoch)
def _to_plot(cm, names):
fig = plt.figure(figsize=(8, 8))
plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
plt.title("Confusion Matrix")
plt.colorbar()
ticks = N.arange(len(names))
plt.xticks(ticks, names, rotation=45)
plt.yticks(ticks, names)
cm = N.around(cm.astype('float') / cm.sum(axis=1)[:, N.newaxis],
decimals=2)
threshold = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
color = "white" if cm[i, j] > threshold else "black"
plt.text(j, i, cm[i, j], horizontalalignment="center", color=color)
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
return fig
def _to_image(fig):
buf = io.BytesIO()
plt.savefig(buf, format='png')
plt.close(fig)
buf.seek(0)
img = T.image.decode_png(buf.getvalue(), channels=4)
img = T.expand_dims(img, 0)
return img
def _to_summary_pb(num_units_list, dropout_rate_list, optimizer_list):
nus_val = struct_pb2.ListValue()
nus_val.extend(num_units_list)
drs_val = struct_pb2.ListValue()
drs_val.extend(dropout_rate_list)
opts_val = struct_pb2.ListValue()
opts_val.extend(optimizer_list)
return hparams.summary.experiment_pb(
hparam_infos=[
hparams.api_pb2.HParamInfo(name='num_units',
display_name='Number of units',
type=hparams.api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=nus_val),
hparams.api_pb2.HParamInfo(name='drop_rate',
display_name='Dropout rate',
type=hparams.api_pb2.DATA_TYPE_FLOAT64,
domain_discrete=drs_val),
hparams.api_pb2.HParamInfo(name='optimizer',
display_name='Optimizer',
type=hparams.api_pb2.DATA_TYPE_STRING,
domain_discrete=opts_val)
],
metric_infos=[
hparams.api_pb2.MetricInfo(
name=hparams.api_pb2.MetricName(tag='accuracy'),
display_name='Accuracy'),
])
def get_assignment_map_from_checkpoint(tvars, init_checkpoint):
import re
import collections as co
assignment_map = {}
initialized_variable_names = {}
name_to_variable = co.OrderedDict()
for var in tvars:
name = var.name
m = re.match("^(.*):\\d+$", name)
if m is not None:
name = m.group(1)
name_to_variable[name] = var
init_vars = T.train.list_variables(init_checkpoint)
assignment_map = co.OrderedDict()
for x in init_vars:
(name, var) = (x[0], x[1])
if name not in name_to_variable:
continue
assignment_map[name] = name
initialized_variable_names[name] = 1
initialized_variable_names[name + ":0"] = 1
return (assignment_map, initialized_variable_names)
"""
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", 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"/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,398
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/config/rag.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
from ... import core as qc
import copy
class PreTrained(qc.PreTrained):
hs = qc.Hypers(
[],
dict(
BOS=None,
dataset_split="train",
dataset="wiki_dpr",
decoder_start_token_id=None,
do_deduplication=True,
do_marginalize=False,
doc_sep=" // ",
EOS=None,
exclude_bos_score=False,
forced_eos_token_id=None,
index_name="compressed",
index_path=None,
is_composition=True,
is_enc_dec=True,
label_smoothing=0.0,
max_combined_length=300,
model_type="rag",
n_docs=5,
output_retrieved=False,
PAD=None,
passages_path=None,
prefix=None,
reduce_loss=False,
retrieval_batch_size=8,
retrieval_vector_size=768,
s_vocab=None,
title_sep=" / ",
use_dummy_dataset=False,
y_cache=True,
),
)
@classmethod
def from_pretrained(cls, *args, **kw):
kw["_fast_init"] = False
return super().from_pretrained(*args, **kw)
@classmethod
def from_pretrained_question_encoder_generator(
cls,
question_encoder_pretrained_model_name_or_path=None,
generator_pretrained_model_name_or_path=None,
retriever=None,
**kw,
):
kw_question_encoder = {
argument[len("question_encoder_") :]: value
for argument, value in kw.items()
if argument.startswith("question_encoder_")
}
kw_generator = {
argument[len("generator_") :]: value
for argument, value in kw.items()
if argument.startswith("generator_")
}
# remove question_encoder, generator kw from kw
for key in kw_question_encoder.keys():
del kw["question_encoder_" + key]
for key in kw_generator.keys():
del kw["generator_" + key]
question_encoder = kw_question_encoder.pop("model", None)
if question_encoder is None:
assert question_encoder_pretrained_model_name_or_path is not None
from ..auto.modeling_auto import AutoModel
if "config" not in kw_question_encoder:
from ..auto.configuration_auto import AutoConfig
question_encoder_config, kw_question_encoder = AutoConfig.from_pretrained(
question_encoder_pretrained_model_name_or_path,
**kw_question_encoder,
return_unused_kw=True,
)
kw_question_encoder["config"] = question_encoder_config
question_encoder = AutoModel.from_pretrained(
question_encoder_pretrained_model_name_or_path, **kw_question_encoder
)
generator = kw_generator.pop("model", None)
if generator is None:
assert generator_pretrained_model_name_or_path is not None
from ..auto.modeling_auto import AutoModelForSeq2SeqLM
if "config" not in kw_generator:
from ..auto.configuration_auto import AutoConfig
generator_config, kw_generator = AutoConfig.from_pretrained(
generator_pretrained_model_name_or_path,
**kw_generator,
return_unused_kw=True,
)
kw_generator["config"] = generator_config
generator = AutoModelForSeq2SeqLM.from_pretrained(
generator_pretrained_model_name_or_path, **kw_generator
)
# instantiate config with corresponding kw
config = kw.get("config", None)
if config is None:
config = RagConfig.from_question_encoder_generator_configs(
question_encoder.config, generator.config, **kw
)
return cls(
question_encoder=question_encoder,
generator=generator,
config=config,
retriever=retriever,
)
@classmethod
def from_question_encoder_generator_configs(
cls, question_encoder_config, generator_config, **kw
):
return cls(
question_encoder=question_encoder_config.to_dict(),
generator=generator_config.to_dict(),
**kw,
)
def to_dict(self):
y = copy.deepcopy(self.__dict__)
y["question_encoder"] = self.question_encoder.to_dict()
y["generator"] = self.generator.to_dict()
y["model_type"] = self.__class__.model_type
return y
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], 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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,399
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/config/funnel.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
from ... import core as qc
class PreTrained(qc.PreTrained):
hs = qc.Hypers(
[],
dict(
act="gelu_new",
attention_type="relative_shift",
block_repeats=None,
block_sizes=[4, 4, 4],
d_head=64,
d_inner=3072,
d_model=768,
drop_act=0.0,
drop_attn=0.1,
drop=0.1,
eps=1e-9,
init_range=0.1,
initializer_std=None,
model_type="funnel",
n_dec_lays=2,
n_heads=12,
n_pos=512,
n_typ=3,
pool_q_only=True,
pooling_type="mean",
s_vocab=30522,
separate_cls=True,
truncate_seq=True,
),
)
def _init_weights(self, module):
classname = module.__class__.__name__
if classname.find("Linear") != -1:
if getattr(module, "weight", None) is not None:
if self.config.initializer_std is None:
fan_out, fan_in = module.weight.shape
std = np.sqrt(1.0 / float(fan_in + fan_out))
else:
std = self.config.initializer_std
qc.init.normal_(module.weight, std=std)
if getattr(module, "bias", None) is not None:
qc.init.constant_(module.bias, 0.0)
elif classname == "FunnelRelMultiheadAttention":
qc.init.uniform_(module.r_w_bias, b=self.config.init_range)
qc.init.uniform_(module.r_r_bias, b=self.config.init_range)
qc.init.uniform_(module.r_kernel, b=self.config.init_range)
qc.init.uniform_(module.r_s_bias, b=self.config.init_range)
qc.init.uniform_(module.seg_embed, b=self.config.init_range)
elif classname == "FunnelEmbeddings":
std = 1.0 if self.config.initializer_std is None else self.config.initializer_std
qc.init.normal_(module.word_embeddings.weight, std=std)
if module.word_embeddings.padding_idx is not None:
module.word_embeddings.weight.data[module.padding_idx].zero_()
MAP = {
"funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/config.json",
"funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json",
"funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/config.json",
"funnel-transformer/medium-base": "https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json",
"funnel-transformer/intermediate": "https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json",
"funnel-transformer/intermediate-base": "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json",
"funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/config.json",
"funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json",
"funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json",
"funnel-transformer/xlarge-base": "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json",
}
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,400
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/ops/blocksparse/__init__.py
|
from .matmul import matmul
from .softmax import softmax
__all__ = [
"matmul",
"softmax",
]
|
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"/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,401
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/config/mbart.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import torch
from collections import OrderedDict
from ... import core as qc
class PreTrained(qc.PreTrained):
hs = qc.Hypers(
[],
dict(
act_fun="gelu",
BOS=0,
d_dec_ffn=4096,
d_enc_ffn=4096,
d_model=1024,
drop_act=0.0,
drop_attn=0.0,
drop_dec=0.0,
drop_enc=0.0,
drop_proj=0.0,
drop=0.1,
EOS=2,
forced_EOS=2,
grad_checkpoint=True,
init_std=0.02,
is_enc_dec=True,
model_type="mbart",
n_dec_heads=16,
n_dec_lays=12,
n_enc_heads=16,
n_enc_lays=12,
n_pos=1024,
PAD=1,
s_vocab=50265,
scale=False,
y_cache=True,
),
)
def _init_weights(self, module):
std = self.cfg.init_std
if isinstance(module, qc.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, qc.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def _set_grad_checkpoint(self, module, value=False):
if isinstance(module, (MBartDecoder, MBartDecoder)):
module.grad_checkpoint = value
@property
def dummy_inputs(self):
pad = self.cfg.PAD
input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad]], device=self.device)
dummy_inputs = {
"mask": input_ids.ne(pad),
"input_ids": input_ids,
}
return dummy_inputs
MAP = {
"facebook/mbart-large-cc25": dict(
add_bias_logits=False,
add_final_norm=True,
archs=["MBartForConditionalGeneration"],
id2label={"0": "LABEL_0", "1": "LABEL_1", "2": "LABEL_2"},
label2id={"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2},
max_len=1024,
n_beams=5,
n_lays=12,
normalize_embedding=True,
num_labels=3,
pre_norm=True,
s_vocab=250027,
scale=True,
static_position_embeddings=False,
task_params={"translation_en_to_ro": {"dec_START": 250020}},
y_prev=True,
),
}
class Onnx:
@property
def inputs(self):
if self.task in ["default", "seq2seq-lm"]:
y = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("mask", {0: "batch", 1: "encoder_sequence"}),
]
)
if self.use_past:
y["decoder_input_ids"] = {0: "batch"}
y["dec_m"] = {
0: "batch",
1: "past_decoder_sequence + sequence",
}
else:
y["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
y["dec_m"] = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(y, direction="inputs")
elif self.task == "causal-lm":
y = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("mask", {0: "batch", 1: "encoder_sequence"}),
]
)
if self.use_past:
n_enc_lays, _ = self.n_lays
for i in range(n_enc_lays):
y[f"prev_kv.{i}.key"] = {
0: "batch",
2: "past_sequence + sequence",
}
y[f"prev_kv.{i}.value"] = {
0: "batch",
2: "past_sequence + sequence",
}
else:
y = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("mask", {0: "batch", 1: "encoder_sequence"}),
("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
("dec_m", {0: "batch", 1: "decoder_sequence"}),
]
)
return y
@property
def outputs(self):
if self.task in ["default", "seq2seq-lm"]:
y = super().outputs
else:
y = super().outputs
if self.use_past:
n_enc_lays, _ = self.n_lays
for i in range(n_enc_lays):
y[f"present.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
y[f"present.{i}.value"] = {
0: "batch",
2: "past_sequence + sequence",
}
return y
def _generate_dummy_inputs_for_default_and_seq2seq_lm(
self,
tokenizer,
batch_size=-1,
seq_length=-1,
is_pair=False,
framework=None,
):
encoder_inputs = (
self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
tokenizer, batch_size, seq_length, is_pair, framework
)
)
decoder_seq_length = seq_length if not self.use_past else 1
decoder_inputs = (
self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
tokenizer, batch_size, decoder_seq_length, is_pair, framework
)
)
decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
y = dict(**encoder_inputs, **decoder_inputs)
if self.use_past:
batch, encoder_seq_length = y["input_ids"].shape
decoder_seq_length = y["decoder_input_ids"].shape[1]
num_encoder_attention_heads, num_decoder_attention_heads = self.n_heads
encoder_shape = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.d_model // num_encoder_attention_heads,
)
decoder_past_length = decoder_seq_length + 3
decoder_shape = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.d_model // num_decoder_attention_heads,
)
y["dec_m"] = torch.cat(
[y["dec_m"], torch.ones(batch, decoder_past_length)],
dim=1,
)
y["prev_kv"] = []
n_enc_lays, n_dec_lays = self.n_lays
min_num_layers = min(n_enc_lays, n_dec_lays)
max_num_layers = max(n_enc_lays, n_dec_lays) - min_num_layers
remaining_side_name = "encoder" if n_enc_lays > n_dec_lays else "decoder"
for _ in range(min_num_layers):
y["prev_kv"].append(
(
torch.zeros(decoder_shape),
torch.zeros(decoder_shape),
torch.zeros(encoder_shape),
torch.zeros(encoder_shape),
)
)
shape = encoder_shape if remaining_side_name == "encoder" else decoder_shape
for _ in range(min_num_layers, max_num_layers):
y["prev_kv"].append((torch.zeros(shape), torch.zeros(shape)))
return y
def _generate_dummy_inputs_for_causal_lm(
self,
tokenizer,
batch_size=-1,
seq_length=-1,
is_pair=False,
framework=None,
):
y = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
tokenizer, batch_size, seq_length, is_pair, framework
)
if self.use_past:
batch, seqlen = y["input_ids"].shape
past_key_values_length = seqlen + 2
n_enc_lays, _ = self.n_lays
num_encoder_attention_heads, _ = self.n_heads
past_shape = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.d_model // num_encoder_attention_heads,
)
y["mask"] = torch.cat([y["mask"], torch.ones(batch, past_key_values_length)], dim=1)
y["prev_kv"] = [
(torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(n_enc_lays)
]
return y
def _generate_dummy_inputs_for_sequence_classification_and_question_answering(
self,
tokenizer,
batch_size=-1,
seq_length=-1,
is_pair=False,
framework=None,
):
batch_size = compute_effective_axis_dimension(
batch_size, fixed_dimension=OnnxConfig.DEFAULT_FIXED_BATCH, num_token_to_add=0
)
token_to_add = tokenizer.num_special_tokens_to_add(is_pair)
seq_length = compute_effective_axis_dimension(
seq_length,
fixed_dimension=OnnxConfig.DEFAULT_FIXED_SEQUENCE,
num_token_to_add=token_to_add,
)
dummy_input = [" ".join([tokenizer.unk]) * seq_length] * batch_size
common_inputs = dict(tokenizer(dummy_input, return_tensors=framework))
return common_inputs
def generate_dummy_inputs(
self,
tokenizer,
batch_size=-1,
seq_length=-1,
is_pair=False,
framework=None,
):
if self.task in ["default", "seq2seq-lm"]:
y = self._generate_dummy_inputs_for_default_and_seq2seq_lm(
tokenizer,
batch_size=batch_size,
seq_length=seq_length,
is_pair=is_pair,
framework=framework,
)
elif self.task == "causal-lm":
y = self._generate_dummy_inputs_for_causal_lm(
tokenizer,
batch_size=batch_size,
seq_length=seq_length,
is_pair=is_pair,
framework=framework,
)
else:
y = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
tokenizer,
batch_size=batch_size,
seq_length=seq_length,
is_pair=is_pair,
framework=framework,
)
return y
def _flatten_past_key_values_(self, flattened_output, name, idx, t):
if self.task in ["default", "seq2seq-lm"]:
flattened_output = super()._flatten_past_key_values_(flattened_output, name, idx, t)
else:
flattened_output = super()._flatten_past_key_values_(flattened_output, name, idx, t)
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,402
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/run/swag.py
|
# Copyright 2021 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
# fine-tune for multiple choice
import logging
import random
import torch
from dataclasses import dataclass
from datasets import load_metric
from itertools import chain
from torch.utils.data import DataLoader
from transformers import default_data_collator, AutoModelForChoicepleChoice, PreTrainedTokenizerBase
from .params import TRAIN, EVAL, ALL, EACH
from .runner import Runner as Base
log = logging.getLogger(__name__)
@dataclass
class DataCollatorForChoicepleChoice:
tokenizer: PreTrainedTokenizerBase
padding = True
max_len = None
pad_to_multiple_of = None
def __call__(self, xs):
label_name = "label" if "label" in xs[0].keys() else "labels"
labels = [x.pop(label_name) for x in xs]
size = len(xs)
choices = len(xs[0]["input_ids"])
ys = [[{k: v[i] for k, v in x.items()} for i in range(choices)] for x in xs]
ys = list(chain(*ys))
ys = self.tokenizer.pad(
ys,
padding=self.padding,
max_len=self.max_len,
pad_to_multiple_of=self.pad_to_multiple_of,
return_tensors="pt",
)
ys = {k: v.view(size, choices, -1) for k, v in ys.items()}
ys["labels"] = torch.tensor(labels, dtype=torch.int64)
return ys
class Runner(Base):
AutoModel = AutoModelForChoicepleChoice
@property
def cols(self):
if self._cols is None:
ds = self.dataset
if ds[TRAIN] is not None:
cs = ds[TRAIN].column_names
else:
cs = ds[EVAL].column_names
e = [f"ending{x}" for x in range(4)]
c = "sent1"
q = "sent2"
l = "label" if "label" in cs else "labels"
self._cols = {ALL: cs, EACH: [e, c, q, l]}
return self._cols
@property
def train_ds(self):
if self._train_ds is None:
ps, mgr, ds = self.params, self.mgr, self.dataset
with mgr.main_process_first():
self._dataset = y = ds.map(
self.prep_for_train,
batched=True,
remove_columns=self.cols[ALL],
desc="Running tokenizer on dataset",
)
y = y[TRAIN]
if ps.max_train_samples is not None:
y = y.select(range(ps.max_train_samples))
for i in random.sample(range(len(y)), 3):
log.info(f"Sample {i} of the training set: {y[i]}")
self._train_ds = y
return self._train_ds
def prep_for_train(self, xs):
ps = self.params
e_col, c_col, q_col, l_col = self.cols[EACH]
firsts = [[x] * 4 for x in xs[c_col]]
qs = xs[q_col]
seconds = [[f"{q} {xs[x][i]}" for x in e_col] for i, q in enumerate(qs)]
firsts = list(chain(*firsts))
seconds = list(chain(*seconds))
ys = self.tokenizer(
firsts,
seconds,
max_len=ps.max_len,
padding=self.padding,
truncation=True,
)
ys = {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in ys.items()}
ys["labels"] = xs[l_col]
return ys
@property
def loaders(self):
if self._loaders is None:
ps, mgr = self.params, self.mgr
if ps.pad_to_max_length:
c = default_data_collator
else:
c = DataCollatorForChoicepleChoice(
self.tokenizer, pad_to_multiple_of=(8 if mgr.use_fp16 else None)
)
t = DataLoader(
self.train_ds, shuffle=True, collate_fn=c, batch_size=ps.train_batch_size
)
e = DataLoader(self.eval_ds, collate_fn=c, batch_size=ps.eval_batch_size)
self._loaders = {TRAIN: t, EVAL: e}
return self._loaders
@property
def metric(self):
if self._metric is None:
self._metric = load_metric("accuracy")
return self._metric
def eval_epoch(self, e):
m, mgr = self.model, self.mgr
m.eval()
for xs in self.loaders[EVAL]:
with torch.no_grad():
ys = m(**xs)
ys = ys.logits.argmax(dim=-1)
self.metric.add_batch(predictions=mgr.gather(ys), references=mgr.gather(xs["labels"]))
y = self.metric.compute()
mgr.print(f"epoch {e}: {y}")
def main():
x = Runner()
x.dataset
x.config
x.tokenizer
x.model
x.model.resize_token_embeddings(len(x.tokenizer))
x.loaders
x.prepare()
x.train()
x.save()
if __name__ == "__main__":
main()
"""
accelerate launch swag.py \
--model_name bert-base-uncased \
--dataset_name swag \
--out_dir /tmp/test-swag-no-trainer \
--pad_to_max_length
"""
|
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"/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], 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["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,403
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/analyzer.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
from .counter import counters
from .contain import Contains
class Analyzer:
san_args = ((('passed', '.'), ('failed', 'F')), 'Sanity:')
def check_sanity(self, src, **kw):
with counters(self.san_args, kw) as cs:
return cs
coh_args = ((('record', ''), ('purged', 'd'), ('equal', '='),
('full', '<'), ('partial', '~')), 'Coherence:')
def check_coherence(self, src, **kw):
gs = Contains()
with counters(self.coh_args, kw) as cs:
gs.grow_from(src, **kw)
mg, fg = gs.record, gs.full
for m in sorted(mg.nodes()):
if m in fg:
for m2 in fg.successors(m):
if m2 in fg and m in fg.successors(m2):
print(m, m2)
print(mg.node[m]['nominal'][:30],
mg.node[m2]['nominal'][:30])
return cs
|
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|
33,404
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/tutorials/03-matrix-multiplication.py
|
"""
Matrix Multiplication
=====================
In this tutorial, you will write a very short high-performance FP16 matrix multiplication kernel that achieves
performance on parallel with cuBLAS.
You will specifically learn about:
* Block-level matrix multiplications.
* Multi-dimensional pointer arithmetics.
* Program re-ordering for improved L2 cache hit rate.
* Automatic performance tuning.
"""
# %%
# Motivations
# -----------
#
# Matrix multiplications are a key building block of most modern high-performance computing systems.
# They are notoriously hard to optimize, hence their implementation is generally done by
# hardware vendors themselves as part of so-called "kernel libraries" (e.g., cuBLAS).
# Unfortunately, these libraries are often proprietary and cannot be easily customized
# to accommodate the needs of modern deep learning workloads (e.g., fused activation functions).
# In this tutorial, you will learn how to implement efficient matrix multiplications by
# yourself with Triton, in a way that is easy to customize and extend.
#
# Roughly speaking, the kernel that we will write will implement the following blocked
# algorithm to multiply a (M, K) by a (K, N) matrix:
#
# .. code-block:: python
#
# # Do in parallel
# for m in range(0, M, BLOCK_SIZE_M):
# # Do in parallel
# for n in range(0, N, BLOCK_SIZE_N):
# acc = zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=float32)
# for k in range(0, K, BLOCK_SIZE_K):
# a = A[m : m+BLOCK_SIZE_M, k : k+BLOCK_SIZE_K]
# b = B[k : k+BLOCK_SIZE_K, n : n+BLOCK_SIZE_N]
# acc += dot(a, b)
# C[m : m+BLOCK_SIZE_M, n : n+BLOCK_SIZE_N] = acc
#
# where each iteration of the doubly-nested for-loop is performed by a dedicated Triton program instance.
# %%
# Compute Kernel
# --------------
#
# The above algorithm is, actually, fairly straightforward to implement in Triton.
# The main difficulty comes from the computation of the memory locations at which blocks
# of :code:`A` and :code:`B` must be read in the inner loop. For that, we need
# multi-dimensional pointer arithmetics.
#
# Pointer Arithmetics
# ~~~~~~~~~~~~~~~~~~~
#
# For a row-major 2D tensor :code:`X`, the memory location of :code:`X[i, j]` is given b
# y :code:`&X[i, j] = X + i*stride_xi + j*stride_xj`.
# Therefore, blocks of pointers for :code:`A[m : m+BLOCK_SIZE_M, k:k+BLOCK_SIZE_K]` and
# :code:`B[k : k+BLOCK_SIZE_K, n : n+BLOCK_SIZE_N]` can be defined in pseudo-code as:
#
# .. code-block:: python
#
# &A[m : m+BLOCK_SIZE_M, k:k+BLOCK_SIZE_K] = a_ptr + (m : m+BLOCK_SIZE_M)[:, None]*A.stride(0) + (k : k+BLOCK_SIZE_K)[None, :]*A.stride(1);
# &B[k : k+BLOCK_SIZE_K, n:n+BLOCK_SIZE_N] = b_ptr + (k : k+BLOCK_SIZE_K)[:, None]*B.stride(0) + (n : n+BLOCK_SIZE_N)[None, :]*B.stride(1);
#
# Which means that pointers for blocks of A and B can be initialized (i.e., :code:`k=0`) in Triton as the following
# code. Also note that we need an extra modulo to handle the case where :code:`M` is not a multiple of
# :code:`BLOCK_SIZE_M` or :code:`N` is not a multiple of :code:`BLOCK_SIZE_N`, in which case we can pad the data with
# some useless values, which will not contribute to the results. For the :code:`K` dimension, we will handle that later
# using masking load semantics.
#
# .. code-block:: python
#
# offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
# offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
# offs_k = tl.arange(0, BLOCK_SIZE_K)
# a_ptrs = a_ptr + (offs_am[:, None]*stride_am + offs_k [None, :]*stride_ak)
# b_ptrs = b_ptr + (offs_k [:, None]*stride_bk + offs_bn[None, :]*stride_bn)
#
# And then updated in the inner loop as follows:
#
# .. code-block:: python
#
# a_ptrs += BLOCK_SIZE_K * stride_ak;
# b_ptrs += BLOCK_SIZE_K * stride_bk;
#
#
# L2 Cache Optimizations
# ~~~~~~~~~~~~~~~~~~~~~~
#
# As mentioned above, each program instance computes a :code:`[BLOCK_SIZE_M, BLOCK_SIZE_N]`
# block of :code:`C`.
# It is important to remember that the order in which these blocks are computed does
# matter, since it affects the L2 cache hit rate of our program. and unfortunately, a
# a simple row-major ordering
#
# .. code-block:: Python
#
# pid = triton.program_id(0);
# grid_m = (M + BLOCK_SIZE_M - 1) // BLOCK_SIZE_M;
# grid_n = (N + BLOCK_SIZE_N - 1) // BLOCK_SIZE_N;
# pid_m = pid / grid_n;
# pid_n = pid % grid_n;
#
# is just not going to cut it.
#
# One possible solution is to launch blocks in an order that promotes data reuse.
# This can be done by 'super-grouping' blocks in groups of :code:`GROUP_M` rows before
# switching to the next column:
#
# .. code-block:: python
#
# # Program ID
# pid = tl.program_id(axis=0)
# # Number of program ids along the M axis
# num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
# # Number of programs ids along the N axis
# num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
# # Number of programs in group
# num_pid_in_group = GROUP_SIZE_M * num_pid_n
# # Id of the group this program is in
# group_id = pid // num_pid_in_group
# # Row-id of the first program in the group
# first_pid_m = group_id * GROUP_SIZE_M
# # If `num_pid_m` isn't divisible by `GROUP_SIZE_M`, the last group is smaller
# group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
# # *Within groups*, programs are ordered in a column-major order
# # Row-id of the program in the *launch grid*
# pid_m = first_pid_m + (pid % group_size_m)
# # Col-id of the program in the *launch grid*
# pid_n = (pid % num_pid_in_group) // group_size_m
#
# For example, in the following matmul where each matrix is 9 blocks by 9 blocks,
# we can see that if we compute the output in row-major ordering, we need to load 90
# blocks into SRAM to compute the first 9 output blocks, but if we do it in grouped
# ordering, we only need to load 54 blocks.
#
# .. image:: grouped_vs_row_major_ordering.png
#
# In practice, this can improve the performance of our matrix multiplication kernel by
# more than 10\% on some hardware architecture (e.g., 220 to 245 TFLOPS on A100).
#
# %%
# Final Result
# ------------
import torch
import triton
import triton.language as tl
# `triton.jit`'ed functions can be auto-tuned by using the `triton.autotune` decorator, which consumes:
# - A list of `triton.Config` objects that define different configurations of
# meta-parameters (e.g., `BLOCK_SIZE_M`) and compilation options (e.g., `num_warps`) to try
# - An auto-tuning *key* whose change in values will trigger evaluation of all the
# provided configs
@triton.autotune(
configs=[
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
],
key=['M', 'N', 'K'],
)
@triton.jit
def matmul_kernel(
# Pointers to matrices
a_ptr, b_ptr, c_ptr,
# Matrix dimensions
M, N, K,
# The stride variables represent how much to increase the ptr by when moving by 1
# element in a particular dimension. E.g. `stride_am` is how much to increase `a_ptr`
# by to get the element one row down (A has M rows).
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
# Meta-parameters
BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
GROUP_SIZE_M: tl.constexpr,
ACTIVATION: tl.constexpr,
):
"""Kernel for computing the matmul C = A x B.
A has shape (M, K), B has shape (K, N) and C has shape (M, N)
"""
# -----------------------------------------------------------
# Map program ids `pid` to the block of C it should compute.
# This is done in a grouped ordering to promote L2 data reuse.
# See above `L2 Cache Optimizations` section for details.
pid = tl.program_id(axis=0)
num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
num_pid_in_group = GROUP_SIZE_M * num_pid_n
group_id = pid // num_pid_in_group
first_pid_m = group_id * GROUP_SIZE_M
group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
pid_m = first_pid_m + (pid % group_size_m)
pid_n = (pid % num_pid_in_group) // group_size_m
# ----------------------------------------------------------
# Create pointers for the first blocks of A and B.
# We will advance this pointer as we move in the K direction
# and accumulate
# `a_ptrs` is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers
# `b_ptrs` is a block of [BLOCK_SIZE_K, BLOCK_SIZE_N] pointers
# See above `Pointer Arithmetics` section for details
offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
# -----------------------------------------------------------
# Iterate to compute a block of the C matrix.
# We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block
# of fp32 values for higher accuracy.
# `accumulator` will be converted back to fp16 after the loop.
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
# Load the next block of A and B, generate a mask by checking the K dimension.
# If it is out of bounds, set it to 0.
a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
# We accumulate along the K dimension.
accumulator += tl.dot(a, b)
# Advance the ptrs to the next K block.
a_ptrs += BLOCK_SIZE_K * stride_ak
b_ptrs += BLOCK_SIZE_K * stride_bk
# You can fuse arbitrary activation functions here
# while the accumulator is still in FP32!
if ACTIVATION == "leaky_relu":
accumulator = leaky_relu(accumulator)
c = accumulator.to(tl.float16)
# -----------------------------------------------------------
# Write back the block of the output matrix C with masks.
offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
tl.store(c_ptrs, c, mask=c_mask)
# We can fuse `leaky_relu` by providing it as an `ACTIVATION` meta-parameter in `_matmul`.
@triton.jit
def leaky_relu(x):
x = x + 1
return tl.where(x >= 0, x, 0.01 * x)
# %%
# We can now create a convenience wrapper function that only takes two input tensors,
# and (1) checks any shape constraint; (2) allocates the output; (3) launches the above kernel.
def matmul(a, b, activation=""):
# Check constraints.
assert a.shape[1] == b.shape[0], "Incompatible dimensions"
assert a.is_contiguous(), "Matrix A must be contiguous"
assert b.is_contiguous(), "Matrix B must be contiguous"
M, K = a.shape
K, N = b.shape
# Allocates output.
c = torch.empty((M, N), device=a.device, dtype=a.dtype)
# 1D launch kernel where each block gets its own program.
grid = lambda META: (
triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),
)
matmul_kernel[grid](
a, b, c,
M, N, K,
a.stride(0), a.stride(1),
b.stride(0), b.stride(1),
c.stride(0), c.stride(1),
ACTIVATION=activation
)
return c
# %%
# Unit Test
# ---------
#
# We can test our custom matrix multiplication operation against a native torch implementation (i.e., cuBLAS).
torch.manual_seed(0)
a = torch.randn((512, 512), device='cuda', dtype=torch.float16)
b = torch.randn((512, 512), device='cuda', dtype=torch.float16)
triton_output = matmul(a, b)
torch_output = torch.matmul(a, b)
print(f"triton_output={triton_output}")
print(f"torch_output={torch_output}")
if torch.allclose(triton_output, torch_output, atol=1e-2, rtol=0):
print("✅ Triton and Torch match")
else:
print("❌ Triton and Torch differ")
# %%
# Benchmark
# ---------
#
# Square Matrix Performance
# ~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# We can now compare the performance of our kernel against that of cuBLAS. Here we focus on square matrices,
# but feel free to arrange this script as you wish to benchmark any other matrix shape.
@triton.testing.perf_report(
triton.testing.Benchmark(
x_names=['M', 'N', 'K'], # Argument names to use as an x-axis for the plot
x_vals=[
128 * i for i in range(2, 33)
], # Different possible values for `x_name`
line_arg='provider', # Argument name whose value corresponds to a different line in the plot
# Possible values for `line_arg`
line_vals=['cublas', 'triton'],
# Label name for the lines
line_names=["cuBLAS", "Triton"],
# Line styles
styles=[('green', '-'), ('blue', '-')],
ylabel="TFLOPS", # Label name for the y-axis
plot_name="matmul-performance", # Name for the plot, used also as a file name for saving the plot.
args={},
)
)
def benchmark(M, N, K, provider):
a = torch.randn((M, K), device='cuda', dtype=torch.float16)
b = torch.randn((K, N), device='cuda', dtype=torch.float16)
quantiles = [0.5, 0.2, 0.8]
if provider == 'cublas':
ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.matmul(a, b), quantiles=quantiles)
if provider == 'triton':
ms, min_ms, max_ms = triton.testing.do_bench(lambda: matmul(a, b), quantiles=quantiles)
perf = lambda ms: 2 * M * N * K * 1e-12 / (ms * 1e-3)
return perf(ms), perf(max_ms), perf(min_ms)
benchmark.run(show_plots=True, print_data=True)
|
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"/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,405
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/mpnet.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import collections
import os
import unicodedata
from ...tokens.utils import (
AddedToken,
PreTrainedTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
VOCAB_FS = {"vocab_file": "vocab.txt"}
VOCAB_MAP = {
"vocab_file": {
"microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt",
}
}
INPUT_CAPS = {
"microsoft/mpnet-base": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"microsoft/mpnet-base": {"do_lower_case": True},
}
def load_vocab(vocab_file):
vocab = collections.OrderedDict()
with open(vocab_file, "r", encoding="utf-8") as reader:
tokens = reader.readlines()
for index, token in enumerate(tokens):
token = token.rstrip("\n")
vocab[token] = index
return vocab
def whitespace_tokenize(text):
text = text.strip()
if not text:
return []
tokens = text.split()
return tokens
class Tokenizer(PreTrainedTokenizer):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
input_caps = INPUT_CAPS
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
do_lower_case=True,
do_basic_tokenize=True,
never_split=None,
bos="<s>",
eos="</s>",
sep="</s>",
cls="<s>",
unk="[UNK]",
pad="<pad>",
msk="<mask>",
tokenize_chinese_chars=True,
strip_accents=None,
**kw,
):
bos = AddedToken(bos, lstrip=False, rstrip=False) if isinstance(bos, str) else bos
eos = AddedToken(eos, lstrip=False, rstrip=False) if isinstance(eos, str) else eos
sep = AddedToken(sep, lstrip=False, rstrip=False) if isinstance(sep, str) else sep
cls = AddedToken(cls, lstrip=False, rstrip=False) if isinstance(cls, str) else cls
unk = AddedToken(unk, lstrip=False, rstrip=False) if isinstance(unk, str) else unk
pad = AddedToken(pad, lstrip=False, rstrip=False) if isinstance(pad, str) else pad
msk = AddedToken(msk, lstrip=True, rstrip=False) if isinstance(msk, str) else msk
super().__init__(
do_lower_case=do_lower_case,
do_basic_tokenize=do_basic_tokenize,
never_split=never_split,
bos=bos,
eos=eos,
unk=unk,
sep=sep,
cls=cls,
pad=pad,
msk=msk,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
**kw,
)
if not os.path.isfile(vocab_file):
raise ValueError(
f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained "
"model use `tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
)
self.vocab = load_vocab(vocab_file)
self.ids_to_tokens = collections.OrderedDict(
[(ids, tok) for tok, ids in self.vocab.items()]
)
self.do_basic_tokenize = do_basic_tokenize
if do_basic_tokenize:
self.basic_tokenizer = BasicTokenizer(
do_lower_case=do_lower_case,
never_split=never_split,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk=self.unk)
@property
def do_lower_case(self):
return self.basic_tokenizer.do_lower_case
@property
def s_vocab(self):
return len(self.vocab)
def get_vocab(self):
return dict(self.vocab, **self.added_tokens_encoder)
def _tokenize(self, text):
split_tokens = []
if self.do_basic_tokenize:
for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
if token in self.basic_tokenizer.never_split:
split_tokens.append(token)
else:
split_tokens += self.wordpiece_tokenizer.tokenize(token)
else:
split_tokens = self.wordpiece_tokenizer.tokenize(text)
return split_tokens
def _convert_token_to_id(self, token):
return self.vocab.get(token, self.vocab.get(self.unk))
def _convert_id_to_token(self, index):
return self.ids_to_tokens.get(index, self.unk)
def convert_tokens_to_string(self, tokens):
out_string = " ".join(tokens).replace(" ##", "").strip()
return out_string
def build_inputs_with_special_tokens(self, toks_0, toks_1=None):
if toks_1 is None:
return [self.cls_token_id] + toks_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + toks_0 + sep + sep + toks_1 + sep
def get_special_tokens_mask(
self,
toks_0,
toks_1=None,
has_specials=False,
):
if has_specials:
return super().get_special_tokens_mask(toks_0=toks_0, toks_1=toks_1, has_specials=True)
if toks_1 is None:
return [1] + ([0] * len(toks_0)) + [1]
return [1] + ([0] * len(toks_0)) + [1, 1] + ([0] * len(toks_1)) + [1]
def create_token_type_ids_from_sequences(self, toks_0, toks_1=None):
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if toks_1 is None:
return len(cls + toks_0 + sep) * [0]
return len(cls + toks_0 + sep + sep + toks_1 + sep) * [0]
def save_vocabulary(self, dir, pre=None):
index = 0
if os.path.isdir(dir):
vocab_file = os.path.join(
dir,
(pre + "-" if pre else "") + VOCAB_FS["vocab_file"],
)
else:
vocab_file = (pre + "-" if pre else "") + dir
with open(vocab_file, "w", encoding="utf-8") as writer:
for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
" Please check that the vocabulary is not corrupted!"
)
index = token_index
writer.write(token + "\n")
index += 1
return (vocab_file,)
class BasicTokenizer(object):
def __init__(
self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True, strip_accents=None
):
if never_split is None:
never_split = []
self.do_lower_case = do_lower_case
self.never_split = set(never_split)
self.tokenize_chinese_chars = tokenize_chinese_chars
self.strip_accents = strip_accents
def tokenize(self, text, never_split=None):
never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
text = self._clean_text(text)
if self.tokenize_chinese_chars:
text = self._tokenize_chinese_chars(text)
orig_tokens = whitespace_tokenize(text)
split_tokens = []
for token in orig_tokens:
if token not in never_split:
if self.do_lower_case:
token = token.lower()
if self.strip_accents is not False:
token = self._run_strip_accents(token)
elif self.strip_accents:
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token, never_split))
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
return "".join(output)
def _run_split_on_punc(self, text, never_split=None):
if never_split is not None and text in never_split:
return [text]
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
char = chars[i]
if _is_punctuation(char):
output.append([char])
start_new_word = True
else:
if start_new_word:
output.append([])
start_new_word = False
output[-1].append(char)
i += 1
return ["".join(x) for x in output]
def _tokenize_chinese_chars(self, text):
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
else:
output.append(char)
return "".join(output)
def _is_chinese_char(self, cp):
if (
(cp >= 0x4E00 and cp <= 0x9FFF)
or (cp >= 0x3400 and cp <= 0x4DBF) #
or (cp >= 0x20000 and cp <= 0x2A6DF) #
or (cp >= 0x2A700 and cp <= 0x2B73F) #
or (cp >= 0x2B740 and cp <= 0x2B81F) #
or (cp >= 0x2B820 and cp <= 0x2CEAF) #
or (cp >= 0xF900 and cp <= 0xFAFF)
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True
return False
def _clean_text(self, text):
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xFFFD or _is_control(char):
continue
if _is_whitespace(char):
output.append(" ")
else:
output.append(char)
return "".join(output)
class WordpieceTokenizer(object):
def __init__(self, vocab, unk, max_input_chars_per_word=100):
self.vocab = vocab
self.unk = unk
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk)
continue
is_bad = False
start = 0
sub_tokens = []
while start < len(chars):
end = len(chars)
cur_substr = None
while start < end:
substr = "".join(chars[start:end])
if start > 0:
substr = "##" + substr
if substr in self.vocab:
cur_substr = substr
break
end -= 1
if cur_substr is None:
is_bad = True
break
sub_tokens.append(cur_substr)
start = end
if is_bad:
output_tokens.append(self.unk)
else:
output_tokens.extend(sub_tokens)
return output_tokens
|
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"/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": 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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,406
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/config/gpt_neo.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
from ... import core as qc
class PreTrained(qc.PreTrained):
hs = qc.Hypers(
[],
dict(
act="gelu_new",
attention_types=[[["global", "local"], 12]],
BOS=50256,
d_ff=None,
d_hidden=2048,
drop_attn=0.0,
drop_embed=0.0,
drop_resid=0.0,
drop_sum_first=0.1,
EOS=50256,
eps=1e-5,
init_range=0.02,
model_type="gpt_neo",
n_heads=16,
n_lays=24,
n_pos=2048,
s_vocab=50257,
s_win=256,
sum_act=None,
sum_proj=True,
sum_type="cls_index",
sum_use_proj=True,
y_cache=True,
),
)
@staticmethod
def expand_attention_types_params(attention_types):
attentions = []
for item in attention_types:
for _ in range(item[1]):
attentions.extend(item[0])
return attentions
def _init_weights(self, module):
if isinstance(module, (qc.Linear,)):
module.weight.data.normal_(mean=0.0, std=self.config.init_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, qc.Embed):
module.weight.data.normal_(mean=0.0, std=self.config.init_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, qc.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, GPTNeoModel):
module.gradient_checkpointing = value
def custom_unfold(input, dimension, size, step):
import torch
shape = input.size()
rank = len(shape)
sizedim = shape[dimension]
low_indices = torch.arange(0, sizedim, step)
min_length = torch.div(sizedim - size, step, rounding_mode="floor") + 1
indices = torch.arange(size) + low_indices[:min_length][:, None]
s = [slice(None)] * rank
s[dimension] = indices
sliced = input[s]
perm = list(range(0, rank + 1))
perm.append(perm.pop(dimension + 1))
return sliced.permute(perm)
def custom_get_block_length_and_num_blocks(seq_length, s_win):
import torch
candidates = torch.arange(1, s_win)
remainders = torch.remainder(seq_length, candidates)
divisor_indices = remainders == 0
divisors = candidates[divisor_indices]
largest_divisor = torch.max(divisors)
return largest_divisor, torch.div(seq_length, largest_divisor, rounding_mode="floor")
GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
}
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": 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"/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,407
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/base.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import os
import re
import sys
import pathlib as pth
import collections as co
from hashlib import blake2b
def num_to_name(n):
return '{:0>3d}0'.format(n)
def digest(value):
return blake2b(value, digest_size=20).hexdigest()
def rst_def(pref, name):
return '\n.. _{0}/{1}:\n\n{1}\n{2}\n'.format(pref, name, '=' * len(name))
def rst_ref(pref, name):
return ':ref:`{}/{}`'.format(pref, name)
def lister(path, rng=(), suffs=('.png', '.jpg', '.mov')):
with os.scandir(path) as es:
for e in es:
p = pth.Path(e.path)
if p.is_file():
if p.suffix in suffs and (not rng or p.stem in rng):
yield p
elif p.is_dir():
yield from lister(p, rng, suffs)
Adr = co.namedtuple('Adr', 'display_name addr_spec')
Adr.__new__.__defaults__ = ('', )
class Adrs(co.namedtuple('Adrs', 'addresses')):
adr_pat = r'[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+'
adr_re = re.compile(r'(?aim)' + adr_pat)
@classmethod
def has_adr(cls, txt):
return bool(cls.adr_re.search(txt))
@classmethod
def from_txt(cls, txt):
s = co.OrderedDict()
txt = txt.replace(';', ',')
for c in (', MD', ', Md', ',M.D.', ' M.D.', "'", '"', '*', '&',
'esquire', 'Esquire'):
txt = txt.replace(c, ' ')
t = txt
for c in (',', '<', '[', '(', '>', ']', ')', 'mailto:'):
t = t.replace(c, ' ')
for w in t.split():
if cls.adr_re.match(w):
s[w] = None
if s:
return cls(tuple(Adr(None, a) for a in s.keys()))
return (','.join(w for w in txt.split(',') if w.strip()), None)
def camelize(txt, upper_first=True):
if upper_first:
return re.sub(r"(?:^|_)(.)", lambda m: m.group(1).upper(), txt)
else:
return txt[0].lower() + camelize(txt)[1:]
def link_class(label):
n = 'lnk_' + label
n = camelize(n[:-1] if n.endswith('_') else n)
d = dict(label=label, directed=label.endswith('ing'))
globals()[n] = t = type(n, (object, ), d)
return t
for l in ('full', 'partial'):
link_class(l)
ls = (
'audience',
'bcc',
'cc',
'date',
'from_',
'including',
'record_id',
'proximity',
'quoting',
'referring',
'replying',
'source',
'subject',
'summary',
'tags',
'title',
'to',
'topic',
)
Hdr = co.namedtuple('Hdr', ls)
Hdr.links = tuple(link_class(l) for l in Hdr._fields)
class Record:
label = 'record'
Traits = co.namedtuple('Traits', 'role background justify slug')
Traits.__new__.__defaults__ = (None, None, 0, None)
class Config:
EQ = 'eq'
LT = 'lt'
GT = 'gt'
TBD = 'TBD'
DEFAULT = 'default'
EXCLUDED = 'excluded'
ENH = '_enh'
HTML = 'html'
ATTM = 'attm'
PLAIN = 'plain'
CHAIN = 'chain'
def_from = ''
include_adrs = ()
exclude_specs = exclude_mids = ()
exclude_doms = exclude_locs = exclude_fulls = ()
ROOT = 'root'
PRIV = 'priv'
PROT = 'prot'
PUBL = 'publ'
OPEN = 'open'
subject_aliases = topic_aliases = ()
def_contacts = contact_aliases = bridge_aliases = {
None: (),
ROOT: (),
PRIV: (),
PROT: (),
PUBL: (),
OPEN: ()
}
SRC = 'src/'
DST = 'dst/'
CTXT = 'ctxt'
DOCS = 'docs'
PICS = 'pics'
RECS = 'recs'
ARCH = '/arch/'
REPO = '/repo/'
QNAR = 'qnar/'
SAFE = '/safe/'
BLOG = '/blog/'
MAIN = '/main/'
MBOX = 'mbox'
TBOX = 'tbox'
BBOX = 'bbox'
# SBOX = 'transcripts'
SBOX = 'try'
IMGS = 'imgs'
ORGS = 'orgs'
nominal_offs = book_names = ()
line_junk = line_replace = fixups = quotes = ()
alloweds = substitutes = all_traits = {}
web_templates = ''
# Base RGB FFC0C0, Hue 0, Dist 90, Lightest Pale Pastel
gray = 'e8e8e8'
green = 'B8F4B8'
lgreen = 'E4FDE4'
blue = 'B7D0EC'
lblue = 'E4EFFB'
red = 'FFC0C0'
lred = 'FFE6E6'
yellow = 'FFF4C0'
lyellow = 'FFFBE6'
right = 8
lright = right - 3
middle = 7
lmiddle = middle - 3
left = 6
lleft = left - 3
@property
def recs_src(self):
return self.SRC + self.RECS
@property
def recs_arch(self):
return self.SRC + self.RECS + self.ARCH
@property
def recs_repo(self):
return self.SRC + self.RECS + self.REPO
@property
def main_src(self):
return self.SRC + self.DOCS + self.MAIN
@property
def blog_src(self):
return self.SRC + self.DOCS + self.BLOG
@property
def priv_src(self):
return self.SRC + self.DOCS + self.SAFE
@property
def docs_src(self):
return self.SRC + self.DOCS
@property
def sbox_src(self):
return self.SRC + self.DOCS + self.REPO + self.SBOX
@property
def mbox_src(self):
return self.recs_repo + self.MBOX
@property
def tbox_src(self):
return self.recs_repo + self.TBOX
@property
def bbox_src(self):
return self.recs_repo + self.BBOX
@property
def qnar_dst(self):
return self.DST + self.QNAR
@property
def html_dst(self):
return self.DST + self.QNAR + self.HTML
@property
def attm_dst(self):
return self.DST + self.QNAR + self.ATTM
config = Config()
sys.path.insert(0, str(pth.Path.cwd()))
try:
import qnarre_settings
qnarre_settings.apply_to(config)
except ImportError as e:
print('Failed to import a qnarre_settings.py', e)
sys.path.pop(0)
def traits_for(key):
ts = config.all_traits.get(str(key), Traits())
if ts.slug:
ts = config.all_traits.get(ts.slug, Traits())
return ts
|
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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,408
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/config/megatron.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
from ... import core as qc
class PreTrained(qc.PreTrained):
hs = qc.Hypers(
[],
dict(
act="gelu",
d_ff=4096,
d_hidden=1024,
drop_attn=0.1,
drop=0.1,
eps=1e-12,
init_range=0.02,
model_type="megatron-bert",
n_heads=16,
n_lays=24,
n_pos=512,
n_typ=2,
PAD=0,
pos_type="absolute",
s_vocab=29056,
y_cache=True,
),
)
def _init_weights(self, module):
if isinstance(module, (qc.Linear, qc.Embed)):
module.weight.data.normal_(mean=0.0, std=self.config.init_range)
elif isinstance(module, qc.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, qc.Linear) and module.bias is not None:
module.bias.data.zero_()
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, MegatronBertEncoder):
module.gradient_checkpointing = value
MAP = {}
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": 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"/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", 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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], 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|
33,409
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/flash/llama.py
|
# Copyright (c) 2023, Tri Dao.
import math
import json
import re
from pathlib import Path
from collections import OrderedDict
import torch
import torch.nn.functional as F
from transformers import GPT2Config, LlamaConfig
def remap_state_dict_meta_llama(state_dict, config):
def key_mapping_layers(key):
return f'transformer.{key}' if not key.startswith('output.') else key
state_dict = OrderedDict((key_mapping_layers(k), v) for k, v in state_dict.items())
# Word embedding
def key_mapping_emb(key):
return re.sub(r'^transformer.tok_embeddings.', 'transformer.embeddings.word_embeddings.', key)
state_dict = OrderedDict((key_mapping_emb(k), v) for k, v in state_dict.items())
word_embeddings = state_dict.pop('transformer.embeddings.word_embeddings.weight')
# It's possible that vocab_size is padded to be a multiple of 8, for example.
pad_vocab_size_multiple = getattr(config, 'pad_vocab_size_multiple', 1)
vocab_size = (math.ceil(word_embeddings.shape[0] / pad_vocab_size_multiple)
* pad_vocab_size_multiple)
state_dict['transformer.embeddings.word_embeddings.weight'] = F.pad(
word_embeddings, (0, 0, 0, vocab_size - word_embeddings.shape[0])
)
if getattr(config, 'tie_word_embeddings'):
state_dict['lm_head.weight'] = state_dict['transformer.embeddings.word_embeddings.weight']
else:
output_embeddings = state_dict.pop('output.weight')
# Need to recompute vocab_size since LLaMa shards the word embeddings and output embeddings
# differently.
vocab_size = (math.ceil(output_embeddings.shape[0] / pad_vocab_size_multiple)
* pad_vocab_size_multiple)
# It's possible that vocab_size is padded to be a multiple of 8, for example.
state_dict['lm_head.weight'] = F.pad(
output_embeddings, (0, 0, 0, vocab_size - output_embeddings.shape[0])
)
# LayerNorm
def key_mapping_ln(key):
key = re.sub(r'^transformer.norm.', r'transformer.ln_f.', key)
key = re.sub(r'^transformer.layers.(\d+).attention_norm.', r'transformer.layers.\1.norm1.', key)
key = re.sub(r'^transformer.layers.(\d+).ffn_norm.', r'transformer.layers.\1.norm2.', key)
return key
state_dict = OrderedDict((key_mapping_ln(k), v) for k, v in state_dict.items())
# MLP
for l in range(config.n_layer):
w1 = state_dict.pop(f'transformer.layers.{l}.feed_forward.w1.weight')
w3 = state_dict.pop(f'transformer.layers.{l}.feed_forward.w3.weight')
# Our ordering is different
state_dict[f'transformer.layers.{l}.mlp.fc1.weight'] = torch.cat([w3, w1], dim=0)
def key_mapping_mlp(key):
return re.sub(r'^transformer.layers.(\d+).feed_forward.w2.',
r'transformer.layers.\1.mlp.fc2.', key)
state_dict = OrderedDict((key_mapping_mlp(k), v) for k, v in state_dict.items())
# Attention
for l in range(config.n_layer):
Wq = state_dict.pop(f'transformer.layers.{l}.attention.wq.weight')
Wk = state_dict.pop(f'transformer.layers.{l}.attention.wk.weight')
Wv = state_dict.pop(f'transformer.layers.{l}.attention.wv.weight')
state_dict[f'transformer.layers.{l}.mixer.Wqkv.weight'] = torch.cat([Wq, Wk, Wv], dim=0)
# We don't store these
state_dict.pop(f'transformer.layers.{l}.attention.inner_attention.rope.freqs', None)
def key_mapping_attn(key):
return re.sub(r'^transformer.layers.(\d+).attention.wo.',
r'transformer.layers.\1.mixer.out_proj.', key)
state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items())
return state_dict
def config_from_checkpoint(checkpoint_path: str, model_name: str) -> LlamaConfig:
"""Load a LlamaConfig from a checkpoint path."""
with open(Path(checkpoint_path) / model_name / 'params.json') as f:
params = json.load(f)
config = LlamaConfig(hidden_size=params['dim'], intermediate_size=None,
num_attention_heads=params['n_heads'],
num_hidden_layers=params['n_layers'],
rms_norm_eps=params['norm_eps'])
return config
def state_dicts_from_checkpoint(checkpoint_path: str, model_name: str) -> dict:
# Need to sort, otherwise we mess up the ordering and the weights are wrong
return [torch.load(path, map_location='cpu')
for path in sorted((Path(checkpoint_path) / model_name).glob('consolidated.*.pth'))]
def llama_config_to_gpt2_config(llama_config: LlamaConfig) -> GPT2Config:
return GPT2Config(
vocab_size=llama_config.vocab_size,
n_positions=0, # No absolute position embedding
n_embd=llama_config.hidden_size,
n_layer=llama_config.num_hidden_layers,
n_head=llama_config.num_attention_heads,
n_inner=llama_config.intermediate_size,
activation_function='swiglu', # Hardcode since HF calls it 'silu'
# Llama doesn't have dropout, idk if it's because they only release the inference code
resid_pdrop=0.0,
embd_pdrop=0.0,
attn_pdrop=0.0,
layer_norm_epsilon=llama_config.rms_norm_eps,
initializer_range=llama_config.initializer_range,
bos_token_id=llama_config.bos_token_id,
eos_token_id=llama_config.eos_token_id,
# These are new arguments not in the original GPT2Config
pad_token_id=llama_config.pad_token_id, # Idk if this does anything
rms_norm=True,
rotary_emb_fraction=1.0,
rotary_emb_interleaved=True,
tie_word_embeddings=False,
qkv_proj_bias=False,
out_proj_bias=False,
mlp_fc1_bias=False,
mlp_fc2_bias=False,
)
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], 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|
33,410
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/ops/matmul_perf_model.py
|
import heapq
import torch
import triton
import triton._C.libtriton.triton as _triton
from triton.runtime import driver
from triton.testing import get_dram_gbps, get_max_simd_tflops, get_max_tensorcore_tflops
def get_tensorcore_tflops(backend, device, num_ctas, num_warps, dtype):
''' return compute throughput in TOPS '''
total_warps = num_ctas * min(num_warps, 4)
num_subcores = driver.utils.get_device_properties(device)["multiprocessor_count"] * 4 # on recent GPUs
tflops = min(num_subcores, total_warps) / num_subcores * get_max_tensorcore_tflops(dtype, backend, device)
return tflops
def get_simd_tflops(backend, device, num_ctas, num_warps, dtype):
''' return compute throughput in TOPS '''
total_warps = num_ctas * min(num_warps, 4)
num_subcores = driver.utils.get_device_properties(device)["multiprocessor_count"] * 4 # on recent GPUs
tflops = min(num_subcores, total_warps) / num_subcores * get_max_simd_tflops(dtype, backend, device)
return tflops
def get_tflops(backend, device, num_ctas, num_warps, dtype):
capability = torch.cuda.get_device_capability(device)
if capability[0] < 8 and dtype == torch.float32:
return get_simd_tflops(backend, device, num_ctas, num_warps, dtype)
return get_tensorcore_tflops(backend, device, num_ctas, num_warps, dtype)
def estimate_matmul_time(
# backend, device,
num_warps, num_stages,
A, B, C,
M, N, K,
BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K,
debug=False, **kwargs
):
''' return estimated running time in ms
= max(compute, loading) + store '''
backend = _triton.runtime.backend.CUDA
device = torch.cuda.current_device()
dtype = A.dtype
dtsize = A.element_size()
num_cta_m = triton.cdiv(M, BLOCK_M)
num_cta_n = triton.cdiv(N, BLOCK_N)
num_cta_k = SPLIT_K
num_ctas = num_cta_m * num_cta_n * num_cta_k
# If the input is smaller than the block size
M, N = max(M, BLOCK_M), max(N, BLOCK_N)
# time to compute
total_ops = 2 * M * N * K / (1024 * 1024 * 1024) # GOPS
tput = get_tflops(backend, device, num_ctas, num_warps, dtype)
compute_ms = total_ops / tput
# time to load data
num_sm = driver.utils.get_device_properties(device)["multiprocessor_count"]
active_cta_ratio = min(1, num_ctas / num_sm)
active_cta_ratio_bw1 = min(1, num_ctas / 32) # 32 active ctas are enough to saturate
active_cta_ratio_bw2 = max(min(1, (num_ctas - 32) / (108 - 32)), 0) # 32-108, remaining 5%
dram_bw = get_dram_gbps(backend, device) * (active_cta_ratio_bw1 * 0.95 + active_cta_ratio_bw2 * 0.05) # in GB/s
l2_bw = dram_bw * 4 # rough estimation (should be 4.7 for A100?)
# assume 80% of (following) loads are in L2 cache
load_a_dram = M * K * dtsize * (1 + 0.2 * (num_cta_n - 1))
load_a_l2 = M * K * dtsize * 0.8 * (num_cta_n - 1)
load_b_dram = N * K * dtsize * (1 + 0.2 * (num_cta_m - 1))
load_b_l2 = N * K * dtsize * 0.8 * (num_cta_m - 1)
# total
total_dram = (load_a_dram + load_b_dram) / (1024 * 1024) # MB
total_l2 = (load_a_l2 + load_b_l2) / (1024 * 1024)
# loading time in ms
load_ms = total_dram / dram_bw + total_l2 / l2_bw
# estimate storing time
store_bw = dram_bw * 0.6 # :o
store_c_dram = M * N * dtsize * SPLIT_K / (1024 * 1024) # MB
if SPLIT_K == 1:
store_ms = store_c_dram / store_bw
else:
reduce_bw = store_bw
store_ms = store_c_dram / reduce_bw
# c.zero_()
zero_ms = M * N * 2 / (1024 * 1024) / store_bw
store_ms += zero_ms
total_time_ms = max(compute_ms, load_ms) + store_ms
if debug:
print(f'Total time: {total_time_ms}ms, compute time: {compute_ms}ms, '
f'loading time: {load_ms}ms, store time: {store_ms}ms, '
f'Activate CTAs: {active_cta_ratio*100}%')
return total_time_ms
def early_config_prune(configs, named_args):
device = torch.cuda.current_device()
capability = torch.cuda.get_device_capability()
# BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps, num_stages
dtsize = named_args['A'].element_size()
dtype = named_args['A'].dtype
# 1. make sure we have enough smem
pruned_configs = []
for config in configs:
kw = config.kwargs
BLOCK_M, BLOCK_N, BLOCK_K, num_stages = \
kw['BLOCK_M'], kw['BLOCK_N'], kw['BLOCK_K'], config.num_stages
max_shared_memory = driver.utils.get_device_properties(device)["max_shared_mem"]
required_shared_memory = (BLOCK_M + BLOCK_N) * BLOCK_K * num_stages * dtsize
if required_shared_memory <= max_shared_memory:
pruned_configs.append(config)
configs = pruned_configs
# Some dtypes do not allow atomic_add
if dtype not in [torch.float16, torch.float32]:
configs = [config for config in configs if config.kwargs['SPLIT_K'] == 1]
# group configs by (BLOCK_M,_N,_K, SPLIT_K, num_warps)
configs_map = {}
for config in configs:
kw = config.kwargs
BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps, num_stages = \
kw['BLOCK_M'], kw['BLOCK_N'], kw['BLOCK_K'], kw['SPLIT_K'], config.num_warps, config.num_stages
key = (BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps)
if key in configs_map:
configs_map[key].append((config, num_stages))
else:
configs_map[key] = [(config, num_stages)]
pruned_configs = []
for k, v in configs_map.items():
BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps = k
if capability[0] >= 8:
# compute cycles (only works for ampere GPUs)
mmas = BLOCK_M * BLOCK_N * BLOCK_K / (16 * 8 * 16)
mma_cycles = mmas / min(4, num_warps) * 8
ldgsts_latency = 300 # Does this matter?
optimal_num_stages = ldgsts_latency / mma_cycles
# nearest stages, prefer large #stages
nearest = heapq.nsmallest(2, v, key=lambda x: 10 + abs(x[1] - optimal_num_stages)
if (x[1] - optimal_num_stages) < 0 else x[1] - optimal_num_stages)
for n in nearest:
pruned_configs.append(n[0])
else: # Volta & Turing only supports num_stages <= 2
random_config = v[0][0]
random_config.num_stages = 2
pruned_configs.append(random_config)
return pruned_configs
|
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"/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,411
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/prophetnet.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import copy
import math
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import functional as F
from transformers.utils import logging
from .. import core as qc
from ..core import utils as qu
from ..core import output as qo
from ..core import attention as qa
from ..core.embed import Embed
from ..core.mlp import Classifier, MLP, Predictor, Pool
from ..prep.config.bert import PreTrained
from dataclasses import dataclass
from ...modeling_utils import PreTrained
log = logging.get_logger(__name__)
LIST = [
"microsoft/prophetnet-large-uncased",
]
def softmax(hidden_state, dim, onnx_trace=False):
if onnx_trace:
return F.softmax(hidden_state.float(), dim=dim)
else:
return F.softmax(hidden_state, dim=dim, dtype=torch.float32)
def ngram_attention_bias(sequence_length, ngram, device, dtype):
left_block = torch.ones(
(ngram, sequence_length, sequence_length), device=device, dtype=dtype
) * float("-inf")
right_block = left_block.detach().clone()
# create bias
for stream_idx in range(ngram):
right_block[stream_idx].fill_diagonal_(0, wrap=False)
left_block[stream_idx].triu_(-stream_idx + 1)
left_block[:, :, 0] = 0
return torch.cat([left_block, right_block], dim=2)
def compute_relative_buckets(num_buckets, max_distance, relative_positions, is_bidirectional=False):
inv_relative_positions = -relative_positions
rel_positions_bucket = 0
if is_bidirectional:
num_buckets = num_buckets // 2
rel_positions_bucket = (
rel_positions_bucket
+ torch.lt(inv_relative_positions, torch.zeros_like(inv_relative_positions)).int()
* num_buckets
)
inv_relative_positions = torch.abs(inv_relative_positions)
else:
inv_relative_positions = torch.max(
inv_relative_positions, torch.zeros_like(inv_relative_positions)
)
max_exact = num_buckets // 2
is_small = torch.lt(inv_relative_positions, max_exact)
val_if_large = max_exact + torch.log(inv_relative_positions.float() / max_exact) / math.log(
max_distance / max_exact
) * (num_buckets - max_exact)
val_if_large = torch.min(val_if_large, torch.ones_like(val_if_large) * (num_buckets - 1)).int()
rel_positions_bucket = rel_positions_bucket + torch.where(
is_small, inv_relative_positions.int(), val_if_large
)
return rel_positions_bucket
def compute_all_stream_relative_buckets(num_buckets, max_distance, position_ids):
# main stream
main_stream_relative_positions = position_ids.unsqueeze(1).repeat(1, position_ids.size(-1), 1)
main_stream_relative_positions = main_stream_relative_positions - position_ids.unsqueeze(-1)
# predicting stream
predicting_stream_relative_positions = torch.cat(
(position_ids - 1, position_ids), dim=-1
).unsqueeze(1)
predicting_stream_relative_positions = predicting_stream_relative_positions.repeat(
1, position_ids.size(-1), 1
)
predicting_stream_relative_positions = (
predicting_stream_relative_positions - position_ids.unsqueeze(-1)
)
# get both position buckets
main_relative_position_buckets = compute_relative_buckets(
num_buckets, max_distance, main_stream_relative_positions, is_bidirectional=False
)
predict_relative_position_buckets = compute_relative_buckets(
num_buckets, max_distance, predicting_stream_relative_positions, is_bidirectional=False
)
return main_relative_position_buckets, predict_relative_position_buckets
@dataclass
class ProphetNetSeq2SeqLMOutput(ModelOutput):
loss = None
logits = None
logits_ngram = None
caches = None
hiddens = None
decoder_ngram_hidden_states = None
attns = None
decoder_ngram_attentions = None
crosses = None
enc_y = None
enc_hiddens = None
enc_attns = None
@dataclass
class ProphetNetSeq2SeqModelOutput(ModelOutput):
y
last_hidden_state_ngram = None
caches = None
hiddens = None
decoder_ngram_hidden_states = None
attns = None
decoder_ngram_attentions = None
crosses = None
enc_y = None
enc_hiddens = None
enc_attns = None
@dataclass
class ProphetNetDecoderModelOutput(ModelOutput):
y
last_hidden_state_ngram = None
caches = None
hiddens = None
hidden_states_ngram = None
attns = None
ngram_attentions = None
crosses = None
@dataclass
class ProphetNetDecoderLMOutput(ModelOutput):
loss = None
logits = None
logits_ngram = None
caches = None
hiddens = None
hidden_states_ngram = None
attns = None
ngram_attentions = None
crosses = None
class ProphetNetPositionalEmbeddings(qc.Embed):
def __init__(self, config):
self.max_length = config.n_pos
super().__init__(config.n_pos, config.d_model, config.PAD)
def forward(self, inputs_shape, device, attention_mask=None, caches=None, position_ids=None):
assert (position_ids is None) or (self.padding_idx is None)
if position_ids is None:
if caches is not None:
prev_num_input_ids = caches[0][0].shape[2]
num_input_ids = inputs_shape[1] + prev_num_input_ids
position_ids = torch.ones((1, 1), dtype=torch.long, device=device) * (
int(self.padding_idx + num_input_ids)
)
else:
if attention_mask is None:
attention_mask = torch.ones(inputs_shape, dtype=torch.long, device=device)
position_ids = (
torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask
).long() + self.padding_idx
position_ids = position_ids.clamp(0, self.max_length - 1)
return super().forward(position_ids), position_ids
def _forward(self, position_ids):
return super().forward(position_ids)
class Attention(qc.Module):
def __init__(
self,
config,
num_attn_heads,
):
super().__init__()
d_model = config.d_model
self.drop_attn = config.drop_attn
self.drop = config.drop
self.num_attn_heads = num_attn_heads
self.head_dim = d_model // num_attn_heads
assert self.head_dim * num_attn_heads == d_model
self.key_proj = qc.Linear(d_model, d_model)
self.value_proj = qc.Linear(d_model, d_model)
self.query_proj = qc.Linear(d_model, d_model)
self.out_proj = qc.Linear(d_model, d_model)
def _shape(self, tensor, seq_len, bsz):
return (
tensor.view(bsz, seq_len, self.num_attn_heads, self.head_dim)
.transpose(1, 2)
.contiguous()
)
def forward(
self,
hiddens,
key_value_states=None,
attention_mask=None,
layer_head_mask=None,
past_key_value=None,
output_attentions=False,
):
batch_size, tgt_len, d_model = hiddens.size()
is_cross_attention = key_value_states is not None
assert list(hiddens.size()) == [
batch_size,
tgt_len,
d_model,
]
query_states = self.query_proj(hiddens) / (self.head_dim**0.5)
if is_cross_attention and past_key_value is not None:
# reuse k,v, crosses
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# crosses
key_states = self._shape(self.key_proj(key_value_states), -1, batch_size)
value_states = self._shape(self.value_proj(key_value_states), -1, batch_size)
else:
# self_attention
key_states = self._shape(self.key_proj(hiddens), -1, batch_size)
value_states = self._shape(self.value_proj(hiddens), -1, batch_size)
if is_cross_attention:
past_key_value = (key_states, value_states)
proj_shape = (batch_size * self.num_attn_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, batch_size).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
assert attn_weights.size() == (
batch_size * self.num_attn_heads,
tgt_len,
src_len,
)
if attention_mask is not None and attention_mask.dim() == 0:
attention_mask = None
assert attention_mask is None or attention_mask.size() == (
self.num_attn_heads * batch_size,
1,
src_len,
)
if attention_mask is not None: # don't attend to padding symbols
attn_weights = attn_weights + attention_mask
if output_attentions:
attn_weights_reshaped = attn_weights.view(
batch_size, self.num_attn_heads, tgt_len, src_len
)
attn_weights = attn_weights_reshaped.view(
batch_size * self.num_attn_heads, tgt_len, src_len
)
else:
attn_weights_reshaped = None
attn_weights = F.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
assert layer_head_mask.size() == (self.num_attn_heads,)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(
batch_size, self.num_attn_heads, tgt_len, src_len
)
attn_weights = attn_weights.view(batch_size * self.num_attn_heads, tgt_len, src_len)
attn_weights_reshaped = layer_head_mask.view(1, -1, 1, 1) * attn_weights_reshaped
attn_probs = F.drop(
attn_weights,
p=self.drop_attn,
training=self.training,
)
attn_output = torch.bmm(attn_probs, value_states)
assert attn_output.size() == (
batch_size * self.num_attn_heads,
tgt_len,
self.head_dim,
)
attn_output = (
attn_output.view(batch_size, self.num_attn_heads, tgt_len, self.head_dim)
.transpose(1, 2)
.reshape(batch_size, tgt_len, d_model)
)
attn_output = self.out_proj(attn_output)
attn_output = F.drop(attn_output, p=self.drop, training=self.training)
return attn_output, attn_weights_reshaped, past_key_value
class ProphetNetFeedForward(qc.Module):
def __init__(self, config, ffn_dim):
super().__init__()
self.act = qu.activation(config.act)
self.intermediate = qc.Linear(config.d_model, ffn_dim)
self.output = qc.Linear(ffn_dim, config.d_model)
self.drop_act = config.drop_act
self.drop = config.drop
def forward(self, x):
y = self.intermediate(x)
y = self.act(y)
y = F.drop(y, p=self.drop_act, training=self.training)
y = self.output(y)
y = F.drop(y, p=self.drop, training=self.training)
return y
class ProphetNetNgramSelfAttention(qc.Module):
def __init__(self, config):
super().__init__()
self.d_model = config.d_model
self.num_buckets = config.num_buckets
self.relative_max_distance = config.relative_max_distance
self.num_attn_heads = config.num_decoder_attention_heads
self.drop = config.drop
self.drop_attn = config.drop_attn
self.head_dim = config.d_model // self.num_attn_heads
self.ngram = config.ngram
assert self.head_dim * self.num_attn_heads == config.d_model
self.key_proj = qc.Linear(config.d_model, config.d_model)
self.value_proj = qc.Linear(config.d_model, config.d_model)
self.query_proj = qc.Linear(config.d_model, config.d_model)
self.out_proj = qc.Linear(config.d_model, config.d_model)
self.relative_pos_embeddings = qc.Linear(
config.d_model, self.num_buckets * self.num_attn_heads
)
self.onnx_trace = False
def _shape(self, tensor, seq_len, batch_size):
return (
tensor.view(batch_size, seq_len, self.num_attn_heads, self.head_dim)
.transpose(1, 2)
.contiguous()
)
def forward(
self,
hiddens,
past_key_value=None,
attention_mask=None,
layer_head_mask=None,
extended_predict_attention_mask=None,
main_relative_position_buckets=None,
predict_relative_position_buckets=None,
position_ids=None,
):
batch_size, ngram_sequence_length, d_model = hiddens.size()
assert list(hiddens.size()) == [
batch_size,
ngram_sequence_length,
d_model,
]
# project
query_states = self.query_proj(hiddens)
key_states = self.key_proj(hiddens)
value_states = self.value_proj(hiddens)
# normalize
query_states = query_states / (self.head_dim**0.5)
# reshape
query_states = self._shape(query_states, ngram_sequence_length, batch_size)
key_states = self._shape(key_states, -1, batch_size)
value_states = self._shape(value_states, -1, batch_size)
proj_shape = (batch_size * self.num_attn_heads, -1, self.head_dim)
query_states = query_states.view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
# chunk into main stream and predict stream
hidden_states_list = hiddens.chunk(1 + self.ngram, dim=1)
query_states_list = query_states.chunk(1 + self.ngram, dim=1)
key_states_list = key_states.chunk(1 + self.ngram, dim=1)
value_states_list = value_states.chunk(1 + self.ngram, dim=1)
main_hidden_states, hidden_states_predict_list = (
hidden_states_list[0],
hidden_states_list[1:],
)
main_query_states, predict_query_states_list = query_states_list[0], query_states_list[1:]
main_key_states, predict_key_states_list = key_states_list[0], key_states_list[1:]
main_value_states, predict_value_states_list = value_states_list[0], value_states_list[1:]
# saved states are stored with shape (batch_size, num_attn_heads, seq_len, head_dim)
if past_key_value is not None:
prev_main_key_states = past_key_value[0].view(
batch_size * self.num_attn_heads, -1, self.head_dim
)
main_key_states = torch.cat((prev_main_key_states, main_key_states), dim=1)
prev_main_value_states = past_key_value[1].view(
batch_size * self.num_attn_heads, -1, self.head_dim
)
main_value_states = torch.cat((prev_main_value_states, main_value_states), dim=1)
# Update cache
past_key_value = (
main_key_states.view(batch_size, self.num_attn_heads, -1, self.head_dim),
main_value_states.view(batch_size, self.num_attn_heads, -1, self.head_dim),
)
# get seq_length of main stream only
sequence_length = ngram_sequence_length // (1 + self.ngram)
# MAIN-STREAM
# main attn weights
main_attn_weights = torch.bmm(main_query_states, main_key_states.transpose(1, 2))
main_relative_pos_embeddings = self.get_main_relative_pos_embeddings(
main_hidden_states, main_attn_weights, position_ids, main_relative_position_buckets
)
main_attn_weights = main_attn_weights + main_relative_pos_embeddings
if attention_mask is not None:
main_attn_weights = main_attn_weights + attention_mask
main_attn_probs = softmax(
main_attn_weights,
dim=-1,
onnx_trace=self.onnx_trace,
).type_as(main_attn_weights)
if layer_head_mask is not None:
assert layer_head_mask.size() == (self.num_attn_heads,)
main_attn_probs = layer_head_mask.view(1, -1, 1, 1) * main_attn_probs.view(
batch_size, self.num_attn_heads, -1, sequence_length
)
main_attn_probs = main_attn_probs.view(
batch_size * self.num_attn_heads, -1, sequence_length
)
main_attn_probs = F.drop(main_attn_probs, p=self.drop_attn, training=self.training)
# project to attn_output
main_attn_output = torch.bmm(main_attn_probs, main_value_states)
# reshape so that n_heads dim is merged into last `head_dim` axis
main_attn_output = (
main_attn_output.view(batch_size, self.num_attn_heads, sequence_length, self.head_dim)
.transpose(1, 2)
.reshape(batch_size, 1, sequence_length, d_model)
)
main_attn_output = self.out_proj(main_attn_output)
# PREDICT-STREAM
# [ngram, B*head, T, c]
predict_query_states = torch.cat(predict_query_states_list, 0).view(
self.ngram, -1, sequence_length, self.head_dim
)
# [ngram, B*head, 2*T, c]
predict_key_states = torch.cat(
[torch.cat([main_key_states, key], 1).unsqueeze(0) for key in predict_key_states_list],
0,
)
# [ngram, T, B, C]
predict_hidden_states = torch.cat(hidden_states_predict_list, 0).view(
self.ngram, sequence_length, batch_size, d_model
)
# [ngram, B*head, 2*T, c]
predict_value_states = torch.cat(
[
torch.cat([main_value_states, v_p], 1).unsqueeze(0)
for v_p in predict_value_states_list
],
0,
)
# [ngram, B*head, T, 2*T]
predict_attn_weights = torch.einsum(
"nbtc,nbsc->nbts", (predict_query_states, predict_key_states)
)
predict_relative_pos_embeddings = self.get_predict_relative_pos_embeddings(
predict_hidden_states,
predict_attn_weights,
position_ids,
predict_relative_position_buckets,
)
# [ngram, B*head, T, 2*T]
predict_attn_weights = predict_attn_weights + predict_relative_pos_embeddings
if extended_predict_attention_mask is not None:
predict_attn_weights = predict_attn_weights + extended_predict_attention_mask.to(
predict_attn_weights.dtype
)
predict_attn_probs = softmax(
predict_attn_weights,
dim=-1,
onnx_trace=self.onnx_trace,
).type_as(predict_attn_weights)
if layer_head_mask is not None:
assert layer_head_mask.size() == (self.num_attn_heads,)
predict_attn_probs = layer_head_mask.view(1, 1, -1, 1, 1) * predict_attn_probs.view(
self.ngram, batch_size, self.num_attn_heads, sequence_length, 2 * sequence_length
)
predict_attn_probs = predict_attn_probs.view(
self.ngram, batch_size * self.num_attn_heads, sequence_length, 2 * sequence_length
)
predict_attn_probs = F.drop(predict_attn_probs, p=self.drop_attn, training=self.training)
# project to attention output
# [ngram, B*head, T, c]
predict_attn_output = torch.einsum(
"nbts,nbsc->nbtc", (predict_attn_probs, predict_value_states)
)
# reshape so that n_heads dim is merged into last `head_dim` axis
# [ngram, B, T, C]
predict_attn_output = (
predict_attn_output.view(
self.ngram, batch_size, self.num_attn_heads, sequence_length, self.head_dim
)
.permute(1, 0, 3, 2, 4)
.reshape(batch_size, self.ngram, sequence_length, d_model)
)
predict_attn_output = self.out_proj(predict_attn_output)
# concat to single attn output
# [B, 1+ngram*T, C]
attn_output = torch.cat([main_attn_output, predict_attn_output], 1).view(
batch_size, -1, d_model
)
# reshape into better form for `config.output_attentions`
main_attn_probs = main_attn_probs.view(batch_size, self.num_attn_heads, sequence_length, -1)
predict_attn_probs = predict_attn_probs.view(
self.ngram, batch_size, self.num_attn_heads, sequence_length, -1
).transpose(0, 1)
attn_output = F.drop(attn_output, p=self.drop, training=self.training)
return attn_output, main_attn_probs, predict_attn_probs, past_key_value
def get_main_relative_pos_embeddings(
self, hiddens, attn_weights, position_ids, main_relative_position_buckets
):
# input hiddens [B,T,C], input attn_weights [T*head,T,S], input position_ids [B,T] or [1,1]
if main_relative_position_buckets is None:
batch_size, sequence_length = hiddens.shape[:2]
relative_positions = (
torch.arange(1, attn_weights.shape[-1] + 1)
.unsqueeze(0)
.unsqueeze(0)
.repeat(batch_size, sequence_length, 1)
.to(position_ids.device)
)
relative_positions = relative_positions - position_ids.unsqueeze(0).repeat(
batch_size, sequence_length, 1
) # [B, T, s]
main_relative_position_buckets = compute_relative_buckets(
self.num_buckets, self.relative_max_distance, relative_positions, False
)
rel_pos_embeddings = self.relative_pos_embeddings(hiddens) # [B,T,Buckets*head]
rel_pos_embeddings = rel_pos_embeddings.view(
rel_pos_embeddings.shape[:2] + (self.num_buckets, self.num_attn_heads)
).permute(
0, 3, 1, 2
) # [B,T,Buckets,head]
rel_pos_embeddings = rel_pos_embeddings.reshape(
attn_weights.shape[:2] + (-1,)
) # [B*head,T,Buckets]
main_relative_position_buckets = (
main_relative_position_buckets.repeat(1, self.num_attn_heads, 1)
.view(-1, main_relative_position_buckets.shape[-1])
.long()
) # [B*head*T, T]
rel_pos_embeddings = rel_pos_embeddings.reshape(
-1, rel_pos_embeddings.size(-1)
) # [B*head*T,Buckets]
main_relative_pos_embeddings = torch.gather(
rel_pos_embeddings, dim=1, index=main_relative_position_buckets
).view(attn_weights.shape[:2] + (-1,))
return main_relative_pos_embeddings
def get_predict_relative_pos_embeddings(
self, hiddens, attn_weights, position_ids, predict_relative_position_buckets
):
# input hiddens [ngram, T,B,C], input attn_weights [ngram, B*head,T,S], input position_ids [B,T] or [1,1], input predict_relative_position_buckets [B,T, 2*T] or None
sequence_length, batch_size = hiddens.shape[1:3]
if predict_relative_position_buckets is None:
key_sequence_length = attn_weights.shape[-1]
assert (
position_ids[0][0] == key_sequence_length - 1
), "`position_ids` are incorrect. They should be of the format 1 2 3 4 5 ... (key_sequence_length - 1)"
relative_positions = (
torch.arange(0, key_sequence_length)
.unsqueeze(0)
.unsqueeze(0)
.repeat(batch_size, sequence_length, 1)
.to(position_ids.device)
)
relative_positions = relative_positions - position_ids.unsqueeze(0).repeat(
batch_size, sequence_length, 1
)
predict_relative_position_buckets = compute_relative_buckets(
self.num_buckets, self.relative_max_distance, relative_positions, False
)
hiddens = hiddens.transpose(1, 2) # [ngram, B, T, C]
rel_pos_embeddings = self.relative_pos_embeddings(hiddens).view(
hiddens.shape[:-1] + (self.num_buckets, self.num_attn_heads)
) # [ngram, B, T, bucket, head]
rel_pos_embeddings = rel_pos_embeddings.permute(0, 1, 4, 2, 3).reshape(
self.ngram * batch_size * self.num_attn_heads, sequence_length, -1
) # [ngram*B*head, T, bucket]
predict_relative_position_buckets = predict_relative_position_buckets.unsqueeze(0).repeat(
self.ngram, 1, self.num_attn_heads, 1
) # [ngram, B, head*T, S]
rel_pos_embeddings = rel_pos_embeddings.reshape(-1, rel_pos_embeddings.size(-1))
predict_relative_position_buckets = predict_relative_position_buckets.view(
-1, predict_relative_position_buckets.size(-1)
).long() # [ngram*B*head*T, S]
predict_relative_pos_embeddings = torch.gather(
rel_pos_embeddings, dim=1, index=predict_relative_position_buckets
).view(
self.ngram, batch_size * self.num_attn_heads, sequence_length, -1
) # [ngram, B*head, T, S]
return predict_relative_pos_embeddings
class EncLayer(qc.Module):
def __init__(self, config):
super().__init__()
# 1st residual block
self.self_attn = Attention(config, config.num_encoder_attention_heads)
self.self_attn_layer_norm = LayerNorm(config.d_model)
# 2nd residual block
self.feed_forward = ProphetNetFeedForward(config, config.encoder_ffn_dim)
self.feed_forward_layer_norm = LayerNorm(config.d_model)
def forward(
self,
hiddens,
attention_mask,
layer_head_mask,
output_attentions=False,
):
# 1st residual block
attention_output, attn_weights, _ = self.self_attn(
hiddens=hiddens,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hiddens = self.self_attn_layer_norm(attention_output + hiddens)
# 2nd residual block
feed_forward_output = self.feed_forward(hiddens)
hiddens = self.feed_forward_layer_norm(feed_forward_output + hiddens)
outputs = (hiddens,)
if output_attentions:
outputs += (attn_weights,)
return outputs
class DecLayer(qc.Module):
def __init__(self, config):
super().__init__()
# 1st residual block
self.self_attn = ProphetNetNgramSelfAttention(config)
self.self_attn_layer_norm = LayerNorm(config.d_model)
# 2nd residual block
if config.add_cross_attention:
self.cross_attn = Attention(config, config.num_decoder_attention_heads)
self.cross_attn_layer_norm = LayerNorm(config.d_model)
# 3rd residual block
self.feed_forward = ProphetNetFeedForward(config, config.decoder_ffn_dim)
self.feed_forward_layer_norm = LayerNorm(config.d_model)
def forward(
self,
hiddens,
attention_mask=None,
enc_hiddens=None,
encoder_attn_mask=None,
layer_head_mask=None,
cross_attn_layer_head_mask=None,
extended_predict_attention_mask=None,
main_relative_position_buckets=None,
predict_relative_position_buckets=None,
position_ids=None,
past_key_value=None,
y_cache=True,
output_attentions=False,
):
# 1st residual block
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
(
ngram_attention_output,
self_attn_weights,
self_attn_weights_ngram,
present_key_value,
) = self.self_attn(
hiddens=hiddens,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
extended_predict_attention_mask=extended_predict_attention_mask,
main_relative_position_buckets=main_relative_position_buckets,
predict_relative_position_buckets=predict_relative_position_buckets,
position_ids=position_ids,
)
hiddens = self.self_attn_layer_norm(hiddens + ngram_attention_output)
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attn_weights = None
if enc_hiddens is not None:
# 2nd residual block
attention_output, cross_attn_weights, cross_attn_present_key_value = self.cross_attn(
hiddens=hiddens,
key_value_states=enc_hiddens,
attention_mask=encoder_attn_mask,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
output_attentions=output_attentions,
)
hiddens = self.cross_attn_layer_norm(attention_output + hiddens)
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# 3rd residual block
feed_forward_output = self.feed_forward(hiddens)
hiddens = self.feed_forward_layer_norm(feed_forward_output + hiddens)
outputs = (hiddens,)
if output_attentions:
outputs += (self_attn_weights, self_attn_weights_ngram, cross_attn_weights)
if y_cache:
outputs += (present_key_value,)
return outputs
class Encoder(PreTrained):
def __init__(self, config, word_embeddings: qc.Embed = None):
super().__init__(config)
self.word_embeddings = (
word_embeddings
if word_embeddings is not None
else qc.Embed(config.s_vocab, config.d_model, padding_idx=config.PAD)
)
self.position_embeddings = ProphetNetPositionalEmbeddings(config)
self.embeddings_layer_norm = LayerNorm(config.d_model)
self.layers = nn.ModuleList([EncLayer(config) for _ in range(config.num_encoder_layers)])
self.gradient_checkpointing = False
def forward(
self,
input_ids=None,
attention_mask=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
output_attentions = (
output_attentions if output_attentions is not None else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is None and inputs_embeds is None:
raise ValueError("Either input_ids or inputs_embeds has to be passed.")
elif input_ids is not None and inputs_embeds is not None:
raise ValueError("Make sure to only pass input_ids or inputs_embeds.")
elif input_ids is not None and inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
# prepare attention mask
if attention_mask is not None:
extended_attention_mask = (
1.0
- attention_mask[:, None, :].repeat(self.config.num_encoder_attention_heads, 1, 1)
) * -10000.0
extended_attention_mask = extended_attention_mask.to(inputs_embeds.dtype)
else:
extended_attention_mask = None
position_embeddings, position_ids = self.position_embeddings(
inputs_embeds.shape[:2], inputs_embeds.device
)
hiddens = inputs_embeds + position_embeddings
hiddens = self.embeddings_layer_norm(hiddens)
hiddens = F.drop(hiddens, p=self.config.drop, training=self.training)
enc_hiddens = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
assert head_mask.size()[0] == (
len(self.layers)
), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
enc_hiddens = enc_hiddens + (hiddens,)
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(encoder_layer),
hiddens,
extended_attention_mask,
(head_mask[idx] if head_mask is not None else None),
)
else:
layer_outputs = encoder_layer(
hiddens,
attention_mask=extended_attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
output_attentions=output_attentions,
)
hiddens = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
enc_hiddens = enc_hiddens + (hiddens,)
if not return_dict:
return tuple(v for v in [hiddens, enc_hiddens, all_attentions] if v is not None)
return qo.Base(
y=hiddens,
hiddens=enc_hiddens,
attns=all_attentions,
)
class Decoder(PreTrained):
def __init__(self, config, word_embeddings: qc.Embed = None):
super().__init__(config)
self.ngram = config.ngram
self.num_buckets = config.num_buckets
self.relative_max_distance = config.relative_max_distance
self.drop = config.drop
self.max_target_positions = config.n_pos
self.word_embeddings = (
word_embeddings
if word_embeddings is not None
else qc.Embed(config.s_vocab, config.d_model, padding_idx=config.PAD)
)
self.position_embeddings = ProphetNetPositionalEmbeddings(config)
self.ngram_embeddings = qc.Embed(self.ngram, config.d_model, None)
self.layers = nn.ModuleList([DecLayer(config) for _ in range(config.n_dec_lays)])
self.embeddings_layer_norm = LayerNorm(config.d_model)
self.gradient_checkpointing = False
def forward(
self,
input_ids=None,
attention_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
caches=None,
inputs_embeds=None,
y_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
y_cache = y_cache if y_cache is not None else self.config.y_cache
output_attentions = (
output_attentions if output_attentions is not None else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is None and inputs_embeds is None:
raise ValueError(
"Either `decoder_input_ids` or `decoder_inputs_embeds` has to be passed."
)
elif input_ids is not None and inputs_embeds is not None:
raise ValueError(
"Make sure to only pass `decoder_input_ids` or `decoder_inputs_embeds`."
)
elif input_ids is not None and inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
batch_size, sequence_length = inputs_embeds.shape[:2]
main_stream_pos_embed, position_ids = self.position_embeddings(
(batch_size, sequence_length),
device=inputs_embeds.device,
caches=caches,
)
if caches is not None:
main_relative_position_buckets, predict_relative_position_buckets = None, None
else:
(
main_relative_position_buckets,
predict_relative_position_buckets,
) = self.compute_buffered_relative_buckets(position_ids)
predicting_stream_pos_embed = self.position_embeddings._forward(position_ids + 1)
# add position embeddings
hiddens = inputs_embeds + main_stream_pos_embed
ngram_embeddings = self.ngram_embeddings.weight
# prepare attention mask
if caches is not None:
assert (
hiddens.size(1) == 1
), "At the moment `y_cache` is only supported for `decoder_input_ids` of length 1"
ngram_hidden_states = [
(ngram_embeddings[ngram - 1] + predicting_stream_pos_embed).repeat(batch_size, 1, 1)
for ngram in range(self.ngram)
]
extended_attention_mask = None
extended_predict_attention_mask = None
else:
ngram_hidden_states = [
(ngram_embeddings[ngram - 1] + predicting_stream_pos_embed)
for ngram in range(self.ngram)
]
extended_attention_mask = self.prepare_attention_mask(hiddens, attention_mask)
extended_predict_attention_mask = self.prepare_predict_attention_mask(
hiddens, attention_mask
)
# prepare encoder attention mask
if encoder_attention_mask is not None:
extended_encoder_attention_mask = (
1.0
- encoder_attention_mask[:, None, :].repeat(
self.config.num_decoder_attention_heads, 1, 1
)
) * -10000.0
extended_encoder_attention_mask = extended_encoder_attention_mask.to(
inputs_embeds.dtype
)
else:
extended_encoder_attention_mask = None
hiddens = torch.cat([hiddens] + ngram_hidden_states, 1)
if self.embeddings_layer_norm:
hiddens = self.embeddings_layer_norm(hiddens)
hiddens = F.drop(hiddens, p=self.drop, training=self.training)
# init attns, hiddens and cache with empty tuples
all_main_stream_hidden_states = () if output_hidden_states else None
all_ngram_stream_hidden_states = (
() if output_hidden_states and self.config.ngram > 0 else None
)
all_main_stream_attns = () if output_attentions else None
all_ngram_stream_attns = () if output_attentions else None
all_cross_attns = () if output_attentions and self.config.add_cross_attention else None
present_key_values = () if y_cache else None
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip(
[head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]
):
if attn_mask is not None:
assert attn_mask.size()[0] == (len(self.layers))
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
# grad cannot be kept because tensor is sliced
all_main_stream_hidden_states += (hiddens[:, :sequence_length],)
if self.config.ngram > 0:
all_ngram_stream_hidden_states += (hiddens[:, sequence_length:],)
past_key_value = caches[idx] if caches is not None else None
if self.gradient_checkpointing and self.training:
if y_cache:
log.warning(
"`y_cache=True` is incompatible with gradient checkpointing. Setting `y_cache=False`..."
)
y_cache = False
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, y_cache, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(decoder_layer),
hiddens,
extended_attention_mask,
enc_hiddens,
extended_encoder_attention_mask,
(head_mask[idx] if head_mask is not None else None),
(cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None),
extended_predict_attention_mask,
main_relative_position_buckets,
predict_relative_position_buckets,
position_ids,
None,
)
else:
layer_outputs = decoder_layer(
hiddens,
attention_mask=extended_attention_mask,
enc_hiddens=enc_hiddens,
encoder_attn_mask=extended_encoder_attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
cross_attn_layer_head_mask=(
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
),
extended_predict_attention_mask=extended_predict_attention_mask,
main_relative_position_buckets=main_relative_position_buckets,
predict_relative_position_buckets=predict_relative_position_buckets,
position_ids=position_ids,
past_key_value=past_key_value,
y_cache=y_cache,
output_attentions=output_attentions,
)
hiddens = layer_outputs[0]
if y_cache:
present_key_values += (layer_outputs[4 if output_attentions else 1],)
if output_attentions:
all_main_stream_attns += (layer_outputs[1],)
all_ngram_stream_attns += (layer_outputs[2],)
if self.config.add_cross_attention:
all_cross_attns += (layer_outputs[3],)
if output_hidden_states:
all_main_stream_hidden_states += (hiddens[:, :sequence_length],)
if self.config.ngram > 0:
all_ngram_stream_hidden_states += (hiddens[:, sequence_length:],)
# split y for return
y = hiddens[:, :sequence_length]
last_hidden_state_ngram = hiddens[:, sequence_length:] if self.config.ngram > 0 else None
if not return_dict:
return tuple(
v
for v in [
y,
last_hidden_state_ngram,
present_key_values,
all_main_stream_hidden_states,
all_ngram_stream_hidden_states,
all_main_stream_attns,
all_ngram_stream_attns,
all_cross_attns,
]
if v is not None
)
return ProphetNetDecoderModelOutput(
y=y,
last_hidden_state_ngram=last_hidden_state_ngram,
caches=present_key_values,
hiddens=all_main_stream_hidden_states,
hidden_states_ngram=all_ngram_stream_hidden_states,
attns=all_main_stream_attns,
ngram_attentions=all_ngram_stream_attns,
crosses=all_cross_attns,
)
def compute_buffered_relative_buckets(self, position_ids):
batch_size, sequence_length = position_ids.shape
position_ids = (
torch.arange(1, self.max_target_positions).to(position_ids.device).repeat(1, 1)
)
main_relative_buckets, predict_relative_buckets = compute_all_stream_relative_buckets(
self.num_buckets, self.relative_max_distance, position_ids
)
# buffer relative buckets
main_relative_buckets = main_relative_buckets[:, :sequence_length, :sequence_length].repeat(
batch_size, 1, 1
)
predict_relative_buckets = torch.cat(
[
predict_relative_buckets[:, :sequence_length, :sequence_length],
predict_relative_buckets[
:,
:sequence_length,
self.max_target_positions : self.max_target_positions + sequence_length,
],
],
2,
).repeat(batch_size, 1, 1)
return main_relative_buckets, predict_relative_buckets
def prepare_attention_mask(self, hiddens, attention_mask):
batch_size, seq_length = hiddens.shape[:2]
causal_mask = torch.full(
(seq_length, seq_length),
-float("inf"),
dtype=hiddens.dtype,
device=hiddens.device,
)
causal_mask = torch.triu(causal_mask, 1)
extended_causal_mask = causal_mask[:seq_length, :seq_length][None, :, :].expand(
(batch_size,) + causal_mask.shape
)
if attention_mask is not None:
extended_attention_mask = (1.0 - attention_mask[:, None, :]) * -10000.0
extended_attention_mask = extended_causal_mask + extended_attention_mask
else:
extended_attention_mask = extended_causal_mask
return extended_attention_mask.repeat(self.config.num_decoder_attention_heads, 1, 1).to(
hiddens.dtype
)
def prepare_predict_attention_mask(self, hiddens, attention_mask):
batch_size, seq_length = hiddens.shape[:2]
predict_causal_mask = ngram_attention_bias(
self.max_target_positions, self.ngram, hiddens.device, hiddens.dtype
)
predict_causal_mask = torch.cat(
[
predict_causal_mask[:, :seq_length, :seq_length],
predict_causal_mask[
:,
:seq_length,
self.max_target_positions : self.max_target_positions + seq_length,
],
],
dim=-1,
)
extended_predict_causal_mask = predict_causal_mask[:, None, :, :].expand(
predict_causal_mask.shape[:1] + (batch_size,) + predict_causal_mask.shape[1:]
)
if attention_mask is not None:
extended_attention_mask = (1.0 - attention_mask[None, :, None, :]) * -10000.0
extended_attention_mask = extended_attention_mask.expand(
(self.ngram, batch_size, seq_length, seq_length)
)
# predicted stream attention_mask should always be 0
extended_attention_mask = torch.cat(
[extended_attention_mask, torch.zeros_like(extended_attention_mask)], dim=-1
)
extended_predict_attention_mask = extended_predict_causal_mask + extended_attention_mask
else:
extended_predict_attention_mask = extended_predict_causal_mask
return extended_predict_attention_mask.repeat(
1, self.config.num_decoder_attention_heads, 1, 1
).to(hiddens.dtype)
class Model(PreTrained):
def __init__(self, config):
super().__init__(config)
self.word_embeddings = qc.Embed(config.s_vocab, config.d_model, padding_idx=config.PAD)
encoder_config = copy.deepcopy(config)
encoder_config.is_enc_dec = False
encoder_config.y_cache = False
self.encoder = Encoder(encoder_config, self.word_embeddings)
decoder_config = copy.deepcopy(config)
decoder_config.is_decoder = True
decoder_config.is_enc_dec = False
self.decoder = Decoder(decoder_config, self.word_embeddings)
def forward(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
encoder_outputs=None,
caches=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
y_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
y_cache = y_cache if y_cache is not None else self.config.y_cache
output_attentions = (
output_attentions if output_attentions is not None else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# decoder outputs consists of (dec_features, caches, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
enc_hiddens=encoder_outputs[0],
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
caches=caches,
inputs_embeds=decoder_inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
y_cache=y_cache,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return ProphetNetSeq2SeqModelOutput(
y=decoder_outputs.y,
last_hidden_state_ngram=decoder_outputs.last_hidden_state_ngram,
caches=decoder_outputs.caches,
hiddens=decoder_outputs.hiddens,
decoder_ngram_hidden_states=decoder_outputs.hidden_states_ngram,
attns=decoder_outputs.attns,
decoder_ngram_attentions=decoder_outputs.ngram_attentions,
crosses=decoder_outputs.crosses,
enc_y=encoder_outputs.y,
enc_hiddens=encoder_outputs.hiddens,
enc_attns=encoder_outputs.attns,
)
class ForCondGen(PreTrained):
def __init__(self, config):
super().__init__(config)
self.prophetnet = Model(config)
self.padding_idx = config.PAD
self.disable_ngram_loss = config.disable_ngram_loss
self.lm_head = qc.Linear(config.d_model, config.s_vocab, bias=False)
def forward(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
encoder_outputs=None,
caches=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
labels=None,
y_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
# get decoder inputs from shifting lm labels to the right
decoder_input_ids = self._shift_right(labels)
outputs = self.prophetnet(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
encoder_outputs=encoder_outputs,
caches=caches,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
y_cache=y_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
batch_size, sequence_length = (
decoder_input_ids.shape
if decoder_input_ids is not None
else decoder_inputs_embeds.shape[:2]
)
predicting_streams = outputs[1].view(batch_size, self.config.ngram, sequence_length, -1)
predict_logits = self.lm_head(predicting_streams)
logits = predict_logits[:, 0]
logits_ngram = predict_logits[:, 1:] if self.config.ngram > 1 else None
# To use .view in loss computation, make sure that logits is contiguous.
if not logits.is_contiguous():
logits = logits.contiguous()
loss = None
if labels is not None:
loss = self._compute_loss(predict_logits, labels)
if not return_dict:
all_logits = tuple(v for v in [logits, logits_ngram] if v is not None)
return (
(loss,) + all_logits + outputs[2:] if loss is not None else all_logits + outputs[2:]
)
else:
return ProphetNetSeq2SeqLMOutput(
loss=loss,
logits=logits,
logits_ngram=logits_ngram,
caches=outputs.caches,
hiddens=outputs.hiddens,
decoder_ngram_hidden_states=outputs.decoder_ngram_hidden_states,
attns=outputs.attns,
decoder_ngram_attentions=outputs.decoder_ngram_attentions,
crosses=outputs.crosses,
enc_y=outputs.enc_y,
enc_hiddens=outputs.enc_hiddens,
enc_attns=outputs.enc_attns,
)
def _compute_loss(self, logits, labels, ignore_index=-100):
expend_targets = labels.new_zeros(self.config.ngram, labels.size(0), labels.size(1)).fill_(
ignore_index
)
for i in range(self.config.ngram):
if i > 0 and self.disable_ngram_loss:
break
expend_targets[i, :, :] = labels
logits = logits.transpose(0, 1).contiguous()
lprobs = F.log_softmax(
logits.view(-1, logits.size(-1)),
dim=-1,
dtype=torch.float32,
)
loss = F.nll_loss(lprobs, expend_targets.view(-1), reduction="mean")
if self.config.eps > 0.0:
smooth_loss = -lprobs.sum(dim=-1, keepdim=True)
non_masked_tokens = expend_targets.ne(ignore_index).view(-1)
smooth_loss = smooth_loss[non_masked_tokens]
smooth_loss = smooth_loss.mean()
eps_i = self.config.eps / lprobs.size(-1)
loss = (1.0 - self.config.eps) * loss + eps_i * smooth_loss
return loss
class ForCausal(PreTrained):
def __init__(self, config):
# set config for CLM
config = copy.deepcopy(config)
config.is_decoder = True
config.is_enc_dec = False
super().__init__(config)
self.prophetnet = ProphetNetDecoderWrapper(config)
self.padding_idx = config.PAD
self.disable_ngram_loss = config.disable_ngram_loss
self.lm_head = qc.Linear(config.d_model, config.s_vocab, bias=False)
def forward(
self,
input_ids=None,
attention_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
caches=None,
inputs_embeds=None,
labels=None,
y_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, caches, dec_hidden, dec_attn)
outputs = self.prophetnet.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
enc_hiddens=enc_hiddens,
encoder_attention_mask=encoder_attention_mask,
head_mask=head_mask,
cross_attn_head_mask=cross_attn_head_mask,
caches=caches,
inputs_embeds=inputs_embeds,
y_cache=y_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
batch_size, sequence_length = (
input_ids.shape if input_ids is not None else inputs_embeds.shape[:2]
)
predicting_streams = outputs[1].view(batch_size, self.config.ngram, sequence_length, -1)
predict_logits = self.lm_head(predicting_streams)
logits = predict_logits[:, 0]
logits_ngram = predict_logits[:, 1:] if self.config.ngram > 1 else None
loss = None
if labels is not None:
loss = self._compute_loss(predict_logits, labels)
if not return_dict:
all_logits = tuple(v for v in [logits, logits_ngram] if v is not None)
return (
(loss,) + all_logits + outputs[2:] if loss is not None else all_logits + outputs[2:]
)
else:
return ProphetNetDecoderLMOutput(
loss=loss,
logits=logits,
logits_ngram=logits_ngram,
caches=outputs.caches,
hiddens=outputs.hiddens,
hidden_states_ngram=outputs.hidden_states_ngram,
attns=outputs.attns,
ngram_attentions=outputs.ngram_attentions,
crosses=outputs.crosses,
)
def _compute_loss(self, logits, labels, ignore_index=-100):
expend_targets = labels.new_zeros(self.config.ngram, labels.size(0), labels.size(1)).fill_(
ignore_index
)
for i in range(self.config.ngram):
if i > 0 and self.disable_ngram_loss:
break
expend_targets[i, :, :] = labels
logits = logits.transpose(0, 1).contiguous()
lprobs = F.log_softmax(
logits.view(-1, logits.size(-1)),
dim=-1,
dtype=torch.float32,
)
loss = F.nll_loss(lprobs, expend_targets.view(-1), reduction="mean")
if self.config.eps > 0.0:
smooth_loss = -lprobs.sum(dim=-1, keepdim=True)
non_masked_tokens = expend_targets.ne(ignore_index).view(-1)
smooth_loss = smooth_loss[non_masked_tokens]
smooth_loss = smooth_loss.mean()
eps_i = self.config.eps / lprobs.size(-1)
loss = (1.0 - self.config.eps) * loss + eps_i * smooth_loss
return loss
class ProphetNetDecoderWrapper(PreTrained):
def __init__(self, config):
super().__init__(config)
self.decoder = Decoder(config)
def forward(self, *args, **kw):
return self.decoder(*args, **kw)
|
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|
33,412
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/gpt2.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
# https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
# https://openai.com/blog/better-language-models/
from dataclasses import dataclass
import torch
from torch import nn
from torch.nn import functional as F
from torch.cuda.amp.autocast_mode import autocast
from transformers.utils import logging
from torch.utils.checkpoint import checkpoint
from .. import core as qc
from ..core import utils as qu
from ..core import output as qo
from ..core import forward as qf
from ..core import mlp as qm
from ..core import attention as qa
from ..core.mlp import Classifier
from ..prep.config.gpt2 import PreTrained
log = logging.get_logger(__name__)
class LMHead(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(**kw)
self.proj = qc.Linear(cfg.n_embed, cfg.s_vocab, bias=False, **kw)
def forward(self, x, labels=None, **kw):
ys = self.model(x, **kw)
y = self.proj(ys[0])
loss = None
if labels is not None:
sl = y[..., :-1, :].contiguous()
ls = labels[..., 1:].contiguous()
loss = nn.CrossEntropyLoss()(sl.view(-1, sl.size(-1)), ls.view(-1))
ys = (y,) + ys[1:] + (loss,)
return qo.LossCrosses(*ys)
@dataclass
class Output(qc.Output):
logits: tuple = None
mc_logits: tuple = None
attns: tuple = None
caches: tuple = None
hiddens: tuple = None
loss: tuple = None
mc_loss: tuple = None
class DualHead(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
cfg.n_labels = 1
self.model = Model(**kw)
self.sum = qc.SeqSummary(**kw)
self.proj = qc.Linear(cfg.n_embed, cfg.s_vocab, bias=False, **kw)
def forward(self, x, mc_token_ids=None, labels=None, mc_labels=None, **kw):
ys = self.model(x, **kw)
y = self.proj(ys[0])
loss = None
if labels is not None:
sl = y[..., :-1, :].contiguous()
ls = labels[..., 1:].contiguous()
loss = nn.CrossEntropyLoss()(sl.view(-1, sl.size(-1)), ls.view(-1))
mc_y = self.sum(ys[0], mc_token_ids).squeeze(-1)
mc_loss = None
if mc_labels is not None:
mc_loss = nn.CrossEntropyLoss()(mc_y.view(-1, mc_y.size(-1)), mc_labels.view(-1))
ys = (y, mc_y) + ys[1:] + (loss, mc_loss)
return Output(*ys)
class ForSeqClass(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(**kw)
self.proj = qc.Linear(cfg.n_embed, cfg.n_labels, bias=False, **kw)
forward = qf.forward_seq
def post_proj(self, x):
cfg = self.cfg
b = (x.shape[:2] if x is not None else x_emb.shape[:2])[0]
if cfg.PAD is None:
n = -1
else:
assert b == 1
n = -1 if x is None else torch.ne(x, cfg.PAD).sum(-1) - 1
return x[torch.arange(b, device=self.device), n]
class ForTokClass(PreTrained):
def __init__(self, drop_proj=0.1, **kw):
super().__init__(**kw)
self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Classifier(drop_proj=drop_proj, **kw)
forward = qf.forward_tok
class Model(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.tok_emb = qc.Embed(cfg.s_vocab, cfg.d_model, **kw)
self.pos_emb = qc.Embed(cfg.n_pos, cfg.d_model, **kw)
self.lays = qc.Stack([Layer(lay_i=i, **kw) for i in range(cfg.n_lays)])
self.norm = qc.LayerNorm(cfg.d_model, cfg.eps, **kw)
self.drop = qc.Dropout(cfg.drop_embed, **kw)
def forward(
self,
x,
cache=None,
enc_m=None,
enc=None,
head_m=None,
mask=None,
pos=None,
typ=None,
x_emb=None,
**kw,
):
cfg = self.cfg
if x is None:
s, d = x_emb.size()[:-1], x_emb.device
else:
assert x_emb is None
s, d = x.size(), x.device
x = x.view(-1, s[-1])
if x_emb is None:
x_emb = self.tok_emb(x)
if cache is None:
c_len = 0
cache = tuple([None] * len(self.lays))
else:
c_len = cache[0][0].size(-2)
if mask is not None:
mask = self.get_mask(mask.view(s[0], -1), s, d)
if pos is not None:
pos = pos.view(-1, s[-1])
else:
pos = (
torch.arange(c_len, s[-1] + c_len, dtype=torch.long, device=d)
.unsqueeze(0)
.view(-1, s[-1])
)
if typ is not None:
typ = typ.view(-1, s[-1])
if cfg.add_cross and enc is not None:
if enc_m is None:
enc_m = torch.ones(enc.size()[:2], device=d)
enc_m = self.invert_mask(enc_m)
else:
enc_m = None
head_m = self.get_head_m(head_m, cfg.n_lays)
y = x_emb + self.pos_emb(pos)
if typ is not None:
y = y + self.tok_emb(typ)
y = self.drop(y)
attns = caches = crosses = hiddens = ()
for i, (lay, c) in enumerate(zip(self.lays, cache)):
hiddens += (y,)
kw.update(enc_m=enc_m, enc=enc, head_m=head_m[i], mask=mask)
if self.grad_checkpoint and self.training:
def create_forward(x):
def forward(*xs):
return x(*xs, cache=c)
return forward
ys = checkpoint(create_forward(lay), y, **kw)
else:
ys = lay(y, cache=c, **kw)
y = ys[0]
attns += (ys[2],)
if cfg.add_cross:
crosses += (ys[3],)
caches += (ys[1],)
y = self.norm(y).view(s + (y.size(-1),))
hiddens += (y,)
return qo.CachesCrosses(y, attns, caches, crosses, hiddens)
class Layer(qc.Module):
hs = qc.Hypers({"d_model", "add_cross", "n_inner"})
def __init__(self, lay_i, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
d = cfg.d_model
self.attn = Attention(lay_i=lay_i, **kw)
self.norm_attn = qc.LayerNorm(d, **kw)
if cfg.add_cross:
self.cross = Attention(is_cross=True, lay_i=lay_i, **kw)
self.norm_cross = qc.LayerNorm(d, **kw)
self.proj = qm.GPT(cfg.n_inner if cfg.n_inner is not None else 4 * d, **kw)
self.norm = qc.LayerNorm(d, **kw)
def forward(self, x, cache=None, enc_m=None, enc=None, head_m=None, mask=None, **kw):
y = self.norm_attn(x)
y, a, kv = self.attn(y, cache=cache, head_m=head_m, mask=mask, **kw)
y = x + y
a2 = None
if enc is not None:
x = y
y = self.norm_cross(y)
y, a2, kv2 = self.cross(y, enc_m=enc_m, enc=enc, head_m=head_m, mask=mask, **kw)
y = x + y
kv = kv + kv2
x = y
return x + self.proj(self.norm(y)), a, a2, kv
class Attention(qc.Module):
hs = qc.Hypers({"d_model", "drop_attn", "drop", "n_heads", "n_pos", "scale", "scale_by_inv"})
def __init__(self, is_cross=False, lay_i=None, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
self.is_cross = is_cross
self.lay_i = lay_i
cfg = self.get_cfg(kw)
d, h = cfg.d_model, cfg.n_heads
assert d % h == 0
cfg.s_head = int(d / h)
if is_cross:
self.attn = qc.Conv1D(2 * d, d, **kw)
self.query = qc.Conv1D(d, d, **kw)
else:
self.attn = qc.Conv1D(3 * d, d, **kw)
self.proj = qc.Conv1D(d, d, **kw)
self.drop_attn = qc.Dropout(cfg.drop_attn, **kw)
self.drop = qc.Dropout(cfg.drop, **kw)
p, t = cfg.n_pos, torch.bool
self.register_buffer("bias", torch.tril(torch.ones((p, p), dtype=t)).view(1, 1, p, p))
# self.register_buffer("bias_m", torch.tensor(-1e4))
def forward(self, x, cache=None, enc_m=None, enc=None, head_m=None, mask=None, **kw):
cfg = self.cfg
if enc is None:
q, k, v = self.attn(x).split(cfg.d_model, dim=2)
else:
q = self.query(x)
k, v = self.attn(enc).split(cfg.d_model, dim=2)
mask = enc_m
q = self.split_heads(q)
k = self.split_heads(k)
v = self.split_heads(v)
if cache is not None:
k = torch.cat((cache[0], k), dim=-2)
v = torch.cat((cache[1], v), dim=-2)
if cfg.reorder:
ys = self.reordered(q, k, v, mask, head_m)
else:
ys = self.scores(q, k, v, mask, head_m)
y = self.join_heads(ys[0])
y = (self.drop(self.proj(y)),)
y += ys[1:] + ((k, v),)
return y
split_heads = qa.split_heads
join_heads = qa.join_heads
def scores(self, q, k, v, mask, head_m, **kw):
cfg = self.cfg
a = torch.matmul(q, k.transpose(-1, -2))
if cfg.scale:
a = a / torch.full([], v.size(-1) ** 0.5, dtype=a.dtype, device=a.device)
if cfg.scale_by_inv:
a = a / float(self.lay_i + 1)
if not self.is_cross:
n_q, n_k = q.size(-2), k.size(-2)
causal = self.bias[:, :, n_k - n_q : n_k, :n_k].bool()
m = torch.tensor(torch.finfo(a.dtype).min, dtype=a.dtype).to(a.device)
a = torch.where(causal, a, m)
if mask is not None:
a = a + mask
a = self.drop_attn(F.softmax(a, dim=-1).type(v.dtype))
if head_m is not None:
a = a * head_m
return torch.matmul(a, v), a
def reordered(self, q, k, v, mask, head_m, **kw):
cfg = self.cfg
b, h, n_q, d = q.size()
_, _, n_k, _ = k.size()
a = torch.empty(b * h, n_q, n_k, dtype=torch.float32, device=q.device)
alpha = 1.0
if cfg.scale:
alpha /= float(v.size(-1)) ** 0.5
if cfg.scale_by_inv:
alpha /= float(self.lay_i + 1)
with autocast(enabled=False):
q, k = q.reshape(-1, n_q, d), k.transpose(-1, -2).reshape(-1, d, n_k)
a = torch.baddbmm(a, q.float(), k.float(), beta=0, alpha=alpha)
a = a.reshape(b, h, n_q, n_k)
if not self.is_cross:
causal = self.bias[:, :, n_k - n_q : n_k, :n_k].bool()
m = torch.tensor(torch.finfo(a.dtype).min, dtype=a.dtype).to(a.device)
a = torch.where(causal, a, m)
if mask is not None:
a = a + mask
a = self.drop_attn(F.softmax(a, dim=-1).type(v.dtype))
if head_m is not None:
a = a * head_m
return torch.matmul(a, v), a
|
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"/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,413
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/core/attention.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import math
import torch
import numpy as np
from torch import nn
from torch.nn import functional as F
from torch.nn.parameter import Parameter, UninitializedBuffer
from .. import core as qc
from . import utils as qu
def split_heads(self, x, k=False):
cfg = self.cfg
y = x.view(x.size()[:-1] + (cfg.n_heads, cfg.s_head))
if k:
return y.permute(0, 2, 3, 1)
else:
return y.permute(0, 2, 1, 3)
def join_heads(self, x):
y = x.permute(0, 2, 1, 3).contiguous()
cfg = self.cfg
return y.view(y.size()[:-2] + (cfg.n_heads * cfg.s_head,))
class Attention(qc.Module):
hs = qc.Hypers(
["d_model", "n_heads", "d_k", "d_v"],
{
"add_b_kv": False,
"add_zero_attn": False,
"batch_first": False,
"bias": True,
"drop": 0.0,
},
)
w_pack, w_q, w_k, w_v = None
b_pack, b_q, b_k, b_v = None
def __init__(self, n_heads, d_model, hs=[], **kw):
if n_heads is not None:
kw.update(n_heads=n_heads)
if d_model is not None:
kw.update(d_model=d_model)
super().__init__([self.hs] + hs, **kw)
cfg = self.cfg
n, h = cfg.n_heads, cfg.d_model
assert h % n == 0
d_k = cfg.d_k if cfg.d_k is not None else h
d_v = cfg.d_v if cfg.d_v is not None else h
self.pack = self.d_k == h and self.d_v == h
kw = {"device": cfg.device, "dtype": cfg.dtype}
if self.pack:
self.w_pack = Parameter(torch.empty((3 * h, h), **kw))
self.register_parameter("w_q", None)
self.register_parameter("w_k", None)
self.register_parameter("w_v", None)
else:
self.register_parameter("w_pack", None)
self.w_q = Parameter(torch.empty((h, h), **kw))
self.w_k = Parameter(torch.empty((h, d_k), **kw))
self.w_v = Parameter(torch.empty((h, d_v), **kw))
if cfg.bias:
self.b_pack = Parameter(torch.empty(3 * h, **kw))
else:
self.register_parameter("b_pack", None)
self.out = Linear(h, h, bias=cfg.bias, **kw)
if cfg.add_b_kv:
self.b_k = Parameter(torch.empty((1, 1, h), **kw))
self.b_v = Parameter(torch.empty((1, 1, h), **kw))
else:
self.register_parameter("b_k", None)
self.register_parameter("b_v", None)
def build(self, _):
if not self.is_built():
with torch.no_grad():
self.reset_params()
def reset_params(self):
if self.pack:
nn.init.xavier_uniform_(self.w_pack)
else:
nn.init.xavier_uniform_(self.w_q)
nn.init.xavier_uniform_(self.w_k)
nn.init.xavier_uniform_(self.w_v)
if self.b_pack is not None:
nn.init.constant_(self.b_pack, 0.0)
nn.init.constant_(self.out.bias, 0.0)
if self.b_k is not None:
nn.init.xavier_normal_(self.b_k)
if self.b_v is not None:
nn.init.xavier_normal_(self.b_v)
def forward(self, q, k, v, mask=None, k_mask=None, need_weights=True, average=True):
cfg = self.cfg
is_batched = q.dim() == 3
if cfg.batch_first and is_batched:
q, k, v = [x.transpose(1, 0) for x in (q, k, v)]
if self.pack:
y, w = self.multi_head_attention_forward(
q,
k,
v,
mask,
k_mask,
self.add_zero_attn,
need_weights=need_weights,
average=average,
)
else:
y, w = self.multi_head_attention_forward(
q,
k,
v,
self.add_zero_attn,
mask,
k_mask,
need_weights=need_weights,
average=average,
)
if self.batch_first and is_batched:
return y.transpose(1, 0), w
else:
return y, w
def project_packed(self, q, k, v):
w, b = self.w_pack, self.b_pack
if k is v:
if q is k:
return F.linear(q, w, b).chunk(3, dim=-1)
else:
H = q.size(-1)
w_q, w_kv = w.split([H, H * 2])
if b is None:
b_q = b_kv = None
else:
b_q, b_kv = b.split([H, H * 2])
return (F.linear(q, w_q, b_q),) + F.linear(k, w_kv, b_kv).chunk(2, dim=-1)
else:
w_q, w_k, w_v = w.chunk(3)
if b is None:
b_q = b_k = b_v = None
else:
b_q, b_k, b_v = b.chunk(3)
return F.linear(q, w_q, b_q), F.linear(k, w_k, b_k), F.linear(v, w_v, b_v)
def project(self, q, k, v, bs):
w_q, w_k, w_v = self.w_q, self.w_k, self.w_v
H, Dk, Dv = q.size(-1), k.size(-1), v.size(-1)
assert w_q.shape == (H, H) and w_k.shape == (H, Dk) and w_v.shape == (H, Dv)
b_q, b_k, b_v = bs
assert b_q is None or b_q.shape == (H,)
assert b_k is None or b_k.shape == (H,)
assert b_v is None or b_v.shape == (H,)
return F.linear(q, w_q, b_q), F.linear(k, w_k, b_k), F.linear(v, w_v, b_v)
def attention(self, q, k, v, mask=None):
cfg = self.cfg
B, Nt, H = q.shape
q = q / math.sqrt(H)
w = torch.bmm(q, k.transpose(-2, -1))
if mask is not None:
w += mask
w = softmax(w, dim=-1)
if self.training and cfg.dropout_p > 0.0:
w = drop(w, p=self.drop)
y = torch.bmm(w, v)
return y, w
def is_batched(self, q, k, v, k_mask, mask):
if q.dim() == 3:
assert k.dim() == 3 and v.dim() == 3
if k_mask is not None:
assert k_mask.dim() == 2
if mask is not None:
assert mask.dim() in (2, 3)
return True
assert q.dim() == 2
assert k.dim() == 2 and v.dim() == 2
if k_mask is not None:
assert k_mask.dim() == 1
if mask is not None:
assert mask.dim() in (2, 3)
if mask.dim() == 3:
assert mask.shape == (self.cfg.n_heads, q.shape[0], k.shape[0])
return False
def multi_head_attention_forward(
self,
q,
k,
v,
mask=None,
k_mask=None,
add_zero_attn=None,
need_weights=True,
static_k=None,
static_v=None,
average=True,
):
if not self.is_batched(q, k, v, k_mask, mask):
q = q.unsqueeze(1)
k = k.unsqueeze(1)
v = v.unsqueeze(1)
if k_mask is not None:
k_mask = k_mask.unsqueeze(0)
cfg = self.cfg
h, n = cfg.d_model, cfg.n_heads
b_q, b_k, b_v = self.b_q, self.b_k, self.b_v
if self.pack:
assert k.shape == v.shape
q, k, v = self.project_packed(q, k, v)
else:
assert k.shape[:2] == v.shape[:2]
if self.b_pack is None:
b_q = b_k = b_v = None
else:
b_q, b_k, b_v = self.b_pack.chunk(3)
q, k, v = self.project(q, k, v, (b_q, b_k, b_v))
d_tgt, d_batch, _ = q.shape
d_src, _, _ = k.shape
if mask is not None:
assert mask.is_floating_point() or mask.dtype == torch.bool
if mask.dim() == 2:
assert mask.shape == (d_tgt, d_src)
mask = mask.unsqueeze(0)
else:
assert mask.shape == (d_batch * n, d_tgt, d_src)
if b_k is not None and b_v is not None:
assert static_k is None
assert static_v is None
k = torch.cat([k, b_k.repeat(1, d_batch, 1)])
v = torch.cat([v, b_v.repeat(1, d_batch, 1)])
if mask is not None:
mask = pad(mask, (0, 1))
if k_mask is not None:
k_mask = pad(k_mask, (0, 1))
else:
assert b_k is None
assert b_v is None
d_head = h // n
q = q.contiguous().view(d_tgt, d_batch * n, d_head).transpose(0, 1)
if static_k is None:
k = k.contiguous().view(k.shape[0], d_batch * n, d_head).transpose(0, 1)
else:
assert static_k.size(0) == d_batch * n
assert static_k.size(2) == d_head
k = static_k
if static_v is None:
v = v.contiguous().view(v.shape[0], d_batch * n, d_head).transpose(0, 1)
else:
assert static_v.size(0) == d_batch * n
assert static_v.size(2) == d_head
v = static_v
if add_zero_attn:
zero_attn_shape = (d_batch * n, 1, d_head)
k = torch.cat([k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=1)
v = torch.cat([v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=1)
if mask is not None:
mask = pad(mask, (0, 1))
if k_mask is not None:
k_mask = pad(k_mask, (0, 1))
d_src = k.size(1)
if k_mask is not None:
assert k_mask.shape == (d_batch, d_src)
k_mask = (
k_mask.view(d_batch, 1, 1, d_src)
.expand(-1, n, -1, -1)
.reshape(d_batch * n, 1, d_src)
)
if mask is None:
mask = k_mask
elif mask.dtype == torch.bool:
mask = mask.logical_or(k_mask)
else:
mask = mask.masked_fill(k_mask, float("-inf"))
if mask is not None and mask.dtype == torch.bool:
mask = torch.zeros_like(mask, dtype=q.dtype).masked_fill_(mask, float("-inf"))
y, w = _scaled_dot_product_attention(q, k, v, mask)
y = y.transpose(0, 1).contiguous().view(d_tgt, d_batch, h)
y = F.linear(y, self.out.weight, self.out.bias)
if need_weights:
w = w.view(d_batch, n, d_tgt, d_src)
if average:
w = w.sum(dim=1) / n
return y, w
else:
return y, None
class Attend(qc.Module):
hs = qc.Hypers(
[
"d_attn_k",
"d_attn_v",
"d_attn",
"d_model",
"drop_attn",
"drop",
"len_mem",
"n_heads",
"pos_type",
"proxim_bias",
],
{},
)
v_w = pos_tim = proxim_b = None
def __init__(self, owner, hs=[], **kw):
super().__init__([self.hs] + hs, **kw)
self.owner = owner
self.pre = owner.pre
self.post = owner.post
self.pos_x_b = owner.pos_x_b
self.pos_p_b = owner.pos_p_b
cfg = self.cfg
h, n = cfg.d_model, cfg.n_heads
assert h % n == 0
k = cfg.d_attn_k or cfg.d_attn or h
assert k % n == 0
self.scale = 1 / (k**0.5)
v = cfg.d_attn_v or k
assert v % n == 0
kw = {"dtype": cfg.dtype, "device": cfg.device}
if k == v:
self.qkv_w = Parameter(torch.empty((h, n * k), **kw))
else:
self.qk_w = Parameter(torch.empty((h, n * k), **kw))
self.v_w = Parameter(torch.empty((h, n * v), **kw))
self.out_w = Parameter(torch.empty((n * v, h), **kw))
if cfg.pos_type == "relative":
self.pos_tim = PosTiming(**kw)
self.pos_w = Parameter(torch.empty((h, n * k), **kw))
if self.pos_x_b is None:
self.pos_x_b = Parameter(torch.empty((n, k), **kw))
if self.pos_p_b is None:
self.pos_p_b = Parameter(torch.empty((n, k), **kw))
if cfg.proxim_bias:
self.proxim_b = Proximity(**kw)
def build(self, x):
if not self.is_built():
cfg = self.cfg
with torch.no_grad():
e = x.shape[1] + cfg.len_mem if cfg.len_mem else 0
if cfg.pos_type == "relative":
self.pos_tim.materialize(cfg.d_model, e)
if cfg.proxim_bias:
self.proxim_b.materialize(e)
def reset_params(self):
if self.is_built():
a = math.sqrt(5)
if self.v_w is None:
nn.init.kaiming_uniform_(self.qkv_w, a=a)
else:
nn.init.kaiming_uniform_(self.qk_w, a=a)
nn.init.kaiming_uniform_(self.v_w, a=a)
if self.pos_tim is not None:
nn.init.kaiming_uniform_(self.pos_w, a=a)
if self.owner.pos_x_b is None:
nn.init.kaiming_uniform_(self.pos_x_b, a=a)
if self.owner.pos_p_b is None:
nn.init.kaiming_uniform_(self.pos_p_b, a=a)
split_heads = split_heads
join_heads = join_heads
def forward(self, x, mask=None):
x, ctx = x[0], x[1] if len(x) > 1 else None
xlen = x.shape[1]
y = x if ctx is None else torch.cat([ctx, x], dim=1)
y = self.pre([y, y])
if self.v_w is None:
y = v = torch.einsum("bih,hk->bik", y, self.qkv_w)
else:
y = torch.einsum("bih,hk->bik", y, self.qk_w)
v = torch.einsum("bih,hv->biv", v, self.v_w)
q = self.split_heads(y[:, -xlen:, :])
k = self.split_heads(y)
if self.pos_tim is None:
qk = torch.einsum("bnik,bnjk->bnij", q, k)
else:
qk = self.to_qk_with_pos(q, k)
v = self.split_heads(v)
y = self.to_scores(qk, mask, v)
y = self.join_heads(y)
y = torch.einsum("biv,vh->bih", y, self.out_w)
y = self.post([x, y])
return y
def to_qk_with_pos(self, q, k):
b = self.pos_x_b[:, None, :, None]
y = torch.einsum("bnik,bnjk->bnij", q + b, k)
p = torch.einsum("ih,hk->ik", self.pos_tim, self.pos_w)
# fmt: off
p = self.split_heads(p)[None,]
# fmt: on
b = self.pos_p_b[:, None, :, None]
p = torch.einsum("bnik,bnjk->bnij", q + b, p)
y += self.shift(p)
return y
def shift(self, x):
s = x.shape
y = torch.pad(x, [[0, 0], [0, 0], [0, 0], [1, 0]])
y = torch.reshape(y, [s[0], s[1], s[3] + 1, s[2]])
y = torch.slice(y, [0, 0, 1, 0], [-1, -1, -1, -1])
y = torch.reshape(y, s)
return y
def to_scores(self, qk, mask, v):
b = 0
if mask is not None:
b = torch.logical_not(mask)
b = torch.cast(b, torch.floatx()) * qu.big_neg()
if self.proxim_b is not None:
b += self.proxim_b
b = b[:, None, :, None]
y = torch.softmax(qk * self.scale + b)
cfg = self.cfg
y = self.drop(y, cfg.drop_attn or cfg.drop)
y = torch.einsum("bnij,bnjv->bniv", y, v)
return y
class Proximity(UninitializedBuffer):
def materialize(self, end, dtype=None, device=None):
dtype = self.data.dtype if dtype is None else dtype
device = self.data.device if device is None else device
kw = {"dtype": dtype, "device": device}
y = torch.arange(end, **kw)
# fmt: off
y = (y[None,] - y[:, None])
y = -torch.log1p(torch.abs(y))
self.data = y[None, None,]
# fmt: on
self.__class__ = self.cls_to_become
class PosTiming(UninitializedBuffer):
def materialize(self, dim, end, dtype=None, device=None):
dtype = self.data.dtype if dtype is None else dtype
device = self.data.device if device is None else device
kw = {"dtype": dtype, "device": device}
t = torch.arange(end - 1, -1, -1.0, **kw)
f = torch.arange(0, dim, 2.0, **kw)
f = 1 / (10000 ** (f / dim))
t = torch.einsum("i,d->id", t, f)
self.data = torch.cat([torch.sin(t), torch.cos(t)], dim=-1)
self.__class__ = self.cls_to_become
class PosTiming(UninitializedBuffer):
def materialize(self, dim, end, p_max, p_min, p_start, dtype=None, device=None):
dtype = self.data.dtype if dtype is None else dtype
device = self.data.device if device is None else device
kw = {"dtype": dtype, "device": device}
t = torch.arange(end, **kw) + p_start
assert dim % 2 == 0
n = dim // 2
f = np.log(p_max / p_min) / max(n - 1, 1)
f = torch.arange(n, **kw) * -f
f = torch.exp(f) * p_min
t = torch.einsum("i,d->id", t, f)
self.data = torch.cat([torch.sin(t), torch.cos(t)], dim=-1)
self.__class__ = self.cls_to_become
|
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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,414
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/old/convert.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import pathlib as pth
import qnarre.core.utils as utils
from qnarre.core.models import pt
from qnarre.core.mnist import dset_for, model_for, params
TRAIN = "train"
def main(_):
ps = utils.Params(params).init_comps()
ds = dset_for(ps, TRAIN)
# with T.distribute.MirroredStrategy().scope():
mdl = model_for(ps, compiled=True)
mdl.train_on_batch(ds)
mp = pth.Path.cwd() / ps.dir_model / ps.model
assert tf.get_checkpoint_state(str(mp))
mdl.load_weights(str(mp / TRAIN))
c = tf.Checkpoint(model=mdl, optimizer=ps.optimizer)
c.restore(str(mp / TRAIN)).expect_partial() # .assert_consumed()
for n, s in tf.list_variables(str(mp)):
print(n)
mp2 = pth.Path.cwd() / ps.dir_model / "mnist_2"
print("saving...")
c.save(str(mp2 / TRAIN))
for n, s in tf.list_variables(str(mp2)):
print(n)
assert tf.get_checkpoint_state(str(mp2))
mdl.load_weights(str(mp2 / "train-1"))
if __name__ == "__main__":
from absl import app, logging
logging.set_verbosity(logging.INFO) # DEBUG
app.run(main)
|
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"/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], 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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], 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"/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": 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["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], 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"/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,415
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/mbart.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import torch
from argparse import ArgumentParser
from torch import nn
from ..config.mbart import PreTrained
from ...run.mbart import ForConditionalGen
def make_linear_from_emb(x):
s_vocab, emb_size = x.weight.shape
y = nn.Linear(s_vocab, emb_size, bias=False)
y.weight.data = x.weight.data
return y
def to_pytorch(src_path, cfg_path, save_path, finetuned=False, mbart_50=False):
d = torch.load(src_path, map_location="cpu")["model"]
for k in _IGNORE:
d.pop(k, None)
s_vocab = d["encoder.embed_tokens.weight"].shape[0]
cfg = PreTrained.from_pretrained(cfg_path, s_vocab=s_vocab)
if mbart_50 and finetuned:
cfg.act_fun = "relu"
print(f"Building from config: {cfg}")
d["shared.weight"] = d["decoder.embed_tokens.weight"]
m = ForConditionalGen(cfg)
m.model.load_state_dict(d)
if finetuned:
m.lm_head = make_linear_from_emb(m.model.shared)
print(f"Saving to: {save_path}")
torch.save(m.state_dict(), save_path)
_IGNORE = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"_float_tensor",
"decoder.output_projection.weight",
]
if __name__ == "__main__":
x = ArgumentParser()
x.add_argument("--src_path", type=str)
x.add_argument("--cfg_path", default="facebook/mbart-large-cc25", type=str)
x.add_argument("--save_path", default=None, type=str)
x.add_argument("--finetuned", action="store_true")
x.add_argument("--mbart_50", action="store_true")
y = x.parse_args()
to_pytorch(y.src_path, y.cfg_path, y.finetuned, y.mbart_50)
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,416
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/try/norm.py
|
"""
Layer Normalization
====================
In this tutorial, you will write a high-performance layer normalization
kernel that runs faster than the PyTorch implementation.
In doing so, you will learn about:
* Implementing backward pass in Triton.
* Implementing parallel reduction in Triton.
"""
# %%
# Motivations
# -----------
#
# The *LayerNorm* operator was first introduced in [BA2016]_ as a way to improve the performance
# of sequential models (e.g., Transformers) or neural networks with small batch size.
# It takes a vector :math:`x` as input and produces a vector :math:`y` of the same shape as output.
# The normalization is performed by subtracting the mean and dividing by the standard deviation of :math:`x`.
# After the normalization, a learnable linear transformation with weights :math:`w` and biases :math:`b` is applied.
# The forward pass can be expressed as follows:
#
# .. math::
# y = \frac{ x - \text{E}[x] }{ \sqrt{\text{Var}(x) + \epsilon} } * w + b
#
# where :math:`\epsilon` is a small constant added to the denominator for numerical stability.
# Let’s first take a look at the forward pass implementation.
import torch
import triton
import triton.language as tl
try:
# This is https://github.com/NVIDIA/apex, NOT the apex on PyPi, so it
# should not be added to extras_require in setup.py.
import apex
HAS_APEX = True
except ModuleNotFoundError:
HAS_APEX = False
@triton.jit
def _layer_norm_fwd_fused(
X, # pointer to the input
Y, # pointer to the output
W, # pointer to the weights
B, # pointer to the biases
Mean, # pointer to the mean
Rstd, # pointer to the 1/std
stride, # how much to increase the pointer when moving by 1 row
N, # number of columns in X
eps, # epsilon to avoid division by zero
BLOCK_SIZE: tl.constexpr,
):
# Map the program id to the row of X and Y it should compute.
row = tl.program_id(0)
Y += row * stride
X += row * stride
# Compute mean
mean = 0
_mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
a = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
_mean += a
mean = tl.sum(_mean, axis=0) / N
# Compute variance
_var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
x = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
x = tl.where(cols < N, x - mean, 0.)
_var += x * x
var = tl.sum(_var, axis=0) / N
rstd = 1 / tl.sqrt(var + eps)
# Write mean / rstd
tl.store(Mean + row, mean)
tl.store(Rstd + row, rstd)
# Normalize and apply linear transformation
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
mask = cols < N
w = tl.load(W + cols, mask=mask)
b = tl.load(B + cols, mask=mask)
x = tl.load(X + cols, mask=mask, other=0.).to(tl.float32)
x_hat = (x - mean) * rstd
y = x_hat * w + b
# Write output
tl.store(Y + cols, y, mask=mask)
# %%
# Backward pass
# -------------
#
# The backward pass for the layer normalization operator is a bit more involved than the forward pass.
# Let :math:`\hat{x}` be the normalized inputs :math:`\frac{ x - \text{E}[x] }{ \sqrt{\text{Var}(x) + \epsilon} }` before the linear transformation,
# the Vector-Jacobian Products (VJP) :math:`\nabla_{x}` of :math:`x` are given by:
#
# .. math::
# \nabla_{x} = \frac{1}{\sigma}\Big( \nabla_{y} \odot w - \underbrace{ \big( \frac{1}{N} \hat{x} \cdot (\nabla_{y} \odot w) \big) }_{c_1} \odot \hat{x} - \underbrace{ \frac{1}{N} \nabla_{y} \cdot w }_{c_2} \Big)
#
# where :math:`\odot` denotes the element-wise multiplication, :math:`\cdot` denotes the dot product, and :math:`\sigma` is the standard deviation.
# :math:`c_1` and :math:`c_2` are intermediate constants that improve the readability of the following implementation.
#
# For the weights :math:`w` and biases :math:`b`, the VJPs :math:`\nabla_{w}` and :math:`\nabla_{b}` are more straightforward:
#
# .. math::
# \nabla_{w} = \nabla_{y} \odot \hat{x} \quad \text{and} \quad \nabla_{b} = \nabla_{y}
#
# Since the same weights :math:`w` and biases :math:`b` are used for all rows in the same batch, their gradients need to sum up.
# To perform this step efficiently, we use a parallel reduction strategy: each kernel instance accumulates
# partial :math:`\nabla_{w}` and :math:`\nabla_{b}` across certain rows into one of :math:`\text{GROUP_SIZE_M}` independent buffers.
# These buffers stay in the L2 cache and then are further reduced by another function to compute the actual :math:`\nabla_{w}` and :math:`\nabla_{b}`.
#
# Let the number of input rows :math:`M = 4` and :math:`\text{GROUP_SIZE_M} = 2`,
# here's a diagram of the parallel reduction strategy for :math:`\nabla_{w}` (:math:`\nabla_{b}` is omitted for brevity):
#
# .. image:: parallel_reduction.png
#
# In Stage 1, the rows of X that have the same color share the same buffer and thus a lock is used to ensure that only one kernel instance writes to the buffer at a time.
# In Stage 2, the buffers are further reduced to compute the final :math:`\nabla_{w}` and :math:`\nabla_{b}`.
# In the following implementation, Stage 1 is implemented by the function :code:`_layer_norm_bwd_dx_fused` and Stage 2 is implemented by the function :code:`_layer_norm_bwd_dwdb`.
@triton.jit
def _layer_norm_bwd_dx_fused(
DX, # pointer to the input gradient
DY, # pointer to the output gradient
DW, # pointer to the partial sum of weights gradient
DB, # pointer to the partial sum of biases gradient
X, # pointer to the input
W, # pointer to the weights
B, # pointer to the biases
Mean, # pointer to the mean
Rstd, # pointer to the 1/std
Lock, # pointer to the lock
stride, # how much to increase the pointer when moving by 1 row
N, # number of columns in X
eps, # epsilon to avoid division by zero
GROUP_SIZE_M: tl.constexpr,
BLOCK_SIZE_N: tl.constexpr
):
# Map the program id to the elements of X, DX, and DY it should compute.
row = tl.program_id(0)
cols = tl.arange(0, BLOCK_SIZE_N)
mask = cols < N
X += row * stride
DY += row * stride
DX += row * stride
# Offset locks and weights/biases gradient pointer for parallel reduction
lock_id = row % GROUP_SIZE_M
Lock += lock_id
Count = Lock + GROUP_SIZE_M
DW = DW + lock_id * N + cols
DB = DB + lock_id * N + cols
# Load data to SRAM
x = tl.load(X + cols, mask=mask, other=0).to(tl.float32)
dy = tl.load(DY + cols, mask=mask, other=0).to(tl.float32)
w = tl.load(W + cols, mask=mask).to(tl.float32)
mean = tl.load(Mean + row)
rstd = tl.load(Rstd + row)
# Compute dx
xhat = (x - mean) * rstd
wdy = w * dy
xhat = tl.where(mask, xhat, 0.)
wdy = tl.where(mask, wdy, 0.)
c1 = tl.sum(xhat * wdy, axis=0) / N
c2 = tl.sum(wdy, axis=0) / N
dx = (wdy - (xhat * c1 + c2)) * rstd
# Write dx
tl.store(DX + cols, dx, mask=mask)
# Accumulate partial sums for dw/db
partial_dw = (dy * xhat).to(w.dtype)
partial_db = (dy).to(w.dtype)
while tl.atomic_cas(Lock, 0, 1) == 1:
pass
count = tl.load(Count)
# First store doesn't accumulate
if count == 0:
tl.atomic_xchg(Count, 1)
else:
partial_dw += tl.load(DW, mask=mask)
partial_db += tl.load(DB, mask=mask)
tl.store(DW, partial_dw, mask=mask)
tl.store(DB, partial_db, mask=mask)
# Release the lock
tl.atomic_xchg(Lock, 0)
@triton.jit
def _layer_norm_bwd_dwdb(
DW, # pointer to the partial sum of weights gradient
DB, # pointer to the partial sum of biases gradient
FINAL_DW, # pointer to the weights gradient
FINAL_DB, # pointer to the biases gradient
M, # GROUP_SIZE_M
N, # number of columns
BLOCK_SIZE_M: tl.constexpr,
BLOCK_SIZE_N: tl.constexpr
):
# Map the program id to the elements of DW and DB it should compute.
pid = tl.program_id(0)
cols = pid * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
dw = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
db = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
# Iterate through the rows of DW and DB to sum the partial sums.
for i in range(0, M, BLOCK_SIZE_M):
rows = i + tl.arange(0, BLOCK_SIZE_M)
mask = (rows[:, None] < M) & (cols[None, :] < N)
offs = rows[:, None] * N + cols[None, :]
dw += tl.load(DW + offs, mask=mask, other=0.)
db += tl.load(DB + offs, mask=mask, other=0.)
# Write the final sum to the output.
sum_dw = tl.sum(dw, axis=0)
sum_db = tl.sum(db, axis=0)
tl.store(FINAL_DW + cols, sum_dw, mask=cols < N)
tl.store(FINAL_DB + cols, sum_db, mask=cols < N)
# %%
# Benchmark
# ---------
#
# We can now compare the performance of our kernel against that of PyTorch.
# Here we focus on inputs that have Less than 64KB per feature.
# Specifically, one can set :code:`'mode': 'backward'` to benchmark the backward pass.
class LayerNorm(torch.autograd.Function):
@staticmethod
def forward(ctx, x, normalized_shape, weight, bias, eps):
# allocate output
y = torch.empty_like(x)
# reshape input data into 2D tensor
x_arg = x.reshape(-1, x.shape[-1])
M, N = x_arg.shape
mean = torch.empty((M, ), dtype=torch.float32, device='cuda')
rstd = torch.empty((M, ), dtype=torch.float32, device='cuda')
# Less than 64KB per feature: enqueue fused kernel
MAX_FUSED_SIZE = 65536 // x.element_size()
BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))
if N > BLOCK_SIZE:
raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.")
# heuristics for number of warps
num_warps = min(max(BLOCK_SIZE // 256, 1), 8)
# enqueue kernel
_layer_norm_fwd_fused[(M,)](x_arg, y, weight, bias, mean, rstd,
x_arg.stride(0), N, eps,
BLOCK_SIZE=BLOCK_SIZE, num_warps=num_warps)
ctx.save_for_backward(x, weight, bias, mean, rstd)
ctx.BLOCK_SIZE = BLOCK_SIZE
ctx.num_warps = num_warps
ctx.eps = eps
return y
@staticmethod
def backward(ctx, dy):
x, w, b, m, v = ctx.saved_tensors
# heuristics for amount of parallel reduction stream for DW/DB
N = w.shape[0]
GROUP_SIZE_M = 64
if N <= 8192: GROUP_SIZE_M = 96
if N <= 4096: GROUP_SIZE_M = 128
if N <= 1024: GROUP_SIZE_M = 256
# allocate output
locks = torch.zeros(2 * GROUP_SIZE_M, dtype=torch.int32, device='cuda')
_dw = torch.empty((GROUP_SIZE_M, w.shape[0]), dtype=x.dtype, device=w.device)
_db = torch.empty((GROUP_SIZE_M, w.shape[0]), dtype=x.dtype, device=w.device)
dw = torch.empty((w.shape[0],), dtype=w.dtype, device=w.device)
db = torch.empty((w.shape[0],), dtype=w.dtype, device=w.device)
dx = torch.empty_like(dy)
# enqueue kernel using forward pass heuristics
# also compute partial sums for DW and DB
x_arg = x.reshape(-1, x.shape[-1])
M, N = x_arg.shape
_layer_norm_bwd_dx_fused[(M,)](dx, dy, _dw, _db, x, w, b, m, v, locks,
x_arg.stride(0), N, ctx.eps,
BLOCK_SIZE_N=ctx.BLOCK_SIZE,
GROUP_SIZE_M=GROUP_SIZE_M,
num_warps=ctx.num_warps)
grid = lambda meta: [triton.cdiv(N, meta['BLOCK_SIZE_N'])]
# accumulate partial sums in separate kernel
_layer_norm_bwd_dwdb[grid](_dw, _db, dw, db, GROUP_SIZE_M, N,
BLOCK_SIZE_M=32,
BLOCK_SIZE_N=128)
return dx, None, dw, db, None
layer_norm = LayerNorm.apply
def test_layer_norm(M, N, dtype, eps=1e-5, device='cuda'):
# create data
x_shape = (M, N)
w_shape = (x_shape[-1], )
weight = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
bias = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
x = -2.3 + 0.5 * torch.randn(x_shape, dtype=dtype, device='cuda')
dy = .1 * torch.randn_like(x)
x.requires_grad_(True)
# forward pass
y_tri = layer_norm(x, w_shape, weight, bias, eps)
y_ref = torch.nn.functional.layer_norm(x, w_shape, weight, bias, eps).to(dtype)
# backward pass (triton)
y_tri.backward(dy, retain_graph=True)
dx_tri, dw_tri, db_tri = [_.grad.clone() for _ in [x, weight, bias]]
x.grad, weight.grad, bias.grad = None, None, None
# backward pass (torch)
y_ref.backward(dy, retain_graph=True)
dx_ref, dw_ref, db_ref = [_.grad.clone() for _ in [x, weight, bias]]
# compare
assert torch.allclose(y_tri, y_ref, atol=1e-2, rtol=0)
assert torch.allclose(dx_tri, dx_ref, atol=1e-2, rtol=0)
assert torch.allclose(db_tri, db_ref, atol=1e-2, rtol=0)
assert torch.allclose(dw_tri, dw_ref, atol=1e-2, rtol=0)
@triton.testing.perf_report(
triton.testing.Benchmark(
x_names=['N'],
x_vals=[512 * i for i in range(2, 32)],
line_arg='provider',
line_vals=['triton', 'torch'] + (['apex'] if HAS_APEX else []),
line_names=['Triton', 'Torch'] + (['Apex'] if HAS_APEX else []),
styles=[('blue', '-'), ('green', '-'), ('orange', '-')],
ylabel='GB/s',
plot_name='layer-norm-backward',
args={'M': 4096, 'dtype': torch.float16, 'mode': 'backward'}
)
)
def bench_layer_norm(M, N, dtype, provider, mode='backward', eps=1e-5, device='cuda'):
# create data
x_shape = (M, N)
w_shape = (x_shape[-1], )
weight = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
bias = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
x = -2.3 + 0.5 * torch.randn(x_shape, dtype=dtype, device='cuda')
dy = .1 * torch.randn_like(x)
x.requires_grad_(True)
quantiles = [0.5, 0.2, 0.8]
# utility functions
if provider == 'triton':
y_fwd = lambda: layer_norm(x, w_shape, weight, bias, eps)
if provider == 'torch':
y_fwd = lambda: torch.nn.functional.layer_norm(x, w_shape, weight, bias, eps)
if provider == 'apex':
apex_layer_norm = apex.normalization.FusedLayerNorm(w_shape).to(x.device).to(x.dtype)
y_fwd = lambda: apex_layer_norm(x)
# forward pass
if mode == 'forward':
gbps = lambda ms: 2 * x.numel() * x.element_size() / ms * 1e-6
ms, min_ms, max_ms = triton.testing.do_bench(y_fwd, quantiles=quantiles, rep=500)
# backward pass
if mode == 'backward':
gbps = lambda ms: 3 * x.numel() * x.element_size() / ms * 1e-6
y = y_fwd()
ms, min_ms, max_ms = triton.testing.do_bench(lambda: y.backward(dy, retain_graph=True),
quantiles=quantiles, grad_to_none=[x], rep=500)
return gbps(ms), gbps(max_ms), gbps(min_ms)
test_layer_norm(1151, 8192, torch.float16)
bench_layer_norm.run(save_path='.', print_data=True)
# %%
# References
# ----------
#
# .. [BA2016] Jimmy Lei Ba and Jamie Ryan Kiros and Geoffrey E. Hinton, "Layer Normalization", Arxiv 2016
|
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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,417
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/core/utils.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import numpy as np
import os
import torch
from itertools import chain
from transformers.activations import ACT2FN
def activation(k, v=None):
return ACT2FN[k] if isinstance(k, str) else (v or k)
def view_2D(*xs):
return [x.view(-1, x.size(-1)) if x is not None else None for x in xs]
def view_3D(*xs):
return [x.view(-1, x.size(-2), x.size(-1)) if x is not None else None for x in xs]
def get_list(xs):
ys = set()
for x in xs:
ys = ys | set(x)
ys = list(ys)
ys.sort()
return ys
def group_texts(size, xs):
ys = {k: list(chain(*xs[k])) for k in xs.keys()}
n = len(ys[list(xs.keys())[0]])
if n >= size:
n = (n // size) * size
ys = {k: [x[i : i + size] for i in range(0, n, size)] for k, x in ys.items()}
ys["labels"] = ys["input_ids"].copy()
return ys
def init_array(xs, dataset, lim):
i = 0
ys = np.full((len(dataset), lim), -100, dtype=np.float32) # float64)
for x in xs:
batch = x.shape[0]
cols = x.shape[1]
if i + batch < len(dataset):
ys[i : i + batch, :cols] = x
else:
ys[i:, :cols] = x[: len(dataset) - i]
i += batch
return ys
def big_neg(dtype=None):
f = dtype
return torch.float16.min if f == "float16" else -1e9
class Dictionary:
def __init__(self):
self.word2idx = {}
self.idx2word = []
def add_word(self, x):
if x not in self.word2idx:
self.idx2word.append(x)
self.word2idx[x] = len(self.idx2word) - 1
return self.word2idx[x]
def __len__(self):
return len(self.idx2word)
class Corpus:
def __init__(self, path):
self.dictionary = Dictionary()
self.train = self.tokenize(os.path.join(path, "train.txt"))
self.eval = self.tokenize(os.path.join(path, "eval.txt"))
self.test = self.tokenize(os.path.join(path, "test.txt"))
def tokenize(self, path):
assert os.path.exists(path)
with open(path, "r", encoding="utf8") as f:
for line in f:
words = line.split() + ["<eos>"]
for word in words:
self.dictionary.add_word(word)
with open(path, "r", encoding="utf8") as f:
idss = []
for line in f:
words = line.split() + ["<eos>"]
ids = []
for word in words:
ids.append(self.dictionary.word2idx[word])
idss.append(torch.tensor(ids).type(torch.int64))
ids = torch.cat(idss)
return ids
def shift_right(x, PAD, dec_START):
y = x.new_zeros(x.shape)
y[:, 1:] = x[:, :-1].clone()
y[:, 0] = dec_START
assert PAD is not None
y.masked_fill_(y == -100, PAD)
return y
def shift_right2(x, PAD):
y = x.clone()
assert PAD is not None
y.masked_fill_(y == -100, PAD)
eos = (y.ne(PAD).sum(dim=1) - 1).unsqueeze(-1)
dec_START = y.gather(1, eos).squeeze()
y[:, 1:] = y[:, :-1].clone()
y[:, 0] = dec_START
return y
def causal_mask(shape, dtype, device, c_len=0): # qpx add device
b, n = shape
y = torch.full((n, n), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
cond = torch.arange(y.size(-1), device=device)
y.masked_fill_(cond < (cond + 1).view(y.size(-1), 1), 0)
y = y.to(dtype)
if c_len > 0:
y = torch.cat([torch.zeros(n, c_len, dtype=dtype, device=device), y], dim=-1)
return y[None, None, :, :].expand(b, 1, n, n + c_len)
def expand_mask(x, dtype, len=None):
b, n = x.size()
len = len if len is not None else n
y = 1.0 - x[:, None, None, :].expand(b, 1, len, n).to(dtype)
return y.masked_fill(y.to(torch.bool), torch.finfo(dtype).min) # * y
|
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"/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], 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"/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,418
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/named.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
from .. import to_class
from ..rectify import QNERR
class Tagged:
_factor = _weight = 1
_bias = 0
def __init__(self, *, tag, factor=None, bias=None, weight=None, **kw):
assert tag == self.tag
for k in ('agent', 'author', 'authority', 'genre', 'preset', 'place'):
kw.pop(k, None)
if kw:
print(kw)
super().__init__(**kw)
if factor is not None:
self._factor = factor
if bias is not None:
self._bias = bias
if weight is not None:
self._weight = weight
@classmethod
def to_tag(cls):
return cls.__name__.lower()
@property
def tag(self):
return type(self).to_tag()
@property
def factor(self):
return self._factor
@property
def bias(self):
return self._bias
@property
def weight(self):
return self.factor * self._weight + self.bias
def partial(self, *parts):
ws = []
for p in parts:
if isinstance(p, tuple):
ws.extend(p)
else:
ws.append(p)
return self.factor * (sum(ws) if self._weight == 1 else len(ws))
class Saved:
root = None
def __init__(self, *, root=None, path=None, text=None, **kw):
super().__init__(**kw)
if root:
self.root = root
if text is None and path:
text = ''
p = self.root / path
if p.exists():
text = p.read_text(encoding='ascii', errors=QNERR)
if text is not None:
self.from_text(text, root=root, **kw)
def __str__(self):
return '{}{}'.format(self.name, self.suff)
@property
def suff(self):
return '.' + self.tag
@property
def path(self):
return self.root / str(self)
def save(self, **kw):
t = self.to_text(**kw)
self.path.write_text(t, encoding='ascii', errors=QNERR)
class Named(Tagged):
sequence = None
_by_name = {}
_by_tag = None
_seq = 0
@classmethod
def next_seq(cls):
cls._seq += 1
return cls._seq
@classmethod
def next_name(cls):
return '{:0>6d}'.format(len(cls._by_name))
@classmethod
def create(cls, *, name, tag=None, **kw):
n = name if ':' in name else (':' + name)
t, n = n.split(':')
t = tag if tag else t
t = t if t else cls.to_tag()
n = cls.next_name() if n == 'fudge' else n
k = t + ':' + n
try:
v = cls._by_name[k]
if len(kw):
v.__init__(tag=t, name=n, **kw)
except KeyError:
c = cls if t == cls.to_tag() else to_class(t)
if not len(kw):
kw['empty'] = True
cls._by_name[k] = v = c(tag=t, name=n, **kw)
cls._by_tag = None
return v
@classmethod
def by_tag(cls, tag):
if cls._by_tag is None:
cls._by_tag = bt = {}
for n in cls._by_name.values():
bt.setdefault(n.tag, []).append(n)
return cls._by_tag.get(tag, ())
def __init__(self, *, name, empty=False, **kw):
super().__init__(**kw)
self.name = name
if not empty and self.sequence is None:
self.sequence = self.next_seq()
def __str__(self):
return "'{}:{}'".format(self.tag, self.name)
@property
def fields(self):
return {'Type': self.tag, 'Name': self.name}
def also_as(self, tag):
k = tag + ':' + self.name
assert k not in self._by_name
self._by_name[k] = self
class Preset(Saved, Named):
props = {}
def from_text(self, txt, **_):
self.props = eval(txt or '{}')
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], 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"/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,419
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/language/semantic.py
|
from __future__ import annotations # remove after python 3.11
from functools import wraps
from typing import List, Optional, Sequence, Tuple, TypeVar
from . import core as tl
from triton._C.libtriton.triton import ir
T = TypeVar('T')
# Create custom exception that prints message "hello"
class IncompatibleTypeErrorImpl(Exception):
def __init__(self, type_a, type_b):
self.type_a = type_a
self.type_b = type_b
self.message = "invalid operands of type " + self.type_a.__repr__() + " and " + self.type_b.__repr__()
super(IncompatibleTypeErrorImpl, self).__init__(self.message)
# ===----------------------------------------------------------------------===##
# Programming Model
# ===----------------------------------------------------------------------===##
def program_id(axis: int, builder: ir.builder) -> tl.tensor:
return tl.tensor(builder.create_get_program_id(axis), tl.int32)
def num_programs(axis: int, builder: ir.builder) -> tl.tensor:
return tl.tensor(builder.create_get_num_programs(axis), tl.int32)
# ===----------------------------------------------------------------------===//
# Implicit Casting Utilities
# ===----------------------------------------------------------------------===//
def integer_promote_impl(a_ty: tl.dtype, b_ty: tl.dtype) -> tl.dtype:
a_rank = a_ty.int_bitwidth
b_rank = b_ty.int_bitwidth
a_sn = a_ty.int_signedness
b_sn = b_ty.int_signedness
# Rules for signedness taken from "Usual arithmetic conversions" on
# https://en.cppreference.com/w/c/language/conversion.
if a_sn == b_sn:
return a_ty if a_rank > b_rank else b_ty
elif a_sn == tl.dtype.SIGNEDNESS.UNSIGNED:
return a_ty if a_rank >= b_rank else b_ty
elif b_sn == tl.dtype.SIGNEDNESS.UNSIGNED:
return b_ty if b_rank >= a_rank else a_ty
assert False
def computation_type_impl(a_ty: tl.dtype, b_ty: tl.dtype, div_or_mod: bool) -> tl.dtype:
# 1) if one operand is double, the other is implicitly
# converted to double
if a_ty.is_fp64() or b_ty.is_fp64():
return tl.float64
# 2) if one operand is float, the other is implicitly
# converted to float
if a_ty.is_fp32() or b_ty.is_fp32():
return tl.float32
# 3 ) if one operand is half, the other is implicitly converted to half
# unless we're doing / or %, which do not exist natively in PTX for fp16.
# Supported PTX op: add, sub, mul, fma, neg, abs, min, max, tanh, ex2, setp
if a_ty.is_fp16() or b_ty.is_fp16():
if div_or_mod:
return tl.float32
else:
return tl.float16
# 4) return bf16 only if both operands are of bf16
if a_ty.is_bf16() or b_ty.is_bf16():
if div_or_mod:
return tl.float32
if a_ty.is_bf16() and b_ty.is_bf16():
return tl.bfloat16
return tl.float32
if not a_ty.is_int() or not b_ty.is_int():
assert False
# 5 ) both operands are integer and undergo
# integer promotion
if div_or_mod and a_ty.int_signedness != b_ty.int_signedness:
raise ValueError("Cannot use /, #, or % with " + a_ty.__repr__() + " and " + b_ty.__repr__() + " because they have different signedness;"
"this is unlikely to result in a useful answer. Cast them to the same signedness.")
return integer_promote_impl(a_ty, b_ty)
# ===----------------------------------------------------------------------===//
# Binary Operators
# ===----------------------------------------------------------------------===//
def check_ptr_type_impl(type_a: tl.dtype, type_b: tl.dtype, allow_ptr_a: bool) -> None:
if type_a.is_ptr():
if not allow_ptr_a:
raise IncompatibleTypeErrorImpl(type_a, type_b)
# T* + U* with T != U
if type_b.is_ptr() and (type_a != type_b):
raise IncompatibleTypeErrorImpl(type_a, type_b)
# T* + float
if type_b.is_floating():
raise IncompatibleTypeErrorImpl(type_a, type_b)
def binary_op_type_checking_impl(lhs: tl.tensor,
rhs: tl.tensor,
builder: ir.builder,
allow_lhs_ptr=False, allow_rhs_ptr=False,
arithmetic_check=True, div_or_mod=False
) -> Tuple[tl.tensor, tl.tensor]:
# implicit broadcasting
lhs, rhs = broadcast_impl_value(lhs, rhs, builder)
# implicit typecasting
lhs_sca_ty = lhs.type.scalar
rhs_sca_ty = rhs.type.scalar
check_ptr_type_impl(lhs_sca_ty, rhs_sca_ty, allow_lhs_ptr)
check_ptr_type_impl(rhs_sca_ty, lhs_sca_ty, allow_rhs_ptr)
if arithmetic_check and not lhs_sca_ty.is_ptr() and not rhs_sca_ty.is_ptr():
ret_sca_ty = computation_type_impl(lhs_sca_ty, rhs_sca_ty, div_or_mod)
lhs = cast(lhs, ret_sca_ty, builder)
rhs = cast(rhs, ret_sca_ty, builder)
return lhs, rhs
def add(input: tl.tensor,
other: tl.tensor,
builder: ir.builder) -> tl.tensor:
input, other = binary_op_type_checking_impl(input, other, builder, True, True)
input_scalar_ty = input.type.scalar
other_scalar_ty = other.type.scalar
# offset + ptr
# ptr + offset
if other_scalar_ty.is_ptr() and not input_scalar_ty.is_ptr():
input, other = other, input
if input_scalar_ty.is_ptr():
return tl.tensor(builder.create_addptr(input.handle, other.handle), input.type)
# float + float
elif input_scalar_ty.is_floating():
return tl.tensor(builder.create_fadd(input.handle, other.handle), input.type)
# int + int
elif input_scalar_ty.is_int():
return tl.tensor(builder.create_add(input.handle, other.handle), input.type)
assert False
def sub(input: tl.tensor,
other: tl.tensor,
builder: ir.builder) -> tl.tensor:
input, other = binary_op_type_checking_impl(input, other, builder, True, False)
scalar_ty = input.type.scalar
# ptr - offset
if scalar_ty.is_ptr():
return tl.tensor(builder.create_addptr(input.handle, minus(other, builder).handle),
input.type)
# float - float
if scalar_ty.is_floating():
return tl.tensor(builder.create_fsub(input.handle, other.handle), input.type)
# int - int
elif scalar_ty.is_int():
return tl.tensor(builder.create_sub(input.handle, other.handle), input.type)
assert False
def mul(input: tl.tensor,
other: tl.tensor,
builder: ir.builder) -> tl.tensor:
input, other = binary_op_type_checking_impl(input, other, builder)
scalar_ty = input.type.scalar
# float * float
if scalar_ty.is_floating():
return tl.tensor(builder.create_fmul(input.handle, other.handle), input.type)
# * int
elif scalar_ty.is_int():
return tl.tensor(builder.create_mul(input.handle, other.handle), input.type)
assert False
def truediv(input: tl.tensor,
other: tl.tensor,
builder: ir.builder) -> tl.tensor:
input, other = binary_op_type_checking_impl(input, other, builder, False, False, True, True)
input_scalar_ty = input.type.scalar
other_scalar_ty = other.type.scalar
# float / int
if input_scalar_ty.is_floating() and other_scalar_ty.is_int():
other = cast(other, input_scalar_ty, builder)
# int / float
elif input_scalar_ty.is_int() and other_scalar_ty.is_floating():
input = cast(input, other_scalar_ty, builder)
# int / int (cast to tl.float32)
elif input_scalar_ty.is_int() and other_scalar_ty.is_int():
input = cast(input, tl.float32, builder)
other = cast(other, tl.float32, builder)
# float / float (cast to highest exponent type)
elif input_scalar_ty.is_floating() and other_scalar_ty.is_floating():
if input_scalar_ty.fp_mantissa_width > other_scalar_ty.fp_mantissa_width:
other = cast(other, input_scalar_ty, builder)
else:
input = cast(input, other_scalar_ty, builder)
# unreachable
else:
assert False
return tl.tensor(builder.create_fdiv(input.handle, other.handle), input.type)
def floordiv(input: tl.tensor,
other: tl.tensor,
builder: ir.builder) -> tl.tensor:
input, other = binary_op_type_checking_impl(input, other, builder, False, False, True, True)
input_scalar_ty = input.type.scalar
other_scalar_ty = other.type.scalar
if input_scalar_ty.is_int() and other_scalar_ty.is_int():
ret_ty = integer_promote_impl(input_scalar_ty, other_scalar_ty)
input = cast(input, ret_ty, builder)
other = cast(other, ret_ty, builder)
if ret_ty.is_int_signed():
return tl.tensor(builder.create_sdiv(input.handle, other.handle), input.type)
else:
return tl.tensor(builder.create_udiv(input.handle, other.handle), input.type)
assert False
def fdiv(input: tl.tensor,
other: tl.tensor,
ieee_rounding: bool,
builder: ir.builder) -> tl.tensor:
input_scalar_ty = input.type.scalar
other_scalar_ty = other.type.scalar
if not input_scalar_ty.is_floating() or not other_scalar_ty.is_floating():
raise ValueError("both operands of fdiv must have floating scalar type")
input, other = binary_op_type_checking_impl(input, other, builder, False, False, False, True)
ret = builder.create_fdiv(input.handle, other.handle)
return tl.tensor(ret, input.type)
def mod(input: tl.tensor,
other: tl.tensor,
builder: ir.builder) -> tl.tensor:
input, other = binary_op_type_checking_impl(input, other, builder, False, False, True, True)
scalar_ty = input.type.scalar
other_scalar_ty = other.type.scalar
# float % float
if scalar_ty.is_floating():
# input - input.div(other, rounding_mode="floor") * other
ret = sub(input, mul(floor(fdiv(input, other, False, builder), builder),
other, builder),
builder)
return ret
# % int
elif scalar_ty.is_int():
if scalar_ty.int_signedness != other_scalar_ty.int_signedness:
raise ValueError("Cannot mod " + scalar_ty.__repr__() + " by " + other_scalar_ty.__repr__() + " "
"because they have different signedness;"
"this is unlikely to result in a useful answer. Cast them to the same signedness.")
if scalar_ty.is_int_signed():
return tl.tensor(builder.create_srem(input.handle, other.handle), input.type)
else:
return tl.tensor(builder.create_urem(input.handle, other.handle), input.type)
assert False
##############
# bitwise ops
##############
def bitwise_op_type_checking_impl(input: tl.tensor,
other: tl.tensor,
builder: ir.builder) -> Tuple[tl.tensor, tl.tensor]:
input, other = binary_op_type_checking_impl(input, other, builder, False, False, False)
input_sca_ty = input.type.scalar
other_sca_ty = other.type.scalar
if not input_sca_ty.is_int() or not other_sca_ty.is_int():
raise IncompatibleTypeErrorImpl(input_sca_ty, other_sca_ty)
ret_sca_ty = integer_promote_impl(input_sca_ty, other_sca_ty)
if ret_sca_ty != input_sca_ty:
input = cast(input, ret_sca_ty, builder)
if ret_sca_ty != other_sca_ty:
other = cast(other, ret_sca_ty, builder)
return input, other
def and_(input: tl.tensor,
other: tl.tensor,
builder: ir.builder) -> tl.tensor:
input, other = bitwise_op_type_checking_impl(input, other, builder)
return tl.tensor(builder.create_and(input.handle, other.handle), input.type)
def or_(input: tl.tensor,
other: tl.tensor,
builder: ir.builder) -> tl.tensor:
input, other = bitwise_op_type_checking_impl(input, other, builder)
return tl.tensor(builder.create_or(input.handle, other.handle), input.type)
def xor_(input: tl.tensor,
other: tl.tensor,
builder: ir.builder) -> tl.tensor:
input, other = bitwise_op_type_checking_impl(input, other, builder)
return tl.tensor(builder.create_xor(input.handle, other.handle), input.type)
def logical_and(input: tl.tensor, other: tl.tensor, builder: ir.builder) -> tl.tensor:
if not input.type.is_int1():
input = bitcast(input, tl.dtype("int1"), builder)
if not other.type.is_int1():
other = bitcast(other, tl.dtype("int1"), builder)
return and_(input, other, builder)
def logical_or(input: tl.tensor, other: tl.tensor, builder: ir.builder) -> tl.tensor:
if not input.type.is_int1():
input = bitcast(input, tl.dtype("int1"), builder)
if not other.type.is_int1():
other = bitcast(other, tl.dtype("int1"), builder)
return or_(input, other, builder)
def not_(input: tl.tensor, builder: ir.builder):
if not input.type.is_int1():
input = bitcast(input, tl.dtype("int1"), builder)
return invert(input, builder)
def lshr(input: tl.tensor,
other: tl.tensor,
builder: ir.builder) -> tl.tensor:
input, other = bitwise_op_type_checking_impl(input, other, builder)
return tl.tensor(builder.create_lshr(input.handle, other.handle), input.type)
def ashr(input: tl.tensor,
other: tl.tensor,
builder: ir.builder) -> tl.tensor:
input, other = bitwise_op_type_checking_impl(input, other, builder)
return tl.tensor(builder.create_ashr(input.handle, other.handle), input.type)
def shl(input: tl.tensor,
other: tl.tensor,
builder: ir.builder) -> tl.tensor:
input, other = bitwise_op_type_checking_impl(input, other, builder)
return tl.tensor(builder.create_shl(input.handle, other.handle), input.type)
# ===----------------------------------------------------------------------===//
# Unary Operators
# ===----------------------------------------------------------------------===//
def plus(input: tl.tensor) -> tl.tensor:
return input
def minus(input: tl.tensor,
builder: ir.builder) -> tl.tensor:
input_sca_ty = input.type.scalar
if input_sca_ty.is_ptr():
raise ValueError("wrong type argument to unary minus (" + input_sca_ty.__repr__() + ")")
_0 = tl.tensor(builder.get_null_value(input_sca_ty.to_ir(builder)), input_sca_ty)
return sub(_0, input, builder)
def invert(input: tl.tensor,
builder: tl.tensor) -> tl.tensor:
input_sca_ty = input.type.scalar
if input_sca_ty.is_ptr() or input_sca_ty.is_floating():
raise ValueError("wrong type argument to unary invert (" + input_sca_ty.__repr__() + ")")
_1 = tl.tensor(builder.get_all_ones_value(input_sca_ty.to_ir(builder)), input_sca_ty)
return xor_(input, _1, builder)
# ===----------------------------------------------------------------------===//
# Comparison Operators
# ===----------------------------------------------------------------------===//
def _bool_like(v: tl.tensor) -> tl.block_type:
if not v.type.is_block():
return tl.int1
shape = v.type.shape
return tl.block_type(tl.int1, shape)
def greater_than(input: tl.tensor,
other: tl.tensor,
builder: ir.builder) -> tl.tensor:
input, other = binary_op_type_checking_impl(input, other, builder)
scalar_ty = input.type.scalar
# float > float
if scalar_ty.is_floating():
return tl.tensor(builder.create_fcmpOGT(input.handle, other.handle), _bool_like(input))
# > int
elif scalar_ty.is_int():
if scalar_ty.is_int_signed():
return tl.tensor(builder.create_icmpSGT(input.handle, other.handle), _bool_like(input))
else:
return tl.tensor(builder.create_icmpUGT(input.handle, other.handle), _bool_like(input))
assert False
def greater_equal(input: tl.tensor,
other: tl.tensor,
builder: ir.builder) -> tl.tensor:
input, other = binary_op_type_checking_impl(input, other, builder)
scalar_ty = input.type.scalar
# float >= float
if scalar_ty.is_floating():
return tl.tensor(builder.create_fcmpOGE(input.handle, other.handle), _bool_like(input))
# >= int
elif scalar_ty.is_int():
if scalar_ty.is_int_signed():
return tl.tensor(builder.create_icmpSGE(input.handle, other.handle), _bool_like(input))
else:
return tl.tensor(builder.create_icmpUGE(input.handle, other.handle), _bool_like(input))
assert False
def less_than(input: tl.tensor,
other: tl.tensor,
builder: ir.builder) -> tl.tensor:
input, other = binary_op_type_checking_impl(input, other, builder)
scalar_ty = input.type.scalar
# float < float
if scalar_ty.is_floating():
return tl.tensor(builder.create_fcmpOLT(input.handle, other.handle), _bool_like(input))
# < int
elif scalar_ty.is_int():
if scalar_ty.is_int_signed():
return tl.tensor(builder.create_icmpSLT(input.handle, other.handle), _bool_like(input))
else:
return tl.tensor(builder.create_icmpULT(input.handle, other.handle), _bool_like(input))
assert False
def less_equal(input: tl.tensor,
other: tl.tensor,
builder: ir.builder) -> tl.tensor:
input, other = binary_op_type_checking_impl(input, other, builder)
scalar_ty = input.type.scalar
# float < float
if scalar_ty.is_floating():
return tl.tensor(builder.create_fcmpOLE(input.handle, other.handle), _bool_like(input))
# < int
elif scalar_ty.is_int():
if scalar_ty.is_int_signed():
return tl.tensor(builder.create_icmpSLE(input.handle, other.handle), _bool_like(input))
else:
return tl.tensor(builder.create_icmpULE(input.handle, other.handle), _bool_like(input))
assert False
def equal(input: tl.tensor,
other: tl.tensor,
builder: ir.builder) -> tl.tensor:
input, other = binary_op_type_checking_impl(input, other, builder)
scalar_ty = input.type.scalar
# float == float
if scalar_ty.is_floating():
return tl.tensor(builder.create_fcmpOEQ(input.handle, other.handle), _bool_like(input))
# == int
elif scalar_ty.is_int():
return tl.tensor(builder.create_icmpEQ(input.handle, other.handle), _bool_like(input))
assert False
def not_equal(input: tl.tensor,
other: tl.tensor,
builder: ir.builder) -> tl.tensor:
input, other = binary_op_type_checking_impl(input, other, builder)
scalar_ty = input.type.scalar
# float == float
if scalar_ty.is_floating():
return tl.tensor(builder.create_fcmpUNE(input.handle, other.handle), _bool_like(input))
# == int
elif scalar_ty.is_int():
return tl.tensor(builder.create_icmpNE(input.handle, other.handle), _bool_like(input))
assert False
# ===----------------------------------------------------------------------===//
# Block Creation
# ===----------------------------------------------------------------------===//
def arange(start: int, end: int, builder: ir.builder) -> tl.tensor:
if not isinstance(start, int) or not isinstance(end, int):
raise ValueError("arange's arguments must be of type tl.constexpr")
is_start_int64 = bool(start >> 32)
is_end_int64 = bool(end >> 32)
if is_start_int64 or is_end_int64:
raise ValueError("arange must fit in int32")
if end <= start:
raise ValueError("arange's end argument must be greater than the start argument")
shape = [end - start]
ret_ty = tl.block_type(tl.int32, shape)
return tl.tensor(builder.create_make_range(start, end), ret_ty)
def full(shape: List[int], value, dtype: tl.dtype, builder: ir.builder) -> tl.tensor:
if isinstance(value, tl.tensor):
assert value.numel.value == 1, "only accepts size-1 tensor"
value = cast(value, dtype, builder)
ret_ty = tl.block_type(value.dtype, shape)
return tl.tensor(builder.create_splat(value.handle, shape), ret_ty)
else:
# scalar
if value == 0:
value = builder.get_null_value(dtype.to_ir(builder))
else:
get_value_fn = getattr(builder, f"get_{dtype.name}")
value = get_value_fn(value)
if dtype is None:
raise ValueError("dtype must be specified when value is not a tensor")
ret_ty = tl.block_type(dtype, shape)
return tl.tensor(builder.create_splat(value, shape), ret_ty)
# ===----------------------------------------------------------------------===//
# Shape Manipulation
# ===----------------------------------------------------------------------===//
def view(input: tl.tensor,
dst_shape: List[int],
builder: ir.builder) -> tl.tensor:
# TODO: disable when TritonToTritonGPU handles views properly
# assert len(input.shape) == len(dst_shape)
numel = 1
for s in dst_shape:
numel *= s
if input.type.numel != numel:
raise ValueError("cannot view block of different shape")
ret_ty = tl.block_type(input.type.scalar, dst_shape)
return tl.tensor(builder.create_view(input.handle, dst_shape), ret_ty)
def reshape(input: tl.tensor,
dst_shape: List[int],
builder: ir.builder) -> tl.tensor:
raise ValueError("`reshape` is not supported yet. Please use `view` instead if applicable. "
"Note that view may reorder elements in an implementation- and context- dependent way.")
def expand_dims(input: tl.tensor, axis: int, builder: ir.builder) -> tl.tensor:
dst_shape = list(input.type.shape)
dst_shape.insert(axis, 1)
ret_ty = tl.block_type(input.type.scalar, dst_shape)
return tl.tensor(builder.create_expand_dims(input.handle, axis), ret_ty)
def cat(lhs: tl.tensor, rhs: tl.tensor, can_reorder: bool, builder: ir.builder) -> tl.tensor:
assert can_reorder, "current implementation of `cat` always may reorder elements"
assert len(lhs.shape) == 1
ret_type = tl.block_type(lhs.type.scalar, [lhs.shape[0] + rhs.shape[0]])
return tl.tensor(builder.create_cat(lhs.handle, rhs.handle), ret_type)
def trans(input: tl.tensor, builder: ir.builder) -> tl.tensor:
if len(input.shape) != 2:
raise ValueError("Only 2D tensors can be transposed")
ret_type = tl.block_type(input.type.scalar, [input.shape[1], input.shape[0]])
return tl.tensor(builder.create_trans(input.handle), ret_type)
def broadcast_impl_shape(input: tl.tensor,
shape: List[int],
builder: ir.builder) -> tl.tensor:
if not input.type.is_block():
ret_ty = tl.block_type(input.type, shape)
return tl.tensor(builder.create_splat(input.handle, shape), ret_ty)
src_shape = input.type.get_block_shapes()
if len(src_shape) != len(shape):
raise ValueError(f"Cannot broadcast, rank mismatch: {src_shape}, {shape}")
if shape == src_shape:
return input
for i, item in enumerate(src_shape):
if shape[i] != item and item != 1:
raise ValueError(f"Cannot broadcast, the expanded size of the tensor ({shape[i]})"
f" must match the existing size ({item}) at non-singleton dimension"
f" {i}: {src_shape}, {shape}")
ret_ty = tl.block_type(input.type.scalar, shape)
return tl.tensor(builder.create_broadcast(input.handle, shape), ret_ty)
def broadcast_impl_value(lhs: tl.tensor,
rhs: tl.tensor,
builder: ir.builder) -> tl.tensor:
lhs_ty = lhs.type
rhs_ty = rhs.type
# make_shape_compatible(block, scalar)
if lhs_ty.is_block() and not rhs_ty.is_block():
rhs_ty = tl.block_type(rhs_ty.scalar, lhs_ty.shape)
rhs = tl.tensor(builder.create_splat(rhs.handle, lhs_ty.get_block_shapes()), rhs_ty)
# make_shape_compatible(scalar, block)
elif not lhs_ty.is_block() and rhs_ty.is_block():
lhs_ty = tl.block_type(lhs_ty.scalar, rhs_ty.shape)
lhs = tl.tensor(builder.create_splat(lhs.handle, rhs_ty.get_block_shapes()), lhs_ty)
# make_shape_compatible(block, block)
elif lhs_ty.is_block() and rhs_ty.is_block():
lhs_shape = lhs_ty.get_block_shapes()
rhs_shape = rhs_ty.get_block_shapes()
if len(lhs_shape) < len(rhs_shape):
# Add new axes to lhs
for dim in range(len(lhs_shape), len(rhs_shape)):
lhs = tl.tensor(builder.create_expand_dims(lhs.handle, 0), tl.block_type(lhs_ty.scalar, [1] + lhs_shape))
lhs_ty = lhs.type
lhs_shape = lhs_ty.get_block_shapes()
elif len(rhs_shape) < len(lhs_shape):
# Add new axes to rhs
for dim in range(len(rhs_shape), len(lhs_shape)):
rhs = tl.tensor(builder.create_expand_dims(rhs.handle, 0), tl.block_type(rhs_ty.scalar, [1] + rhs_shape))
rhs_ty = rhs.type
rhs_shape = rhs_ty.get_block_shapes()
assert len(rhs_shape) == len(lhs_shape)
ret_shape = []
for i, left in enumerate(lhs_shape):
right = rhs_shape[i]
if left == 1:
ret_shape.append(right)
elif right == 1:
ret_shape.append(left)
elif left == right:
ret_shape.append(left)
else:
raise ValueError("Cannot make_shape_compatible: incompatible dimensions "
"at index " + str(i) + ": " + str(left) + " and " + str(right))
if lhs_shape != ret_shape:
ret_ty = tl.block_type(lhs_ty.scalar, ret_shape)
lhs = tl.tensor(builder.create_broadcast(lhs.handle, ret_shape), ret_ty)
if rhs_shape != ret_shape:
ret_ty = tl.block_type(rhs_ty.scalar, ret_shape)
rhs = tl.tensor(builder.create_broadcast(rhs.handle, ret_shape), ret_ty)
# (scalar, scalar) => returns original blocks
return lhs, rhs
#######
# cast
#######
def bitcast(input: tl.tensor,
dst_ty: tl.dtype,
builder: ir.builder) -> tl.tensor:
src_ty = input.type
if src_ty.is_block():
dst_ty = tl.block_type(dst_ty.scalar, input.type.get_block_shapes())
if src_ty == dst_ty:
return input
src_sca_ty = src_ty.scalar
dst_sca_ty = dst_ty.scalar
if src_sca_ty.is_ptr() or dst_sca_ty.is_ptr():
return cast(input, dst_ty, builder)
# Bitcast
src_bits = src_sca_ty.primitive_bitwidth
dst_bits = dst_sca_ty.primitive_bitwidth
if src_bits != dst_bits:
raise ValueError("Cannot bitcast data-type of size " + str(src_bits) + " to "
"data-type of size " + str(dst_bits))
return tl.tensor(builder.create_bitcast(input.handle, dst_ty.to_ir(builder)),
dst_ty)
def cast(input: tl.tensor,
dst_ty: tl.dtype,
builder: ir.builder) -> tl.tensor:
src_ty = input.type
if isinstance(dst_ty, tl.constexpr):
dst_ty = dst_ty.value
if src_ty.is_block():
dst_ty = tl.block_type(dst_ty.scalar, input.type.get_block_shapes())
if src_ty == dst_ty:
return input
src_sca_ty = src_ty.scalar
dst_sca_ty = dst_ty.scalar
# Casting with customized floating types involved: fp8 <=> bf16, fp16, fp32, fp64
if (src_sca_ty.is_fp8() and dst_sca_ty.is_floating()) or \
(src_sca_ty.is_floating() and dst_sca_ty.is_fp8()):
return tl.tensor(builder.create_fp_to_fp(input.handle, dst_ty.to_ir(builder)),
dst_ty)
# bf16 <=> (not fp32)
if (src_sca_ty.is_fp16() and not dst_sca_ty.is_fp32()) or \
(src_sca_ty.is_bf16() and not dst_sca_ty.is_fp32()):
return cast(cast(input, tl.float32, builder), dst_sca_ty, builder)
# Standard floating types' casting: truncation
# fp64 => fp32, fp16, bf16
# fp32 => fp16, bf16
truncate_fp = src_sca_ty.is_floating() and \
dst_sca_ty.is_floating() and \
src_sca_ty.primitive_bitwidth > dst_sca_ty.primitive_bitwidth
if truncate_fp:
return tl.tensor(builder.create_fp_trunc(input.handle,
dst_ty.to_ir(builder)),
dst_ty)
# Standard floating types' casting: extension
# fp32 => fp64
# fp16 => fp32, fp64
# bf16 => fp32, fp64
ext_fp = src_sca_ty.is_floating() and \
dst_sca_ty.is_floating() and \
src_sca_ty.primitive_bitwidth < dst_sca_ty.primitive_bitwidth
if ext_fp:
return tl.tensor(builder.create_fp_ext(input.handle,
dst_ty.to_ir(builder)),
dst_ty)
# Casting between integer types
if src_sca_ty.is_int() and dst_sca_ty.is_int() and \
(src_sca_ty.int_bitwidth != dst_sca_ty.int_bitwidth or src_sca_ty.int_signedness != dst_sca_ty.int_signedness):
sign_extend = src_sca_ty.is_int_signed() and not src_sca_ty.is_bool()
if dst_sca_ty.is_bool():
ty = input.dtype.to_ir(builder)
_0 = tl.tensor(builder.get_null_value(ty), input.dtype)
return not_equal(input, _0, builder)
else:
return tl.tensor(builder.create_int_cast(input.handle,
dst_ty.to_ir(builder), sign_extend),
dst_ty)
# Casting standard floating types to integer types
if src_sca_ty.is_standard_floating() and dst_sca_ty.is_int():
if dst_sca_ty.is_bool():
ty = input.dtype.to_ir(builder)
_0 = tl.tensor(builder.get_null_value(ty), input.dtype)
return not_equal(input, _0, builder)
elif dst_sca_ty.is_int_signed():
return tl.tensor(builder.create_fp_to_si(input.handle,
dst_ty.to_ir(builder)),
dst_ty)
else:
return tl.tensor(builder.create_fp_to_ui(input.handle,
dst_ty.to_ir(builder)),
dst_ty)
# Casting integer types to standard floating types
if src_sca_ty.is_int() and dst_sca_ty.is_standard_floating():
if src_sca_ty.is_bool() or not src_sca_ty.is_int_signed():
return tl.tensor(builder.create_ui_to_fp(input.handle,
dst_ty.to_ir(builder)),
dst_ty)
else:
return tl.tensor(builder.create_si_to_fp(input.handle,
dst_ty.to_ir(builder)),
dst_ty)
# Casting pointer types to integer types
if src_sca_ty.is_ptr() and dst_sca_ty.is_int():
bitwidth = dst_sca_ty.int_bitwidth
if bitwidth == 64:
return tl.tensor(builder.create_ptr_to_int(input.handle, dst_ty.to_ir(builder)),
dst_ty)
if bitwidth == 1:
return not_equal(cast(input, tl.int64, builder),
tl.tensor(builder.get_int64(0), tl.int64),
builder)
# Casting integer types to pointer types
if src_sca_ty.is_int() and dst_sca_ty.is_ptr():
return tl.tensor(builder.create_int_to_ptr(input.handle, dst_ty.to_ir(builder)), dst_ty)
# Casting pointer types to pointer types
if src_sca_ty.is_ptr() and dst_sca_ty.is_ptr():
return tl.tensor(builder.create_bitcast(input.handle, dst_ty.to_ir(builder)), dst_ty)
assert False, f'cannot cast {input} to {dst_ty}'
# ===----------------------------------------------------------------------===//
# Memory Operators
# ===----------------------------------------------------------------------===//
def _str_to_cache_modifier(cache_modifier):
cache = ir.CACHE_MODIFIER.NONE # default
if cache_modifier:
if cache_modifier == ".ca":
cache = ir.CACHE_MODIFIER.CA
elif cache_modifier == ".cg":
cache = ir.CACHE_MODIFIER.CG
else:
raise ValueError(f"Cache modifier {cache_modifier} not supported")
return cache
def _str_to_eviction_policy(eviction_policy):
eviction = ir.EVICTION_POLICY.NORMAL # default
if eviction_policy:
if eviction_policy == "evict_last":
eviction = ir.EVICTION_POLICY.EVICT_LAST
elif eviction_policy == "evict_first":
eviction = ir.EVICTION_POLICY.EVICT_FIRST
else:
raise ValueError(f"Eviction policy {eviction_policy} not supported")
return eviction
def _str_to_padding_option(padding_option):
padding = None # default
if padding_option:
if padding_option == "zero":
padding = ir.PADDING_OPTION.PAD_ZERO
elif padding_option == "nan":
padding = ir.PADDING_OPTION.PAD_NAN
else:
raise ValueError(f"Padding option {padding_option} not supported")
return padding
def _canonicalize_boundary_check(boundary_check, block_shape):
if boundary_check:
if not hasattr(boundary_check, "__iter__"):
boundary_check = [boundary_check]
boundary_check = [elem.value if isinstance(elem, tl.constexpr) else elem for elem in boundary_check]
for dim in boundary_check:
assert isinstance(dim, int) and 0 <= dim < len(block_shape)
assert len(boundary_check) > 0
assert len(boundary_check) == len(set(boundary_check)), "Duplicate dimension in `boundary_check`"
return sorted(boundary_check)
return tuple()
def _load_block_pointer(ptr, mask, other, boundary_check, padding, cache, eviction, is_volatile, builder):
# Load by a block pointer: `pointer_type<block_type<>>`
# Block pointer can not have `mask` and `other` arguments
if mask or other:
raise ValueError("`mask` and `other` arguments cannot be specified for loading block pointers")
elt_ty = ptr.type.element_ty.element_ty
assert elt_ty != tl.int1, "`tl.int1` should be rewrited in `tl.make_block_ptr`"
if elt_ty.is_int() and padding == ir.PADDING_OPTION.PAD_NAN:
raise ValueError("Padding option `nan` is not supported for integer block pointers")
# `dst_ty` is de-referenced type of the pointer type
dst_ty = ptr.type.element_ty
# Check `boundary_check` argument
boundary_check = _canonicalize_boundary_check(boundary_check, dst_ty.get_block_shapes())
# Build IR
return tl.tensor(builder.create_tensor_pointer_load(ptr.handle, boundary_check, padding, cache, eviction,
is_volatile), dst_ty)
def _load_legacy(ptr, mask, other, boundary_check, padding, cache, eviction, is_volatile, builder):
# Load by a tensor of pointers or a pointer of scalar: `block_type<pointer_type<>>` or `pointer_type<>`
if not ptr.type.scalar.is_ptr():
raise ValueError(f"Unsupported ptr type {ptr.type.__repr__()} in `tl.load`")
# Check `mask`, `other`, `boundary_check`, and `padding` arguments
if not mask and other:
raise ValueError("`other` cannot be provided without `mask`")
if padding or boundary_check:
raise ValueError("`padding_option` or `boundary_check` argument is not supported for loading a tensor of"
"pointers or loading a scalar. Because the compiler does not know the boundary; please "
"use block pointers (defined by `make_block_ptr`) instead")
# For a pointer of scalar, check the type of `mask` and `other`
if not ptr.type.is_block():
if mask and mask.type.is_block():
raise ValueError("Mask argument cannot be block type if pointer argument is not a block")
if other and other.type.is_block():
raise ValueError("Other argument cannot be block type if pointer argument is not a block")
# Make `mask` and `other` into the same shape as `ptr`
if ptr.type.is_block():
if mask:
mask = broadcast_impl_shape(mask, ptr.type.get_block_shapes(), builder)
if other:
other = broadcast_impl_shape(other, ptr.type.get_block_shapes(), builder)
# Get `pointer_type<elt_ty>` and `elt_ty`
ptr_ty = ptr.type.scalar
elt_ty = ptr_ty.element_ty
# Treat `pointer_type<tl.int1>` as `pointer_type<tl.int8>`
if elt_ty == tl.int1:
elt_ty = tl.int8
ptr_ty = tl.pointer_type(elt_ty, ptr_ty.address_space)
ptr = cast(ptr, ptr_ty, builder)
# Cast `other` into `ele_ty` type
if other:
other = cast(other, elt_ty, builder)
# Create loaded result type `dst_ty`
if ptr.type.is_block():
shape = ptr.type.get_block_shapes()
dst_ty = tl.block_type(elt_ty, shape)
else:
# Load by de-referencing the pointer of scalar
dst_ty = elt_ty
# Build IR
if not mask:
return tl.tensor(builder.create_load(ptr.handle, cache, eviction, is_volatile), dst_ty)
else:
return tl.tensor(builder.create_masked_load(ptr.handle, mask.handle, other.handle if other else None, cache,
eviction, is_volatile), dst_ty)
def load(ptr: tl.tensor,
mask: Optional[tl.tensor],
other: Optional[tl.tensor],
boundary_check,
padding_option: str,
cache_modifier: str,
eviction_policy: str,
is_volatile: bool,
builder: ir.builder) -> tl.tensor:
# Cache, eviction and padding options
cache = _str_to_cache_modifier(cache_modifier)
eviction = _str_to_eviction_policy(eviction_policy)
padding = _str_to_padding_option(padding_option)
if ptr.type.is_ptr() and ptr.type.element_ty.is_block():
# Load by a block pointer: `pointer_type<block_type<>>`
return _load_block_pointer(ptr, mask, other, boundary_check, padding, cache, eviction, is_volatile, builder)
else:
# Load by a tensor of pointers or a pointer of scalar: `block_type<pointer_type<>>` or `pointer_type<>`
return _load_legacy(ptr, mask, other, boundary_check, padding, cache, eviction, is_volatile, builder)
def _store_block_pointer(ptr, val, mask, boundary_check, cache, eviction, builder):
# Store by a block pointer: `pointer_type<block_type<>>`
# Block pointers can not have the `mask` argument
if mask:
raise ValueError("`mask` and `other` arguments cannot be specified for loading block pointers")
# Check same shape and element type
block_shape = ptr.type.element_ty.get_block_shapes()
if not val.type.is_block():
val = broadcast_impl_shape(val, block_shape, builder)
assert val.type.is_block(), "Value argument must be block type or a scalar"
assert block_shape == val.type.get_block_shapes(), "Block shape and value shape mismatch"
assert ptr.type.element_ty.element_ty == val.type.element_ty, "Block element type and value element type mismatch"
elt_ty = ptr.type.element_ty.element_ty
assert elt_ty != tl.int1, "`tl.int1` should be rewrited in `tl.make_block_ptr`"
# Check `boundary_check` argument
boundary_check = _canonicalize_boundary_check(boundary_check, block_shape)
# Build IR
return tl.tensor(builder.create_tensor_pointer_store(ptr.handle, val.handle, boundary_check, cache, eviction),
tl.void)
def _store_legacy(ptr, val, mask, boundary_check, cache, eviction, builder):
# Store by a tensor of pointers or a pointer of scalar: `block_type<pointer_type<>>` or `pointer_type<>`
if not ptr.type.scalar.is_ptr():
raise ValueError(f"Unsupported ptr type {ptr.type.__repr__()} in `tl.store`")
# Check `boundary_check` argument
if boundary_check:
raise ValueError("`boundary_check` argument is not supported for storing a tensor of pointers or storing a "
"scalar. Because the compiler does not know the boundary; please use block pointers "
"(defined by `make_block_ptr`) instead")
# For a pointer of scalar, check the type of `val` and `mask`
if not ptr.type.is_block():
if val.type.is_block():
raise ValueError("Value argument cannot be block type if pointer argument is not a block")
if mask and mask.type.is_block():
raise ValueError("Mask argument cannot be block type if pointer argument is not a block")
# Make `mask` and `val` into the same shape as `ptr`
if ptr.type.is_block():
val = broadcast_impl_shape(val, ptr.type.get_block_shapes(), builder)
if mask:
mask = broadcast_impl_shape(mask, ptr.type.get_block_shapes(), builder)
ptr_ty = ptr.type.scalar
elt_ty = ptr_ty.element_ty
# Treat `pointer_type<tl.int1>` as `pointer_type<tl.int8>`
if elt_ty == tl.int1:
elt_ty = tl.int8
ptr_ty = tl.pointer_type(elt_ty, ptr_ty.address_space)
ptr = cast(ptr, ptr_ty, builder)
# Cast to target data type
val = cast(val, elt_ty, builder)
# Build IR
if not mask:
return tl.tensor(builder.create_store(ptr.handle, val.handle, cache, eviction), tl.void)
if not mask.type.scalar.is_bool():
raise ValueError("Mask must have boolean scalar type")
return tl.tensor(builder.create_masked_store(ptr.handle, val.handle, mask.handle, cache, eviction), tl.void)
def store(ptr: tl.tensor,
val: tl.tensor,
mask: Optional[tl.tensor],
boundary_check,
cache_modifier: str,
eviction_policy: str,
builder: ir.builder) -> tl.tensor:
# Cache and eviction options
cache = _str_to_cache_modifier(cache_modifier)
eviction = _str_to_eviction_policy(eviction_policy)
if ptr.type.is_ptr() and ptr.type.element_ty.is_block():
# Store by a block pointer: `pointer_type<block_type<>>`
return _store_block_pointer(ptr, val, mask, boundary_check, cache, eviction, builder)
else:
# Store by a tensor of pointers or a pointer of scalar: `block_type<pointer_type<>>` or `pointer_type<>`
return _store_legacy(ptr, val, mask, boundary_check, cache, eviction, builder)
#########
# atomic
#########
def atomic_cas(ptr: tl.tensor,
cmp: tl.tensor,
val: tl.tensor,
builder: ir.builder) -> tl.tensor:
element_ty = ptr.type.scalar.element_ty
if element_ty.primitive_bitwidth not in [16, 32, 64]:
raise ValueError("atomic_cas only supports elements with width {16, 32, 64}")
return tl.tensor(builder.create_atomic_cas(ptr.handle, cmp.handle, val.handle), val.type)
def atom_red_typechecking_impl(ptr: tl.tensor,
val: tl.tensor,
mask: tl.tensor,
op: str,
builder: ir.builder) -> Tuple[tl.tensor, tl.tensor, tl.tensor]:
if not ptr.type.scalar.is_ptr():
raise ValueError("Pointer argument of store instruction is " + ptr.type.__repr__())
element_ty = ptr.type.scalar.element_ty
if element_ty is tl.float16 and op != 'add':
raise ValueError("atomic_" + op + " does not support fp16")
if element_ty in [tl.int1, tl.int8, tl.int16, tl.bfloat16]:
raise ValueError("atomic_" + op + " does not support " + str(element_ty))
if ptr.type.is_block():
if mask:
mask = broadcast_impl_shape(mask, ptr.type.get_block_shapes(), builder)
if val:
val = broadcast_impl_shape(val, ptr.type.get_block_shapes(), builder)
val = cast(val, ptr.type.scalar.element_ty, builder)
if not mask:
mask_ir = builder.get_int1(True)
mask_ty = tl.int1
if ptr.type.is_block():
mask_ir = builder.create_splat(mask_ir, ptr.type.get_block_shapes())
mask_ty = tl.block_type(tl.int1, ptr.type.get_block_shapes())
mask = tl.tensor(mask_ir, mask_ty)
return ptr, val, mask
def atomic_max(ptr: tl.tensor,
val: tl.tensor,
mask: tl.tensor,
builder: ir.builder) -> tl.tensor:
ptr, val, mask = atom_red_typechecking_impl(ptr, val, mask, 'max', builder)
sca_ty = val.type.scalar
# direct call to atomic_max for integers
if sca_ty.is_int():
if sca_ty.is_int_signed():
return tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.MAX,
ptr.handle,
val.handle,
mask.handle),
val.type)
else:
return tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.UMAX,
ptr.handle,
val.handle,
mask.handle),
val.type)
# for float
# return atomic_smax(i_ptr, i_val) if val >= 0
# return atomic_umin(i_ptr, i_val) if val < 0
i_val = bitcast(val, tl.int32, builder)
i_ptr = bitcast(ptr, tl.pointer_type(tl.int32, 1), builder)
pos = greater_equal(val, tl.tensor(builder.get_fp32(0), sca_ty), builder)
neg = less_than(val, tl.tensor(builder.get_fp32(0), sca_ty), builder)
pos_ret = tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.MAX, i_ptr.handle, i_val.handle, and_(mask, pos, builder).handle), i_val.type)
neg_ret = tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.UMIN, i_ptr.handle, i_val.handle, and_(mask, neg, builder).handle), i_val.type)
return where(pos, pos_ret, neg_ret, builder)
def atomic_min(ptr: tl.tensor,
val: tl.tensor,
mask: tl.tensor,
builder: ir.builder) -> tl.tensor:
ptr, val, mask = atom_red_typechecking_impl(ptr, val, mask, 'min', builder)
sca_ty = val.type.scalar
# direct call to atomic_min for integers
if sca_ty.is_int():
if sca_ty.is_int_signed():
return tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.MIN,
ptr.handle,
val.handle,
mask.handle),
val.type)
else:
return tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.UMIN,
ptr.handle,
val.handle,
mask.handle),
val.type)
# for float
# return atomic_smin(i_ptr, i_val) if val >= 0
# return atomic_umax(i_ptr, i_val) if val < 0
i_val = bitcast(val, tl.int32, builder)
i_ptr = bitcast(ptr, tl.pointer_type(tl.int32, 1), builder)
pos = greater_equal(val, tl.tensor(builder.get_fp32(0), sca_ty), builder)
neg = less_than(val, tl.tensor(builder.get_fp32(0), sca_ty), builder)
pos_ret = tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.MIN,
i_ptr.handle,
i_val.handle,
and_(mask, pos, builder).handle),
i_val.type)
neg_ret = tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.UMAX,
i_ptr.handle,
i_val.handle,
and_(mask, neg, builder).handle),
i_val.type)
return where(pos, pos_ret, neg_ret, builder)
def atomic_add(ptr: tl.tensor,
val: tl.tensor,
mask: tl.tensor,
builder: ir.builder) -> tl.tensor:
ptr, val, mask = atom_red_typechecking_impl(ptr, val, mask, 'add', builder)
sca_ty = val.type.scalar
op = ir.ATOMIC_OP.FADD if sca_ty.is_floating() else ir.ATOMIC_OP.ADD
return tl.tensor(builder.create_atomic_rmw(op, ptr.handle, val.handle, mask.handle), val.type)
def atomic_and(ptr: tl.tensor,
val: tl.tensor,
mask: tl.tensor,
builder: ir.builder) -> tl.tensor:
ptr, val, mask = atom_red_typechecking_impl(ptr, val, mask, 'and', builder)
return tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.AND, ptr.handle, val.handle, mask.handle), val.type)
def atomic_or(ptr: tl.tensor,
val: tl.tensor,
mask: tl.tensor,
builder: ir.builder) -> tl.tensor:
ptr, val, mask = atom_red_typechecking_impl(ptr, val, mask, 'or', builder)
return tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.OR, ptr.handle, val.handle, mask.handle), val.type)
def atomic_xor(ptr: tl.tensor,
val: tl.tensor,
mask: tl.tensor,
builder: ir.builder) -> tl.tensor:
ptr, val, mask = atom_red_typechecking_impl(ptr, val, mask, 'xor', builder)
return tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.XOR, ptr.handle, val.handle, mask.handle), val.type)
def atomic_xchg(ptr: tl.tensor,
val: tl.tensor,
mask: tl.tensor,
builder: ir.builder) -> tl.tensor:
ptr, val, mask = atom_red_typechecking_impl(ptr, val, mask, 'xchg', builder)
return tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.XCHG, ptr.handle, val.handle, mask.handle), val.type)
# ===----------------------------------------------------------------------===//
# Linear Algebra
# ===----------------------------------------------------------------------===//
def dot(lhs: tl.tensor,
rhs: tl.tensor,
allow_tf32: bool,
out_dtype: tl.dtype,
builder: ir.builder) -> tl.tensor:
assert lhs.type.is_block() and rhs.type.is_block()
assert lhs.dtype == rhs.dtype, "lhs and rhs must have the same dtype!"
assert len(lhs.shape) == 2 and len(rhs.shape) == 2
assert lhs.shape[1].value == rhs.shape[0].value
assert lhs.shape[0].value >= 16 and lhs.shape[1].value >= 16 \
and rhs.shape[1].value >= 16,\
"small blocks not supported!"
if lhs.type.scalar.is_int():
assert lhs.type.scalar == tl.int8, "only int8 supported!"
# TODO: This is CUDA specific, check if ROCm has the same limitation
assert lhs.shape[1].value >= 32, "small blocks not supported!"
_0 = builder.get_int32(0)
ret_scalar_ty = tl.int32
elif lhs.type.scalar.is_fp32() or lhs.type.scalar.is_bf16():
_0 = builder.get_fp32(0)
ret_scalar_ty = tl.float32
else:
_0 = builder.get_fp16(0) if out_dtype.is_fp16() else builder.get_fp32(0)
ret_scalar_ty = out_dtype
M = lhs.type.shape[0]
N = rhs.type.shape[1]
_0 = builder.create_splat(_0, [M, N])
ret_ty = tl.block_type(ret_scalar_ty, [M, N])
return tl.tensor(builder.create_dot(lhs.handle, rhs.handle, _0, allow_tf32),
ret_ty)
# ===----------------------------------------------------------------------===//
# Indexing
# ===----------------------------------------------------------------------===//
def where(condition: tl.tensor,
x: tl.tensor,
y: tl.tensor,
builder: ir.builder) -> tl.tensor:
condition = cast(condition, tl.int1, builder)
if condition.type.is_block():
condition, x = broadcast_impl_value(condition, x, builder)
x, y = broadcast_impl_value(x, y, builder)
condition, x = broadcast_impl_value(condition, x, builder)
x, y = binary_op_type_checking_impl(x, y, builder, True, True)
if not condition.type.is_block():
condition, _ = broadcast_impl_value(condition, x, builder)
ret_ty = x.type
return tl.tensor(builder.create_select(condition.handle, x.handle, y.handle), ret_ty)
# ===----------------------------------------------------------------------===//
# Reduction
# ===----------------------------------------------------------------------===
def reduction(
inputs: Sequence[tl.tensor], axis: int, region_builder_fn, builder: ir.builder
) -> Tuple[tl.tensor, ...]:
# get result shape
shape = inputs[0].type.shape
ret_shape = [s for i, s in enumerate(shape) if i != axis]
for t in inputs:
assert t.type.shape == shape
def wrap_tensor(x, scalar_ty):
if ret_shape:
res_ty = tl.block_type(scalar_ty, ret_shape)
else:
# 0d-tensor -> scalar
res_ty = scalar_ty
return tl.tensor(x, res_ty)
reduce_op = builder.create_reduce([t.handle for t in inputs], axis)
region_builder_fn(reduce_op)
reduce_op.verify()
return tuple(
wrap_tensor(reduce_op.get_result(i), inputs[i].type.scalar)
for i in range(len(inputs))
)
# ===----------------------------------------------------------------------===
# Math
# ===----------------------------------------------------------------------===
def _check_dtype(dtypes: List[str]) -> T:
"""
We following libdevice's convention to check accepted data types for math functions.
It is not a good practice to support all data types as accelerators/GPUs don't support
many float16 and bfloat16 math operations.
We should let the users know that they are using and invoke explicit cast to convert
the data type to the supported one.
"""
def wrapper(fn):
@wraps(fn)
def check(*args, **kwargs):
# concatenate args and kwargs
all_args = list(args) + list(kwargs.values())
for arg in [a for a in all_args if isinstance(a, tl.tensor)]:
if arg.type.scalar.name not in dtypes:
raise ValueError(f"Expected dtype {dtypes} but got {arg.type.scalar.name}")
return fn(*args, **kwargs)
return check
return wrapper
def umulhi(x: tl.tensor, y: tl.tensor, builder: ir.builder) -> tl.tensor:
x, y = binary_op_type_checking_impl(x, y, builder)
# FIXME(Keren): not portable, should be fixed
from . import math
return math.mulhi(x, y, _builder=builder)
@_check_dtype(dtypes=["fp32", "fp64"])
def floor(x: tl.tensor, builder: ir.builder) -> tl.tensor:
# FIXME(Keren): not portable, should be fixed
from . import math
return math.floor(x, _builder=builder)
@_check_dtype(dtypes=["fp32", "fp64"])
def exp(x: tl.tensor, builder: ir.builder) -> tl.tensor:
return tl.tensor(builder.create_exp(x.handle), x.type)
@_check_dtype(dtypes=["fp32", "fp64"])
def log(x: tl.tensor, builder: ir.builder) -> tl.tensor:
return tl.tensor(builder.create_log(x.handle), x.type)
@_check_dtype(dtypes=["fp32", "fp64"])
def cos(x: tl.tensor, builder: ir.builder) -> tl.tensor:
return tl.tensor(builder.create_cos(x.handle), x.type)
@_check_dtype(dtypes=["fp32", "fp64"])
def sin(x: tl.tensor, builder: ir.builder) -> tl.tensor:
return tl.tensor(builder.create_sin(x.handle), x.type)
@_check_dtype(dtypes=["fp32", "fp64"])
def sqrt(x: tl.tensor, builder: ir.builder) -> tl.tensor:
return tl.tensor(builder.create_sqrt(x.handle), x.type)
def abs(x: tl.tensor, builder: ir.builder) -> tl.tensor:
dtype = x.dtype
if dtype.is_floating():
return tl.tensor(builder.create_fabs(x.handle), x.type)
elif dtype.is_int_signed():
return tl.tensor(builder.create_iabs(x.handle), x.type)
elif dtype.is_int_unsigned():
return x # no-op
else:
assert False, f"Unexpected dtype {dtype}"
##
def multiple_of(x: tl.tensor, values: List[int]) -> tl.tensor:
if len(x.shape) != len(values):
raise ValueError("Shape of input to multiple_of does not match the length of values")
x.handle.set_attr("tt.divisibility", ir.make_attr(values, x.handle.get_context()))
return x
def max_contiguous(x: tl.tensor, values: List[int]) -> tl.tensor:
if len(x.shape) != len(values):
raise ValueError("Shape of input to max_contiguous does not match the length of values")
x.handle.set_attr("tt.contiguity", ir.make_attr(values, x.handle.get_context()))
return x
def debug_barrier(builder: ir.builder) -> tl.tensor:
return tl.tensor(builder.create_barrier(), tl.void)
def device_print(prefix: str, args: List[tl.tensor], builder: ir.builder) -> tl.tensor:
new_args = []
for arg in args:
new_args.append(arg.handle)
return tl.tensor(builder.create_print(prefix, new_args), tl.void)
def device_assert(cond: tl.tensor, msg: str, file_name: str, func_name, lineno: int, builder: ir.builder) -> tl.tensor:
cond_ty = cond.type
if not cond_ty.is_block():
cond_ty = tl.block_type(cond_ty.scalar, (1,))
cond = tl.tensor(builder.create_splat(cond.handle, (1,)), cond_ty)
return tl.tensor(builder.create_assert(cond.handle, msg, file_name, func_name, lineno), tl.void)
def _convert_elem_to_ir_value(builder, elem, require_i64):
if isinstance(elem, tl.constexpr):
return builder.get_int64(elem.value) if require_i64 else builder.get_int32(elem.value)
elif isinstance(elem, tl.tensor):
assert elem.numel.value == 1, "Expected a scalar in shape/strides/offsets"
assert elem.dtype.is_int(), "Expected an integer scalar type in shape/strides/offsets"
if elem.dtype != tl.int64 and require_i64:
return builder.create_int_cast(elem.handle, builder.get_int64_ty(), elem.dtype.is_int_signed())
elif elem.dtype != tl.int32:
return builder.create_int_cast(elem.handle, builder.get_int32_ty(), elem.dtype.is_int_signed())
return elem.handle
assert False, f"Unsupported element type in shape/strides/offsets: {type(elem)}"
def _convert_to_ir_values(builder, list_like, require_i64=True):
if hasattr(list_like, "__iter__"):
return [_convert_elem_to_ir_value(builder, elem, require_i64) for elem in list_like]
return [_convert_elem_to_ir_value(builder, list_like, require_i64)]
def make_block_ptr(base: tl.tensor, shape, strides, offsets, block_shape, order, builder: ir.builder) -> tl.tensor:
# Convert dynamic arguments to IR values
# NOTES(Chenggang): current `shape/strides` are `int64_t`, while `offsets/block_shape` are `int32_t`
shape = _convert_to_ir_values(builder, shape)
strides = _convert_to_ir_values(builder, strides)
offsets = _convert_to_ir_values(builder, offsets, require_i64=False)
# Check `base` type
if not base.type.is_ptr() or base.type.element_ty.is_block():
raise ValueError("Expected `base` to be a pointer type (but not a block pointer type or others)")
# Treat `pointer_type<tl.int1>` as `pointer_type<tl.int8>`
if base.type.element_ty == tl.int1:
base = cast(base, tl.pointer_type(tl.int8, base.type.address_space), builder)
# Check whether `block_shape` is static
if not hasattr(block_shape, "__iter__"):
block_shape = [block_shape]
block_shape = [elem.value if isinstance(elem, tl.constexpr) else elem for elem in block_shape]
assert all([isinstance(elem, int) and -2**31 <= elem < 2**31 for elem in block_shape]), \
"Expected a list of constant integers (`int32_t` range) in `block_shape`"
# Check `order`
if not hasattr(order, "__iter__"):
order = [order]
order = [elem.value if isinstance(elem, tl.constexpr) else elem for elem in order]
assert sorted(order) == list(range(len(order))), "Expected a permutation of (0, 1, ..., len(order)-1) in order"
# Must have same length
assert all([len(block_shape) == len(list_like) for list_like in [shape, strides, offsets, order]]), \
"Expected shape/strides/offsets/block_shape to have the same length"
# Build value, the type is:
# `pointer_type<blocked<shape, element_type>>` in Python
# `tt.ptr<tensor<shape, element_type>>` in MLIR
handle = builder.create_make_block_ptr(base.handle, shape, strides, offsets, block_shape, order)
return tl.tensor(handle, tl.pointer_type(tl.block_type(base.type.element_ty, block_shape)))
def advance(base: tl.tensor, offsets, builder: ir.builder) -> tl.tensor:
# Convert dynamic offsets to IR values
offsets = _convert_to_ir_values(builder, offsets, require_i64=False)
# Advanced block pointer type is the same as before
return tl.tensor(builder.create_advance(base.handle, offsets), base.type)
|
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"/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,420
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/dpr.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import collections
from ...tokens.utils import BatchEncoding
from .bert import Bert
VOCAB_FS = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
CONTEXT_ENCODER_PRETRAINED_VOCAB_MAP = {
"vocab_file": {
"facebook/dpr-ctx_encoder-single-nq-base": "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt",
"facebook/dpr-ctx_encoder-multiset-base": "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt",
},
"tokenizer_file": {
"facebook/dpr-ctx_encoder-single-nq-base": "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json",
"facebook/dpr-ctx_encoder-multiset-base": "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json",
},
}
QUESTION_ENCODER_PRETRAINED_VOCAB_MAP = {
"vocab_file": {
"facebook/dpr-question_encoder-single-nq-base": "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt",
"facebook/dpr-question_encoder-multiset-base": "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt",
},
"tokenizer_file": {
"facebook/dpr-question_encoder-single-nq-base": "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json",
"facebook/dpr-question_encoder-multiset-base": "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json",
},
}
READER_PRETRAINED_VOCAB_MAP = {
"vocab_file": {
"facebook/dpr-reader-single-nq-base": "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt",
"facebook/dpr-reader-multiset-base": "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt",
},
"tokenizer_file": {
"facebook/dpr-reader-single-nq-base": "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json",
"facebook/dpr-reader-multiset-base": "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json",
},
}
CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"facebook/dpr-ctx_encoder-single-nq-base": 512,
"facebook/dpr-ctx_encoder-multiset-base": 512,
}
QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"facebook/dpr-question_encoder-single-nq-base": 512,
"facebook/dpr-question_encoder-multiset-base": 512,
}
READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"facebook/dpr-reader-single-nq-base": 512,
"facebook/dpr-reader-multiset-base": 512,
}
CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION = {
"facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION = {
"facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
READER_PRETRAINED_INIT_CONFIGURATION = {
"facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
"facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}
class DPRContextEncoderTokenizer(Bert):
vocab_fs = VOCAB_FS
vocab_map = CONTEXT_ENCODER_PRETRAINED_VOCAB_MAP
input_caps = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
class DPRQuestionEncoderTokenizer(Bert):
vocab_fs = VOCAB_FS
vocab_map = QUESTION_ENCODER_PRETRAINED_VOCAB_MAP
input_caps = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
DPRSpanPrediction = collections.namedtuple(
"DPRSpanPrediction",
["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"],
)
DPRReaderOutput = collections.namedtuple(
"DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"]
)
class Mixin:
def __call__(
self,
questions,
titles=None,
texts=None,
padding=False,
truncation=False,
max_length=None,
return_tensors=None,
return_attention_mask=None,
**kw,
):
if titles is None and texts is None:
return super().__call__(
questions,
padding=padding,
truncation=truncation,
max_length=max_length,
return_tensors=return_tensors,
return_attention_mask=return_attention_mask,
**kw,
)
elif titles is None or texts is None:
text_pair = titles if texts is None else texts
return super().__call__(
questions,
text_pair,
padding=padding,
truncation=truncation,
max_length=max_length,
return_tensors=return_tensors,
return_attention_mask=return_attention_mask,
**kw,
)
titles = titles if not isinstance(titles, str) else [titles]
texts = texts if not isinstance(texts, str) else [texts]
n_passages = len(titles)
questions = questions if not isinstance(questions, str) else [questions] * n_passages
if len(titles) != len(texts):
raise ValueError(
f"There should be as many titles than texts but got {len(titles)} titles and {len(texts)} texts."
)
encoded_question_and_titles = super().__call__(
questions, titles, padding=False, truncation=False
)["input_ids"]
encoded_texts = super().__call__(
texts, add_special_tokens=False, padding=False, truncation=False
)["input_ids"]
encoded_inputs = {
"input_ids": [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(
encoded_question_and_titles, encoded_texts
)
]
}
if return_attention_mask is not False:
attention_mask = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.PAD) for input_id in input_ids])
encoded_inputs["attention_mask"] = attention_mask
return self.pad(
encoded_inputs, padding=padding, max_length=max_length, return_tensors=return_tensors
)
def decode_best_spans(
self,
reader_input: BatchEncoding,
reader_output: DPRReaderOutput,
num_spans=16,
max_answer_length=64,
num_spans_per_passage=4,
):
input_ids = reader_input["input_ids"]
start_logits, end_logits, relevance_logits = reader_output[:3]
n_passages = len(relevance_logits)
sorted_docs = sorted(range(n_passages), reverse=True, key=relevance_logits.__getitem__)
nbest_spans_predictions = []
for doc_id in sorted_docs:
sequence_ids = list(input_ids[doc_id])
# assuming question & title information is at the beginning of the sequence
passage_offset = sequence_ids.index(self.sep_token_id, 2) + 1 # second sep id
if sequence_ids[-1] == self.PAD:
sequence_len = sequence_ids.index(self.PAD)
else:
sequence_len = len(sequence_ids)
best_spans = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len],
end_logits=end_logits[doc_id][passage_offset:sequence_len],
max_answer_length=max_answer_length,
top_spans=num_spans_per_passage,
)
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index]
+ end_logits[doc_id][end_index],
relevance_score=relevance_logits[doc_id],
doc_id=doc_id,
start_index=start_index,
end_index=end_index,
text=self.decode(sequence_ids[start_index : end_index + 1]),
)
)
if len(nbest_spans_predictions) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def _get_best_spans(
self,
start_logits,
end_logits,
max_answer_length,
top_spans,
):
scores = []
for (start_index, start_score) in enumerate(start_logits):
for (answer_length, end_score) in enumerate(
end_logits[start_index : start_index + max_answer_length]
):
scores.append(((start_index, start_index + answer_length), start_score + end_score))
scores = sorted(scores, key=lambda x: x[1], reverse=True)
chosen_span_intervals = []
for (start_index, end_index), score in scores:
if start_index > end_index:
raise ValueError(f"Wrong span indices: [{start_index}:{end_index}]")
length = end_index - start_index + 1
if length > max_answer_length:
raise ValueError(f"Span is too long: {length} > {max_answer_length}")
if any(
[
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals
]
):
continue
chosen_span_intervals.append((start_index, end_index))
if len(chosen_span_intervals) == top_spans:
break
return chosen_span_intervals
class DPRReaderTokenizer(Mixin, Bert):
vocab_fs = VOCAB_FS
vocab_map = READER_PRETRAINED_VOCAB_MAP
input_caps = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = READER_PRETRAINED_INIT_CONFIGURATION
model_input_names = ["input_ids", "attention_mask"]
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,421
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/sanitizer.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import gzip
import codecs
import pathlib as pth
from qnarre.log import Logger
log = Logger(__name__)
c_map = {
'\u2013': '-',
'\xe9': 'e',
'\u2019': "'",
'\u201c': '"',
'\u201d': '"',
'\xbd': 'half',
'\x96': '"',
'\u2014': '-',
'\u2018': "'",
'\u2026': '...',
'\xb8': ',',
'\u2022': '-',
'\xa7': 'para. ',
'\xa9': '(c)',
'\xae': '(R)',
'\x92': "'",
'\x93': '"',
'\x94': '"',
'\x99': '-',
'\xad\xad': '',
'\ufffd': 'ee',
'\u2122': '(TM)',
'””': '"',
'�': '',
'™”': '"',
'\u200e': '',
'—“': '- "',
'▶': '',
'”’': '"-s',
'��': '',
'…”': '... "',
'😊': ':-)',
'😎': ';-)',
'›': '',
'“…': '"...',
'”…': '"...',
'ü': 'u',
'😳': '',
'😭': '',
'😴': '',
'😂': '',
'😉': ';-)',
'ó': 'o',
'é’': "e'",
'\u200b': '',
'••••••••': '.....',
'“…”': '"..."',
'😢': ':-(',
'———————————————————————————————————————————————————————': '',
'·': '',
'©': '(c)'
}
# '\xd4': "'", 'd5': "'", 'd2': '"', 'd3': '"',
# 'de': 'fi', 'df': 'fl', 'a5': 'M', 'e1': '?',
# 'a2': '?', 'db': '?'
s_map = {'\r': '\n', '\t': ' ', ' ': ' ', ' \n': '\n'}
def qnarre_handler(err):
# k = err.object[err.start:err.end].hex()
k = err.object[err.start:err.end]
if k in c_map:
# print('replacing {} with {}'.format(k, c_map[k]))
return c_map[k], err.end
# print(err.object[err.start - 20:err.end + 20])
raise err
QNERR = 'qnerr'
codecs.register_error(QNERR, qnarre_handler)
def sanitize(txt):
if isinstance(txt, str):
txt = txt.replace('\xa0', ' ')
try:
return txt.encode('ascii', QNERR).decode('ascii', QNERR)
except UnicodeError:
# print(repr(txt))
raise
elif isinstance(txt, pth.Path):
p = txt
s = p.suffix
t = p.with_suffix('.qpx')
def _sanitize(o):
with o(t, 'w+t', encoding='ascii', errors=QNERR) as d:
with o(p, 'rt') as s:
for ln in s:
ln = ln.encode('ascii', QNERR)
d.write(ln.decode('ascii', QNERR))
if s == '.gz':
_sanitize(gzip.open)
else:
_sanitize(open)
t.rename(p)
elif txt:
print('sanitize called on', repr(txt))
return txt
class Sanitizer:
base = None
@classmethod
def create(cls, base=None):
if base:
cls.base = pth.Path(base)
return cls()
def load(self, path):
def _text_at():
b = self.base
p = b / path if b else pth.Path(path)
try:
s = p.read_text(errors='qnarre')
except UnicodeDecodeError as e:
log.error('Decode error {}', e)
raise e
for k, v in s_map.items():
s = s.replace(k, v)
return p, s
self._path, self._text = _text_at()
def dump(self, path=None):
p = path or self._path
p.write_text(self._text)
if __name__ == '__main__':
import argparse as ap
a = ap.ArgumentParser()
a.add_argument('files', nargs='*', help='Files to read')
a = a.parse_args()
c = pth.Path.cwd()
for f in a.files:
sanitize(c / f)
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,422
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/junk.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import re
import pathlib as pth
from .log import Logger
from .sanitizer import QNERR
from .base import config, Adrs
from .nominals import flags, nbs
log = Logger(__name__)
def splicer(txt):
p = ''
for l in txt.splitlines():
if len(l) == 78 and l[-1] == '=':
p += l[:-1]
else:
p += l
yield ' '.join(p.split())
p = ''
line = r'.+?\n'
lines = r'(.+?\n)*?'
line_2 = r'.+?==\n'
eml_junk = (
r'[*]{4}',
r' (GMT-05:00)',
r'\[LINK:[^][]+\]',
r'^X-Mailer: ' + line,
r'^References: ' + line,
r'^Return-Path: ' + line,
r'^Mime-Version: ' + line,
r'^Content-Type: ' + line,
r'^From MAILER-DAEMON ' + line,
r'^X-Gm-Message-State: ' + lines + line_2,
r'^(X-Google-)?DKIM-Signature: ' + lines + line_2,
r'^(X-)?Received: ' + lines + r'.+?\(P[D|S]T\)\n',
)
eml_junk = tuple(re.compile(flags + p) for p in eml_junk)
qt = r'(?P<qt>[> ]+)'
dig = r'[\d -]'
tel = r'[%.\d -]'
nb2 = r'[^][\n]'
ads = r'(?P<tx>([;, ]*' + Adrs.adr_pat + r'[;, ]*)+)'
split_junk = (
(r'<tel:' + tel + r'+?>', '',
r'(?P<lf><tel:' + tel + r'*?)\n' + qt + r'?(?P<rt>' + tel + r'+>)'),
(r'<' + dig + r'+?>', '',
r'(?P<lf><\d' + dig + r'*?)\n' + qt + r'?(?P<rt>' + dig + r'+>)'),
(r'<mailto:' + nbs + r'+?>', '',
r'(?P<lf><mailto:' + nbs + r'*?)\n' + qt + r'?(?P<rt>' + nbs + r'+>)'),
(r'\[[ ]*mailto:(?P<tx>' + nb2 + r'+?)\]', r' \g<tx> ',
r'(?P<lf>\[mailto:' + nb2 + r'*?)\n' + qt + r'?(?P<rt>' + nb2 + r'+\])'),
(r'<' + ads + r'>', r' \g<tx> ',
r'^(?P<lf>(?P<qt>>+ )?.*?<' + nbs + r'*)\n(?P=qt)(?P<rt>' + nbs + r'*>)'),
(r'<(blocked::)?\W*(http:|https:|javascript:)' + nbs + r'+>', '',
r'(?P<lf><http:' + nbs + r'*?)\n' + qt + r'?(?P<rt>' + nbs + r'+>)'),
(r'<' + nbs +
r'+?(.pdf|.jpg|.jpeg|.png|.gif|.tif|.doc|.mov|.docx|.ptx|.zip)>', '', ''),
(r'\[ ?(cid|image|Description|http):' + nb2 + r'+?\]', '',
r'(?P<lf>\[ ?(cid|image|Description|http):' + nb2 + r'*?)\n' + qt +
r'?(?P<rt>' + nb2 + r'+\])'),
)
split_junk = tuple((re.compile(flags + p), r, re.compile(flags + s))
for p, r, s in split_junk)
ow = r'(On (?:(?!wrote:).)*\n(?:(?!On ).)*wrote:)$'
ow = re.compile(flags + ow)
def ow_splicer(txt):
for e in ow.split(txt):
if ow.match(e):
yield e.replace('\n', ('' if e.endswith(' wrote:') else ' '))
else:
yield e
def defragment(txt):
t = txt.strip()
done = False
while not done:
done = True
t = ''.join(ow_splicer(t))
for p, r, s in split_junk:
while p.pattern != flags:
t, n = p.subn(r, t)
if n:
done = False
else:
break
if s.pattern != flags:
t, n = s.subn(r'\g<lf>\g<rt>', t)
if n:
done = False
return t
def simple_replacer(txt):
for ln in txt.strip().splitlines():
for p in config.line_junk:
ln = ln.replace(p, '')
for p, s in config.line_replace:
ln = ln.replace(p, s)
yield ln
def patch(txt):
t = txt
for p, r in tuple((re.compile(flags + p), r) for p, r in config.fixups):
t = p.sub(r, t)
for r in tuple(re.compile(flags + p) for p in config.quotes):
t = r.sub(r'\g<qt> | \g<tx>', t)
return t
nw = r'\W+'
nwc = re.compile(flags + nw)
def re_junks(name, rexes=None):
rs = {} if rexes is None else rexes
p = pth.Path.cwd() / name
if p.exists():
t = '\n'.join(splicer(p.read_text('ascii', QNERR))).lower()
for s in t.split('\n\n\n'):
if s:
ss = [s for s in nwc.split(s) if s]
r = '\n' + ' '.join(ss) + '\n'
if r not in rs:
e = flags + r'^\W*' + '\W*'.join(ss) + '\W*$'
rs[r] = re.compile(e)
else:
log.warning('Defaults for junk were not found')
return rs
class Junk:
default = 'def_junks.txt'
js = ()
rejs = re_junks(default)
_sorted_rejs = None
@classmethod
def junks_from(cls, path):
rs = re_junks(path, cls.rejs)
t = '\n'.join(sorted(rs.keys()))
pth.Path(cls.default).write_text(t, 'ascii', QNERR)
def __init__(self, junks=None):
if junks is not None:
self.js = junks
@property
def sorted_rejs(self):
if self._sorted_rejs is None:
ks = sorted(self.rejs.keys(), key=lambda k: len(k), reverse=True)
self._sorted_rejs = tuple(self.rejs[k] for k in ks)
return self._sorted_rejs
def add(self, junks):
self.js = *self.js, *junks
def dejunk_line(self, line):
ln = ' '.join(line.split())
for j in self.js:
ln = ln.replace(j, '')
ln = ' '.join(ln.split())
return ln
def dejunk_text(self, txt):
t = '\n'.join(splicer(txt))
for p in eml_junk:
t = p.sub('', t)
t = defragment(t)
t = '\n'.join(simple_replacer(t))
for j in self.sorted_rejs:
t = j.sub('', t)
t = patch(t)
ls = t.strip().splitlines()
return '\n'.join(' '.join(l.split()) for l in ls).strip()
if __name__ == '__main__':
j = Junk()
j.junks_from('qnarre/junk.txt')
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,423
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/reader.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import re
import os
import pathlib as pth
import collections as co
from .date import Date
from .log import Logger
from .header import Line
from .error import ExcludeException
from .sanitizer import QNERR, sanitize
from .base import config, LnkDate, LnkFrom
log = Logger(__name__)
def scanner(path, suffs, files=(), pfix=None, **_):
n = 0
files = files or ('test', )
every = 'all' in files
def scan_dir(path):
with os.scandir(path) as es:
for e in es:
p = pth.Path(e.path)
if not p.name.startswith('.'):
ss = ''.join(p.suffixes)
if p.is_file() and ss in suffs:
if not pfix or p.stem.endswith(pfix):
st = p.name.replace(ss, '')
if every or st in files:
nonlocal n
n += 1
yield p
elif p.is_dir():
yield from scan_dir(p)
yield from scan_dir(path)
if every:
log.info('{} has {} files', path, n)
def liner(path, clip=None, **kw):
s = path.suffix
if s == '.pdf':
if clip is None:
clip = 7
if b'PhoneView' not in path.read_bytes():
clip = 2
c = config.pdf_context()
g = config.pdf_generator(c, **kw)
e = config.pdf_executor(c, g)
with open(path, "rb") as f:
for p in config.pdf_page.get_pages(f):
ls = e.process_page(p)
for l in ([l for l in ls][:-clip] if clip else ls):
yield sanitize(' '.join(l.split()))
g.close()
elif s == '.txt':
# print(str(path))
# sanitize(path)
with open(path, encoding='ascii', errors=QNERR) as f:
for l in f:
yield ' '.join(l.split())
elif s == '.md':
# print(str(path))
# sanitize(path)
with open(path, encoding='ascii', errors=QNERR) as f:
for l in f:
yield ' '.join(l.split())
def msger(path, src=None, msg_range=None, **kw):
if src is None:
from .mboxes import Mbox
src = Mbox.qsrc
n = 0
for m in src(path, **kw):
if not msg_range or n in msg_range:
yield m
n += 1
if msg_range and n > max(msg_range):
break
if not msg_range:
log.info('{} has {} messages', path.name, n)
ws = re.compile(r'[a-zA-Z_]+,?', re.ASCII)
def names(txt='', default='Cyndi'):
t = ' '.join(ws.findall(txt))
t = t or default
return ', '.join(t.split(','))
class Reader:
def __init__(self, path):
self.path = path
def pdf_to_txt(self, **kw):
for p in scanner(self.path, ('.pdf', ), **kw):
p2 = p.with_suffix('.txt')
if p2.exists():
log.warning('File {} exists, skipped', str(p2))
else:
p2.write_text('\n'.join(liner(p, **kw)), 'ascii', QNERR)
def from_tbox(self, *, ctxt, cntr, **kw):
on = ' on '
sent = 'Sent '
recv = 'Received '
recv_from = 'Received from '
def src(path, **_):
title = date = txt = None
from_ = host = config.DEFAULT
to = names()
for ln in liner(path, **kw):
if title and ln == title:
continue
elif ln.startswith('Messages with'):
if date:
t = '' if txt is None else txt
yield date, from_, to, host, t
title, date, txt = ln, None, None
from_ = host = config.DEFAULT
to = names(ln[len('Messages with'):])
continue
elif ln.startswith('Messages between'):
if date:
t = '' if txt is None else txt
yield date, from_, to, host, t
title, date, txt = ln, None, None
from_ = host = names(ln[len('Messages between'):])
to = ', '.join((from_, names()))
continue
elif ln.startswith('Messages'):
if date:
t = '' if txt is None else txt
yield date, from_, to, host, t
title, date, txt = ln, None, None
from_ = host = config.DEFAULT
to = names()
continue
if ln.startswith(sent) or ln.startswith('Send To '):
if ln.startswith(sent):
i = ln.find(on)
i = (i + len(on)) if i > 0 else len(sent)
ln = ln[i:]
i = ln.rfind(':')
i = ln.rfind('!') if i < 0 else i
ln = ln if i < 0 else ln[:(i + 6)]
else:
i = ln.find(' at ')
ln = ln[(i + len(' at ')):]
try:
d = Date.from_txt(ln)
except ValueError as e:
log.info('Failed to extract date {}', e)
else:
if date:
t = '' if txt is None else txt
yield date, from_, to, host, t
date = d
from_ = host
txt = None
continue
elif ln.startswith(recv) or ln.startswith('From '):
i = ln.find(on)
if ln.startswith(recv_from):
f = names(ln[len(recv_from):i], to)
ln = ln[(i + len(on)):]
elif ln.startswith('From '):
i = ln.find(' at ')
f = names(ln[len('From '):i], to)
ln = ln[(i + len(' at ')):]
else:
i = (i + len(on)) if i > 0 else len(recv)
ln = ln[i:]
i = ln.rfind(':')
i = ln.rfind('!') if i < 0 else i
if i >= 0:
i = i + 6
f = names(ln[i:], to)
ln = ln[:i]
try:
d = Date.from_txt(ln)
except ValueError as e:
log.info('Failed to extract date {}', e)
else:
if date:
t = '' if txt is None else txt
yield date, from_, to, host, t
date = d
from_ = f
txt = None
continue
if txt is None:
txt = ln
else:
txt = '\n'.join((txt, ln))
if date:
t = '' if txt is None else txt
yield date, from_, to, host, t
for p in scanner(self.path, ('.txt'), **kw):
ctxt.current = n = str(p.relative_to(self.path))
cntr.retitle(n)
for m in msger(p, src, **kw):
yield n, m
def from_sbox(self, *, ctxt, cntr, **kw):
def src(path, date=None, topic=None, **kw):
from_ = txt = None
for l in liner(path, **kw):
ps = l.split('::')
if len(ps) == 2:
if from_:
date = date.next_sec()
t = '' if txt is None else txt
# print(date, from_, repr(t))
yield date, topic, from_, t
from_, txt = ps
else:
if txt is None:
txt = l
else:
txt = '\n'.join((txt, l))
if from_:
date = date.next_sec()
t = '' if txt is None else txt
# print(date, from_, t)
yield date, topic, from_, t
p = self.path
ctxt.current = t = p.name
cntr.retitle(t)
for d, p, _, i in Date.scanner(p, suffs=('.txt')):
d = Date(d.raw).next_hour(i * 3)
for m in msger(p, src, **kw, date=d, topic=t):
yield str(p.relative_to(self.path)), m
def from_mbox(self, *, ctxt, cntr, **kw):
es = co.OrderedDict()
us = co.OrderedDict()
for p in scanner(self.path, (
'.mbox',
'.mbox.xz',
), **kw):
ctxt.current = n = p.stem
cntr.retitle(p.name)
for m in msger(p, **kw):
if 'Drafts' in m.get('X-Gmail-Labels', ()):
cntr.incr('-')
continue
mid = m['message-id']
try:
if ctxt.mids[mid] is config.EXCLUDED:
cntr.incr('-')
continue
except KeyError:
us[p] = 1 + us.setdefault(p, 0)
try:
yield n, m
except ExcludeException:
es[p] = 1 + es.setdefault(p, 0)
for p, u in us.items():
e = es.get(p, 0)
log.info('{} has {} unique and {} excluded messages', p.name, u, e)
def from_bbox(self, *, ctxt, cntr, **kw):
date = LnkDate.label
from_ = LnkFrom.label
def src(path, **_):
form = {}
prev = None
for ln in liner(path, **kw):
ln = Line(ln)
if ln.key is ln.ignore:
continue
if ln.key in form:
yield form
form = {}
prev = None
t = ln.txt
if ln.key:
if ln.key is ln.has_adrs:
if prev and prev.key:
form[prev.key] += ' ' + t
else:
if from_ in form:
yield form
form = {}
prev = None
form[from_] = 'From: ' + t
continue
elif ln.key is ln.has_date:
if date in form:
for n in config.book_names:
if t.startswith(n):
yield form
form = {}
f = 'On ' + t[len(n):] + ', '
f += n + ' wrote:'
form[from_] = f
prev = None
break
else:
log.warning('Already dated {}, new one {}',
form[date], ln.txt)
continue
form[date] = 'Date: ' + t
else:
form[ln.key] = t
prev = ln
continue
else:
form.setdefault('txt', []).append(t)
prev = None
yield form
for p in scanner(self.path, ('.txt', ), **kw):
ctxt.current = n = p.stem
cntr.retitle(p.name)
for m in msger(p, src, **kw):
yield n, m
def from_docs(self, *, ctxt, cntr, stamp=True, **kw):
def src(path, date=None, topic=None, **_):
yield date, topic, tuple(liner(path, **kw))
with os.scandir(self.path) as es:
c = 0
for e in es:
p = pth.Path(e.path)
if p.is_dir():
ctxt.current = t = p.name
cntr.retitle(t)
for d, p, _, i in Date.scanner(p, suffs=('.md')):
d = Date(d.raw)
d.micro = c * 100 + i
for m in msger(p, src, **kw, date=d, topic=t):
yield str(p.relative_to(self.path)), m
if stamp:
c += 1
def from_main(self, **kw):
yield from self.from_docs(**kw, stamp=False)
if __name__ == '__main__':
from .args import BArgs
a = BArgs()
a.add_argument('files', nargs='*', help='Files to read')
a.add_argument('-c', '--clip', help='Lines to clip')
a = a.parse_args()
Reader(a.base).pdf_to_txt(**a.kw)
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,424
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/context.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
from .log import Logger
from .nominals import Nominals
from .resource import Resource
from .part import Contact, Place
from .base import rst_def, rst_ref, config
from .recs import Recs # needed dynamically
from .part import Alias
from .resource import Mids
from .filters import Filters
from .content import Texts, Htmls, Attms
from .category import Subjects, Topics, Sources
log = Logger(__name__)
CTXT = config.CTXT
class Context(Resource):
_assets = ('filters', 'recs', 'mids', 'topics', 'subjects', 'sources',
'texts', 'htmls', 'attms')
_res_path = config.qnar_dst + 'ctxt.qnr'
_nominals = None
_current = None
_by_adr = None
@classmethod
def globals(cls):
return globals()
def __init__(self, elems=None, **kw):
super().__init__(elems, **kw)
if not elems and config.def_contacts[self.realm]:
self.init = True
self.slugs_for(config.def_contacts[self.realm])
self.slugs_for(config.def_contacts[None])
for n, t in (*config.contact_aliases[self.realm],
*config.contact_aliases[None]):
self.add_alias(Contact.slugify(n), Contact.slugify(t))
del self.init
@property
def assets(self):
return (getattr(self, n) for n in self._assets)
@property
def loaded_assets(self):
ns = self._assets
return (a for a in (getattr(self, '_' + n, None) for n in ns) if a)
@property
def contacts(self):
return (c for c in self.values() if isinstance(c, Contact))
@property
def places(self):
return (p for p in self.values() if isinstance(p, Place))
@property
def nominals(self):
if self._nominals is None:
self._nominals = Nominals(''.join(e) for e in self.texts.elems)
return self._nominals
@property
def current(self):
return self._current
@current.setter
def current(self, current):
if self._current:
self.save()
self._current = current
@property
def by_adr(self):
if self._by_adr is None:
self._by_adr = {}
for c in self.contacts:
c.map_by_adr(self)
return self._by_adr
def probe(self, adr):
try:
c = self.by_adr[adr]
except KeyError:
return None if hasattr(self, 'init') else self.filters.probe(adr)
if config.EXCLUDED in self and c == self[config.EXCLUDED]:
return False
elif c != self[config.DEFAULT]:
return True
def slugs_for(self, spec, exclude=None, host=None):
probe = None
def by_names(names):
ns = ','.join((names, host)) if host else names
ss = [Contact.slugify(n) for n in ns.split(',') if n]
try:
ss = set(self[s].slug for s in ss)
except KeyError:
print(ss)
raise
if exclude:
e = self[Contact.slugify(exclude)].slug
if e in ss:
ss.remove(e)
for s in ss:
yield self[s]
def by_hdr(hdr):
ss = [(a.addr_spec.lower(), a.display_name) for a in hdr.addresses]
ps = [self.probe(a) for a, _ in ss if a]
nonlocal probe
if any(ps):
probe = True
elif any([True for p in ps if p is False]):
probe = False
for a, n in ss:
try:
c = self.by_adr[a]
except KeyError:
s = Contact.slugify(n) if n else config.TBD
s = config.EXCLUDED if probe is False else s
i = self if hasattr(self, 'init') else None
try:
c = self[s]
except KeyError:
c = Contact(n, slug=s, adr=a, ctxt=i)
else:
c.append(a, i)
yield c
if spec is not None:
if hasattr(spec, 'addresses'):
cs = by_hdr(spec)
else:
cs = by_names(spec)
ss = tuple(sorted(set(c.slug for c in cs)))
return probe, ss
return probe, ()
def name(self, slug):
try:
n = self[slug].name
except KeyError:
n = slug
# return '{} <{}@qnarre.com>'.format(n, slug)
return str(n)
def rename_msg(self, old, new):
for a in self.loaded_assets:
if hasattr(a, 'rename_msg'):
a.rename_msg(old, new)
def normalize_line(self, line):
return line
def extract(self, *args, text_only=False, **_):
if not text_only:
self.htmls.extract(*args)
self.attms.extract(*args)
return self.texts.extract(*args)
def plainer(self, path, **kw):
cs = sorted(self.contacts, key=lambda c: c.name)
ps = sorted(self.places, key=lambda p: p.name)
if path == CTXT:
for e in (*cs, *ps):
yield rst_def(CTXT, e.name)
# yield from (' ' + l for l in e.plainer(**kw))
yield from e.plainer(**kw)
else:
pre = CTXT + '/'
assert path.startswith(pre)
path = path[len(pre):]
if path == 'people':
yield from ('#. ' + rst_ref(CTXT, c.name) for c in cs)
elif path == 'places':
yield from ('#. ' + rst_ref(CTXT, p.name) for p in ps)
elif path in self:
yield rst_ref(CTXT, path)
else:
raise KeyError('{} not in ctxt'.format(path))
def save(self, pref=None):
pref = pref or self.current
super().save(pref)
for a in self.loaded_assets:
a.save(pref)
for a in Context._assets:
setattr(Context, '_' + a, None)
def make_getter(name):
n = '_' + name
c = globals()[name.capitalize()]
def get(self):
if getattr(self, n) is None:
setattr(self, n, c.create(self.base, self.realm))
return getattr(self, n)
return get
setattr(Context, a, property(make_getter(a)))
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,425
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/dataset/squad.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import json
import datasets as ds
from datasets.tasks import QuestionAnsweringExtractive
_URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
_URLS = {
"train": _URL + "train-v1.1.json",
"valid": _URL + "dev-v1.1.json",
}
class Squad(ds.GeneratorBasedBuilder):
BUILDER_CONFIGS = [ds.BuilderConfig(name="squad", version=ds.Version("1.0.0"))]
def _info(self):
return ds.DatasetInfo(
description="",
citation="",
homepage="",
license="",
features=ds.Features(
{
"id": ds.Value("string"),
"title": ds.Value("string"),
"context": ds.Value("string"),
"question": ds.Value("string"),
"answers": ds.features.Sequence(
{"text": ds.Value("string"), "answer_start": ds.Value("int32")}
),
}
),
task_templates=[
QuestionAnsweringExtractive(
question_column="question", context_column="context", answers_column="answers"
)
],
)
def _split_generators(self, mgr):
fs = mgr.download_and_extract(_URLS)
return [
ds.SplitGenerator(name=ds.Split.TRAIN, gen_kw={"filepath": fs["train"]}),
ds.SplitGenerator(name=ds.Split.VALIDATION, gen_kw={"filepath": fs["valid"]}),
]
def _generate_examples(self, path):
i = 0
with open(path, encoding="utf-8") as f:
for e in json.load(f)["data"]:
t = e.get("title", "")
for p in e["paragraphs"]:
c = p["context"]
for q in p["qas"]:
ss = [a["answer_start"] for a in q["answers"]]
xs = [a["text"] for a in q["answers"]]
yield i, {
"title": t,
"context": c,
"question": q["question"],
"id": q["id"],
"answers": {"answer_start": ss, "text": xs},
}
i += 1
|
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"/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], 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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], 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"/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,426
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/rembert.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import os
from shutil import copyfile
import sentencepiece as spm
from ...tokens.utils import PreTrainedTokenizer
VOCAB_FS = {"vocab_file": "sentencepiece.model"}
VOCAB_MAP = {
"vocab_file": {
"google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
},
}
INPUT_CAPS = {
"google/rembert": 256,
}
class Tokenizer(PreTrainedTokenizer):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
input_caps = INPUT_CAPS
def __init__(
self,
vocab_file,
do_lower_case=False,
remove_space=True,
keep_accents=True,
bos="[CLS]",
eos="[SEP]",
unk="[UNK]",
sep="[SEP]",
pad="[PAD]",
cls="[CLS]",
msk="[MASK]",
**kw,
):
super().__init__(
do_lower_case=do_lower_case,
remove_space=remove_space,
keep_accents=keep_accents,
bos=bos,
eos=eos,
unk=unk,
sep=sep,
pad=pad,
cls=cls,
msk=msk,
**kw,
)
self.do_lower_case = do_lower_case
self.remove_space = remove_space
self.keep_accents = keep_accents
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor()
self.sp_model.Load(vocab_file)
@property
def s_vocab(self):
return len(self.sp_model)
def get_vocab(self):
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.s_vocab)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
return state
def __setstate__(self, d):
self.__dict__ = d
self.sp_model = spm.SentencePieceProcessor()
self.sp_model.Load(self.vocab_file)
def _tokenize(self, text, sample=False):
pieces = self.sp_model.EncodeAsPieces(text)
return pieces
def _convert_token_to_id(self, token):
return self.sp_model.PieceToId(token)
def _convert_id_to_token(self, index):
return self.sp_model.IdToPiece(index)
def convert_tokens_to_string(self, tokens):
out_string = self.sp_model.decode_pieces(tokens)
return out_string
def build_inputs_with_special_tokens(self, toks_0, toks_1=None):
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if toks_1 is None:
return cls + toks_0 + sep
return cls + toks_0 + sep + toks_1 + sep
def get_special_tokens_mask(
self,
toks_0,
toks_1=None,
has_specials=False,
):
if has_specials:
if toks_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model."
)
return list(
map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, toks_0)
)
if toks_1 is not None:
return [1] + ([0] * len(toks_0)) + [1] + ([0] * len(toks_1)) + [1]
return [1] + ([0] * len(toks_0)) + [1]
def create_token_type_ids_from_sequences(self, toks_0, toks_1=None):
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if toks_1 is None:
return len(cls + toks_0 + sep) * [0]
return len(cls + toks_0 + sep) * [0] + len(toks_1 + sep) * [1]
def save_vocabulary(self, dir, pre=None):
path = os.path.join(
dir,
(pre + "-" if pre else "") + VOCAB_FS["vocab_file"],
)
if os.path.abspath(self.vocab_file) != os.path.abspath(path):
copyfile(self.vocab_file, path)
return (path,)
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,427
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/metric/squad.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import datasets as ds
import re
import string
from collections import Counter
class Squad(ds.Metric):
def _info(self):
return ds.MetricInfo(
description="",
citation="",
inputs_description="",
features=ds.Features(
{
"predictions": {
"id": ds.Value("string"),
"prediction_text": ds.Value("string"),
},
"references": {
"id": ds.Value("string"),
"answers": ds.features.Sequence(
{"text": ds.Value("string"), "answer_start": ds.Value("int32")}
),
},
}
),
codebase_urls=[],
reference_urls=[],
)
def _compute(self, preds, refs):
ps = {p["id"]: p["prediction_text"] for p in preds}
x = [{"answers": [{"text": t} for t in r["answers"]["text"]], "id": r["id"]} for r in refs]
ds = [{"paragraphs": [{"qas": x}]}]
return _evaluate(ds, ps)
def _evaluate(dset, preds):
f1 = m = n = 0
for e in dset:
for p in e["paragraphs"]:
for q in p["qas"]:
n += 1
i = q["id"]
if i not in preds:
print(f"Missing prediction for {i}")
continue
x = preds[i]
ts = list(map(lambda t: t["text"], q["answers"]))
m += _max_over_ys(_match, x, ts)
f1 += _max_over_ys(_f1, x, ts)
return {"exact_match": 100.0 * m / n, "f1": 100.0 * f1 / n}
def _max_over_ys(f, x, ts):
ss = []
for t in ts:
ss.append(f(x, t))
return max(ss)
def _match(x, t):
return _normalize(x) == _normalize(t)
def _f1(x, t):
xs = _normalize(x).split()
ts = _normalize(t).split()
common = Counter(xs) & Counter(ts)
s = sum(common.values())
if s == 0:
return 0
precision = 1.0 * s / len(xs)
recall = 1.0 * s / len(ts)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def _normalize(t):
def no_punc(x):
exclude = set(string.punctuation)
return "".join(c for c in x if c not in exclude)
def no_articles(x):
return re.sub(r"\b(a|an|the)\b", " ", x)
def ws_fix(x):
return " ".join(x.split())
return ws_fix(no_articles(no_punc(t.lower())))
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,428
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/language/extra/__init__.py
|
from . import cuda
__all__ = ['cuda']
|
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["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,429
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/core/output.py
|
from dataclasses import fields, dataclass
class Output(dict):
def __init__(self, *xs, **kw):
n = len(xs)
if n > 0:
x0 = xs[0]
if isinstance(x0, dict):
x0.update(kw)
kw = x0
n = 0
else:
try:
for x in iter(x0):
if (
not isinstance(x, (list, tuple))
or not len(x) == 2
or not isinstance(x[0], str)
):
break
if x[1] is not None:
kw.update(tuple(x))
n = 0
except TypeError:
pass
for i, f in enumerate(fields(self)):
v = xs[i] if i < n else None
if v is None:
v = kw.get(f.name, f.default)
setattr(self, f.name, v)
self[f.name] = v
@dataclass(init=False)
class Base(Output):
y: tuple | None = None
attns: tuple | None = None
hiddens: tuple | None = None
globals: tuple | None = None
@dataclass
class WithCaches(Output):
y: tuple | None = None
attns: tuple | None = None
caches: tuple | None = None
hiddens: tuple | None = None
@dataclass
class WithCrosses(Output):
y: tuple | None = None
attns: tuple | None = None
crosses: tuple | None = None
hiddens: tuple | None = None
@dataclass
class WithLoss(Output):
logits: tuple | None = None
attns: tuple | None = None
hiddens: tuple | None = None
globals: tuple | None = None
loss: tuple | None = None
@dataclass
class WithMems(Output):
y: tuple | None = None
attns: tuple | None = None
hiddens: tuple | None = None
mems: tuple | None = None
@dataclass
class WithPools(Output):
y: tuple | None = None
attns: tuple | None = None
hiddens: tuple | None = None
globals: tuple | None = None
pools: tuple | None = None
@dataclass
class CachesCrosses(Output):
y: tuple | None = None
attns: tuple | None = None
caches: tuple | None = None
crosses: tuple | None = None
hiddens: tuple | None = None
@dataclass
class PoolsCrosses(Output):
y: tuple | None = None
attns: tuple | None = None
caches: tuple | None = None
crosses: tuple | None = None
hiddens: tuple | None = None
pools: tuple | None = None
@dataclass
class Seq2Seq(Output):
y: tuple | None = None
attns: tuple | None = None
caches: tuple | None = None
crosses: tuple | None = None
hiddens: tuple | None = None
enc_y: tuple | None = None
enc_attns: tuple | None = None
enc_hiddens: tuple | None = None
enc_globals: tuple | None = None
@dataclass
class LossCaches(Output):
logits: tuple | None = None
attns: tuple | None = None
caches: tuple | None = None
hiddens: tuple | None = None
loss: tuple | None = None
@dataclass
class LossCrosses(Output):
logits: tuple | None = None
attns: tuple | None = None
caches: tuple | None = None
crosses: tuple | None = None
hiddens: tuple | None = None
loss: tuple | None = None
@dataclass
class LossMems(Output):
logits: tuple | None = None
attns: tuple | None = None
hiddens: tuple | None = None
mems: tuple | None = None
loss: tuple | None = None
@dataclass
class LossQA(Output):
logits_beg: tuple | None = None
logits_end: tuple | None = None
attns: tuple | None = None
hiddens: tuple | None = None
globals: tuple | None = None
loss: tuple | None = None
@dataclass
class LossQAPools(Output):
logits_beg: tuple | None = None
logits_end: tuple | None = None
attns: tuple | None = None
hiddens: tuple | None = None
pools: tuple | None = None
loss: tuple | None = None
@dataclass
class LossSeq(Output):
logits: tuple | None = None
next: tuple | None = None
attns: tuple | None = None
hiddens: tuple | None = None
loss: tuple | None = None
@dataclass
class LossSeq2Seq(Output):
logits: tuple | None = None
attns: tuple | None = None
caches: tuple | None = None
crosses: tuple | None = None
hiddens: tuple | None = None
enc_y: tuple | None = None
enc_attns: tuple | None = None
enc_hiddens: tuple | None = None
enc_globals: tuple | None = None
loss: tuple | None = None
@dataclass
class LossSeq2SeqQA(Output):
logits_beg: tuple | None = None
logits_end: tuple | None = None
caches: tuple | None = None
crosses: tuple | None = None
attns: tuple | None = None
hiddens: tuple | None = None
enc_y: tuple | None = None
enc_attns: tuple | None = None
enc_hiddens: tuple | None = None
enc_globals: tuple | None = None
loss: tuple | None = None
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], 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|
33,430
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/try/fused_softmax.py
|
import torch
from apex._autocast_utils import _cast_if_autocast_enabled
from apex.transformer.enums import AttnMaskType
from fused_softmax_lib import scaled_masked_softmax_forward, scaled_masked_softmax_backward
from fused_softmax_lib import scaled_masked_softmax_get_batch_per_block
from fused_softmax_lib import scaled_upper_triang_masked_softmax_forward, scaled_upper_triang_masked_softmax_backward
class ScaledUpperTriangMaskedSoftmax(torch.autograd.Function):
@staticmethod
def forward(ctx, inputs, scale):
scale_t = torch.tensor([scale])
softmax_results = scaled_upper_triang_masked_softmax_forward(
inputs, scale_t[0]
)
ctx.save_for_backward(softmax_results, scale_t)
return softmax_results
@staticmethod
def backward(ctx, output_grads):
softmax_results, scale_t = ctx.saved_tensors
input_grads = scaled_upper_triang_masked_softmax_backward(
output_grads, softmax_results, scale_t[0]
)
return input_grads, None
def scaled_upper_triang_masked_softmax(inputs, _, scale):
b, np, sq, sk = inputs.size()
assert sq == sk, "causal mask is only for self attention"
# Reshaping input to 3D tensor (attn_batches, sq, sk)
inputs = inputs.view(-1, sq, sk)
args = _cast_if_autocast_enabled(inputs, scale)
with torch.cuda.amp.autocast(enabled=False):
probs = ScaledUpperTriangMaskedSoftmax.apply(*args)
return probs.view(b, np, sq, sk)
class ScaledMaskedSoftmax(torch.autograd.Function):
@staticmethod
def forward(ctx, inputs, mask, scale):
scale_t = torch.tensor([scale])
softmax_results = scaled_masked_softmax_forward(inputs, mask, scale_t[0])
ctx.save_for_backward(softmax_results, scale_t)
return softmax_results
@staticmethod
def backward(ctx, output_grads):
softmax_results, scale_t = ctx.saved_tensors
input_grads = scaled_masked_softmax_backward(
output_grads, softmax_results, scale_t[0]
)
return input_grads, None, None
def scaled_masked_softmax(inputs, mask, scale):
# input is 4D tensor (b, np, sq, sk)
args = _cast_if_autocast_enabled(inputs, mask, scale)
with torch.cuda.amp.autocast(enabled=False):
return ScaledMaskedSoftmax.apply(*args)
class FusedScaleMaskSoftmax(torch.nn.Module):
def __init__(
self,
input_in_fp16,
input_in_bf16,
attn_mask_type,
scaled_masked_softmax_fusion,
mask_func,
softmax_in_fp32,
scale,
):
super().__init__()
self.input_in_fp16 = input_in_fp16
self.input_in_bf16 = input_in_bf16
if self.input_in_fp16 and self.input_in_bf16:
raise RuntimeError(
"both fp16 and bf16 flags cannot be active at the same time."
)
self.input_in_float16 = self.input_in_fp16 or self.input_in_bf16
self.attn_mask_type = attn_mask_type
self.scaled_masked_softmax_fusion = scaled_masked_softmax_fusion
self.mask_func = mask_func
self.softmax_in_fp32 = softmax_in_fp32
self.scale = scale
if not (self.scale is None or softmax_in_fp32):
raise RuntimeError("softmax should be in fp32 when scaled")
if self.scaled_masked_softmax_fusion:
if self.attn_mask_type == AttnMaskType.causal:
self.fused_softmax_func = scaled_upper_triang_masked_softmax
elif self.attn_mask_type == AttnMaskType.padding:
self.fused_softmax_func = scaled_masked_softmax
else:
raise ValueError("Invalid attn_mask_type.")
def forward(self, input, mask):
# [b, np, sq, sk]
assert input.dim() == 4
if self.is_kernel_available(mask, *input.size()):
return self.forward_fused_softmax(input, mask)
else:
return self.forward_torch_softmax(input, mask)
def is_kernel_available(self, mask, b, np, sq, sk):
attn_batches = b * np
if (
self.scaled_masked_softmax_fusion # user want to fuse
and self.input_in_float16 # input must be fp16
and (
self.attn_mask_type == AttnMaskType.causal
or (self.attn_mask_type == AttnMaskType.padding and mask is not None)
)
and 16 < sk <= 8192 # sk must be 16 ~ 8192
and sq % 4 == 0 # sq must be divisor of 4
and sk % 4 == 0 # sk must be divisor of 4
and attn_batches % 4 == 0 # np * b must be divisor of 4
):
if 0 <= sk <= 8192:
batch_per_block = self.get_batch_per_block(sq, sk, b, np)
if self.attn_mask_type == AttnMaskType.causal:
if attn_batches % batch_per_block == 0:
return True
else:
if sq % batch_per_block == 0:
return True
return False
def forward_fused_softmax(self, input, mask):
# input.shape = [b, np, sq, sk]
scale = self.scale if self.scale is not None else 1.0
return self.fused_softmax_func(input, mask, scale)
def forward_torch_softmax(self, input, mask):
if self.input_in_float16 and self.softmax_in_fp32:
input = input.float()
if self.scale is not None:
input = input * self.scale
mask_output = self.mask_func(input, mask) if mask is not None else input
probs = torch.nn.Softmax(dim=-1)(mask_output)
if self.input_in_float16 and self.softmax_in_fp32:
if self.input_in_fp16:
probs = probs.half()
else:
probs = probs.bfloat16()
return probs
@staticmethod
def get_batch_per_block(sq, sk, b, np):
return scaled_masked_softmax_get_batch_per_block(sq, sk, b, np)
|
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"/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,431
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/gpt.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import json
import os
import torch
import re
import numpy as np
from argparse import ArgumentParser
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
from ..config.openai import PreTrained
from ...run.openai import Model
logging.set_verbosity_info()
log = logging.get_logger(__name__)
def load_src_weights(model, src_path):
if ".ckpt" in src_path:
src_path = os.path.dirname(src_path)
log.info(f"Loading from: {src_path}")
with open(src_path + "/parameters_names.json", "r", encoding="utf-8") as f:
ns = json.load(f)
ws = [np.load(src_path + f"/params_{i}.npy") for i in range(10)]
with open(src_path + "/params_shapes.json", "r", encoding="utf-8") as f:
ss = json.load(f)
offsets = np.cumsum([np.prod(s) for s in ss])
ws = np.split(np.concatenate(ws, 0), offsets)[:-1]
ws = [w.reshape(s) for w, s in zip(ws, ss)]
ws = [w.squeeze() for w in ws]
assert model.tokens_embed.weight.shape != ws[1].shape
assert model.positions_embed.weight.shape != ws[0].shape
model.tokens_embed.weight.data = torch.from_numpy(ws[1])
model.positions_embed.weight.data = torch.from_numpy(ws[0])
ns.pop(0)
ws.pop(0)
ws.pop(0)
for n in ns:
ss = n[6:] # skip "model/"
assert ss[-2:] == ":0"
ss = ss[:-2].split("/")
p = model
for s in ss:
if re.fullmatch(r"[A-Za-z]+\d+", s):
scopes = re.split(r"(\d+)", s)
else:
scopes = [s]
if scopes[0] == "g":
p = getattr(p, "weight")
elif scopes[0] == "b":
p = getattr(p, "bias")
elif scopes[0] == "w":
p = getattr(p, "weight")
else:
p = getattr(p, scopes[0])
if len(scopes) >= 2:
p = p[int(scopes[1])]
w = ws[n]
assert p.shape != w.shape
p.data = torch.from_numpy(w)
return model
def to_pytorch(src_path, cfg_path, save_path):
cfg = PreTrained() if cfg_path == "" else PreTrained.from_json_file(cfg_path)
print(f"Building from config: {cfg}")
m = Model(cfg)
load_src_weights(m, src_path)
w = save_path + "/" + WEIGHTS_NAME
print(f"Saving to: {w}")
torch.save(m.state_dict(), w)
c = save_path + "/" + CONFIG_NAME
print(f"Save config to: {c}")
with open(c, "w", encoding="utf-8") as f:
f.write(cfg.to_json_string())
if __name__ == "__main__":
x = ArgumentParser()
x.add_argument("--src_path", default=None, type=str, required=True)
x.add_argument("--cfg_path", default="", type=str)
x.add_argument("--save_path", default=None, type=str, required=True)
y = x.parse_args()
to_pytorch(y.src_path, y.cfg_path, y.save_path)
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,432
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/llama.py
|
# Copyright 2023 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import functional as F
from transformers.utils import logging
from .. import core as qc
from ..core import utils as qu
from ..core import output as qo
from ..core import forward as qf
from ..core import attention as qa
from ..core import mlp as qm
from ..core import embed as qe
from ..core import norm as qn
from ..prep.config.llama import PreTrained
import math
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
log = logging.get_logger(__name__)
class PreTrained(PreTrainedModel):
config_class = LlamaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["LlamaDecoderLayer"]
_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, Model):
module.gradient_checkpointing = value
class ForCausal(PreTrained):
def __init__(self, config):
super().__init__(config)
self.model = Model(config)
self.lm_head = nn.Linear(cfg.d_model, config.vocab_size, bias=False)
self.post_init()
def forward(
self,
input_ids=None,
mask=None,
position_ids=None,
past_key_values=None,
inputs_embeds=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
output_attentions = (
output_attentions if output_attentions is not None else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.model(
input_ids=input_ids,
mask=mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class ForSeqClass(PreTrained):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Model(config)
self.score = nn.Linear(cfg.d_model, self.num_labels, bias=False)
self.post_init()
def forward(
self,
input_ids=None,
mask=None,
position_ids=None,
past_key_values=None,
inputs_embeds=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.model(
input_ids,
mask=mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(
logits.device
)
else:
sequence_lengths = -1
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
loss = None
if labels is not None:
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (
labels.dtype == torch.long or labels.dtype == torch.int
):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(pooled_logits, labels)
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
class Model(PreTrained):
def __init__(self, config):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, cfg.d_model, self.padding_idx)
self.layers = nn.ModuleList([Layer(config) for _ in range(config.num_hidden_layers)])
self.norm = qn.RMS(cfg.d_model, eps=config.rms_norm_eps)
self.gradient_checkpointing = False
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_mask
def _prepare_decoder_mask(self, mask, input_shape, inputs_embeds, past_key_values_length):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_mask = None
if input_shape[-1] > 1:
combined_mask = qu.causal_mask(
input_shape,
inputs_embeds.dtype,
device=inputs_embeds.device,
c_len=past_key_values_length,
)
if mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = qu.expand_mask(mask, inputs_embeds.dtype, len=input_shape[-1]).to(
inputs_embeds.device
)
combined_mask = (
expanded_attn_mask if combined_mask is None else expanded_attn_mask + combined_mask
)
return combined_mask
def forward(
self,
input_ids=None,
mask=None,
position_ids=None,
past_key_values=None,
inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
output_attentions = (
output_attentions if output_attentions is not None else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError(
"You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
)
elif input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError(
"You have to specify either decoder_input_ids or decoder_inputs_embeds"
)
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(
past_key_values_length,
seq_length + past_key_values_length,
dtype=torch.long,
device=device,
)
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
else:
position_ids = position_ids.view(-1, seq_length).long()
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
if mask is None:
mask = torch.ones(
(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
)
mask = self._prepare_decoder_mask(
mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
)
hidden_states = inputs_embeds
if self.gradient_checkpointing and self.training:
if use_cache:
log.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, output_attentions, None)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(decoder_layer),
hidden_states,
mask,
position_ids,
None,
)
else:
layer_outputs = decoder_layer(
hidden_states,
mask=mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
if v is not None
)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
class Layer(qc.Module):
hs = qc.Hypers({"d_model", "add_cross", "n_inner"})
def __init__(self, lay_i, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
d = cfg.d_model
self.attn = Attention(**kw)
self.proj = qm.Llama(d, **kw)
self.norm_attn = qn.RMS(d, **kw)
self.norm = qn.RMS(d, **kw)
def forward(self, x, mask=None, pos=None, cache=None, **kw):
y = self.norm_attn(x)
y, a, kv = self.attn(y, mask=mask, pos=pos, cache=cache, **kw)
y = x + y
x = y
return x + self.proj(self.norm(y)), a, kv
class Attention(qc.Module):
hs = qc.Hypers({"d_model", "n_heads", "n_pos"})
def __init__(self, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
d, h = cfg.d_model, cfg.n_heads
assert d % h == 0
cfg.s_head = s = int(d / h)
self.emb = qe.RotaryEmbed(s, **kw)
self.query = qc.Linear(d, h * s, bias=False, **kw)
self.key = qc.Linear(d, h * s, bias=False, **kw)
self.value = qc.Linear(d, h * s, bias=False, **kw)
self.proj = qc.Linear(h * s, d, bias=False, **kw)
def _shape(self, tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, h, s).transpose(1, 2).contiguous()
def forward(self, x, mask=None, pos=None, cache=None, **kw):
cfg = self.cfg
b, n_q, _ = x.size()
d, h, s = cfg.d_model, cfg.n_heads, cfg.s_head
q = self.query(x).view(b, n_q, h, s).transpose(1, 2)
k = self.key(x).view(b, n_q, h, s).transpose(1, 2)
v = self.value(x).view(b, n_q, h, s).transpose(1, 2)
n_kv = k.shape[-2]
if cache is not None:
n_kv += cache[0].shape[-2]
cos, sin = self.emb(v, seq_len=n_kv)
q, k = qe.apply_rotary_pos_emb(q, k, cos, sin, pos)
if cache is not None:
k = torch.cat([cache[0], k], dim=2)
v = torch.cat([cache[1], v], dim=2)
a = torch.matmul(q, k.transpose(2, 3)) / math.sqrt(s)
assert a.size() == (b, h, n_q, n_kv)
if mask is not None:
assert mask.size() == (b, 1, n_q, n_kv)
a = a + mask
a = torch.max(a, torch.tensor(torch.finfo(a.dtype).min))
a = F.softmax(a, dim=-1, dtype=torch.float32).to(q.dtype)
y = torch.matmul(a, v)
assert y.size() == (b, h, n_q, s)
y = y.transpose(1, 2)
y = y.reshape(b, n_q, d)
return self.proj(y), a, (k, v)
|
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["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,433
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/debugger/debugger.py
|
import itertools
import random
from typing import Tuple
import triton
import triton.language as tl
from .core import ExecutionContext
from .memory_map import MemoryMap
from .tl_lang import (TritonLangProxy, WrappedTensor, _primitive_to_tensor,
debugger_constexpr)
from triton.debugger import torch_wrapper
torch = torch_wrapper.torch
tl_method_backup = {}
def get_proxy_method(proxy, name):
method = getattr(proxy, name)
def fun(*args, **kwarg):
return method(*args, **kwarg)
return fun
def attach_triton(module, proxy):
method_list = [func for func in dir(TritonLangProxy) if func[0] != "_"]
for name in method_list:
if hasattr(module, name):
attr = getattr(module, name)
tl_method_backup[name] = attr
if callable(attr):
setattr(module, name, get_proxy_method(proxy, name))
else:
setattr(module, name, getattr(proxy, name))
def detach_triton(module):
for name, method in tl_method_backup.items():
setattr(module, name, method)
def program_ids_from_grid(grid: Tuple[int, ...]) -> Tuple[int, ...]:
# reverse the grid dimensions and generate the range for each dimension
reversed_grid = reversed(grid)
ranges_for_each_dimension = [range(dim) for dim in reversed_grid]
# gen all combinations
index_combinations = list(itertools.product(*ranges_for_each_dimension))
random.shuffle(index_combinations)
for index_combination in index_combinations:
yield index_combination
class DebuggerFunction:
def __init__(self, func, grid=(1,)):
self.func = func
self.grid = grid
def _is_constexpr(self, name):
return name in self.func.__annotations__ and self.func.__annotations__[name] is triton.language.core.constexpr
def _get_constexpr(self):
result = []
for name, annotation in self.func.__annotations__.items():
if annotation is triton.language.core.constexpr:
result.append(name)
return result
def _assert_constexpr(self, **kwargs):
constexp = self._get_constexpr()
missing = [i for i in constexp if i not in kwargs.keys()]
assert len(missing) == 0, f"You must specify constexpr {missing}"
def _get_grid(self, **kwargs):
if callable(self.grid):
return self.grid(kwargs)
else:
return self.grid
def __call__(self, *args, **kwargs):
self._assert_constexpr(**kwargs)
memory = MemoryMap()
def convert_arg(v):
name, arg = v
if torch.is_tensor(arg):
ptr = memory.add_tensor(arg)
return WrappedTensor(torch.tensor([ptr], dtype=torch.int64, device="cuda"))
if self._is_constexpr(name):
return debugger_constexpr(arg)
return WrappedTensor(_primitive_to_tensor(arg))
new_args = tuple(map(convert_arg, zip(self.func.__code__.co_varnames, args)))
new_kwargs = {k: convert_arg((k, v)) for (k, v) in kwargs.items() if k not in ["num_warps", "num_stages"]}
grid = self._get_grid(**kwargs)
for program_id in program_ids_from_grid(grid):
proxy = TritonLangProxy(memory, ExecutionContext(program_id, grid))
attach_triton(tl, proxy)
self.func(*new_args, **new_kwargs)
detach_triton(tl)
class GridSelector:
"""
Entry point of the debugger
"""
def __init__(self, func):
version = torch.__version__
assert version[0] == "2", f"Triton Debugger only supports torch >= 2.0, using {version}"
self.func = func
def __getitem__(self, grid):
return DebuggerFunction(self.func, grid)
def __call__(self, *args, **kwargs):
return DebuggerFunction(self.func)(*args, **kwargs)
class AutotuneGridSelector:
def __init__(self, func, autotune_params):
self.func = func
self.autotune_params = autotune_params
def __getitem__(self, grid):
return AutotuneRunner(self.func, self.autotune_params, grid)
def __call__(self, *args, **kwargs):
return AutotuneRunner(self.func, self.autotune_params)(*args, **kwargs)
class AutotuneRunner:
def __init__(self, func, autotune_params, grid=None):
self.func = func
self.autotune_params = autotune_params
self.grid = grid
def __call__(self, *args, **kwargs):
assert len(self.autotune_params["configs"]) >= 1
for config in self.autotune_params["configs"][1:]:
def convert_arg(v):
if torch.is_tensor(v):
return torch.clone(v)
return v
new_args = tuple(map(convert_arg, args))
new_kwargs = {k: convert_arg(v) for k, v in kwargs.items()}
if self.grid:
self.func[self.grid](*new_args, **new_kwargs, **config.kwargs)
else:
self.func(*new_args, **new_kwargs, **config.kwargs)
main_config = self.autotune_params["configs"][0]
if self.grid:
self.func[self.grid](*args, **kwargs, **main_config.kwargs)
else:
self.func(*args, **kwargs, **main_config.kwargs)
def triton_debug_autotune(**kwars):
def wrapper(func):
return AutotuneGridSelector(func, kwars)
return wrapper
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
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33,434
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quantapix/qnarre
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refs/heads/main
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/qnarre/base/doc/log.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import logging as lg
import contextlib as cl
lg.basicConfig(
level=lg.DEBUG,
format="%(asctime)s %(name)-12s %(levelname)-8s %(message)s",
datefmt="%m-%d %H:%M",
# filename='/tmp/qnarre.log',
filename="/tmp/qnarre.log",
# filemode='w'
filemode="w",
) # 'a'
ch = lg.StreamHandler()
ch.setLevel(lg.WARNING)
ch.setFormatter(lg.Formatter("%(name)-12s: %(levelname)-8s %(message)s"))
lg.getLogger().addHandler(ch)
class Logger(lg.LoggerAdapter):
def __init__(self, name, extra=None):
super().__init__(lg.getLogger(name), extra or {})
def log(self, level, msg, *args, **kw):
if self.isEnabledFor(level):
msg, kw = self.process(msg, kw)
class Msg:
def __init__(self, fmt, args):
self.fmt = fmt
self.args = args
def __str__(self):
return self.fmt.format(*self.args)
self.logger._log(level, Msg(msg, args), (), **kw)
@cl.contextmanager
def start_stop_log(log, msg):
m = msg + "..."
log.info(m)
print(m, end="")
yield
m += " done"
log.info(m)
print("\n" + m)
log = Logger(__name__)
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], 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|
33,435
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/test/regression/test_functional_regressions.py
|
import numpy as np
import torch
from numpy.random import RandomState
import triton
import triton.language as tl
def test_chained_matmul():
# Regression test for issue #1601
def chained_matmul_reference(a, b, c):
intermediate = torch.einsum('MK,NK->MN', a, b)
return torch.einsum('MN,NK->MK', intermediate, c)
@triton.jit
def chained_matmul_kernel(
A, # shape: (m, k)
B, # shape: (n, k)
C, # shape: (n, k)
out, # shape: (m, k)
m, n, k: tl.constexpr,
block_m: tl.constexpr,
block_n: tl.constexpr,
block_k: tl.constexpr):
tl.static_assert(block_k == k,
f"expected block_k == k but got {block_k} != {k}")
block_ix = tl.program_id(0)
a_tile = (block_ix * block_m + tl.arange(0, block_m))[:, None] * block_k \
+ tl.arange(0, block_k)[None, :]
a = tl.load(A + a_tile, mask=a_tile < m * k, other=0.0)
acc = tl.zeros([block_m, block_k], dtype=tl.float32)
for loop_block_start in range(0, n, block_n):
bc_tile = (loop_block_start + tl.arange(0, block_n))[:, None] * block_k \
+ tl.arange(0, block_k)[None, :]
b = tl.load(B + bc_tile, mask=bc_tile < n * k, other=0.0)
intermediate = tl.dot(a, tl.trans(b))
intermediate_mask = ((loop_block_start + tl.arange(0, block_n)) < n)[None, :] \
* (tl.arange(0, block_m) < m)[:, None]
intermediate = tl.where(intermediate_mask, intermediate, 0.0)
c = tl.load(C + bc_tile, mask=bc_tile < n * k)
acc += tl.dot(intermediate.to(A.dtype.element_ty), c)
tl.store(out + a_tile, acc.to(A.dtype.element_ty), mask=a_tile < m * k)
m, n, k = 32, 64, 128
block_m, block_n, block_k = 16, 32, k
grid = (triton.cdiv(m, block_m),)
a = torch.randint(low=0, high=2, size=(m, k), dtype=torch.float16,
device='cuda')
b = torch.randint(low=0, high=2, size=(n, k), dtype=torch.float16,
device='cuda')
c = torch.randint_like(b, low=0, high=2)
triton_result = torch.zeros_like(a)
torch_result = chained_matmul_reference(a, b, c)
chained_matmul_kernel[grid](a, b, c, triton_result, m, n, k,
block_m=block_m, block_n=block_n,
block_k=block_k)
assert (torch_result == triton_result).all()
def test_vecmat():
@triton.jit
def batched_vecmat(
# inputs
A, # shape: [dim_m, dim_k]
B, # shape: [dim_m, dim_n, dim_k]
# dimensions
dim_m, dim_n, dim_k,
# outputs
output,
# block information
block_m: tl.constexpr, block_n: tl.constexpr, block_k: tl.constexpr
):
m_index = tl.program_id(0)
n_index = tl.program_id(1)
# Output tile
output_tile = (m_index * block_m + tl.arange(0, block_m))[:, None] * dim_n \
+ (n_index * block_n + tl.arange(0, block_n))[None, :]
vecmat = tl.zeros([block_m, block_n], dtype=A.dtype.element_ty)
k_blocks = dim_k // block_k
for k_index in range(k_blocks):
# Load A tile
a_tile = (m_index * block_m + tl.arange(0, block_m))[:, None] * dim_k \
+ (k_index * block_k + tl.arange(0, block_k))[None, :]
a = tl.load(A + a_tile)
# Load B tile, transposed to [n, m, k] in order to broadcast A on a
# leading dimension.
b_tile = (m_index * block_m + tl.arange(0, block_m))[None, :, None] * dim_n * dim_k \
+ (n_index * block_n + tl.arange(0, block_n))[:, None, None] * dim_k \
+ (k_index * block_k + tl.arange(0, block_k))[None, None, :]
b = tl.load(B + b_tile)
expanded_a, _ = tl.broadcast(a, b)
vecmat += tl.trans(tl.sum(expanded_a * b, axis=2))
tl.store(output + output_tile, vecmat)
M, N, K = 128, 128, 128
block_m, block_n, block_k = 16, 32, 64
rs = RandomState(17)
A_vec = rs.randint(0, 4, (M, K)).astype('float32')
B_vec = rs.randint(0, 4, (M, N, K)).astype('float32')
A = A_vec
B = B_vec
A_tri = torch.tensor(A, device='cuda')
B_tri = torch.tensor(B, device='cuda')
C_tri = torch.zeros((M, N), dtype=torch.float32, device='cuda')
grid = (M // block_m, N // block_n)
batched_vecmat[grid](A_tri, B_tri, M, N, K, C_tri,
block_m=block_m, block_n=block_n, block_k=block_k,
num_warps=4, num_stages=1)
A_expanded = A[:, np.newaxis, :]
A_broadcasted = np.broadcast_to(A_expanded, (M, N, K))
AB = A_broadcasted * B
C_ref = np.sum(AB, axis=2)
np.testing.assert_allclose(C_ref, C_tri.cpu().numpy(), rtol=0.01, atol=1e-3)
|
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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,436
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/reformer.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import pickle
import numpy as np
import torch
from argparse import ArgumentParser
from torch import nn
from transformers.utils import logging
from ..config.reformer import PreTrained
from ...models.reformer import ReformerModelWithLMHead
logging.set_verbosity_info()
def set_param(torch_layer, weight, bias=None):
assert torch_layer.weight.shape == weight.shape, f"{torch_layer} layer.weight does not match"
torch_layer.weight = nn.Parameter(weight)
if bias is not None:
assert torch_layer.bias.shape == bias.shape, f"{torch_layer} layer.bias does not match"
torch_layer.bias = nn.Parameter(bias)
def set_layer_weights_in_torch_lsh(weights, torch_layer, d_hidden):
np_query_key = np.asarray(weights[0])
np_value = np.asarray(weights[1])
np_dense = np.asarray(weights[2])
set_param(
torch_layer.self_attention.query_key,
torch.tensor(np_query_key).transpose(1, 2).contiguous().view(-1, d_hidden),
)
set_param(
torch_layer.self_attention.value,
torch.tensor(np_value).transpose(1, 2).contiguous().view(-1, d_hidden),
)
set_param(
torch_layer.output.dense,
torch.tensor(np_dense).view(-1, d_hidden).contiguous().transpose(0, 1),
)
def set_layer_weights_in_torch_local(weights, torch_layer, d_hidden):
np_query = np.asarray(weights[0])
np_key = np.asarray(weights[1])
np_value = np.asarray(weights[2])
np_dense = np.asarray(weights[3])
set_param(
torch_layer.self_attention.query,
torch.tensor(np_query).transpose(1, 2).contiguous().view(-1, d_hidden),
)
set_param(
torch_layer.self_attention.key,
torch.tensor(np_key).transpose(1, 2).contiguous().view(-1, d_hidden),
)
set_param(
torch_layer.self_attention.value,
torch.tensor(np_value).transpose(1, 2).contiguous().view(-1, d_hidden),
)
set_param(
torch_layer.output.dense,
torch.tensor(np_dense).view(-1, d_hidden).contiguous().transpose(0, 1),
)
def set_block_weights_in_torch(weights, torch_block, d_hidden):
layer_norm_1 = weights[0][0][0]
layer_norm_1_weight = np.asarray(layer_norm_1[0])
layer_norm_1_bias = np.asarray(layer_norm_1[1])
set_param(
torch_block.attention.layer_norm,
torch.tensor(layer_norm_1_weight),
torch.tensor(layer_norm_1_bias),
)
attn_weights = weights[0][1]
if len(attn_weights) < 4:
set_layer_weights_in_torch_lsh(attn_weights, torch_block.attention, d_hidden)
else:
set_layer_weights_in_torch_local(attn_weights, torch_block.attention, d_hidden)
intermediate_weights = weights[2][0][1][2]
if len(intermediate_weights) == 4:
intermediate_weights = intermediate_weights[2]
layer_norm_2_weight = np.asarray(intermediate_weights[0][0])
layer_norm_2_bias = np.asarray(intermediate_weights[0][1])
set_param(
torch_block.feed_forward.layer_norm,
torch.tensor(layer_norm_2_weight),
torch.tensor(layer_norm_2_bias),
)
inter_dense_weight = np.asarray(intermediate_weights[1][0])
inter_dense_bias = np.asarray(intermediate_weights[1][1])
set_param(
torch_block.feed_forward.dense.dense,
torch.tensor(inter_dense_weight).transpose(0, 1).contiguous(),
torch.tensor(inter_dense_bias),
)
out_dense_weight = np.asarray(intermediate_weights[4][0])
out_dense_bias = np.asarray(intermediate_weights[4][1])
set_param(
torch_block.feed_forward.output.dense,
torch.tensor(out_dense_weight).transpose(0, 1).contiguous(),
torch.tensor(out_dense_bias),
)
def load_src_weights(weights, torch_model, d_hidden):
torch_model_reformer = torch_model.reformer
word_embeddings = np.asarray(weights[1])
set_param(
torch_model_reformer.embeddings.word_embeddings,
torch.tensor(word_embeddings),
)
if isinstance(weights[3], tuple):
position_embeddings = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights)):
emb_weights = np.asarray(weights[3][emb_idx][0])
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), f"{position_embeddings[emb_idx]} emb does not match"
position_embeddings.weights[emb_idx] = nn.Parameter(torch.tensor(emb_weights))
trax_layer_weights = weights[5]
assert len(torch_model_reformer.encoder.layers) * 4 == len(
trax_layer_weights
), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers):
block_weights = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(block_weights, layer, d_hidden)
layer_norm_out_weight = np.asarray(weights[7][0])
layer_norm_out_bias = np.asarray(weights[7][1])
set_param(
torch_model_reformer.encoder.layer_norm,
torch.tensor(layer_norm_out_weight),
torch.tensor(layer_norm_out_bias),
)
output_embed_weights = np.asarray(weights[9][0])
output_embed_bias = np.asarray(weights[9][1])
set_param(
torch_model.lm_head.decoder,
torch.tensor(output_embed_weights).transpose(0, 1).contiguous(),
torch.tensor(output_embed_bias),
)
def to_pytorch(src_path, cfg_path, save_path):
cfg = PreTrained.from_json_file(cfg_path)
print(f"Building from config: {cfg}")
m = ReformerModelWithLMHead(cfg)
with open(src_path, "rb") as f:
model_weights = pickle.load(f)["weights"]
load_src_weights(model_weights, m, cfg.d_hidden)
print(f"Saving to: {save_path}")
torch.save(m.state_dict(), save_path)
if __name__ == "__main__":
x = ArgumentParser()
x.add_argument("--src_path", default=None, type=str, required=True)
x.add_argument("--cfg_path", default=None, type=str, required=True)
x.add_argument("--save_path", default=None, type=str, required=True)
y = x.parse_args()
to_pytorch(y.src_path, y.cfg_path, y.save_path)
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,437
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/perceiver.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
from ...tokens.utils import AddedToken, PreTrainedTokenizer
class Tokenizer(PreTrainedTokenizer):
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
pad="[PAD]",
bos="[BOS]",
eos="[EOS]",
msk="[MASK]",
cls="[CLS]",
sep="[SEP]",
model_max_length=2048,
**kw,
):
pad = AddedToken(pad, lstrip=False, rstrip=False) if isinstance(pad, str) else pad
bos = AddedToken(bos, lstrip=False, rstrip=False) if isinstance(bos, str) else bos
eos = AddedToken(eos, lstrip=False, rstrip=False) if isinstance(eos, str) else eos
msk = AddedToken(msk, lstrip=False, rstrip=False) if isinstance(msk, str) else msk
cls = AddedToken(cls, lstrip=False, rstrip=False) if isinstance(cls, str) else cls
sep = AddedToken(sep, lstrip=False, rstrip=False) if isinstance(sep, str) else sep
super().__init__(
pad=pad,
bos=bos,
eos=eos,
msk=msk,
cls=cls,
sep=sep,
model_max_length=model_max_length,
**kw,
)
self._utf_vocab_size = 2**8
self.special_tokens_encoder = {
self.pad: 0,
self.bos: 1,
self.eos: 2,
self.msk: 3,
self.cls: 4,
self.sep: 5,
}
self._num_special_tokens = len(self.special_tokens_encoder)
self.special_tokens_decoder = {v: k for k, v in self.special_tokens_encoder.items()}
def get_vocab(self):
vocab = self.special_tokens_encoder.copy()
vocab.update(self.added_tokens_encoder)
for i in range(self._utf_vocab_size):
token = chr(i)
vocab[token] = i + len(self.special_tokens_encoder)
return vocab
@property
def s_vocab(self):
return self._utf_vocab_size + self._num_special_tokens
def get_special_tokens_mask(
self,
toks_0,
toks_1=None,
has_specials=False,
):
if has_specials:
return super().get_special_tokens_mask(toks_0=toks_0, toks_1=toks_1, has_specials=True)
if toks_1 is None:
return [1] + [0] * len(toks_0) + [1]
return [1] + ([0] * len(toks_0)) + [1] + ([0] * len(toks_1)) + [1]
def build_inputs_with_special_tokens(self, toks_0, toks_1=None):
if toks_1 is None:
return [self.cls_token_id] + toks_0 + [self.sep_token_id]
else:
return [self.cls_token_id] + toks_0 + [self.sep_token_id] + toks_1 + [self.sep_token_id]
def _tokenize(self, text):
tokens = [chr(i) for i in text.encode("utf-8")]
return tokens
def _convert_token_to_id(self, token):
if token in self.special_tokens_encoder:
token_id = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
token_id = self.added_tokens_encoder[token]
elif len(token) != 1:
token_id = self.unk_token_id
else:
token_id = ord(token) + self._num_special_tokens
return token_id
def _convert_id_to_token(self, index):
if index in self.special_tokens_decoder:
token = self.special_tokens_decoder[index]
elif index in self.added_tokens_decoder:
token = self.added_tokens_decoder[index]
else:
token = chr(index - self._num_special_tokens)
return token
def convert_tokens_to_string(self, tokens):
bstring = b""
for token in tokens:
if token in self.special_tokens_decoder:
tok_string = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.added_tokens_decoder:
tok_string = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.special_tokens_encoder:
tok_string = token.encode("utf-8")
elif token in self.added_tokens_encoder:
tok_string = token.encode("utf-8")
else:
tok_string = bytes([ord(token)])
bstring += tok_string
string = bstring.decode("utf-8", errors="replace")
return string
def save_vocabulary(self, dir, pre=None):
return ()
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], 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|
33,438
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/debugger/core.py
|
from typing import Tuple
import dataclasses
@dataclasses.dataclass
class ExecutionContext:
program_id: Tuple[int]
program_size: Tuple[int]
|
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"/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,439
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/core/modeling_utils.py
|
import os
import re
from contextlib import contextmanager
from dataclasses import dataclass
from functools import partial
import psutil
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from requests import HTTPError
from .activations import get_activation
from .configuration_utils import PretrainedConfig
from .deepspeed import deepspeed_config, is_deepspeed_zero3_enabled
from .dynamic_module_utils import custom_object_save
from .file_utils import (
DUMMY_INPUTS,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
WEIGHTS_NAME,
EntryNotFoundError,
ModelOutput,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
cached_path,
copy_func,
has_file,
hf_bucket_url,
is_offline_mode,
is_remote_url,
)
from .generation_utils import GenerationMixin
from .utils import logging
from .utils.versions import require_version_core
logger = logging.get_logger(__name__)
_init_weights = True
@contextmanager
def no_init_weights(_enable=True):
global _init_weights
if _enable:
_init_weights = False
try:
yield
finally:
_init_weights = True
def get_parameter_device(x):
try:
return next(x.parameters()).device
except StopIteration:
def find_tensor_attributes(x):
return [(k, v) for k, v in x.__dict__.items() if torch.is_tensor(v)]
gen = x._named_members(get_members_fn=find_tensor_attributes)
first_tuple = next(gen)
return first_tuple[1].device
class ModuleUtilsMixin:
@staticmethod
def _hook_rss_memory_pre_forward(module, *args, **kw):
process = psutil.Process(os.getpid())
mem = process.memory_info()
module.mem_rss_pre_forward = mem.rss
return None
@staticmethod
def _hook_rss_memory_post_forward(module, *args, **kw):
process = psutil.Process(os.getpid())
mem = process.memory_info()
module.mem_rss_post_forward = mem.rss
mem_rss_diff = module.mem_rss_post_forward - module.mem_rss_pre_forward
module.mem_rss_diff = mem_rss_diff + (
module.mem_rss_diff if hasattr(module, "mem_rss_diff") else 0
)
return None
def add_memory_hooks(self):
for module in self.modules():
module.register_forward_pre_hook(self._hook_rss_memory_pre_forward)
module.register_forward_hook(self._hook_rss_memory_post_forward)
self.reset_memory_hooks_state()
def reset_memory_hooks_state(self):
for module in self.modules():
module.mem_rss_diff = 0
module.mem_rss_post_forward = 0
module.mem_rss_pre_forward = 0
@property
def device(self):
return get_parameter_device(self)
@property
def dtype(self):
return get_parameter_dtype(self)
def num_parameters(self, only_trainable=False, exclude_embeddings=False):
if exclude_embeddings:
embedding_param_names = [
f"{name}.weight"
for name, module_type in self.named_modules()
if isinstance(module_type, nn.Embedding)
]
non_embedding_parameters = [
parameter
for name, parameter in self.named_parameters()
if name not in embedding_param_names
]
return sum(
p.numel() for p in non_embedding_parameters if p.requires_grad or not only_trainable
)
else:
return sum(
p.numel() for p in self.parameters() if p.requires_grad or not only_trainable
)
def estimate_tokens(self, input_dict):
if self.main_input_name in input_dict:
return input_dict[self.main_input_name].numel()
else:
logger.warning(
"Could not estimate the number of tokens of the input, floating-point operations will not be computed"
)
return 0
def floating_point_ops(self, input_dict, exclude_embeddings=True):
return (
6
* self.estimate_tokens(input_dict)
* self.num_parameters(exclude_embeddings=exclude_embeddings)
)
class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMixin):
config_class = None
base_model_prefix = ""
main_input_name = "input_ids"
_auto_class = None
# a list of re pattern of tensor names to ignore from the model when loading the model weights
# (and avoid unnecessary warnings).
_keys_to_ignore_on_load_missing = None
# a list of re pattern of tensor names to ignore from the weights when loading the model weights
# (and avoid unnecessary warnings).
_keys_to_ignore_on_load_unexpected = None
# a list of of tensor names to ignore when saving the model (useful for keys that aren't
# trained, but which are deterministic, or tied variables)
_keys_to_ignore_on_save = None
is_parallelizable = False
grad_checkpoint = False
@property
def dummy_inputs(self):
"""
`Dict[str, torch.Tensor]`: Dummy inputs to do a forward pass in the network.
"""
return {"input_ids": torch.tensor(DUMMY_INPUTS)}
@property
def framework(self):
"""
:str: Identifies that this is a PyTorch model.
"""
return "pt"
def __init__(self, config, *inputs, **kw):
super().__init__()
if not isinstance(config, PretrainedConfig):
raise ValueError(
f"Parameter config in `{self.__class__.__name__}(config)` should be an instance of class "
"`PretrainedConfig`. To create a model from a pretrained model use "
f"`model = {self.__class__.__name__}.from_pretrained(PRETRAINED_MODEL_NAME)`"
)
# Save config and origin of the pretrained weights if given in model
self.config = config
self.name_or_path = config.name_or_path
def post_init(self):
self.init_weights()
self._backward_compatibility_grad_checkpoint()
def _backward_compatibility_grad_checkpoint(self):
if self.grad_checkpoint and getattr(self.config, "grad_checkpoint", False):
self.grad_checkpoint_enable()
# Remove the attribute now that is has been consumed, so it's no saved in the config.
delattr(self.config, "grad_checkpoint")
@classmethod
def _from_config(cls, config, **kw):
torch_dtype = kw.pop("torch_dtype", None)
# override default dtype if needed
dtype_orig = None
if torch_dtype is not None:
dtype_orig = cls._set_default_torch_dtype(torch_dtype)
if is_deepspeed_zero3_enabled():
import deepspeed
logger.info("Detected DeepSpeed ZeRO-3: activating zero.init() for this model")
# this immediately partitions the model across all gpus, to avoid the overhead in time
# and memory copying it on CPU or each GPU first
with deepspeed.zero.Init(config_dict_or_path=deepspeed_config()):
model = cls(config, **kw)
else:
model = cls(config, **kw)
# restore default dtype if it was modified
if dtype_orig is not None:
torch.set_default_dtype(dtype_orig)
return model
@classmethod
def _set_default_torch_dtype(cls, dtype: torch.dtype):
if not dtype.is_floating_point:
raise ValueError(
f"Can't instantiate {cls.__name__} model under dtype={dtype} since it is not a floating point dtype"
)
logger.info(f"Instantiating {cls.__name__} model under default dtype {dtype}.")
dtype_orig = torch.get_default_dtype()
torch.set_default_dtype(dtype)
return dtype_orig
@property
def base_model(self):
return getattr(self, self.base_model_prefix, self)
def get_input_embeddings(self):
base_model = getattr(self, self.base_model_prefix, self)
if base_model is not self:
return base_model.get_input_embeddings()
else:
raise NotImplementedError
def set_input_embeddings(self, value):
base_model = getattr(self, self.base_model_prefix, self)
if base_model is not self:
base_model.set_input_embeddings(value)
else:
raise NotImplementedError
def get_output_embeddings(self):
return None
def _init_weights(self, module):
raise NotImplementedError(f"Make sure `_init_weights` is implemented for {self.__class__}")
def tie_weights(self):
output_embeddings = self.get_output_embeddings()
if output_embeddings is not None and getattr(self.config, "tie_word_embeddings", True):
self._tie_or_clone_weights(output_embeddings, self.get_input_embeddings())
if getattr(self.config, "is_enc_dec", False) and getattr(
self.config, "tie_encoder_decoder", False
):
if hasattr(self, self.base_model_prefix):
self = getattr(self, self.base_model_prefix)
self._tie_encoder_decoder_weights(self.encoder, self.decoder, self.base_model_prefix)
for module in self.modules():
if hasattr(module, "_tie_weights"):
module._tie_weights()
@staticmethod
def _tie_encoder_decoder_weights(encoder, decoder, base_model_prefix):
uninitialized_encoder_weights = []
if decoder.__class__ != encoder.__class__:
logger.info(
f"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized."
)
def tie_encoder_to_decoder_recursively(
decoder_pointer,
encoder_pointer,
module_name,
uninitialized_encoder_weights,
depth=0,
):
assert isinstance(decoder_pointer, nn.Module) and isinstance(
encoder_pointer, nn.Module
), f"{decoder_pointer} and {encoder_pointer} have to be of type nn.Module"
if hasattr(decoder_pointer, "weight"):
assert hasattr(encoder_pointer, "weight")
encoder_pointer.weight = decoder_pointer.weight
if hasattr(decoder_pointer, "bias"):
assert hasattr(encoder_pointer, "bias")
encoder_pointer.bias = decoder_pointer.bias
return
encoder_modules = encoder_pointer._modules
decoder_modules = decoder_pointer._modules
if len(decoder_modules) > 0:
assert (
len(encoder_modules) > 0
), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}"
all_encoder_weights = set(
[module_name + "/" + sub_name for sub_name in encoder_modules.keys()]
)
encoder_layer_pos = 0
for name, module in decoder_modules.items():
if name.isdigit():
encoder_name = str(int(name) + encoder_layer_pos)
decoder_name = name
if not isinstance(
decoder_modules[decoder_name], type(encoder_modules[encoder_name])
) and len(encoder_modules) != len(decoder_modules):
# this can happen if the name corresponds to the position in a list module list of layers
# in this case the decoder has added a cross-attention that the encoder does not have
# thus skip this step and subtract one layer pos from encoder
encoder_layer_pos -= 1
continue
elif name not in encoder_modules:
continue
elif depth > 500:
raise ValueError(
"Max depth of recursive function `tie_encoder_to_decoder` reached. It seems that there is a circular dependency between two or more `nn.Modules` of your model."
)
else:
decoder_name = encoder_name = name
tie_encoder_to_decoder_recursively(
decoder_modules[decoder_name],
encoder_modules[encoder_name],
module_name + "/" + name,
uninitialized_encoder_weights,
depth=depth + 1,
)
all_encoder_weights.remove(module_name + "/" + encoder_name)
uninitialized_encoder_weights += list(all_encoder_weights)
# tie weights recursively
tie_encoder_to_decoder_recursively(
decoder, encoder, base_model_prefix, uninitialized_encoder_weights
)
if len(uninitialized_encoder_weights) > 0:
logger.warning(
f"The following encoder weights were not tied to the decoder {uninitialized_encoder_weights}"
)
def _tie_or_clone_weights(self, output_embeddings, input_embeddings):
"""Tie or clone module weights depending of whether we are using TorchScript or not"""
if self.config.torchscript:
output_embeddings.weight = nn.Parameter(input_embeddings.weight.clone())
else:
output_embeddings.weight = input_embeddings.weight
if getattr(output_embeddings, "bias", None) is not None:
output_embeddings.bias.data = nn.functional.pad(
output_embeddings.bias.data,
(
0,
output_embeddings.weight.shape[0] - output_embeddings.bias.shape[0],
),
"constant",
0,
)
if hasattr(output_embeddings, "out_features") and hasattr(
input_embeddings, "num_embeddings"
):
output_embeddings.out_features = input_embeddings.num_embeddings
def resize_token_embeddings(self, new_num_tokens=None):
model_embeds = self._resize_token_embeddings(new_num_tokens)
if new_num_tokens is None:
return model_embeds
# Update base model and current model config
self.config.s_vocab = new_num_tokens
self.s_vocab = new_num_tokens
# Tie weights again if needed
self.tie_weights()
return model_embeds
def _resize_token_embeddings(self, new_num_tokens):
old_embeddings = self.get_input_embeddings()
new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
self.set_input_embeddings(new_embeddings)
# if word embeddings are not tied, make sure that lm head is resized as well
if self.get_output_embeddings() is not None and not self.config.tie_word_embeddings:
old_lm_head = self.get_output_embeddings()
new_lm_head = self._get_resized_lm_head(old_lm_head, new_num_tokens)
self.set_output_embeddings(new_lm_head)
return self.get_input_embeddings()
def _get_resized_embeddings(self, old_embeddings: nn.Embedding, new_num_tokens=None):
if new_num_tokens is None:
return old_embeddings
if is_deepspeed_zero3_enabled():
import deepspeed
with deepspeed.zero.GatheredParameters(old_embeddings.weight, modifier_rank=None):
old_num_tokens, old_embedding_dim = old_embeddings.weight.size()
else:
old_num_tokens, old_embedding_dim = old_embeddings.weight.size()
if old_num_tokens == new_num_tokens:
return old_embeddings
if not isinstance(old_embeddings, nn.Embedding):
raise TypeError(
f"Old embeddings are of type {type(old_embeddings)}, which is not an instance of {nn.Embedding}. "
f"You should either use a different resize function or make sure that `old_embeddings` are an instance of {nn.Embedding}."
)
# Build new embeddings
new_embeddings = nn.Embedding(new_num_tokens, old_embedding_dim)
new_embeddings.to(self.device, dtype=old_embeddings.weight.dtype)
# initialize all new embeddings (in particular added tokens)
self._init_weights(new_embeddings)
# Copy token embeddings from the previous weights
# numbers of tokens to copy
n = min(old_num_tokens, new_num_tokens)
if is_deepspeed_zero3_enabled():
import deepspeed
with deepspeed.zero.GatheredParameters(old_embeddings.weight, modifier_rank=0):
if torch.distributed.get_rank() == 0:
new_embeddings.weight.data[:n, :] = old_embeddings.weight.data[:n, :]
else:
new_embeddings.weight.data[:n, :] = old_embeddings.weight.data[:n, :]
return new_embeddings
def _get_resized_lm_head(
self,
old_lm_head: nn.Linear,
new_num_tokens=None,
transposed=False,
):
if new_num_tokens is None:
return old_lm_head
if is_deepspeed_zero3_enabled():
import deepspeed
with deepspeed.zero.GatheredParameters(old_lm_head.weight, modifier_rank=None):
old_num_tokens, old_lm_head_dim = (
old_lm_head.weight.size() if not transposed else old_lm_head.weight.t().size()
)
else:
old_num_tokens, old_lm_head_dim = (
old_lm_head.weight.size() if not transposed else old_lm_head.weight.t().size()
)
if old_num_tokens == new_num_tokens:
return old_lm_head
if not isinstance(old_lm_head, nn.Linear):
raise TypeError(
f"Old language model head is of type {type(old_lm_head)}, which is not an instance of {nn.Linear}. "
f"You should either use a different resize function or make sure that `old_lm_head` are an instance of {nn.Linear}."
)
# Build new lm head
new_lm_head_shape = (
(old_lm_head_dim, new_num_tokens)
if not transposed
else (new_num_tokens, old_lm_head_dim)
)
has_new_lm_head_bias = old_lm_head.bias is not None
new_lm_head = nn.Linear(*new_lm_head_shape, bias=has_new_lm_head_bias)
new_lm_head = new_lm_head.to(self.device, dtype=old_lm_head.weight.dtype)
# initialize new lm head (in particular added tokens)
self._init_weights(new_lm_head)
num_tokens_to_copy = min(old_num_tokens, new_num_tokens)
# XXX: put the long block of code in a wrapper
if is_deepspeed_zero3_enabled():
import deepspeed
with deepspeed.zero.GatheredParameters(old_lm_head.weight, modifier_rank=0):
if torch.distributed.get_rank() == 0:
# Copy old lm head weights to new lm head
if not transposed:
new_lm_head.weight.data[:num_tokens_to_copy, :] = old_lm_head.weight.data[
:num_tokens_to_copy, :
]
else:
new_lm_head.weight.data[:, :num_tokens_to_copy] = old_lm_head.weight.data[
:, :num_tokens_to_copy
]
# Copy bias weights to new lm head
if has_new_lm_head_bias:
new_lm_head.bias.data[:num_tokens_to_copy] = old_lm_head.bias.data[
:num_tokens_to_copy
]
else:
# Copy old lm head weights to new lm head
if not transposed:
new_lm_head.weight.data[:num_tokens_to_copy, :] = old_lm_head.weight.data[
:num_tokens_to_copy, :
]
else:
new_lm_head.weight.data[:, :num_tokens_to_copy] = old_lm_head.weight.data[
:, :num_tokens_to_copy
]
# Copy bias weights to new lm head
if has_new_lm_head_bias:
new_lm_head.bias.data[:num_tokens_to_copy] = old_lm_head.bias.data[
:num_tokens_to_copy
]
return new_lm_head
def resize_position_embeddings(self, new_num_position_embeddings):
raise NotImplementedError(
f"`resize_position_embeddings` is not implemented for {self.__class__}`. To implement it, you should "
f"overwrite this method in the class {self.__class__} in `modeling_{self.__class__.__module__}.py`"
)
def get_position_embeddings(self):
raise NotImplementedError(
f"`get_position_embeddings` is not implemented for {self.__class__}`. To implement it, you should "
f"overwrite this method in the class {self.__class__} in `modeling_{self.__class__.__module__}.py`"
)
def init_weights(self):
if self.config.pruned_heads:
self.prune_heads(self.config.pruned_heads)
if _init_weights:
# Initialize weights
self.apply(self._init_weights)
# Tie weights should be skipped when not initializing all weights
# since from_pretrained(...) calls tie weights anyways
self.tie_weights()
def prune_heads(self, heads_to_prune: Dict[int, List[int]]):
for layer, heads in heads_to_prune.items():
union_heads = set(self.config.pruned_heads.get(layer, [])) | set(heads)
self.config.pruned_heads[layer] = list(
union_heads
) # Unfortunately we have to store it as list for JSON
self.base_model._prune_heads(heads_to_prune)
def grad_checkpoint_enable(self):
if not self.grad_checkpoint:
raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
self.apply(partial(self._set_grad_checkpoint, value=True))
def grad_checkpoint_disable(self):
if self.grad_checkpoint:
self.apply(partial(self._set_grad_checkpoint, value=False))
@property
def is_grad_checkpoint(self):
return any(hasattr(m, "grad_checkpoint") and m.grad_checkpoint for m in self.modules())
def save_pretrained(
self,
save_directory,
save_config=True,
state_dict=None,
save_function=torch.save,
push_to_hub=False,
**kw,
):
if os.path.isfile(save_directory):
logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
return
if push_to_hub:
commit_message = kw.pop("commit_message", None)
repo = self._create_or_get_repo(save_directory, **kw)
os.makedirs(save_directory, exist_ok=True)
# Only save the model itself if we are using distributed training
model_to_save = unwrap_model(self)
# save the string version of dtype to the config, e.g. convert torch.float32 => "float32"
# we currently don't use this setting automatically, but may start to use with v5
dtype = get_parameter_dtype(model_to_save)
model_to_save.config.torch_dtype = str(dtype).split(".")[1]
# Attach architecture to the config
model_to_save.config.archs = [model_to_save.__class__.__name__]
# If we have a custom model, we copy the file defining it in the folder and set the attributes so it can be
# loaded from the Hub.
if self._auto_class is not None:
custom_object_save(self, save_directory, config=self.config)
# Save the config
if save_config:
model_to_save.config.save_pretrained(save_directory)
# Save the model
if state_dict is None:
state_dict = model_to_save.state_dict()
# Handle the case where some state_dict keys shouldn't be saved
if self._keys_to_ignore_on_save is not None:
for ignore_key in self._keys_to_ignore_on_save:
if ignore_key in state_dict.keys():
del state_dict[ignore_key]
# If we save using the predefined names, we can load using `from_pretrained`
output_model_file = os.path.join(save_directory, WEIGHTS_NAME)
save_function(state_dict, output_model_file)
logger.info(f"Model weights saved in {output_model_file}")
if push_to_hub:
url = self._push_to_hub(repo, commit_message=commit_message)
logger.info(f"Model pushed to the hub in this commit: {url}")
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kw):
config = kw.pop("config", None)
state_dict = kw.pop("state_dict", None)
cache_dir = kw.pop("cache_dir", None)
from_tf = kw.pop("from_tf", False)
from_flax = kw.pop("from_flax", False)
ignore_mismatched_sizes = kw.pop("ignore_mismatched_sizes", False)
force_download = kw.pop("force_download", False)
resume_download = kw.pop("resume_download", False)
proxies = kw.pop("proxies", None)
output_loading_info = kw.pop("output_loading_info", False)
local_files_only = kw.pop("local_files_only", False)
use_auth_token = kw.pop("use_auth_token", None)
revision = kw.pop("revision", None)
mirror = kw.pop("mirror", None)
from_pipeline = kw.pop("_from_pipeline", None)
from_auto_class = kw.pop("_from_auto", False)
_fast_init = kw.pop("_fast_init", True)
torch_dtype = kw.pop("torch_dtype", None)
low_cpu_mem_usage = kw.pop("low_cpu_mem_usage", False)
from_pt = not (from_tf | from_flax)
user_agent = {
"file_type": "model",
"framework": "pytorch",
"from_auto_class": from_auto_class,
}
if from_pipeline is not None:
user_agent["using_pipeline"] = from_pipeline
if is_offline_mode() and not local_files_only:
logger.info("Offline mode: forcing local_files_only=True")
local_files_only = True
# Load config if we don't provide a configuration
if not isinstance(config, PretrainedConfig):
config_path = config if config is not None else pretrained_model_name_or_path
config, model_kw = cls.config_class.from_pretrained(
config_path,
cache_dir=cache_dir,
return_unused_kw=True,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
_from_auto=from_auto_class,
_from_pipeline=from_pipeline,
**kw,
)
else:
model_kw = kw
# Load model
if pretrained_model_name_or_path is not None:
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
if os.path.isdir(pretrained_model_name_or_path):
if from_tf and os.path.isfile(
os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME + ".index")
):
# Load from a TF 1.0 checkpoint in priority if from_tf
archive_file = os.path.join(
pretrained_model_name_or_path, TF_WEIGHTS_NAME + ".index"
)
elif from_tf and os.path.isfile(
os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)
):
# Load from a TF 2.0 checkpoint in priority if from_tf
archive_file = os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)
elif from_flax and os.path.isfile(
os.path.join(pretrained_model_name_or_path, FLAX_WEIGHTS_NAME)
):
# Load from a Flax checkpoint in priority if from_flax
archive_file = os.path.join(pretrained_model_name_or_path, FLAX_WEIGHTS_NAME)
elif os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)):
# Load from a PyTorch checkpoint
archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
# At this stage we don't have a weight file so we will raise an error.
elif os.path.isfile(
os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME + ".index")
) or os.path.isfile(os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)):
raise EnvironmentError(
f"Error no file named {WEIGHTS_NAME} found in directory {pretrained_model_name_or_path} but "
"there is a file for TensorFlow weights. Use `from_tf=True` to load this model from those "
"weights."
)
elif os.path.join(pretrained_model_name_or_path, FLAX_WEIGHTS_NAME):
raise EnvironmentError(
f"Error no file named {WEIGHTS_NAME} found in directory {pretrained_model_name_or_path} but "
"there is a file for Flax weights. Use `from_flax=True` to load this model from those "
"weights."
)
else:
raise EnvironmentError(
f"Error no file named {WEIGHTS_NAME}, {TF2_WEIGHTS_NAME}, {TF_WEIGHTS_NAME + '.index'} or "
f"{FLAX_WEIGHTS_NAME} found in directory {pretrained_model_name_or_path}."
)
elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(
pretrained_model_name_or_path
):
archive_file = pretrained_model_name_or_path
elif os.path.isfile(pretrained_model_name_or_path + ".index"):
if not from_tf:
raise ValueError(
f"We found a TensorFlow checkpoint at {pretrained_model_name_or_path + '.index'}, please set "
"from_tf to True to load from this checkpoint."
)
archive_file = pretrained_model_name_or_path + ".index"
else:
# set correct filename
if from_tf:
filename = TF2_WEIGHTS_NAME
elif from_flax:
filename = FLAX_WEIGHTS_NAME
else:
filename = WEIGHTS_NAME
archive_file = hf_bucket_url(
pretrained_model_name_or_path,
filename=filename,
revision=revision,
mirror=mirror,
)
try:
# Load from URL or cache if already cached
resolved_archive_file = cached_path(
archive_file,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
user_agent=user_agent,
)
except RepositoryNotFoundError:
raise EnvironmentError(
f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
"listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
"token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
"login` and pass `use_auth_token=True`."
)
except RevisionNotFoundError:
raise EnvironmentError(
f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
"this model name. Check the model page at "
f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."
)
except EntryNotFoundError:
if filename == WEIGHTS_NAME:
has_file_kw = {
"revision": revision,
"mirror": mirror,
"proxies": proxies,
"use_auth_token": use_auth_token,
}
if has_file(pretrained_model_name_or_path, TF2_WEIGHTS_NAME, **has_file_kw):
raise EnvironmentError(
f"{pretrained_model_name_or_path} does not appear to have a file named {WEIGHTS_NAME} but "
"there is a file for TensorFlow weights. Use `from_tf=True` to load this model from those "
"weights."
)
elif has_file(pretrained_model_name_or_path, FLAX_WEIGHTS_NAME, **has_file_kw):
raise EnvironmentError(
f"{pretrained_model_name_or_path} does not appear to have a file named {WEIGHTS_NAME} but "
"there is a file for Flax weights. Use `from_flax=True` to load this model from those "
"weights."
)
else:
raise EnvironmentError(
f"{pretrained_model_name_or_path} does not appear to have a file named {WEIGHTS_NAME}, "
f"{TF2_WEIGHTS_NAME}, {TF_WEIGHTS_NAME} or {FLAX_WEIGHTS_NAME}."
)
else:
raise EnvironmentError(
f"{pretrained_model_name_or_path} does not appear to have a file named {filename}."
)
except HTTPError:
raise EnvironmentError(
"We couldn't connect to 'https://huggingface.co/' to load this model and it looks like "
f"{pretrained_model_name_or_path} is not the path to a directory conaining a a file named "
f"{WEIGHTS_NAME}, {TF2_WEIGHTS_NAME}, {TF_WEIGHTS_NAME} or {FLAX_WEIGHTS_NAME}.\n"
"Checkout your internet connection or see how to run the library in offline mode at "
"'https://huggingface.co/docs/transformers/installation#offline-mode'."
)
except EnvironmentError:
raise EnvironmentError(
f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
"'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
f"containing a file named {WEIGHTS_NAME}, {TF2_WEIGHTS_NAME}, {TF_WEIGHTS_NAME} or "
f"{FLAX_WEIGHTS_NAME}."
)
if resolved_archive_file == archive_file:
logger.info(f"loading weights file {archive_file}")
else:
logger.info(
f"loading weights file {archive_file} from cache at {resolved_archive_file}"
)
else:
resolved_archive_file = None
# load pt weights early so that we know which dtype to init the model under
if from_pt:
if state_dict is None:
try:
state_dict = torch.load(resolved_archive_file, map_location="cpu")
except Exception as e:
try:
with open(resolved_archive_file) as f:
if f.read().startswith("version"):
raise OSError(
"You seem to have cloned a repository without having git-lfs installed. Please install "
"git-lfs and run `git lfs install` followed by `git lfs pull` in the folder "
"you cloned."
)
else:
raise ValueError from e
except (UnicodeDecodeError, ValueError):
raise OSError(
f"Unable to load weights from pytorch checkpoint file for '{pretrained_model_name_or_path}' "
f"at '{resolved_archive_file}'. "
"If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True."
)
# set dtype to instantiate the model under:
# 1. If torch_dtype is not None, we use that dtype
# 2. If torch_dtype is "auto", we auto-detect dtype from the loaded state_dict, by checking its first
# weights entry - we assume all weights are of the same dtype
# we also may have config.torch_dtype available, but we won't rely on it till v5
dtype_orig = None
if torch_dtype is not None:
if isinstance(torch_dtype, str):
if torch_dtype == "auto":
torch_dtype = next(iter(state_dict.values())).dtype
else:
raise ValueError(
f"`torch_dtype` can be either a `torch.dtype` or `auto`, but received {torch_dtype}"
)
dtype_orig = cls._set_default_torch_dtype(torch_dtype)
if low_cpu_mem_usage:
# save the keys
loaded_state_dict_keys = [k for k in state_dict.keys()]
del state_dict # free CPU memory - will reload again later
config.name_or_path = pretrained_model_name_or_path
# Instantiate model.
if is_deepspeed_zero3_enabled():
import deepspeed
logger.info("Detected DeepSpeed ZeRO-3: activating zero.init() for this model")
# this immediately partitions the model across all gpus, to avoid the overhead in time
# and memory copying it on CPU or each GPU first
with deepspeed.zero.Init(config_dict_or_path=deepspeed_config()):
with no_init_weights(_enable=_fast_init):
model = cls(config, *model_args, **model_kw)
else:
with no_init_weights(_enable=_fast_init):
model = cls(config, *model_args, **model_kw)
if from_pt:
# restore default dtype
if dtype_orig is not None:
torch.set_default_dtype(dtype_orig)
if from_tf:
if resolved_archive_file.endswith(".index"):
# Load from a TensorFlow 1.X checkpoint - provided by original authors
model = cls.load_tf_weights(
model, config, resolved_archive_file[:-6]
) # Remove the '.index'
else:
# Load from our TensorFlow 2.0 checkpoints
try:
from .modeling_tf_pytorch_utils import load_tf2_checkpoint_in_pytorch_model
model = load_tf2_checkpoint_in_pytorch_model(
model, resolved_archive_file, allow_missing_keys=True
)
except ImportError:
logger.error(
"Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be installed. Please see "
"https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions."
)
raise
elif from_flax:
try:
from .modeling_flax_pytorch_utils import load_flax_checkpoint_in_pytorch_model
model = load_flax_checkpoint_in_pytorch_model(model, resolved_archive_file)
except ImportError:
logger.error(
"Loading a Flax model in PyTorch, requires both PyTorch and Flax to be installed. Please see "
"https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation instructions."
)
raise
elif from_pt:
if low_cpu_mem_usage:
cls._load_state_dict_into_model_low_mem(
model, loaded_state_dict_keys, resolved_archive_file
)
else:
(
model,
missing_keys,
unexpected_keys,
mismatched_keys,
error_msgs,
) = cls._load_state_dict_into_model(
model,
state_dict,
pretrained_model_name_or_path,
ignore_mismatched_sizes=ignore_mismatched_sizes,
_fast_init=_fast_init,
)
# make sure token embedding weights are still tied if needed
model.tie_weights()
# Set model in evaluation mode to deactivate DropOut modules by default
model.eval()
if output_loading_info:
loading_info = {
"missing_keys": missing_keys,
"unexpected_keys": unexpected_keys,
"mismatched_keys": mismatched_keys,
"error_msgs": error_msgs,
}
return model, loading_info
return model
@classmethod
def _load_state_dict_into_model(
cls,
model,
state_dict,
pretrained_model_name_or_path,
ignore_mismatched_sizes=False,
_fast_init=True,
):
# Convert old format to new format if needed from a PyTorch state_dict
old_keys = []
new_keys = []
for key in state_dict.keys():
new_key = None
if "gamma" in key:
new_key = key.replace("gamma", "weight")
if "beta" in key:
new_key = key.replace("beta", "bias")
if new_key:
old_keys.append(key)
new_keys.append(new_key)
for old_key, new_key in zip(old_keys, new_keys):
state_dict[new_key] = state_dict.pop(old_key)
# Retrieve missing & unexpected_keys
model_state_dict = model.state_dict()
expected_keys = list(model_state_dict.keys())
loaded_keys = list(state_dict.keys())
prefix = model.base_model_prefix
has_prefix_module = any(s.startswith(prefix) for s in loaded_keys)
expects_prefix_module = any(s.startswith(prefix) for s in expected_keys)
# key re-naming operations are never done on the keys
# that are loaded, but always on the keys of the newly initialized model
remove_prefix_from_model = not has_prefix_module and expects_prefix_module
add_prefix_to_model = has_prefix_module and not expects_prefix_module
if remove_prefix_from_model:
expected_keys_not_prefixed = [s for s in expected_keys if not s.startswith(prefix)]
expected_keys = [
".".join(s.split(".")[1:]) if s.startswith(prefix) else s for s in expected_keys
]
elif add_prefix_to_model:
expected_keys = [".".join([prefix, s]) for s in expected_keys]
missing_keys = list(set(expected_keys) - set(loaded_keys))
unexpected_keys = list(set(loaded_keys) - set(expected_keys))
# Mistmatched keys contains tuples key/shape1/shape2 of weights in the checkpoint that have a shape not
# matching the weights in the model.
mismatched_keys = []
if ignore_mismatched_sizes:
for checkpoint_key in loaded_keys:
model_key = checkpoint_key
if remove_prefix_from_model:
# The model key starts with `prefix` but `checkpoint_key` doesn't so we add it.
model_key = f"{prefix}.{checkpoint_key}"
elif add_prefix_to_model:
# The model key doesn't start with `prefix` but `checkpoint_key` does so we remove it.
model_key = ".".join(checkpoint_key.split(".")[1:])
if (
model_key in model_state_dict
and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape
):
mismatched_keys.append(
(
checkpoint_key,
state_dict[checkpoint_key].shape,
model_state_dict[model_key].shape,
)
)
del state_dict[checkpoint_key]
# Some models may have keys that are not in the state by design, removing them before needlessly warning
# the user.
if cls._keys_to_ignore_on_load_missing is not None:
for pat in cls._keys_to_ignore_on_load_missing:
missing_keys = [k for k in missing_keys if re.search(pat, k) is None]
if cls._keys_to_ignore_on_load_unexpected is not None:
for pat in cls._keys_to_ignore_on_load_unexpected:
unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
if _fast_init:
# retrieve unintialized modules and initialize
uninitialized_modules = model.retrieve_modules_from_names(
missing_keys, add_prefix=add_prefix_to_model, remove_prefix=remove_prefix_from_model
)
for module in uninitialized_modules:
model._init_weights(module)
# copy state_dict so _load_from_state_dict can modify it
metadata = getattr(state_dict, "_metadata", None)
state_dict = state_dict.copy()
if metadata is not None:
state_dict._metadata = metadata
error_msgs = []
# PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants
# so we need to apply the function recursively.
def load(module, prefix=""):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
args = (state_dict, prefix, local_metadata, True, [], [], error_msgs)
if is_deepspeed_zero3_enabled():
import deepspeed
# because zero3 puts placeholders in model params, this context
# manager gathers (unpartitions) the params of the current layer, then loads from
# the state dict and then re-partitions them again
with deepspeed.zero.GatheredParameters(
list(module.parameters(recurse=False)), modifier_rank=0
):
if torch.distributed.get_rank() == 0:
module._load_from_state_dict(*args)
else:
module._load_from_state_dict(*args)
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + ".")
# Make sure we are able to load base models as well as derived models (with heads)
beg_prefix = ""
model_to_load = model
if not hasattr(model, cls.base_model_prefix) and has_prefix_module:
beg_prefix = cls.base_model_prefix + "."
if hasattr(model, cls.base_model_prefix) and not has_prefix_module:
model_to_load = getattr(model, cls.base_model_prefix)
if any(key in expected_keys_not_prefixed for key in loaded_keys):
raise ValueError(
"The state dictionary of the model you are training to load is corrupted. Are you sure it was "
"properly saved?"
)
load(model_to_load, prefix=beg_prefix)
if len(error_msgs) > 0:
error_msg = "\n\t".join(error_msgs)
raise RuntimeError(
f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}"
)
if len(unexpected_keys) > 0:
logger.warning(
f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when "
f"initializing {model.__class__.__name__}: {unexpected_keys}\n"
f"- This IS expected if you are initializing {model.__class__.__name__} from the checkpoint of a model trained on another task "
f"or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n"
f"- This IS NOT expected if you are initializing {model.__class__.__name__} from the checkpoint of a model that you expect "
f"to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model)."
)
else:
logger.info(
f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n"
)
if len(missing_keys) > 0:
logger.warning(
f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at {pretrained_model_name_or_path} "
f"and are newly initialized: {missing_keys}\n"
f"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference."
)
elif len(mismatched_keys) == 0:
logger.info(
f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at {pretrained_model_name_or_path}.\n"
f"If your task is similar to the task the model of the checkpoint was trained on, "
f"you can already use {model.__class__.__name__} for predictions without further training."
)
if len(mismatched_keys) > 0:
mismatched_warning = "\n".join(
[
f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated"
for key, shape1, shape2 in mismatched_keys
]
)
logger.warning(
f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at {pretrained_model_name_or_path} "
f"and are newly initialized because the shapes did not match:\n{mismatched_warning}\n"
f"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference."
)
return model, missing_keys, unexpected_keys, mismatched_keys, error_msgs
def retrieve_modules_from_names(self, names, add_prefix=False, remove_prefix=False):
module_keys = set([".".join(key.split(".")[:-1]) for key in names])
# torch.nn.ParameterList is a special case where two parameter keywords
# are appended to the module name, *e.g.* bert.special_embeddings.0
module_keys = module_keys.union(
set([".".join(key.split(".")[:-2]) for key in names if key[-1].isdigit()])
)
retrieved_modules = []
# retrieve all modules that has at least one missing weight name
for name, module in self.named_modules():
if remove_prefix:
name = (
".".join(name.split(".")[1:])
if name.startswith(self.base_model_prefix)
else name
)
elif add_prefix:
name = (
".".join([self.base_model_prefix, name])
if len(name) > 0
else self.base_model_prefix
)
if name in module_keys:
retrieved_modules.append(module)
return retrieved_modules
@classmethod
def _load_state_dict_into_model_low_mem(
cls, model, loaded_state_dict_keys, resolved_archive_file
):
require_version_core("torch>=1.9")
if is_deepspeed_zero3_enabled():
raise ValueError("low_cpu_mem_usage arg cannot be used with DeepSpeed ZeRO-3")
# a helper util to find the last sub-module and the param/buffer name
def find_submodule_and_param_name(model, long_key):
split_key = long_key.split(".")
submodule = model
while len(split_key) > 1:
if hasattr(submodule, split_key[0]):
submodule = getattr(submodule, split_key[0])
del split_key[0]
else:
submodule = None
break
return submodule, split_key[0]
# dematerialize param storage for keys that are going to be replaced by state_dict, by
# putting those on the meta device
for k in loaded_state_dict_keys:
submodule, param_name = find_submodule_and_param_name(model, k)
if submodule is not None:
# selectively switch to the meta device only those params/buffers that will
# be next replaced from state_dict. This a complex way to do p.to_("meta")
# since we have no in-place to_ for tensors.
new_val = getattr(submodule, param_name)
if isinstance(new_val, torch.nn.Parameter):
# isinstance returns False for Params on meta device, so switch after the check
new_val = torch.nn.Parameter(new_val.to("meta"))
else:
new_val = new_val.to("meta")
setattr(submodule, param_name, new_val)
# only now can load state_dict
state_dict = torch.load(resolved_archive_file, map_location="cpu")
# materialize state_dict entries one by one on CPU
for k in loaded_state_dict_keys:
submodule, param_name = find_submodule_and_param_name(model, k)
if submodule is not None:
new_val = state_dict[k]
if isinstance(getattr(submodule, param_name), torch.nn.Parameter):
new_val = torch.nn.Parameter(new_val)
setattr(submodule, param_name, new_val)
del state_dict
@classmethod
def register_for_auto_class(cls, auto_class="AutoModel"):
"""
Register this class with a given auto class. This should only be used for custom models as the ones in the
library are already mapped with an auto class.
<Tip warning={true}>
This API is experimental and may have some slight breaking changes in the next releases.
</Tip>
Args:
auto_class (`str` or `type`, *optional*, defaults to `"AutoModel"`):
The auto class to register this new model with.
"""
if not isinstance(auto_class, str):
auto_class = auto_class.__name__
import transformers.models.auto as auto_module
if not hasattr(auto_module, auto_class):
raise ValueError(f"{auto_class} is not a valid auto class.")
cls._auto_class = auto_class
PreTrainedModel.push_to_hub = copy_func(PreTrainedModel.push_to_hub)
PreTrainedModel.push_to_hub.__doc__ = PreTrainedModel.push_to_hub.__doc__.format(
object="model", object_class="AutoModel", object_files="model checkpoint"
)
class Conv1D(nn.Module):
def __init__(self, nf, nx):
super().__init__()
self.nf = nf
w = torch.empty(nx, nf)
nn.init.normal_(w, std=0.02)
self.weight = nn.Parameter(w)
self.bias = nn.Parameter(torch.zeros(nf))
def forward(self, x):
size_out = x.size()[:-1] + (self.nf,)
x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight)
x = x.view(size_out)
return x
@dataclass
class SquadHeadOutput(ModelOutput):
loss = None
top_beg_log_probs = None
top_beg_index = None
top_end_log_probs = None
top_end_index = None
cls_logits = None
class SQuADHead(nn.Module):
def __init__(self, config):
super().__init__()
self.beg_n_top = config.beg_n_top
self.end_n_top = config.end_n_top
self.logits_beg = PoolerStartLogits(config)
self.logits_end = PoolerEndLogits(config)
self.answer_class = PoolerAnswerClass(config)
def forward(
self,
hiddens,
beg_positions=None,
end_positions=None,
cls_index=None,
is_impossible=None,
p_mask=None,
return_dict=False,
):
logits_beg = self.logits_beg(hiddens, p_mask=p_mask)
if beg_positions is not None and end_positions is not None:
# If we are on multi-GPU, let's remove the dimension added by batch splitting
for x in (beg_positions, end_positions, cls_index, is_impossible):
if x is not None and x.dim() > 1:
x.squeeze_(-1)
# during training, compute the end logits based on the ground truth of the start position
logits_end = self.logits_end(hiddens, beg_positions=beg_positions, p_mask=p_mask)
loss_fct = CrossEntropyLoss()
beg_loss = loss_fct(logits_beg, beg_positions)
end_loss = loss_fct(logits_end, end_positions)
total_loss = (beg_loss + end_loss) / 2
if cls_index is not None and is_impossible is not None:
# Predict answerability from the representation of CLS and START
cls_logits = self.answer_class(
hiddens, beg_positions=beg_positions, cls_index=cls_index
)
loss_fct_cls = nn.BCEWithLogitsLoss()
cls_loss = loss_fct_cls(cls_logits, is_impossible)
# note(zhiliny): by default multiply the loss by 0.5 so that the scale is comparable to beg_loss and end_loss
total_loss += cls_loss * 0.5
return SquadHeadOutput(loss=total_loss) if return_dict else (total_loss,)
else:
# during inference, compute the end logits based on beam search
bsz, slen, hsz = hiddens.size()
beg_log_probs = nn.functional.softmax(logits_beg, dim=-1) # shape (bsz, slen)
top_beg_log_probs, top_beg_index = torch.topk(
beg_log_probs, self.beg_n_top, dim=-1
) # shape (bsz, beg_n_top)
top_beg_index_exp = top_beg_index.unsqueeze(-1).expand(
-1, -1, hsz
) # shape (bsz, beg_n_top, hsz)
x_beg = torch.gather(hiddens, -2, top_beg_index_exp) # shape (bsz, beg_n_top, hsz)
x_beg = x_beg.unsqueeze(1).expand(-1, slen, -1, -1) # shape (bsz, slen, beg_n_top, hsz)
hidden_states_expanded = hiddens.unsqueeze(2).expand_as(
x_beg
) # shape (bsz, slen, beg_n_top, hsz)
p_mask = p_mask.unsqueeze(-1) if p_mask is not None else None
logits_end = self.logits_end(hidden_states_expanded, x_beg=x_beg, p_mask=p_mask)
end_log_probs = nn.functional.softmax(logits_end, dim=1) # shape (bsz, slen, beg_n_top)
top_end_log_probs, top_end_index = torch.topk(
end_log_probs, self.end_n_top, dim=1
) # shape (bsz, end_n_top, beg_n_top)
top_end_log_probs = top_end_log_probs.view(-1, self.beg_n_top * self.end_n_top)
top_end_index = top_end_index.view(-1, self.beg_n_top * self.end_n_top)
x_beg = torch.einsum("blh,bl->bh", hiddens, beg_log_probs)
cls_logits = self.answer_class(hiddens, x_beg=x_beg, cls_index=cls_index)
if not return_dict:
return (
top_beg_log_probs,
top_beg_index,
top_end_log_probs,
top_end_index,
cls_logits,
)
else:
return SquadHeadOutput(
top_beg_log_probs=top_beg_log_probs,
top_beg_index=top_beg_index,
top_end_log_probs=top_end_log_probs,
top_end_index=top_end_index,
cls_logits=cls_logits,
)
class SequenceSummary(nn.Module):
def __init__(self, config):
super().__init__()
self.summy_type = getattr(config, "summy_type", "last")
if self.summy_type == "attn":
raise NotImplementedError
self.summary = Identity()
if hasattr(config, "sum_use_proj") and config.sum_use_proj:
if hasattr(config, "sum_proj") and config.sum_proj and config.num_labels > 0:
num_classes = config.num_labels
else:
num_classes = config.d_model
self.summary = nn.Linear(config.d_model, num_classes)
activation_string = getattr(config, "sum_act", None)
self.activation = get_activation(activation_string) if activation_string else Identity()
self.first_dropout = Identity()
if hasattr(config, "drop_sum_first") and config.drop_sum_first > 0:
self.first_dropout = nn.Dropout(config.drop_sum_first)
self.last_dropout = Identity()
if hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0:
self.last_dropout = nn.Dropout(config.summary_last_dropout)
def forward(self, hiddens, cls_index=None):
if self.summy_type == "last":
output = hiddens[:, -1]
elif self.summy_type == "first":
output = hiddens[:, 0]
elif self.summy_type == "mean":
output = hiddens.mean(dim=1)
elif self.summy_type == "cls_index":
if cls_index is None:
cls_index = torch.full_like(
hiddens[..., :1, :],
hiddens.shape[-2] - 1,
dtype=torch.long,
)
else:
cls_index = cls_index.unsqueeze(-1).unsqueeze(-1)
cls_index = cls_index.expand((-1,) * (cls_index.dim() - 1) + (hiddens.size(-1),))
# shape of cls_index: (bsz, XX, 1, d_model) where XX are optional leading dim of hiddens
output = hiddens.gather(-2, cls_index).squeeze(-2) # shape (bsz, XX, d_model)
elif self.summy_type == "attn":
raise NotImplementedError
output = self.first_dropout(output)
output = self.summary(output)
output = self.activation(output)
output = self.last_dropout(output)
return output
def unwrap_model(model):
if hasattr(model, "module"):
return unwrap_model(model.module)
else:
return model
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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,440
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/test/unit/runtime/test_launch.py
|
import gc
# import importlib
# import os
# import sys
# import tempfile
# import textwrap
# import time
import tracemalloc
import torch
import triton
import triton.language as tl
# from typing import Tuple
def test_memory_leak() -> None:
@triton.jit
def kernel(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 10
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tl.store(out_ptr0 + (x0 + tl.zeros([XBLOCK], tl.int32)), tmp0, xmask)
tracemalloc.start()
try:
inp = torch.randn(10, device='cuda')
out = torch.randn(10, device='cuda')
kernel[(10,)](inp, out, 10, XBLOCK=16)
gc.collect()
begin, _ = tracemalloc.get_traced_memory()
for _ in range(100):
kernel[(10,)](inp, out, 10, XBLOCK=16)
gc.collect()
end, _ = tracemalloc.get_traced_memory()
assert end - begin < 1000
finally:
tracemalloc.stop()
# LATENCY_THRESHOLD_US = 46
# def test_kernel_launch_latency() -> None:
# def define_kernel(kernel_name: str, num_tensor_args: int) -> str:
# arg_str = ",".join([f"arg{i}: torch.Tensor" for i in range(num_tensor_args)])
# arg_str += ", n_elements: int, BLOCK_SIZE: tl.constexpr"
# func_str = f"""
# import torch
# import triton
# import triton.language as tl
# @triton.jit
# def {kernel_name}({arg_str}):
# pass
# """
# with tempfile.NamedTemporaryFile(mode="w+t", suffix=".py", delete=False) as temp_file:
# temp_file.write(textwrap.dedent(func_str))
# temp_file_path = temp_file.name
# return temp_file_path
# def import_kernel(file_path, kernel_name):
# directory, filename = os.path.split(file_path)
# module_name, _ = os.path.splitext(filename)
# sys.path.insert(0, directory)
# module = importlib.import_module(module_name)
# kernel = getattr(module, kernel_name)
# return kernel
# def empty(*kernel_args: Tuple[torch.Tensor]):
# first_arg = kernel_args[0]
# n_elements = first_arg.numel()
# grid = (triton.cdiv(n_elements, 1024),)
# device = torch.cuda.current_device()
# # Warmup
# empty_kernel[grid](*kernel_args, n_elements, BLOCK_SIZE=1024, device=device)
# torch.cuda.synchronize()
# # Measure launch overhead at steady state
# num_runs = 1000
# start_time = time.time()
# for i in range(num_runs):
# empty_kernel[grid](*kernel_args, n_elements, BLOCK_SIZE=1024, device=device)
# end_time = time.time()
# latency_us = (end_time - start_time) / num_runs * 1e6
# assert latency_us < LATENCY_THRESHOLD_US, "Kernel launch time has increased!"
# num_tensor_args = 40
# kernel_name = 'empty_kernel'
# file_path = define_kernel(kernel_name, num_tensor_args)
# empty_kernel = import_kernel(file_path, kernel_name)
# # Initialize random tensors for the empty_kernel
# torch.manual_seed(0)
# size = 1024
# kernel_args = (torch.rand(size, device='cuda') for i in range(num_tensor_args))
# # Run empty, which would run empty_kernel internally
# empty(*kernel_args)
|
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"/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,441
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/tokens/utils.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import bisect
import itertools
import re
import unicodedata
from collections import OrderedDict
from .file_utils import PaddingStrategy
from .base import (
AddedToken,
BatchEncoding,
PreTrainedTokenizerBase,
TextInput,
TruncationStrategy,
)
from transformers.utils import logging
log = logging.get_logger(__name__)
# Slow tokenizers are saved in a vocabulary plus three separated files
SPECIAL_TOKENS_MAP_FILE = "special_tokens_map.json"
ADDED_TOKENS_FILE = "added_tokens.json"
TOKENIZER_CONFIG_FILE = "tokenizer_config.json"
class Trie:
def __init__(self):
self.data = {}
def add(self, word):
if not word:
# Prevent empty string
return
ref = self.data
for char in word:
ref[char] = char in ref and ref[char] or {}
ref = ref[char]
ref[""] = 1
def split(self, text):
states = OrderedDict()
offsets = [0]
skip = 0
for current, current_char in enumerate(text):
if skip and current < skip:
continue
to_remove = set()
reset = False
for start, trie_pointer in states.items():
if "" in trie_pointer:
for lookstart, looktrie_pointer in states.items():
if lookstart > start:
break
elif lookstart < start:
lookahead_index = current + 1
end = current + 1
else:
lookahead_index = current
end = current
next_char = text[lookahead_index] if lookahead_index < len(text) else None
if "" in looktrie_pointer:
start = lookstart
end = lookahead_index
skip = lookahead_index
while next_char in looktrie_pointer:
looktrie_pointer = looktrie_pointer[next_char]
lookahead_index += 1
if "" in looktrie_pointer:
start = lookstart
end = lookahead_index
skip = lookahead_index
if lookahead_index == len(text):
# End of string
break
next_char = text[lookahead_index]
# End lookahead
# Storing and resetting
offsets.append(start)
offsets.append(end)
reset = True
break
elif current_char in trie_pointer:
# The current character being looked at has a match within the trie
# update the pointer (it will be stored back into states later).
trie_pointer = trie_pointer[current_char]
# Storing back the new pointer into the states.
# Partial matches got longer by one.
states[start] = trie_pointer
else:
# The new character has not match in the trie, we need
# to stop keeping track of this partial match.
# We can't do it directly within the loop because of how
# python iteration works
to_remove.add(start)
# Either clearing the full start (we found a real match)
# Or clearing only the partial matches that didn't work.
if reset:
states = {}
else:
for start in to_remove:
del states[start]
# If this character is a starting character within the trie
# start keeping track of this partial match.
if current >= skip and current_char in self.data:
states[current] = self.data[current_char]
# We have a cut at the end with states.
for start, trie_pointer in states.items():
if "" in trie_pointer:
# This is a final match, we need to reset and
# store the results in `offsets`.
end = len(text)
offsets.append(start)
offsets.append(end)
# Longest cut is always the one with lower start so the first
# item so we need to break.
break
return self.cut_text(text, offsets)
def cut_text(self, text, offsets):
# We have all the offsets now, we just need to do the actual splitting.
# We need to eventually add the first part of the string and the eventual
# last part.
offsets.append(len(text))
tokens = []
start = 0
for end in offsets:
if start > end:
log.error(
"There was a bug in Trie algorithm in tokenization. Attempting to recover. Please report it anyway."
)
continue
elif start == end:
# This might happen if there's a match at index 0
# we're also preventing zero-width cuts in case of two
# consecutive matches
continue
tokens.append(text[start:end])
start = end
return tokens
def _is_whitespace(char):
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
cat = unicodedata.category(char)
if cat == "Zs":
return True
return False
def _is_control(char):
if char == "\t" or char == "\n" or char == "\r":
return False
cat = unicodedata.category(char)
if cat.startswith("C"):
return True
return False
def _is_punctuation(char):
cp = ord(char)
# We treat all non-letter/number ASCII as punctuation.
# Characters such as "^", "$", and "`" are not in the Unicode
# Punctuation class but we treat them as punctuation anyways, for
# consistency.
if (
(cp >= 33 and cp <= 47)
or (cp >= 58 and cp <= 64)
or (cp >= 91 and cp <= 96)
or (cp >= 123 and cp <= 126)
):
return True
cat = unicodedata.category(char)
if cat.startswith("P"):
return True
return False
def _is_end_of_word(text):
last_char = text[-1]
return bool(_is_control(last_char) | _is_punctuation(last_char) | _is_whitespace(last_char))
def _is_start_of_word(text):
first_char = text[0]
return bool(_is_control(first_char) | _is_punctuation(first_char) | _is_whitespace(first_char))
def _insert_one_token_to_ordered_list(token_list, new_token):
insertion_idx = bisect.bisect_left(token_list, new_token)
# Checks if new_token is already in the ordered token_list
if insertion_idx < len(token_list) and token_list[insertion_idx] == new_token:
# new_token is in token_list, don't add
return
else:
token_list.insert(insertion_idx, new_token)
class PreTrainedTokenizer(PreTrainedTokenizerBase):
def __init__(self, **kw):
super().__init__(**kw)
self.added_tokens_encoder = {}
self.added_tokens_decoder = {}
self.unique_no_split_tokens = []
self.tokens_trie = Trie()
self._decode_use_source_tokenizer = False
@property
def is_fast(self):
return False
@property
def s_vocab(self):
raise NotImplementedError
def get_added_vocab(self):
return self.added_tokens_encoder
def __len__(self):
return self.s_vocab + len(self.added_tokens_encoder)
def _add_tokens(self, new_tokens, special_tokens=False):
new_tokens = [str(tok) for tok in new_tokens]
tokens_to_add = []
for token in new_tokens:
if not isinstance(token, str):
raise TypeError(f"Token {token} is not a string but a {type(token)}.")
if not special_tokens and hasattr(self, "do_lower_case") and self.do_lower_case:
token = token.lower()
if (
token != self.unk
and self.convert_tokens_to_ids(token) == self.convert_tokens_to_ids(self.unk)
and token not in tokens_to_add
):
tokens_to_add.append(token)
if self.verbose:
log.info(f"Adding {token} to the vocabulary")
added_tok_encoder = dict((tok, len(self) + i) for i, tok in enumerate(tokens_to_add))
added_tok_decoder = {v: k for k, v in added_tok_encoder.items()}
self.added_tokens_encoder.update(added_tok_encoder)
self.added_tokens_decoder.update(added_tok_decoder)
if special_tokens:
if len(new_tokens) == 1:
_insert_one_token_to_ordered_list(self.unique_no_split_tokens, new_tokens[0])
else:
self.unique_no_split_tokens = sorted(
set(self.unique_no_split_tokens).union(set(new_tokens))
)
else:
# Or on the newly added tokens
if len(tokens_to_add) == 1:
_insert_one_token_to_ordered_list(self.unique_no_split_tokens, tokens_to_add[0])
else:
self.unique_no_split_tokens = sorted(
set(self.unique_no_split_tokens).union(set(tokens_to_add))
)
self._create_trie(self.unique_no_split_tokens)
return len(tokens_to_add)
def _create_trie(self, unique_no_split_tokens):
trie = Trie()
for token in unique_no_split_tokens:
if (
hasattr(self, "do_lower_case")
and self.do_lower_case
and token not in self.all_special_tokens
):
trie.add(token.lower())
else:
trie.add(token)
self.tokens_trie = trie
def num_special_tokens_to_add(self, pair=False):
toks_0 = []
toks_1 = []
return len(self.build_inputs_with_special_tokens(toks_0, toks_1 if pair else None))
def tokenize(self, text: TextInput, **kw):
all_special_tokens_extended = dict(
(str(t), t) for t in self.all_special_tokens_extended if isinstance(t, AddedToken)
)
text, kw = self.prepare_for_tokenization(text, **kw)
if kw:
log.warning(f"Keyword arguments {kw} not recognized.")
# TODO: should this be in the base class?
if hasattr(self, "do_lower_case") and self.do_lower_case:
# convert non-special tokens to lowercase
escaped_special_toks = [
re.escape(s_tok)
for s_tok in (self.unique_no_split_tokens + self.all_special_tokens)
]
pattern = r"(" + r"|".join(escaped_special_toks) + r")|" + r"(.+?)"
text = re.sub(pattern, lambda m: m.groups()[0] or m.groups()[1].lower(), text)
no_split_token = set(self.unique_no_split_tokens)
tokens = self.tokens_trie.split(text)
# ["This is something", "<special_token_1>", " else"]
for i, token in enumerate(tokens):
if token in no_split_token:
tok_extended = all_special_tokens_extended.get(token, None)
left = tokens[i - 1] if i > 0 else None
right = tokens[i + 1] if i < len(tokens) - 1 else None
if isinstance(tok_extended, AddedToken):
if tok_extended.rstrip and right:
# A bit counter-intuitive but we strip the left of the string
# since tok_extended.rstrip means the special token is eating all white spaces on its right
tokens[i + 1] = right.lstrip()
# Strip white spaces on the left
if tok_extended.lstrip and left:
tokens[i - 1] = left.rstrip() # Opposite here
else:
# We strip left and right by default
if right:
tokens[i + 1] = right.lstrip()
if left:
tokens[i - 1] = left.rstrip()
# ["This is something", "<special_token_1>", "else"]
tokenized_text = []
for token in tokens:
# Need to skip eventual empty (fully stripped) tokens
if not token:
continue
if token in no_split_token:
tokenized_text.append(token)
else:
tokenized_text.extend(self._tokenize(token))
# ["This", " is", " something", "<special_token_1>", "else"]
return tokenized_text
def _tokenize(self, text, **kw):
raise NotImplementedError
def convert_tokens_to_ids(self, tokens):
if tokens is None:
return None
if isinstance(tokens, str):
return self._convert_token_to_id_with_added_voc(tokens)
ids = []
for token in tokens:
ids.append(self._convert_token_to_id_with_added_voc(token))
return ids
def _convert_token_to_id_with_added_voc(self, token):
if token is None:
return None
if token in self.added_tokens_encoder:
return self.added_tokens_encoder[token]
return self._convert_token_to_id(token)
def _convert_token_to_id(self, token):
raise NotImplementedError
def _encode_plus(
self,
text,
text_pair=None,
add_special_tokens=True,
padding_strategy=PaddingStrategy.DO_NOT_PAD,
truncation_strategy=TruncationStrategy.DO_NOT_TRUNCATE,
max_len=None,
stride=0,
is_split_into_words=False,
pad_to_multiple_of=None,
return_tensors=None,
return_token_type_ids=None,
return_attention_mask=None,
return_overflowing_tokens=False,
return_special_tokens_mask=False,
return_offsets_mapping=False,
return_length=False,
verbose=True,
**kw,
):
def get_input_ids(text):
if isinstance(text, str):
tokens = self.tokenize(text, **kw)
return self.convert_tokens_to_ids(tokens)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str):
if is_split_into_words:
tokens = list(
itertools.chain(
*(self.tokenize(t, is_split_into_words=True, **kw) for t in text)
)
)
return self.convert_tokens_to_ids(tokens)
else:
return self.convert_tokens_to_ids(text)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
return text
else:
if is_split_into_words:
raise ValueError(
f"Input {text} is not valid. Should be a string or a list/tuple of strings when `is_split_into_words=True`."
)
else:
raise ValueError(
f"Input {text} is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
)
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers. "
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast. "
"More information on available tokenizers at "
"https://github.com/huggingface/transformers/pull/2674"
)
first_ids = get_input_ids(text)
second_ids = get_input_ids(text_pair) if text_pair is not None else None
return self.prepare_for_model(
first_ids,
pair_ids=second_ids,
add_special_tokens=add_special_tokens,
padding=padding_strategy.value,
truncation=truncation_strategy.value,
max_len=max_len,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
prepend_batch_axis=True,
return_attention_mask=return_attention_mask,
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
verbose=verbose,
)
def _batch_encode_plus(
self,
batch_text_or_text_pairs,
add_special_tokens=True,
padding_strategy=PaddingStrategy.DO_NOT_PAD,
truncation_strategy=TruncationStrategy.DO_NOT_TRUNCATE,
max_len=None,
stride=0,
is_split_into_words=False,
pad_to_multiple_of=None,
return_tensors=None,
return_token_type_ids=None,
return_attention_mask=None,
return_overflowing_tokens=False,
return_special_tokens_mask=False,
return_offsets_mapping=False,
return_length=False,
verbose=True,
**kw,
):
def get_input_ids(text):
if isinstance(text, str):
tokens = self.tokenize(text, **kw)
return self.convert_tokens_to_ids(tokens)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str):
if is_split_into_words:
tokens = list(
itertools.chain(
*(self.tokenize(t, is_split_into_words=True, **kw) for t in text)
)
)
return self.convert_tokens_to_ids(tokens)
else:
return self.convert_tokens_to_ids(text)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
return text
else:
raise ValueError(
"Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
)
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers. "
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast."
)
input_ids = []
for ids_or_pair_ids in batch_text_or_text_pairs:
if not isinstance(ids_or_pair_ids, (list, tuple)):
ids, pair_ids = ids_or_pair_ids, None
elif is_split_into_words and not isinstance(ids_or_pair_ids[0], (list, tuple)):
ids, pair_ids = ids_or_pair_ids, None
else:
ids, pair_ids = ids_or_pair_ids
first_ids = get_input_ids(ids)
second_ids = get_input_ids(pair_ids) if pair_ids is not None else None
input_ids.append((first_ids, second_ids))
batch_outputs = self._batch_prepare_for_model(
input_ids,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_len=max_len,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
return_tensors=return_tensors,
verbose=verbose,
)
return BatchEncoding(batch_outputs)
def _batch_prepare_for_model(
self,
batch_ids_pairs,
add_special_tokens=True,
padding_strategy=PaddingStrategy.DO_NOT_PAD,
truncation_strategy=TruncationStrategy.DO_NOT_TRUNCATE,
max_len=None,
stride=0,
pad_to_multiple_of=None,
return_tensors=None,
return_token_type_ids=None,
return_attention_mask=None,
return_overflowing_tokens=False,
return_special_tokens_mask=False,
return_length=False,
verbose=True,
):
batch_outputs = {}
for first_ids, second_ids in batch_ids_pairs:
outputs = self.prepare_for_model(
first_ids,
second_ids,
add_special_tokens=add_special_tokens,
padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward
truncation=truncation_strategy.value,
max_len=max_len,
stride=stride,
pad_to_multiple_of=None, # we pad in batch afterward
return_attention_mask=False, # we pad in batch afterward
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
return_tensors=None, # We convert the whole batch to tensors at the end
prepend_batch_axis=False,
verbose=verbose,
)
for key, value in outputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
batch_outputs[key].append(value)
batch_outputs = self.pad(
batch_outputs,
padding=padding_strategy.value,
max_len=max_len,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
return batch_outputs
def prepare_for_tokenization(self, text, is_split_into_words=False, **kw):
return (text, kw)
def get_special_tokens_mask(
self,
toks_0,
toks_1=None,
has_specials=False,
):
if has_specials:
if toks_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model."
)
return super().get_special_tokens_mask(toks_0=toks_0, toks_1=toks_1, has_specials=True)
return [0] * ((len(toks_1) if toks_1 else 0) + len(toks_0))
def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
...
def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
...
def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
if isinstance(ids, int):
if ids in self.added_tokens_decoder:
return self.added_tokens_decoder[ids]
else:
return self._convert_id_to_token(ids)
tokens = []
for index in ids:
index = int(index)
if skip_special_tokens and index in self.all_special_ids:
continue
if index in self.added_tokens_decoder:
tokens.append(self.added_tokens_decoder[index])
else:
tokens.append(self._convert_id_to_token(index))
return tokens
def _convert_id_to_token(self, index):
raise NotImplementedError
def convert_tokens_to_string(self, tokens):
return " ".join(tokens)
def _decode(
self,
token_ids,
skip_special_tokens=False,
clean_up_tokenization_spaces=True,
spaces_between_special_tokens=True,
**kw,
):
self._decode_use_source_tokenizer = kw.pop("use_source_tokenizer", False)
filtered_tokens = self.convert_ids_to_tokens(
token_ids, skip_special_tokens=skip_special_tokens
)
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
sub_texts = []
current_sub_text = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(current_sub_text))
current_sub_text = []
sub_texts.append(token)
else:
current_sub_text.append(token)
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(current_sub_text))
if spaces_between_special_tokens:
text = " ".join(sub_texts)
else:
text = "".join(sub_texts)
if clean_up_tokenization_spaces:
clean_text = self.clean_up_tokenization(text)
return clean_text
else:
return text
|
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|
33,442
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/test/unit/debugger/test_debugger.py
|
import random
import torch
import triton
import triton.language as tl
from triton.debugger.debugger import program_ids_from_grid
def test_addition():
@triton.jit(interpret=True)
def add_kernel(
x_ptr,
y_ptr,
output_ptr,
n_elements,
BLOCK_SIZE: tl.constexpr,
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = tl.load(x_ptr + offsets, mask=mask)
y = tl.load(y_ptr + offsets, mask=mask)
output = x + y
tl.store(output_ptr + offsets, output, mask=mask)
a = torch.rand((128,), device="cuda")
b = torch.rand((128,), device="cuda")
expected = a + b
output = torch.empty((128,), device="cuda")
def grid(meta):
return (triton.cdiv(128, meta["BLOCK_SIZE"]),)
add_kernel[grid](a, b, output, 128, BLOCK_SIZE=32)
assert torch.allclose(expected, output, atol=1e-2, rtol=0)
def test_program_ids_from_grid():
random.seed(123)
grid = (3, 4)
expected_combinations = 3 * 4
unique_combinations = set(program_ids_from_grid(grid))
assert len(unique_combinations) == expected_combinations
first_run = list(program_ids_from_grid(grid))
second_run = list(program_ids_from_grid(grid))
assert first_run != second_run
def test_atomic():
@triton.jit(interpret=True)
def atomic(
x_ptr,
):
pid = tl.program_id(axis=0)
tl.atomic_add(x_ptr + pid, 1)
t = tl.atomic_xchg(x_ptr + pid, 3)
t += 1 # 2
tl.atomic_cas(x_ptr + pid, 3, t) # match
tl.atomic_cas(x_ptr + pid, 40, 9) # no match
nb_dim = 16
a = torch.zeros((nb_dim, ), dtype=torch.int32, device="cuda")
atomic[(nb_dim, )](a)
assert torch.allclose(a, torch.full_like(a, 2))
|
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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,443
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/convert/xlm.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import json
import numpy
import torch
from argparse import ArgumentParser
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.models.xlm.tokenization_xlm import VOCAB_FS
from transformers.utils import logging
logging.set_verbosity_info()
def to_pytorch(src_path, save_path):
chkpt = torch.load(src_path, map_location="cpu")
state_dict = chkpt["model"]
two_levels_state_dict = {}
for k, v in state_dict.items():
if "pred_layer" in k:
two_levels_state_dict[k] = v
else:
two_levels_state_dict["transformer." + k] = v
cfg = chkpt["params"]
cfg = dict(
(n, v) for n, v in cfg.items() if not isinstance(v, (torch.FloatTensor, numpy.ndarray))
)
vocab = chkpt["dico_word2id"]
vocab = dict(
(s + "</w>" if s.find("@@") == -1 and i > 13 else s.replace("@@", ""), i)
for s, i in vocab.items()
)
w = save_path + "/" + WEIGHTS_NAME
c = save_path + "/" + CONFIG_NAME
v = save_path + "/" + VOCAB_FS["vocab_file"]
print(f"Saving to: {w}")
torch.save(two_levels_state_dict, w)
print(f"Saving config to: {c}")
with open(c, "w", encoding="utf-8") as f:
f.write(json.dumps(cfg, indent=2) + "\n")
print(f"Saving vocab to: {v}")
with open(v, "w", encoding="utf-8") as f:
f.write(json.dumps(vocab, indent=2) + "\n")
if __name__ == "__main__":
x = ArgumentParser()
x.add_argument("--src_path", default=None, type=str, required=True)
x.add_argument("--save_path", default=None, type=str, required=True)
y = x.parse_args()
to_pytorch(y.src_path, y.save_path)
|
{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/base/doc/patcher.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/nominals.py"], "/qnarre/prep/convert/funnel.py": ["/qnarre/prep/config/funnel.py", "/qnarre/models/funnel.py"], "/qnarre/prep/tokens/fast/realm.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py", "/qnarre/tokens/utils.py"], "/qnarre/models/decision_transfo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/decision_transfo.py"], "/qnarre/models/fsmt.py": ["/qnarre/core/embed.py"], "/qnarre/models/roformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/xlnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc.py": ["/qnarre/base/author.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/util/table.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/tree.py", "/qnarre/base/doc/util/utils.py"], "/tools/triton/python/triton/language/standard.py": ["/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/language/__init__.py"], "/qnarre/base/doc/filters.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/args.py"], "/qnarre/prep/convert/gpt_neo.py": ["/qnarre/prep/config/gpt_neo.py", "/qnarre/models/gpt_neo.py"], "/qnarre/base/doc/section.py": ["/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/message.py"], "/qnarre/models/funnel.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/funnel.py"], "/tools/triton/python/triton/common/__init__.py": ["/tools/triton/python/triton/common/build.py"], "/qnarre/base/doc/qnn.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,444
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/language/__init__.py
|
"""isort:skip_file"""
# Import order is significant here.
from . import math
from . import extra
from .standard import (
cdiv,
sigmoid,
softmax,
ravel,
swizzle2d,
zeros,
zeros_like,
)
from .core import (
abs,
advance,
arange,
argmin,
argmax,
atomic_add,
atomic_and,
atomic_cas,
atomic_max,
atomic_min,
atomic_or,
atomic_xchg,
atomic_xor,
bfloat16,
block_type,
broadcast,
broadcast_to,
cat,
constexpr,
cos,
debug_barrier,
device_assert,
device_print,
dot,
dtype,
exp,
expand_dims,
full,
fdiv,
float16,
float32,
float64,
float8e4,
float8e5,
function_type,
int1,
int16,
int32,
int64,
int8,
load,
log,
make_block_ptr,
max,
max_contiguous,
maximum,
min,
minimum,
multiple_of,
num_programs,
pi32_t,
pointer_type,
program_id,
reduce,
reshape,
sin,
sqrt,
static_assert,
static_print,
store,
sum,
static_range,
tensor,
trans,
triton,
uint16,
uint32,
uint64,
uint8,
umulhi,
view,
void,
where,
xor_sum,
)
from .random import (
pair_uniform_to_normal,
philox,
philox_impl,
rand,
rand4x,
randint,
randint4x,
randn,
randn4x,
uint32_to_uniform_float,
)
__all__ = [
"abs",
"advance",
"arange",
"argmin",
"argmax",
"atomic_add",
"atomic_and",
"atomic_cas",
"atomic_max",
"atomic_min",
"atomic_or",
"atomic_xchg",
"atomic_xor",
"bfloat16",
"block_type",
"broadcast",
"broadcast_to",
"builtin",
"cat",
"cdiv",
"constexpr",
"cos",
"debug_barrier",
"device_assert",
"device_print",
"dot",
"dtype",
"exp",
"expand_dims",
"extra",
"fdiv",
"float16",
"float32",
"float64",
"float8e4",
"float8e5",
"full",
"function_type",
"int1",
"int16",
"int32",
"int64",
"int8",
"ir",
"math",
"load",
"log",
"make_block_ptr",
"max",
"max_contiguous",
"maximum",
"min",
"minimum",
"multiple_of",
"num_programs",
"pair_uniform_to_normal",
"philox",
"philox_impl",
"pi32_t",
"pointer_type",
"program_id",
"rand",
"rand4x",
"randint",
"randint4x",
"randn",
"randn4x",
"ravel",
"reduce",
"reshape",
"sigmoid",
"sin",
"softmax",
"sqrt",
"static_range",
"static_assert",
"static_print",
"store",
"sum",
"swizzle2d",
"tensor",
"trans",
"triton",
"uint16",
"uint32",
"uint32_to_uniform_float",
"uint64",
"uint8",
"umulhi",
"view",
"void",
"where",
"xor_sum",
"zeros",
"zeros_like",
]
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", 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"/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,445
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/contain.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import networkx as nx
from .graph import Graphs
from .nominals import nominal
from .base import Record, LnkFull, LnkPartial
def is_partial(src, dst, sample=20, chunk=120):
n = len(src)
if n > chunk and len(dst) > chunk:
if src[:sample] in dst or src[-(sample + 1):-1] in dst:
return True
ms, mc = sample // 2, chunk // 2
for i in range(n // chunk):
i *= chunk
s = src[i:i + chunk]
if len(s) == chunk:
if s[mc - ms:mc + ms] in dst:
return True
class Contains(Graphs):
_graphs = tuple(a.label for a in (Record, LnkFull, LnkPartial))
def msg_attrs(self, txt, kind, **kw):
n = nominal(txt)
kw.update(empty=len(n) < 5, nominal=n, kind=kind)
return kw
def grow_full(self, cntr, **_):
mg, fg = self.record, self.full
ns = ((m, mg.node[m]['nominal']) for m in mg.nodes())
ns = sorted(ns, key=lambda t: len(t[1]))
for i, (m, n) in enumerate(ns):
ns2 = ns[i + 1:]
while ns2:
m2, n2 = ns2.pop(0)
if n in n2:
fg.add_edge(m, m2)
if len(n) == len(n2):
fg.add_edge(m2, m)
cntr.incr('=')
else:
cntr.incr('<')
ss = nx.dfs_successors(fg, m2).values()
ss = {s for sl in ss for s in sl}
ns2 = [(m, n) for m, n in ns2 if m not in ss]
def grow_partial(self, cntr, **_):
mg, fg, pg = self.record, self.full, self.partial
ns = [(m, mg.node[m]['nominal']) for m in mg.nodes()]
for i, (m, n) in enumerate(ns):
ns2 = ns[:]
del ns2[i]
while ns2:
m2, n2 = ns2.pop(0)
if is_partial(n, n2):
pg.add_edge(m, m2)
cntr.incr('~')
ss = nx.dfs_successors(fg, m2).values()
ss = {s for sl in ss for s in sl}
ns2 = [(m, n) for m, n in ns2 if m not in ss]
def grow_from(self, src, **kw):
super().grow_from(src, **kw)
self.purge_empty(**kw)
self.grow_full(**kw)
# self.grow_partial(**kw)
Contains.init_class()
|
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"/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], 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|
33,446
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/fast/t5.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import os
from shutil import copyfile
from ....tokens.fast import PreTrainedTokenizerFast
from ..t5 import Tokenizer as T5
VOCAB_FS = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
VOCAB_MAP = {
"vocab_file": {
"t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
"t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
"t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
},
"tokenizer_file": {
"t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
},
}
INPUT_CAPS = {
"t5-small": 512,
"t5-base": 512,
"t5-large": 512,
"t5-3b": 512,
"t5-11b": 512,
}
class Tokenizer(PreTrainedTokenizerFast):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
input_caps = INPUT_CAPS
model_input_names = ["input_ids", "mask"]
slow_tokenizer_class = T5
prefix_tokens = []
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
eos="</s>",
unk="<unk>",
pad="<pad>",
extra_ids=100,
additional_special_tokens=None,
**kw,
):
if extra_ids > 0 and additional_special_tokens is None:
additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
elif extra_ids > 0 and additional_special_tokens is not None:
extra_tokens = len(
set(filter(lambda x: ("extra_id_" in str(x)), additional_special_tokens))
)
assert extra_tokens == extra_ids
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
eos=eos,
unk=unk,
pad=pad,
extra_ids=extra_ids,
additional_special_tokens=additional_special_tokens,
**kw,
)
self.vocab_file = vocab_file
self.can_save_slow_tokenizer = False if not self.vocab_file else True
self._extra_ids = extra_ids
def save_vocabulary(self, dir, pre=None):
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer."
)
path = os.path.join(dir, (pre + "-" if pre else "") + VOCAB_FS["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(path):
copyfile(self.vocab_file, path)
return (path,)
def build_inputs_with_special_tokens(self, toks_0, toks_1=None):
toks_0 = toks_0 + [self.EOS]
if toks_1 is None:
return self.prefix_tokens + toks_0
else:
toks_1 = toks_1 + [self.EOS]
return self.prefix_tokens + toks_0 + toks_1
def create_token_type_ids_from_sequences(self, toks_0, toks_1=None):
eos = [self.EOS]
if toks_1 is None:
return len(toks_0 + eos) * [0]
return len(toks_0 + eos + toks_1 + eos) * [0]
|
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"/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,447
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/language/core.py
|
from __future__ import annotations
from contextlib import contextmanager
from enum import Enum
from functools import wraps
from typing import Callable, List, Sequence, TypeVar
import triton
from . import semantic
from triton._C.libtriton.triton import ir
T = TypeVar('T')
TRITON_MAX_TENSOR_NUMEL = 131072
TRITON_BUILTIN = "__triton_builtin__"
def builtin(fn: T) -> T:
"""Mark a function as a builtin."""
assert callable(fn)
@wraps(fn)
def wrapper(*args, **kwargs):
if "_builder" not in kwargs or kwargs["_builder"] is None:
raise ValueError(
"Did you forget to add @triton.jit ? "
"(`_builder` argument must be provided outside of JIT functions.)"
)
return fn(*args, **kwargs)
setattr(wrapper, TRITON_BUILTIN, True)
return wrapper
def is_builtin(fn) -> bool:
"""Is this a registered triton builtin function?"""
return getattr(fn, TRITON_BUILTIN, False)
def _to_tensor(x, builder):
if isinstance(x, bool):
return tensor(builder.get_int1(x), int1)
# Note: compile-time const integers are represented by unsigned values
elif isinstance(x, int):
if -2**31 <= x < 2**31:
return tensor(builder.get_int32(x), int32)
elif 2**31 <= x < 2**32:
return tensor(builder.get_int32(x), uint32)
elif -2**63 <= x < 2**63:
return tensor(builder.get_int64(x), int64)
elif 2**63 <= x < 2**64:
return tensor(builder.get_int64(x), uint64)
else:
raise RuntimeError(f'Nonrepresentable integer {x}.')
elif isinstance(x, float):
min_float32 = 2 ** -126
max_float32 = (2 - 2**-23) * 2**127
abs_x = __builtins__['abs'](x)
if abs_x == float("inf") or\
abs_x == 0.0 or \
x != x or \
min_float32 <= abs_x <= max_float32:
return tensor(builder.get_fp32(x), float32)
else:
return tensor(builder.get_fp64(x), float64)
elif isinstance(x, constexpr):
return _to_tensor(x.value, builder)
elif isinstance(x, tensor):
return x
assert False, f"cannot convert {x} of type {type(x)} to tensor"
class dtype:
SINT_TYPES = ['int8', 'int16', 'int32', 'int64']
UINT_TYPES = ['int1', 'uint8', 'uint16', 'uint32', 'uint64']
FP_TYPES = ['fp8e4', 'fp8e5', 'fp16', 'bf16', 'fp32', 'fp64']
STANDARD_FP_TYPES = ['fp16', 'bf16', 'fp32', 'fp64']
OTHER_TYPES = ['void']
class SIGNEDNESS(Enum):
SIGNED = 0
UNSIGNED = 1
def __init__(self, name):
self.name = name
assert name in dtype.SINT_TYPES + dtype.UINT_TYPES + dtype.FP_TYPES + dtype.OTHER_TYPES, name
if name in dtype.SINT_TYPES:
self.int_signedness = dtype.SIGNEDNESS.SIGNED
self.int_bitwidth = int(name.split('int')[-1])
self.primitive_bitwidth = self.int_bitwidth
elif name in dtype.UINT_TYPES:
self.int_signedness = dtype.SIGNEDNESS.UNSIGNED
self.int_bitwidth = int(name.split('int')[-1])
self.primitive_bitwidth = self.int_bitwidth
elif name in dtype.FP_TYPES:
if name == 'fp8e4':
self.fp_mantissa_width = 3
self.primitive_bitwidth = 8
elif name == 'fp8e5':
self.fp_mantissa_width = 2
self.primitive_bitwidth = 8
elif name == 'fp16':
self.fp_mantissa_width = 10
self.primitive_bitwidth = 16
elif name == 'bf16':
self.fp_mantissa_width = 7
self.primitive_bitwidth = 16
elif name == 'fp32':
self.fp_mantissa_width = 23
self.primitive_bitwidth = 32
elif name == 'fp64':
self.fp_mantissa_width = 53
self.primitive_bitwidth = 64
else:
raise RuntimeError(f'Unsupported floating-point type {name}')
elif name == 'void':
self.primitive_bitwidth = 0
def is_fp8(self):
return 'fp8' in self.name
def is_fp16(self):
return self.name == 'fp16'
def is_bf16(self):
return self.name == 'bf16'
def is_fp32(self):
return self.name == 'fp32'
def is_fp64(self):
return self.name == 'fp64'
def is_int1(self):
return self.name == 'int1'
def is_int8(self):
return self.name == 'int8'
def is_int16(self):
return self.name == 'int16'
def is_int32(self):
return self.name == 'int32'
def is_int64(self):
return self.name == 'int64'
def is_uint8(self):
return self.name == 'uint8'
def is_uint16(self):
return self.name == 'uint16'
def is_uint32(self):
return self.name == 'uint32'
def is_uint64(self):
return self.name == 'uint64'
def is_floating(self):
return self.name in dtype.FP_TYPES
def is_standard_floating(self):
return self.name in dtype.STANDARD_FP_TYPES
def is_int_signed(self):
return self.name in dtype.SINT_TYPES
def is_int_unsigned(self):
return self.name in dtype.UINT_TYPES
def is_int(self):
return self.name in dtype.SINT_TYPES + dtype.UINT_TYPES
def is_bool(self):
return self.is_int1()
@staticmethod
def is_void():
raise RuntimeError("Not implemented")
@staticmethod
def is_block():
return False
@staticmethod
def is_ptr():
return False
def __eq__(self, other: dtype):
if not isinstance(other, dtype):
return False
return self.name == other.name
def __ne__(self, other: dtype):
return not self.__eq__(other)
def __hash__(self):
return hash((self.name,))
@property
def scalar(self):
return self
def to_ir(self, builder: ir.builder) -> ir.type:
if self.name == 'void':
return builder.get_void_ty()
elif self.name == 'int1':
return builder.get_int1_ty()
elif self.name in ('int8', 'uint8'):
return builder.get_int8_ty()
elif self.name in ('int16', 'uint16'):
return builder.get_int16_ty()
elif self.name in ('int32', 'uint32'):
return builder.get_int32_ty()
elif self.name in ('int64', 'uint64'):
return builder.get_int64_ty()
elif self.name == 'fp8e5':
return builder.get_fp8e5_ty()
elif self.name == 'fp8e4':
return builder.get_fp8e4_ty()
elif self.name == 'fp16':
return builder.get_half_ty()
elif self.name == 'bf16':
return builder.get_bf16_ty()
elif self.name == 'fp32':
return builder.get_float_ty()
elif self.name == 'fp64':
return builder.get_double_ty()
raise ValueError(f'fail to convert {self} to ir type')
def __str__(self):
return self.name
@property
def cache_key_part(self) -> str:
"""See cache_key_part() in triton.cc."""
return self.name
def __repr__(self):
return f'triton.language.{self.name}'
class pointer_type(dtype):
def __init__(self, element_ty: dtype, address_space: int = 1):
if not isinstance(element_ty, dtype):
raise TypeError('element_ty is a {type(element_ty).__name__}.')
self.element_ty = element_ty
self.address_space = address_space
self.name = self.__str__()
def to_ir(self, builder: ir.builder) -> ir.pointer_type:
return builder.get_ptr_ty(self.element_ty.to_ir(builder), 1)
def __str__(self):
return f'pointer<{self.element_ty}>'
def __repr__(self):
return self.__str__()
def is_ptr(self):
return True
def __eq__(self, other: pointer_type) -> bool:
if not isinstance(other, pointer_type):
return False
return self.element_ty == other.element_ty and self.address_space == other.address_space
def __ne__(self, other: pointer_type) -> bool:
return not self.__eq__(other)
@property
def scalar(self):
return self
class block_type(dtype):
def __init__(self, element_ty: dtype, shape: List):
self.element_ty = element_ty
# Note that block_type's shape is a list of int
# while tensor's shape is a list of constexpr.
# shape can be empty ([]) when an input is a 0D tensor.
if not shape:
raise TypeError('0d block_type is forbidden')
if isinstance(shape[0], constexpr):
shape = [s.value for s in shape]
self.shape = shape
self.numel = 1
for s in self.shape:
self.numel *= s
if self.numel > TRITON_MAX_TENSOR_NUMEL:
raise ValueError(f"numel ({self.numel}) exceeds triton maximum tensor numel ({TRITON_MAX_TENSOR_NUMEL})")
self.name = self.__str__()
def to_ir(self, builder: ir.builder) -> ir.block_type:
return builder.get_block_ty(self.element_ty.to_ir(builder), self.shape)
def __str__(self):
return f'<{self.shape}, {self.element_ty}>'
def __repr__(self):
return self.__str__()
def is_block(self):
return True
def get_block_shapes(self) -> List[int]:
return self.shape
def __eq__(self, other: block_type) -> bool:
if not isinstance(other, block_type):
return False
return self.element_ty == other.element_ty and self.shape == other.shape
def __ne__(self, other: block_type) -> bool:
return not self.__eq__(other)
@property
def scalar(self):
return self.element_ty
class function_type(dtype):
def __init__(self, ret_types: List[dtype], param_types: List[dtype]) -> None:
self.ret_types = ret_types
self.param_types = param_types
def __str__(self):
return f'fn ({self.param_types}) -> {self.ret_types}'
def to_ir(self, builder: ir.builder):
ir_param_types = [ty.to_ir(builder) for ty in self.param_types]
ret_types = [ret_type.to_ir(builder) for ret_type in self.ret_types]
return builder.get_function_ty(ir_param_types, ret_types)
# scalar types
void = dtype('void')
int1 = dtype('int1')
int8 = dtype('int8')
int16 = dtype('int16')
int32 = dtype('int32')
int64 = dtype('int64')
uint8 = dtype('uint8')
uint16 = dtype('uint16')
uint32 = dtype('uint32')
uint64 = dtype('uint64')
float8e5 = dtype('fp8e5')
float8e4 = dtype('fp8e4')
float16 = dtype('fp16')
bfloat16 = dtype('bf16')
float32 = dtype('fp32')
float64 = dtype('fp64')
# pointer types
pi32_t = pointer_type(int32)
# -----------------------
# constexpr
# -----------------------
class constexpr:
"""
This class is used to store a value that is known at compile-time.
"""
def __init__(self, value):
if isinstance(value, constexpr):
self.value = value.value
else:
self.value = value
def __repr__(self) -> str:
return f"constexpr[{self.value}]"
def __add__(self, other):
return constexpr(self.value + other.value)
def __radd__(self, other):
return constexpr(other.value + self.value)
def __sub__(self, other):
return constexpr(self.value - other.value)
def __rsub__(self, other):
return constexpr(other.value - self.value)
def __mul__(self, other):
return constexpr(self.value * other.value)
def __mod__(self, other):
return constexpr(self.value % other.value)
def __rmul__(self, other):
return constexpr(other.value * self.value)
def __truediv__(self, other):
return constexpr(self.value / other.value)
def __rtruediv__(self, other):
return constexpr(other.value / self.value)
def __floordiv__(self, other):
return constexpr(self.value // other.value)
def __rfloordiv__(self, other):
return constexpr(other.value // self.value)
def __gt__(self, other):
return constexpr(self.value > other.value)
def __rgt__(self, other):
return constexpr(other.value > self.value)
def __ge__(self, other):
return constexpr(self.value >= other.value)
def __rge__(self, other):
return constexpr(other.value >= self.value)
def __lt__(self, other):
return constexpr(self.value < other.value)
def __rlt__(self, other):
return constexpr(other.value < self.value)
def __le__(self, other):
return constexpr(self.value <= other.value)
def __rle__(self, other):
return constexpr(other.value <= self.value)
def __eq__(self, other):
return constexpr(self.value == other.value)
def __ne__(self, other):
return constexpr(self.value != other.value)
def __bool__(self):
return bool(self.value)
def __neg__(self):
return constexpr(-self.value)
def __and__(self, other):
return constexpr(self.value & other.value)
def logical_and(self, other):
return constexpr(self.value and other.value)
def __or__(self, other):
return constexpr(self.value | other.value)
def __xor__(self, other):
return constexpr(self.value ^ other.value)
def logical_or(self, other):
return constexpr(self.value or other.value)
def __pos__(self):
return constexpr(+self.value)
def __invert__(self):
return constexpr(~self.value)
def __pow__(self, other):
return constexpr(self.value ** other.value)
def __rshift__(self, other):
return constexpr(self.value >> other.value)
def __lshift__(self, other):
return constexpr(self.value << other.value)
def __not__(self):
return constexpr(not self.value)
def __call__(self, *args, **kwds):
return self.value(*args, **kwds)
class tensor:
def __init__(self, handle, type: dtype):
# IR handle
self.handle = handle
# Block shape
self.shape = (1, )
if type.is_block():
self.shape = type.shape
self.numel = 1
for s in self.shape:
self.numel *= s
self.numel = constexpr(self.numel)
self.type = type # Tensor type (can be block_type)
# Following the practice in pytorch, dtype is scalar type
self.dtype = type.scalar
self.shape = [constexpr(s) for s in self.shape]
def __str__(self) -> str:
# ex. "float32[3,4]"
return str(self.dtype) + '[' + ','.join(str(s) for s in self.shape) + ']'
@builtin
def __add__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.add(self, other, _builder)
def __radd__(self, other, _builder=None):
return self.__add__(other, _builder=_builder)
@builtin
def __sub__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.sub(self, other, _builder)
def __rsub__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.sub(other, self, _builder)
@builtin
def __mul__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.mul(self, other, _builder)
def __rmul__(self, other, _builder=None):
return self.__mul__(other, _builder=_builder)
@builtin
def __truediv__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.truediv(self, other, _builder)
def __rtruediv__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.truediv(other, self, _builder)
@builtin
def __floordiv__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.floordiv(self, other, _builder)
@builtin
def __rfloordiv__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.floordiv(other, self, _builder)
@builtin
def __mod__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.mod(self, other, _builder)
@builtin
def __rmod__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.mod(other, self, _builder)
# unary operators
@builtin
def __neg__(self, _builder=None):
return semantic.minus(self, _builder)
@builtin
def __invert__(self, _builder=None):
return semantic.invert(self, _builder)
# bitwise operators
@builtin
def __and__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.and_(self, other, _builder)
@builtin
def __rand__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.and_(other, self, _builder)
@builtin
def __or__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.or_(self, other, _builder)
@builtin
def __ror__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.or_(other, self, _builder)
@builtin
def __xor__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.xor_(self, other, _builder)
@builtin
def __rxor__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.xor_(other, self, _builder)
@builtin
def __lshift__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.shl(self, other, _builder)
@builtin
def __rlshift__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.shl(other, self, _builder)
@builtin
def __rshift__(self, other, _builder=None):
other = _to_tensor(other, _builder)
if self.dtype.is_int_signed():
return semantic.ashr(self, other, _builder)
else:
return semantic.lshr(self, other, _builder)
@builtin
def __rrshift__(self, other, _builder=None):
other = _to_tensor(other, _builder)
if self.dtype.is_int_signed():
return semantic.ashr(other, self, _builder)
else:
return semantic.lshr(other, self, _builder)
# comparison operators
# >
@builtin
def __gt__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.greater_than(self, other, _builder)
@builtin
def __rgt__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.greater_than(other, self, _builder)
# >=
@builtin
def __ge__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.greater_equal(self, other, _builder)
@builtin
def __rge__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.greater_equal(other, self, _builder)
# <
@builtin
def __lt__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.less_than(self, other, _builder)
@builtin
def __rlt__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.less_than(other, self, _builder)
# <=
@builtin
def __le__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.less_equal(self, other, _builder)
@builtin
def __rle__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.less_equal(other, self, _builder)
# ==
@builtin
def __eq__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.equal(self, other, _builder)
@builtin
def __ne__(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.not_equal(self, other, _builder)
@builtin
def logical_and(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.logical_and(self, other, _builder)
@builtin
def logical_or(self, other, _builder=None):
other = _to_tensor(other, _builder)
return semantic.logical_or(self, other, _builder)
# note: __not__ isn't actually a magic method in python
# but it's ok because our ASTVisitor handles it
@builtin
def __not__(self, _builder=None):
return semantic.not_(self, _builder)
@builtin
def __getitem__(self, slices, _builder=None):
if isinstance(slices, slice):
slices = [slices]
ret = self
for dim, sl in enumerate(slices):
if isinstance(sl, constexpr) and sl.value is None:
ret = semantic.expand_dims(ret, dim, _builder)
elif sl == slice(None, None, None):
pass
else:
assert False, f"unsupported tensor index: {sl}"
return ret
@property
def T(self):
assert False, "Transposition must be created by the AST Visitor"
@builtin
def to(self, dtype, bitcast=False, _builder=None):
if isinstance(bitcast, constexpr):
bitcast = bitcast.value
if bitcast:
return semantic.bitcast(self, dtype, _builder)
return semantic.cast(self, dtype, _builder)
# -----------------------
# SPMD Programming Model
# -----------------------
def _constexpr_to_value(v):
if isinstance(v, constexpr):
return v.value
return v
@builtin
def program_id(axis, _builder=None):
"""
Returns the id of the current program instance along the given :code:`axis`.
:param axis: The axis of the 3D launch grid. Has to be either 0, 1 or 2.
:type axis: int
"""
# if axis == -1:
# pid0 = program_id(0, _builder)
# pid1 = program_id(1, _builder)
# pid2 = program_id(2, _builder)
# npg0 = num_programs(0, _builder)
# npg1 = num_programs(0, _builder)
# return pid0 + pid1*npg0 + pid2*npg0*npg1
axis = _constexpr_to_value(axis)
return semantic.program_id(axis, _builder)
@builtin
def num_programs(axis, _builder=None):
"""
Returns the number of program instances launched along the given :code:`axis`.
:param axis: The axis of the 3D launch grid. Has to be either 0, 1 or 2.
:type axis: int
"""
axis = _constexpr_to_value(axis)
return semantic.num_programs(axis, _builder)
# -----------------------
# Block Initialization
# -----------------------
@builtin
def arange(start, end, _builder=None):
"""
Returns contiguous values within the left-closed and right-open interval [:code:`start`, :code:`end`). \
End - Start must be less than or equal to TRITON_MAX_TENSOR_NUMEL = 131072
:param start: Start of the interval. Must be a power of two.
:type start: int32
:param end: End of the interval. Must be a power of two > start.
:type end: int32
"""
start = _constexpr_to_value(start)
end = _constexpr_to_value(end)
return semantic.arange(start, end, _builder)
def _shape_check_impl(shape):
shape = _constexpr_to_value(shape)
for i, d in enumerate(shape):
if not isinstance(d, constexpr):
raise TypeError(f"Shape element {i} must have type `constexpr`")
if not isinstance(d.value, int):
raise TypeError(f"Shape element {i} must have type `constexpr[int]`, got `constexpr[{type(d.value)}]")
return [_constexpr_to_value(x) for x in shape]
@builtin
def full(shape, value, dtype, _builder=None):
"""
Returns a tensor filled with the scalar value for the given :code:`shape` and :code:`dtype`.
:param shape: Shape of the new array, e.g., (8, 16) or (8, )
:value value: A scalar value to fill the array with
:type shape: tuple of ints
:param dtype: Data-type of the new array, e.g., :code:`tl.float16`
:type dtype: DType
"""
shape = _shape_check_impl(shape)
value = _constexpr_to_value(value)
dtype = _constexpr_to_value(dtype)
return semantic.full(shape, value, dtype, _builder)
# -----------------------
# Shape Manipulation
# -----------------------
@builtin
def broadcast(input, other, _builder=None):
"""
Tries to broadcast the two given blocks to a common compatible shape.
:param input: The first input tensor.
:type input: Block
:param other: The second input tensor.
:type other: Block
"""
return semantic.broadcast_impl_value(input, other, _builder)
@builtin
def broadcast_to(input, shape, _builder=None):
"""
Tries to broadcast the given tensor to a new :code:`shape`.
:param input: The input tensor.
:type input: Block
:param shape: The desired shape.
:type shape: Tuple[int]
"""
shape = _shape_check_impl(shape)
return semantic.broadcast_impl_shape(input, shape, _builder)
@builtin
def trans(input, _builder=None):
return semantic.trans(input, _builder)
@builtin
def cat(input, other, can_reorder=False, _builder=None):
"""
Concatenate the given blocks
:param input: The first input tensor.
:type input:
:param other: The second input tensor.
:type other:
:param reorder: Compiler hint. If true, the compiler is
allowed to reorder elements while concatenating inputs.
Only use if the order does not matter (e.g., result is
only used in reduction ops)
"""
return semantic.cat(input, other, can_reorder, _builder)
@builtin
def view(input, shape, _builder=None):
"""
Returns a tensor with the same elements as `input` but a different shape.
The order of the elements may not be preserved.
:param input: The input tensor.
:type input:
:param shape: The desired shape.
:type shape: Tuple[int]
"""
shape = _shape_check_impl(shape)
return semantic.view(input, shape, _builder)
@builtin
def reshape(input, shape, _builder=None):
shape = _shape_check_impl(shape)
return semantic.reshape(input, shape, _builder)
def _wrap_axis(axis, ndim):
if not (-ndim <= axis < ndim):
raise ValueError(f"invalid axis {axis}. Expected {-ndim} <= axis < {ndim}")
return axis if axis >= 0 else axis + ndim
@builtin
def expand_dims(input, axis, _builder=None):
"""
Expand the shape of a tensor, by inserting new length-1 dimensions.
Axis indices are with respect to the resulting tensor, so
``result.shape[axis]`` will be 1 for each axis.
:param input: The input tensor.
:type input: tl.tensor
:param axis: The indices to add new axes
:type axis: int | Sequence[int]
"""
axis = _constexpr_to_value(axis)
axes = list(axis) if isinstance(axis, Sequence) else [axis]
new_ndim = len(input.shape) + len(axes)
axes = [_wrap_axis(_constexpr_to_value(d), new_ndim) for d in axes]
if len(set(axes)) != len(axes):
raise ValueError(f"expand_dims recieved duplicate axes, normalized axes = {axes}")
ret = input
for a in sorted(axes):
ret = semantic.expand_dims(ret, a, _builder)
return ret
# -----------------------
# Linear Algebra
# -----------------------
@builtin
def dot(input, other, allow_tf32=True, out_dtype=float32, _builder=None):
"""
Returns the matrix product of two blocks.
The two blocks must be two-dimensional and have compatible inner dimensions.
:param input: The first tensor to be multiplied.
:type input: 2D tensor of scalar-type in {:code:`float16`, :code:`bfloat16`, :code:`float32`}
:param other: The second tensor to be multiplied.
:type other: 2D tensor of scalar-type in {:code:`float16`, :code:`bfloat16`, :code:`float32`}
"""
allow_tf32 = _constexpr_to_value(allow_tf32)
out_dtype = _constexpr_to_value(out_dtype)
return semantic.dot(input, other, allow_tf32, out_dtype, _builder)
# -----------------------
# Non-Atomic Memory Operations
# -----------------------
@builtin
def load(pointer, mask=None, other=None, boundary_check=tuple(), padding_option="", cache_modifier="",
eviction_policy="", volatile=False, _builder=None):
"""
Return a tensor of data whose values are loaded from memory at location defined by `pointer`:
(1) `pointer` could be a single element pointer, then a scalar will be loaded
- `mask` and `other` must be scalar too
- `other` is implicitly typecast to `pointer.dtype.element_ty`
- `boundary_check` and `padding_option` must be empty
(2) `pointer` could be element-wise tensor of pointers, in which case:
- `mask` and `other` are implicitly broadcast to `pointer.shape`
- `other` is implicitly typecast to `pointer.dtype.element_ty`
- `boundary_check` and `padding_option` must be empty
(3) `pointer` could be a block pointer defined by `make_block_ptr`, in which case:
- `mask` and `other` must be None
- `boundary_check` and `padding_option` can be specified to control the behavior of out-of-bound access
:param pointer: Pointer to the data to be loaded
:type pointer: `triton.PointerType`, or block of `dtype=triton.PointerType`
:param mask: if `mask[idx]` is false, do not load the data at address `pointer[idx]`
(must be `None` with block pointers)
:type mask: Block of `triton.int1`, optional
:param other: if `mask[idx]` is false, return `other[idx]`
:type other: Block, optional
:param boundary_check: tuple of integers, indicating the dimensions which should do the boundary check
:type boundary_check: tuple of ints, optional
:param padding_option: should be one of {"", "zero", "nan"}, do padding while out of bound
:param cache_modifier: changes cache option in NVIDIA PTX
:type cache_modifier: str, optional
:param eviction_policy: changes eviction policy in NVIDIA PTX
:type eviction_policy: str, optional
:param volatile: changes volatile option in NVIDIA PTX
:type volatile: bool, optional
"""
# `mask` and `other` can be constexpr
if _constexpr_to_value(mask) is not None:
mask = _to_tensor(mask, _builder)
if _constexpr_to_value(other) is not None:
other = _to_tensor(other, _builder)
padding_option = _constexpr_to_value(padding_option)
cache_modifier = _constexpr_to_value(cache_modifier)
eviction_policy = _constexpr_to_value(eviction_policy)
volatile = _constexpr_to_value(volatile)
return semantic.load(pointer, mask, other, boundary_check, padding_option, cache_modifier, eviction_policy,
volatile, _builder)
@builtin
def store(pointer, value, mask=None, boundary_check=(), cache_modifier="", eviction_policy="", _builder=None):
"""
Store a tensor of data into memory locations defined by `pointer`:
(1) `pointer` could be a single element pointer, then a scalar will be stored
- `mask` must be scalar too
- `boundary_check` and `padding_option` must be empty
(2) `pointer` could be element-wise tensor of pointers, in which case:
- `mask` is implicitly broadcast to `pointer.shape`
- `boundary_check` must be empty
(3) or `pointer` could be a block pointer defined by `make_block_ptr`, in which case:
- `mask` must be None
- `boundary_check` can be specified to control the behavior of out-of-bound access
`value` is implicitly broadcast to `pointer.shape` and typecast to `pointer.dtype.element_ty`.
:param pointer: The memory location where the elements of `value` are stored
:type pointer: `triton.PointerType`, or block of `dtype=triton.PointerType`
:param value: The tensor of elements to be stored
:type value: Block
:param mask: If `mask[idx]` is false, do not store `value[idx]` at `pointer[idx]`
:type mask: Block of triton.int1, optional
:param boundary_check: tuple of integers, indicating the dimensions which should do the boundary check
:type boundary_check: tuple of ints, optional
:param cache_modifier: changes cache option in NVIDIA PTX
:type cache_modifier: str, optional
:param eviction_policy: changes eviction policy in NVIDIA PTX
:type eviction_policy: str, optional
"""
# `value` can be constexpr
value = _to_tensor(value, _builder)
if _constexpr_to_value(mask) is not None:
mask = _to_tensor(mask, _builder)
cache_modifier = _constexpr_to_value(cache_modifier)
eviction_policy = _constexpr_to_value(eviction_policy)
return semantic.store(pointer, value, mask, boundary_check, cache_modifier, eviction_policy, _builder)
@builtin
def make_block_ptr(base: tensor, shape, strides, offsets, block_shape, order, _builder=None):
"""
Returns a pointer to a block in a parent tensor
:param base: The base pointer to the parent tensor
:param shape: The shape of the parent tensor
:param strides: The strides of the parent tensor
:param offsets: The offsets to the block
:param block_shape: The shape of the block
:param order: The order of the original data format
"""
return semantic.make_block_ptr(base, shape, strides, offsets, block_shape, order, _builder)
@builtin
def advance(base: tensor, offsets, _builder=None):
"""
Advance a block pointer
:param base: the block pointer to advance
:param offsets: the offsets to advance, a tuple by dimension
"""
return semantic.advance(base, offsets, _builder)
# -----------------------
# Atomic Memory Operations
# -----------------------
def _add_atomic_docstr(name: str) -> Callable[[T], T]:
def _decorator(func: T) -> T:
docstr = """
Performs an atomic {name} at the memory location specified by :code:`pointer`.
Return the data stored at :code:`pointer` before the atomic operation.
:param pointer: The memory locations to compare-and-swap.
:type pointer: Block of dtype=triton.PointerDType
:param cmp: The values expected to be found in the atomic object
:type cmp: Block of dtype=`pointer.dtype.element_ty`
:param val: The values to copy in case the expected value matches the contained value.
:type val: Block of dtype=`pointer.dtype.element_ty`
"""
func.__doc__ = docstr.format(name=name)
return func
return _decorator
@builtin
@_add_atomic_docstr("compare-and-swap")
def atomic_cas(pointer, cmp, val, _builder=None):
cmp = _to_tensor(cmp, _builder)
val = _to_tensor(val, _builder)
return semantic.atomic_cas(pointer, cmp, val, _builder)
@builtin
@_add_atomic_docstr("exchange")
def atomic_xchg(pointer, val, mask=None, _builder=None):
val = _to_tensor(val, _builder)
return semantic.atomic_xchg(pointer, val, mask, _builder)
@builtin
@_add_atomic_docstr("add")
def atomic_add(pointer, val, mask=None, _builder=None):
val = _to_tensor(val, _builder)
return semantic.atomic_add(pointer, val, mask, _builder)
@builtin
@_add_atomic_docstr("max")
def atomic_max(pointer, val, mask=None, _builder=None):
val = _to_tensor(val, _builder)
return semantic.atomic_max(pointer, val, mask, _builder)
@builtin
@_add_atomic_docstr("min")
def atomic_min(pointer, val, mask=None, _builder=None):
val = _to_tensor(val, _builder)
return semantic.atomic_min(pointer, val, mask, _builder)
@builtin
@_add_atomic_docstr("logical and")
def atomic_and(pointer, val, mask=None, _builder=None):
val = _to_tensor(val, _builder)
return semantic.atomic_and(pointer, val, mask, _builder)
@builtin
@_add_atomic_docstr("logical or")
def atomic_or(pointer, val, mask=None, _builder=None):
val = _to_tensor(val, _builder)
return semantic.atomic_or(pointer, val, mask, _builder)
@builtin
@_add_atomic_docstr("logical xor")
def atomic_xor(pointer, val, mask=None, _builder=None):
val = _to_tensor(val, _builder)
return semantic.atomic_xor(pointer, val, mask, _builder)
# -----------------------
# Conditioning
# -----------------------
@builtin
def where(condition, x, y, _builder=None):
"""
Returns a tensor of elements from either :code:`x` or :code:`y`, depending on :code:`condition`.
Note that :code:`x` and :code:`y` are always evaluated regardless of the value of :code:`condition`.
If you want to avoid unintended memory operations, use the :code:`mask` arguments in `triton.load` and `triton.store` instead.
The shape of :code:`x` and :code:`y` are both broadcast to the shape of :code:`condition`.
:code:`x` and :code:`y` must have the same data type.
:param condition: When True (nonzero), yield x, otherwise yield y.
:type condition: Block of triton.bool
:param x: values selected at indices where condition is True.
:param y: values selected at indices where condition is False.
"""
condition = _to_tensor(condition, _builder)
x = _to_tensor(x, _builder)
y = _to_tensor(y, _builder)
return semantic.where(condition, x, y, _builder)
# -----------------------
# Math
# -----------------------
@builtin
def umulhi(x, y, _builder=None):
x = _to_tensor(x, _builder)
y = _to_tensor(y, _builder)
return semantic.umulhi(x, y, _builder)
@builtin
def fdiv(x, y, ieee_rounding=False, _builder=None):
ieee_rounding = _constexpr_to_value(ieee_rounding)
return semantic.fdiv(x, y, ieee_rounding, _builder)
def _add_math_1arg_docstr(name: str) -> Callable[[T], T]:
def _decorator(func: T) -> T:
docstr = """
Computes the element-wise {name} of :code:`x`.
:param x: the input values
:type x: Block
"""
func.__doc__ = docstr.format(name=name)
return func
return _decorator
@builtin
@_add_math_1arg_docstr("exponential")
def exp(x, _builder=None):
return semantic.exp(x, _builder)
@builtin
@_add_math_1arg_docstr("natural logarithm")
def log(x, _builder=None):
return semantic.log(x, _builder)
@builtin
@_add_math_1arg_docstr("cosine")
def cos(x, _builder=None):
return semantic.cos(x, _builder)
@builtin
@_add_math_1arg_docstr("sine")
def sin(x, _builder=None):
return semantic.sin(x, _builder)
@builtin
@_add_math_1arg_docstr("square root")
def sqrt(x, _builder=None):
return semantic.sqrt(x, _builder)
@builtin
@_add_math_1arg_docstr("absolute value")
def abs(x, _builder=None):
return semantic.abs(x, _builder)
# -----------------------
# Reductions
# -----------------------
def _add_reduction_docstr(name: str) -> Callable[[T], T]:
def _decorator(func: T) -> T:
docstr = """
Returns the {name} of all elements in the :code:`input` tensor along the provided :code:`axis`
:param input: the input values
:param axis: the dimension along which the reduction should be done
"""
func.__doc__ = docstr.format(name=name)
return func
return _decorator
@contextmanager
def _insertion_guard(builder):
ip = builder.get_insertion_point()
yield
builder.restore_insertion_point(ip)
@builtin
def reduce(input, axis, combine_fn, _builder=None, _generator=None):
"""Applies the combine_fn to all elements in :code:`input` tensors along the provided :code:`axis`
:param input: the input tensor, or tuple of tensors
:param axis: the dimension along which the reduction should be done
:param combine_fn: a function to combine two groups of scalar tensors (must be marked with @triton.jit)
"""
if isinstance(input, tensor):
return reduce((input,), axis, combine_fn,
_builder=_builder, _generator=_generator)[0]
def make_combine_region(reduce_op):
in_scalar_tys = [t.type.scalar for t in input]
prototype = function_type(in_scalar_tys, in_scalar_tys * 2)
region = reduce_op.get_region(0)
with _insertion_guard(_builder):
param_types = [ty.to_ir(_builder) for ty in prototype.param_types]
block = _builder.create_block_with_parent(region, param_types)
args = [tensor(block.arg(i), ty)
for i, ty in enumerate(prototype.param_types)]
results = _generator.call_JitFunction(combine_fn, args, kwargs={})
if isinstance(results, tensor):
handles = [results.handle]
else:
handles = [r.handle for r in results]
_builder.create_reduce_ret(*handles)
axis = _constexpr_to_value(axis)
return semantic.reduction(input, axis, make_combine_region, _builder)
@builtin
def _promote_reduction_input(t, _builder=None):
scalar_ty = t.type.scalar
# input is extended to 32-bits if necessary
# this increases numerical accuracy and can be done pretty much for free
# on GPUs
if scalar_ty.is_int() and scalar_ty.int_bitwidth < 32:
return t.to(int32, _builder=_builder)
# hardware doesn't support FMAX, FMIN, CMP for bfloat16
if scalar_ty is bfloat16:
return t.to(float32, _builder=_builder)
return t
@builtin
def _argreduce(input, axis, combine_fn, _builder=None, _generator=None):
axis = _constexpr_to_value(axis)
n = input.shape[axis]
index = arange(0, n, _builder=_builder)
if len(input.shape) > 1:
# Broadcast index across the non-reduced axes
axes_to_expand = [constexpr(d) for d in range(len(input.shape))]
del axes_to_expand[axis]
index = expand_dims(index, axes_to_expand, _builder=_builder)
index = broadcast_to(index, input.shape, _builder=_builder)
rvalue, rindices = reduce((input, index), axis, combine_fn,
_builder=_builder, _generator=_generator)
return rindices
@triton.jit
def minimum(x, y):
"""
Computes the element-wise minimum of :code:`x` and :code:`y`.
:param input: the first input tensor
:type input: Block
:param other: the second input tensor
:type other: Block
"""
return where(x < y, x, y)
@triton.jit
def maximum(x, y):
"""
Computes the element-wise maximum of :code:`x` and :code:`y`.
:param input: the first input tensor
:type input: Block
:param other: the second input tensor
:type other: Block
"""
return where(x > y, x, y)
@triton.jit
def _max_combine(a, b):
return maximum(a, b)
@triton.jit
@_add_reduction_docstr("maximum")
def max(input, axis):
input = _promote_reduction_input(input)
return reduce(input, axis, _max_combine)
@triton.jit
def _argmax_combine(value1, index1, value2, index2):
gt = value1 > value2
lt = value1 < value2
index_min = minimum(index1, index2)
index_ret = where(gt, index1, where(lt, index2, index_min))
value_ret = maximum(value1, value2)
return value_ret, index_ret
@triton.jit
@_add_reduction_docstr("maximum index")
def argmax(input, axis):
input = _promote_reduction_input(input)
return _argreduce(input, axis, _argmax_combine)
@triton.jit
def _min_combine(a, b):
# TODO: minimum/maximum doesn't get lowered to fmin/fmax...
return minimum(a, b)
@triton.jit
@_add_reduction_docstr("minimum")
def min(input, axis):
input = _promote_reduction_input(input)
return reduce(input, axis, _min_combine)
@triton.jit
def _argmin_combine(value1, index1, value2, index2):
lt = value1 < value2
gt = value1 > value2
index_min = minimum(index1, index2)
index_ret = where(lt, index1, where(gt, index2, index_min))
value_ret = minimum(value1, value2)
return value_ret, index_ret
@triton.jit
@_add_reduction_docstr("minimum index")
def argmin(input, axis):
input = _promote_reduction_input(input)
return _argreduce(input, axis, _argmin_combine)
@triton.jit
def _sum_combine(a, b):
return a + b
@triton.jit
@_add_reduction_docstr("sum")
def sum(input, axis):
input = _promote_reduction_input(input)
return reduce(input, axis, _sum_combine)
@triton.jit
def _xor_combine(a, b):
return a ^ b
@builtin
@_add_reduction_docstr("xor sum")
def xor_sum(input, axis, _builder=None, _generator=None):
scalar_ty = input.type.scalar
if not scalar_ty.is_int():
raise ValueError("xor_sum only supported for integers")
input = _promote_reduction_input(input, _builder=_builder)
return reduce(input, axis, _xor_combine,
_builder=_builder, _generator=_generator)
# -----------------------
# Internal for debugging
# -----------------------
@builtin
def debug_barrier(_builder=None):
return semantic.debug_barrier(_builder)
@builtin
def multiple_of(input, values, _builder=None):
"""
Let the compiler knows that the values in :code:`input` are all multiples of :code:`value`.
"""
if isinstance(values, constexpr):
values = [values]
for i, d in enumerate(values):
if not isinstance(d, constexpr):
raise TypeError(f"values element {i} must have type `constexpr`")
if not isinstance(d.value, int):
raise TypeError(f"values element {i} must have type `constexpr[int]`, got `constexpr[{type(d.value)}]")
values = [x.value for x in values]
return semantic.multiple_of(input, values)
@builtin
def max_contiguous(input, values, _builder=None):
"""
Let the compiler knows that the `value` first values in :code:`input` are contiguous.
"""
if isinstance(values, constexpr):
values = [values]
for i, d in enumerate(values):
if not isinstance(d, constexpr):
raise TypeError(f"values element {i} must have type `constexpr`")
if not isinstance(d.value, int):
raise TypeError(f"values element {i} must have type `constexpr[int]`, got `constexpr[{type(d.value)}]")
values = [x.value for x in values]
return semantic.max_contiguous(input, values)
# -----------------------
# Debugging functions
# -----------------------
@builtin
def static_print(*values, sep: str = " ", end: str = "\n", file=None, flush=False, _builder=None):
pass
@builtin
def static_assert(cond, msg="", _builder=None):
pass
@builtin
def device_print(prefix, *args, _builder=None):
import string
prefix = _constexpr_to_value(prefix)
assert isinstance(prefix, str), f"{prefix} is not string"
b_ascii = True
for ch in prefix:
if ch not in string.printable:
b_ascii = False
break
assert b_ascii, f"{prefix} is not an ascii string"
new_args = []
for arg in args:
new_args.append(_to_tensor(arg, _builder))
return semantic.device_print(prefix, new_args, _builder)
@builtin
def device_assert(cond, msg="", _builder=None):
msg = _constexpr_to_value(msg)
import inspect
frame = inspect.currentframe()
module = inspect.getmodule(frame)
# The triton function module doesn't have the name attribute.
# We use this trick to find the caller.
while hasattr(module, "__name__"):
frame = frame.f_back
module = inspect.getmodule(frame)
func_name = frame.f_code.co_name
file_name = frame.f_back.f_code.co_filename
# TODO: The line number currently indicates the line
# where the triton function is called but not where the
# device_assert is called. Need to enhance this.
lineno = frame.f_back.f_lineno
return semantic.device_assert(_to_tensor(cond, _builder), msg, file_name, func_name, lineno, _builder)
# -----------------------
# Iterators
# -----------------------
class static_range:
"""Iterator that counts upward forever."""
def __init__(self, arg1, arg2=None, step=None):
assert isinstance(arg1, constexpr)
if step is None:
self.step = constexpr(1)
else:
assert isinstance(step, constexpr)
self.step = step
if arg2 is None:
self.start = constexpr(0)
self.end = arg1
else:
assert isinstance(arg2, constexpr)
self.start = arg1
self.end = arg2
def __iter__(self):
raise RuntimeError("static_range can only be used in @triton.jit'd functions")
def __next__(self):
raise RuntimeError("static_range can only be used in @triton.jit'd functions")
# -----------------------
# Extern functions
# -----------------------
def dispatch(func, lib_name: str, lib_path: str, args: list, arg_type_symbol_dict: dict, ret_shape: tuple, is_pure: bool, _builder=None):
'''
Dispatch a function to a library
:param func: the function to dispatch
:param lib_name: the name of the library
:param lib_path: the path of the library
:param args: the arguments of the function
:param arg_type_symbol_dict: the type of the arguments
:param ret_shape: the shape of the return value
:param _builder: the builder
:return: the return value of the function
'''
if len(arg_type_symbol_dict) == 0:
raise ValueError("arg_type_symbol_dict is empty")
num_args = len(list(arg_type_symbol_dict.keys())[0])
if len(args) != num_args:
raise ValueError(f"length of input args does not match."
f"Expect {len(args)}, got {num_args}")
arg_types = []
arg_list = []
for arg in args:
if isinstance(arg, tensor):
arg_types.append(arg.dtype)
arg_list.append(arg.handle)
else:
arg_types.append(type(arg))
arg_list.append(arg)
arg_types = tuple(arg_types)
if arg_types not in arg_type_symbol_dict:
raise ValueError(f"input arg type does not match."
f"Expect one of {arg_type_symbol_dict.keys()}, got {arg_types}")
else:
symbol = arg_type_symbol_dict[arg_types][0]
ret_type = arg_type_symbol_dict[arg_types][1]
if ret_shape:
ret_type = block_type(ret_type, ret_shape)
return tensor(func(lib_name, lib_path, symbol, arg_list, ret_type.to_ir(_builder), is_pure), ret_type)
def extern_elementwise(lib_name: str, lib_path: str, args: list, arg_type_symbol_dict: dict, is_pure: bool, _builder=None):
'''
Dispatch an elementwise function to a library
:param lib_name: the name of the library
:param lib_path: the path of the library
:param args: the arguments of the function
:param arg_type_symbol_dict: the type of the arguments
:param is_pure: whether the function is pure
:param _builder: the builder
:return: the return value of the function
'''
dispatch_args = args.copy()
all_scalar = True
ret_shape = None
arg_types = []
for i in range(len(dispatch_args)):
dispatch_args[i] = _to_tensor(dispatch_args[i], _builder)
arg_types.append(dispatch_args[i].dtype)
if dispatch_args[i].type.is_block():
all_scalar = False
if len(arg_types) > 0:
arg_types = tuple(arg_types)
arithmetic_check = True
# If there's a type tuple that is not supported by the library, we will do arithmetic check
if arg_types in arg_type_symbol_dict:
arithmetic_check = False
broadcast_arg = dispatch_args[0]
# Get the broadcast shape over all the arguments
for i, item in enumerate(dispatch_args):
_, broadcast_arg = semantic.binary_op_type_checking_impl(
item, broadcast_arg, _builder, arithmetic_check=arithmetic_check)
# Change the shape of each argument based on the broadcast shape
for i in range(len(dispatch_args)):
dispatch_args[i], _ = semantic.binary_op_type_checking_impl(
dispatch_args[i], broadcast_arg, _builder, arithmetic_check=arithmetic_check)
if not all_scalar:
ret_shape = broadcast_arg.shape
func = getattr(_builder, "create_extern_elementwise")
return dispatch(func, lib_name, lib_path, dispatch_args, arg_type_symbol_dict, ret_shape, is_pure, _builder)
def extern(fn):
"""A decorator for external functions."""
return builtin(fn)
|
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["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,448
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/megatron.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import math
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import functional as F
from transformers.utils import logging
from .. import core as qc
from ..core import utils as qu
from ..core import forward as qf
from ..core import output as qo
from ..core import attention as qa
from ..core.embed import Embed
from ..core.mlp import Classifier, MLP, Predictor, Pool
from ..prep.config.megatron import PreTrained
from torch.nn import CrossEntropyLoss
from ...pytorch_utils import (
apply_chunking_to_forward,
)
log = logging.get_logger(__name__)
LIST = [
"nvidia/megatron-bert-cased-345m",
]
class MegatronBertEmbeddings(qc.Module):
def __init__(self, config):
super().__init__()
self.word_embeddings = qc.Embed(config.s_vocab, config.d_model, padding_idx=config.PAD)
self.position_embeddings = qc.Embed(config.n_pos, config.d_model)
self.token_type_embeddings = qc.Embed(config.n_typ, config.d_model)
self.drop = qc.Dropout(config.drop)
self.register_buffer("position_ids", torch.arange(config.n_pos).expand((1, -1)))
self.pos_type = getattr(config, "pos_type", "absolute")
def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
past_key_values_length=0,
):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[
:, past_key_values_length : seq_length + past_key_values_length
]
if token_type_ids is None:
token_type_ids = torch.zeros(
input_shape, dtype=torch.long, device=self.position_ids.device
)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
if self.pos_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.drop(embeddings)
return embeddings
# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->MegatronBert
class MegatronBertSelfAttention(qc.Module):
def __init__(self, config, pos_type=None):
super().__init__()
if config.d_model % config.n_heads != 0 and not hasattr(config, "d_embed"):
raise ValueError(
f"The hidden size ({config.d_model}) is not a multiple of the number of attention "
f"heads ({config.n_heads})"
)
self.n_heads = config.n_heads
self.attention_head_size = int(config.d_model / config.n_heads)
self.all_head_size = self.n_heads * self.attention_head_size
self.query = qc.Linear(config.d_model, self.all_head_size)
self.key = qc.Linear(config.d_model, self.all_head_size)
self.value = qc.Linear(config.d_model, self.all_head_size)
self.drop = qc.Dropout(config.drop_attn)
self.pos_type = pos_type or getattr(config, "pos_type", "absolute")
if self.pos_type == "relative_key" or self.pos_type == "relative_key_query":
self.n_pos = config.n_pos
self.distance_embedding = qc.Embed(2 * config.n_pos - 1, self.attention_head_size)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.n_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
mixed_query_layer = self.query(hiddens)
is_cross_attention = enc_hiddens is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, crosses
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(enc_hiddens))
value_layer = self.transpose_for_scores(self.value(enc_hiddens))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hiddens))
value_layer = self.transpose_for_scores(self.value(hiddens))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hiddens))
value_layer = self.transpose_for_scores(self.value(hiddens))
query_layer = self.transpose_for_scores(mixed_query_layer)
if self.is_decoder:
past_key_value = (key_layer, value_layer)
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.pos_type == "relative_key" or self.pos_type == "relative_key_query":
seq_length = hiddens.size()[1]
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hiddens.device).view(
-1, 1
)
position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hiddens.device).view(
1, -1
)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.n_pos - 1)
positional_embedding = positional_embedding.to(
dtype=query_layer.dtype
) # fp16 compatibility
if self.pos_type == "relative_key":
relative_position_scores = torch.einsum(
"bhld,lrd->bhlr", query_layer, positional_embedding
)
attention_scores = attention_scores + relative_position_scores
elif self.pos_type == "relative_key_query":
relative_position_scores_query = torch.einsum(
"bhld,lrd->bhlr", query_layer, positional_embedding
)
relative_position_scores_key = torch.einsum(
"bhrd,lrd->bhlr", key_layer, positional_embedding
)
attention_scores = (
attention_scores + relative_position_scores_query + relative_position_scores_key
)
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in MegatronBertModel forward() function)
attention_scores = attention_scores + attention_mask
attention_probs = F.softmax(attention_scores, dim=-1)
attention_probs = self.drop(attention_probs)
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
# Based transformers.models.bert.modeling_bert.BertSelfOutput. Moved LayerNorm to MegatronBertAttention below.
class MegatronBertSelfOutput(qc.Module):
def __init__(self, config):
super().__init__()
self.dense = qc.Linear(config.d_model, config.d_model)
self.drop = qc.Dropout(config.drop)
def forward(self, hiddens, residual):
hiddens = self.dense(hiddens)
hiddens = self.drop(hiddens)
return residual + hiddens
# Based transformers.models.bert.modeling_bert.BertAttention. Added LayerNorm.
class Attention(qc.Module):
def __init__(self, config):
super().__init__()
self.ln = qc.LayerNorm(config.d_model, eps=config.eps)
self.self = MegatronBertSelfAttention(config)
self.output = MegatronBertSelfOutput(config)
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
ln_outputs = self.ln(hiddens)
self_outputs = self.self(
ln_outputs,
attention_mask,
head_mask,
enc_hiddens,
encoder_attention_mask,
past_key_value,
output_attentions,
)
attention_output = self.output(self_outputs[0], hiddens)
outputs = (attention_output,) + self_outputs[1:] # add attns if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->MegatronBert
class MegatronBertIntermediate(qc.Module):
def __init__(self, cfg):
super().__init__()
self.dense = qc.Linear(cfg.d_model, cfg.d_ff)
self.act = qu.activation(cfg.act)
def forward(self, x):
y = self.dense(x)
y = self.act(y)
return y
# Based on transformers.models.bert.modeling_bert.BertOutput. Moved LayerNorm to MegatronBertLayer below.
class MegatronBertOutput(qc.Module):
def __init__(self, config):
super().__init__()
self.dense = qc.Linear(config.d_ff, config.d_model)
self.drop = qc.Dropout(config.drop)
def forward(self, hiddens, input_tensor):
hiddens = self.dense(hiddens)
hiddens = self.drop(hiddens)
return input_tensor + hiddens
# Based on transformers.models.bert.modeling_bert.BertLayer. Added LayerNorm.
class Layer(qc.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = Attention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
if not self.is_decoder:
raise TypeError(
f"{self} should be used as a decoder model if cross attention is added"
)
self.crossattention = Attention(config)
self.ln = qc.LayerNorm(config.d_model, eps=config.eps)
self.intermediate = MegatronBertIntermediate(config)
self.output = MegatronBertOutput(config)
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
hiddens,
attention_mask,
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attns if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and enc_hiddens is not None:
if not hasattr(self, "crossattention"):
raise AttributeError(
f"If `enc_hiddens` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
)
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
head_mask,
enc_hiddens,
encoder_attention_mask,
cross_attn_past_key_value,
output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = (
outputs + cross_attention_outputs[1:-1]
) # add cross attns if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk,
self.chunk_size_feed_forward,
self.seq_len_dim,
attention_output,
)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
def feed_forward_chunk(self, attention_output):
ln_output = self.ln(attention_output)
intermediate_output = self.intermediate(ln_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
class Encoder(qc.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([Layer(config) for _ in range(config.n_lays)])
self.ln = qc.LayerNorm(config.d_model, eps=config.eps)
self.gradient_checkpointing = False
def forward(
self,
hiddens,
attention_mask=None,
head_mask=None,
enc_hiddens=None,
encoder_attention_mask=None,
caches=None,
y_cache=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
next_decoder_cache = () if y_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hiddens,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = caches[i] if caches is not None else None
if self.gradient_checkpointing and self.training:
if y_cache:
log.warning(
"`y_cache=True` is incompatible with gradient checkpointing. Setting `y_cache=False`..."
)
y_cache = False
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, past_key_value, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(layer_module),
hiddens,
attention_mask,
layer_head_mask,
enc_hiddens,
encoder_attention_mask,
)
else:
layer_outputs = layer_module(
hiddens,
attention_mask,
layer_head_mask,
enc_hiddens,
encoder_attention_mask,
past_key_value,
output_attentions,
)
# Because we moved the layer-norm at the end of the hidden layer, we have non-normali-
# zed data here. If that's really needed, we must apply LN to match Transformer's BERT.
hiddens = layer_outputs[0]
if y_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
# Finalize the hidden states.
hiddens = self.ln(hiddens)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hiddens,)
if not return_dict:
return tuple(
v
for v in [
hiddens,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return qo.CachesCrosses(
y=hiddens,
caches=next_decoder_cache,
hiddens=all_hidden_states,
attns=all_self_attentions,
crosses=all_cross_attentions,
)
# Copied from transformers.models.bert.modeling_bert.BertOnlyNSPHead with Bert->MegatronBert
class MegatronBertOnlyNSPHead(qc.Module):
def __init__(self, config):
super().__init__()
self.seq_relationship = qc.Linear(config.d_model, 2)
def forward(self, pooled_output):
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score
# Copied from transformers.models.bert.modeling_bert.BertPreTrainingHeads with Bert->MegatronBert
class MegatronBertPreTrainingHeads(qc.Module):
def __init__(self, config):
super().__init__()
self.predictions = Predictor(config)
self.seq_relationship = qc.Linear(config.d_model, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.predictions(sequence_output)
seq_relationship_score = self.seq_relationship(pooled_output)
return prediction_scores, seq_relationship_score
class Model(PreTrained):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = MegatronBertEmbeddings(config)
self.encoder = Encoder(config)
self.pool = Pool(config) if add_pooling_layer else None
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
enc_hiddens=None,
encoder_attention_mask=None,
caches=None,
y_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
output_attentions = (
output_attentions if output_attentions is not None else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.config.is_decoder:
y_cache = y_cache if y_cache is not None else self.config.y_cache
else:
y_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
# past_key_values_length
past_key_values_length = caches[0][0].shape[2] if caches is not None else 0
if attention_mask is None:
attention_mask = torch.ones(
((batch_size, seq_length + past_key_values_length)), device=device
)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
extended_attention_mask = self.get_extended_attention_mask(
attention_mask, input_shape, device
)
if self.config.is_decoder and enc_hiddens is not None:
encoder_batch_size, encoder_sequence_length, _ = enc_hiddens.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
head_mask = self.get_head_mask(head_mask, self.config.n_lays)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
enc_hiddens=enc_hiddens,
encoder_attention_mask=encoder_extended_attention_mask,
caches=caches,
y_cache=y_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pool(sequence_output) if self.pool is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return qo.BaseWithPoolingAndCrossAttentions(
y=sequence_output,
pools=pooled_output,
caches=encoder_outputs.caches,
hiddens=encoder_outputs.hiddens,
attns=encoder_outputs.attns,
crosses=encoder_outputs.crosses,
)
class ForPreTraining(PreTrained):
def __init__(self, config, add_binary_head=True):
super().__init__(config)
self.bert = Model(config)
self.cls = MegatronBertPreTrainingHeads(config)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
next_sentence_label=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output, pooled_output = outputs[:2]
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
total_loss = None
if labels is not None and next_sentence_label is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(
prediction_scores.view(-1, self.config.s_vocab), labels.view(-1)
)
next_sentence_loss = loss_fct(
seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)
)
total_loss = masked_lm_loss + next_sentence_loss
if not return_dict:
output = (prediction_scores, seq_relationship_score) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return qo.LossSeq(
loss=total_loss,
logits=prediction_scores,
orders=seq_relationship_score,
hiddens=outputs.hiddens,
attns=outputs.attns,
)
class ForCausal(PreTrained):
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
log.warning("If you want to use `ForCausal` as a standalone, add `is_decoder=True.`")
self.bert = Model(config, add_pooling_layer=False)
self.cls = Predictor(config)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
enc_hiddens=None,
encoder_attention_mask=None,
labels=None,
caches=None,
y_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
y_cache = False
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
enc_hiddens=enc_hiddens,
encoder_attention_mask=encoder_attention_mask,
caches=caches,
y_cache=y_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
lm_loss = None
if labels is not None:
# we are doing next-token prediction; shift prediction scores and input ids by one
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
lm_loss = loss_fct(
shifted_prediction_scores.view(-1, self.config.s_vocab), labels.view(-1)
)
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((lm_loss,) + output) if lm_loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=lm_loss,
logits=prediction_scores,
caches=outputs.caches,
hiddens=outputs.hiddens,
attns=outputs.attns,
crosses=outputs.crosses,
)
class ForMasked(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
self.get_cfg(kw)
self.model = Model(add_pool=False, **kw)
self.proj = Predictor(**kw)
forward = qf.forward_masked
class MegatronBertForNextPrediction(PreTrained):
def __init__(self, config):
super().__init__(config)
self.bert = Model(config)
self.cls = MegatronBertOnlyNSPHead(config)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kw,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
seq_relationship_scores = self.cls(pooled_output)
next_sentence_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1))
if not return_dict:
output = (seq_relationship_scores,) + outputs[2:]
return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
return NextSentencePredictorOutput(
loss=next_sentence_loss,
logits=seq_relationship_scores,
hiddens=outputs.hiddens,
attns=outputs.attns,
)
class ForChoice(PreTrained):
def __init__(self, config):
super().__init__(config)
self.bert = Model(config)
self.drop = qc.Dropout(config.drop)
self.classifier = qc.Linear(config.d_model, 1)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = (
attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
)
token_type_ids = (
token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
)
position_ids = (
position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
)
inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.drop(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return qo.WithLoss(
loss=loss,
logits=reshaped_logits,
hiddens=outputs.hiddens,
attns=outputs.attns,
)
class ForSeqClass(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Classifier(**kw)
forward = qf.forward_seq
class ForTokClass(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
self.get_cfg(kw)
self.model = Model(add_pool=False, **kw)
self.proj = Classifier(**kw)
forward = qf.forward_tok
class ForQA(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(add_pool=False, **kw)
self.proj = qc.Linear(cfg.d_model, cfg.n_labels, **kw)
forward = qf.forward_qa
|
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|
33,449
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/fnet.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import torch
import torch.utils.checkpoint
from functools import partial
from torch import nn
from torch.nn import functional as F
from transformers.utils import logging
from .. import core as qc
from ..core import utils as qu
from ..core import forward as qf
from ..core import output as qo
from ..core import attention as qa
from ..core.embed import Embed
from ..core.mlp import Classifier, MLP, Predictor, Pool
from ..prep.config.fnet import PreTrained
log = logging.get_logger(__name__)
from torch.nn import CrossEntropyLoss
from ...utils import is_scipy_available
if is_scipy_available():
from scipy import linalg
from ...pytorch_utils import apply_chunking_to_forward
LIST = ["google/fnet-base", "google/fnet-large"]
# Adapted from https://github.com/google-research/google-research/blob/master/f_net/fourier.py
def _two_dim_matmul(x, matrix_dim_one, matrix_dim_two):
seq_length = x.shape[1]
matrix_dim_one = matrix_dim_one[:seq_length, :seq_length]
x = x.type(torch.complex64)
return torch.einsum("bij,jk,ni->bnk", x, matrix_dim_two, matrix_dim_one)
# # Adapted from https://github.com/google-research/google-research/blob/master/f_net/fourier.py
def two_dim_matmul(x, matrix_dim_one, matrix_dim_two):
return _two_dim_matmul(x, matrix_dim_one, matrix_dim_two)
# Adapted from https://github.com/google-research/google-research/blob/master/f_net/fourier.py
def fftn(x):
out = x
for axis in reversed(range(x.ndim)[1:]): # We don't need to apply FFT to last axis
out = torch.fft.fft(out, axis=axis)
return out
class FNetEmbeddings(qc.Module):
def __init__(self, config):
super().__init__()
self.word_embeddings = qc.Embed(config.s_vocab, config.d_model, padding_idx=config.PAD)
self.position_embeddings = qc.Embed(config.n_pos, config.d_model)
self.token_type_embeddings = qc.Embed(config.n_typ, config.d_model)
self.norm = qc.LayerNorm(config.d_model, eps=config.eps)
self.projection = qc.Linear(config.d_model, config.d_model)
self.drop = qc.Dropout(config.drop)
self.register_buffer("position_ids", torch.arange(config.n_pos).expand((1, -1)))
self.register_buffer(
"token_type_ids",
torch.zeros(self.position_ids.size(), dtype=torch.long),
persistent=False,
)
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, :seq_length]
if token_type_ids is None:
if hasattr(self, "token_type_ids"):
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
input_shape[0], seq_length
)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(
input_shape, dtype=torch.long, device=self.position_ids.device
)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.norm(embeddings)
embeddings = self.projection(embeddings)
embeddings = self.drop(embeddings)
return embeddings
class FNetBasicFourierTransform(qc.Module):
def __init__(self, config):
super().__init__()
self._init_fourier_transform(config)
def _init_fourier_transform(self, config):
if not config.use_tpu_fourier_optimizations:
self.fourier_transform = partial(torch.fft.fftn, dim=(1, 2))
elif config.n_pos <= 4096:
if is_scipy_available():
self.register_buffer(
"dft_mat_hidden",
torch.tensor(linalg.dft(config.d_model), dtype=torch.complex64),
)
self.register_buffer(
"dft_mat_seq",
torch.tensor(linalg.dft(config.tpu_short_seq_length), dtype=torch.complex64),
)
self.fourier_transform = partial(
two_dim_matmul,
matrix_dim_one=self.dft_mat_seq,
matrix_dim_two=self.dft_mat_hidden,
)
else:
self.fourier_transform = fftn
else:
self.fourier_transform = fftn
def forward(self, hiddens):
outputs = self.fourier_transform(hiddens).real
return (outputs,)
class FNetBasicOutput(qc.Module):
def __init__(self, config):
super().__init__()
self.norm = qc.LayerNorm(config.d_model, eps=config.eps)
def forward(self, hiddens, input_tensor):
hiddens = self.norm(input_tensor + hiddens)
return hiddens
class FNetFourierTransform(qc.Module):
def __init__(self, config):
super().__init__()
self.self = FNetBasicFourierTransform(config)
self.output = FNetBasicOutput(config)
def forward(self, hiddens):
self_outputs = self.self(hiddens)
fourier_output = self.output(self_outputs[0], hiddens)
outputs = (fourier_output,)
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->FNet
class FNetIntermediate(qc.Module):
def __init__(self, cfg):
super().__init__()
self.dense = qc.Linear(cfg.d_model, cfg.d_ff)
self.act = qu.activation(cfg.act)
def forward(self, x):
y = self.dense(x)
y = self.act(y)
return y
# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->FNet
class FNetOutput(qc.Module):
def __init__(self, config):
super().__init__()
self.dense = qc.Linear(config.d_ff, config.d_model)
self.norm = qc.LayerNorm(config.d_model, eps=config.eps)
self.drop = qc.Dropout(config.drop)
def forward(self, hiddens, input_tensor):
hiddens = self.dense(hiddens)
hiddens = self.drop(hiddens)
hiddens = self.norm(hiddens + input_tensor)
return hiddens
class FNetLayer(qc.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1 # The dimension which has the sequence length
self.fourier = FNetFourierTransform(config)
self.intermediate = FNetIntermediate(config)
self.output = FNetOutput(config)
def forward(self, hiddens):
self_fourier_outputs = self.fourier(hiddens)
fourier_output = self_fourier_outputs[0]
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, fourier_output
)
outputs = (layer_output,)
return outputs
def feed_forward_chunk(self, fourier_output):
intermediate_output = self.intermediate(fourier_output)
layer_output = self.output(intermediate_output, fourier_output)
return layer_output
class FNetEncoder(qc.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([FNetLayer(config) for _ in range(config.n_lays)])
self.gradient_checkpointing = False
def forward(self, hiddens, output_hidden_states=False, return_dict=True):
all_hidden_states = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hiddens,)
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(layer_module), hiddens
)
else:
layer_outputs = layer_module(hiddens)
hiddens = layer_outputs[0]
if output_hidden_states:
all_hidden_states = all_hidden_states + (hiddens,)
if not return_dict:
return tuple(v for v in [hiddens, all_hidden_states] if v is not None)
return qo.Base(y=hiddens, hiddens=all_hidden_states)
# Copied from transformers.models.bert.modeling_bert.BertPreTrainingHeads with Bert->FNet
class FNetPreTrainingHeads(qc.Module):
def __init__(self, config):
super().__init__()
self.predictions = Predictor(config)
self.seq_relationship = qc.Linear(config.d_model, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.predictions(sequence_output)
seq_relationship_score = self.seq_relationship(pooled_output)
return prediction_scores, seq_relationship_score
class Model(PreTrained):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = FNetEmbeddings(config)
self.encoder = FNetEncoder(config)
self.pool = Pool(config) if add_pooling_layer else None
def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
output_hidden_states=None,
return_dict=None,
):
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
batch_size, seq_length = input_shape
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size, seq_length = input_shape
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if (
self.config.use_tpu_fourier_optimizations
and seq_length <= 4096
and self.config.tpu_short_seq_length != seq_length
):
raise ValueError(
"The `tpu_short_seq_length` in FNetConfig should be set equal to the sequence length being passed to the model when using TPU optimizations."
)
device = input_ids.device if input_ids is not None else inputs_embeds.device
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
batch_size, seq_length
)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
)
encoder_outputs = self.encoder(
embedding_output,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pools = self.pool(sequence_output) if self.pool is not None else None
if not return_dict:
return (sequence_output, pools) + encoder_outputs[1:]
return qo.BaseWithPooling(
y=sequence_output,
pools=pools,
hiddens=encoder_outputs.hiddens,
)
class FNetForPreTraining(PreTrained):
def __init__(self, config):
super().__init__(config)
self.fnet = Model(config)
self.cls = FNetPreTrainingHeads(config)
self.post_init()
def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
labels=None,
next_sentence_label=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.fnet(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output, pooled_output = outputs[:2]
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
total_loss = None
if labels is not None and next_sentence_label is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(
prediction_scores.view(-1, self.config.s_vocab), labels.view(-1)
)
next_sentence_loss = loss_fct(
seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)
)
total_loss = masked_lm_loss + next_sentence_loss
if not return_dict:
output = (prediction_scores, seq_relationship_score) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return qo.LossSeq(
loss=total_loss,
logits=prediction_scores,
orders=seq_relationship_score,
hiddens=outputs.hiddens,
)
class ForMasked(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Predictor(**kw)
forward = qf.forward_masked
class FNetForNextPrediction(PreTrained):
def __init__(self, config):
super().__init__(config)
self.fnet = Model(config)
self.cls = qc.Linear(config.d_model, 2)
self.post_init()
def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
labels=None,
output_hidden_states=None,
return_dict=None,
**kw,
):
if "next_sentence_label" in kw:
labels = kw.pop("next_sentence_label")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.fnet(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
seq_relationship_scores = self.cls(pooled_output)
next_sentence_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1))
if not return_dict:
output = (seq_relationship_scores,) + outputs[2:]
return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
return NextSentencePredictorOutput(
loss=next_sentence_loss,
logits=seq_relationship_scores,
hiddens=outputs.hiddens,
)
class ForChoice(PreTrained):
def __init__(self, config):
super().__init__(config)
self.fnet = Model(config)
self.drop = qc.Dropout(config.drop)
self.classifier = qc.Linear(config.d_model, 1)
self.post_init()
def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
labels=None,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
token_type_ids = (
token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
)
position_ids = (
position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
)
inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.fnet(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.drop(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return qo.WithLoss(loss=loss, logits=reshaped_logits, hiddens=outputs.hiddens)
class ForSeqClass(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Classifier(**kw)
forward = qf.forward_seq
class ForTokClass(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Classifier(**kw)
forward = qf.forward_tok
class ForQA(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(**kw)
self.proj = qc.Linear(cfg.d_model, cfg.n_labels, **kw)
forward = qf.forward_qa
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", 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"/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], 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["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": 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"/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], 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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,450
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/core/flash.py
|
import math
import torch
import triton
import triton.language as tl
import flash_attn_cuda
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from flash_attn import flash_attn_triton
from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
from flash_attn.bert_padding import unpad_input, pad_input
class FlashAttention(nn.Module):
def __init__(self, softmax_scale=None, attention_dropout=0.0):
super().__init__()
self.softmax_scale = softmax_scale
self.dropout_p = attention_dropout
def forward(
self,
qkv,
key_padding_mask=None,
causal=False,
cu_seqlens=None,
max_s=None,
need_weights=False,
):
assert not need_weights
assert qkv.dtype in [torch.float16, torch.bfloat16]
assert qkv.is_cuda
if cu_seqlens is None:
batch_size = qkv.shape[0]
seqlen = qkv.shape[1]
if key_padding_mask is None:
qkv = rearrange(qkv, "b s ... -> (b s) ...")
max_s = seqlen
cu_seqlens = torch.arange(
0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32, device=qkv.device
)
output = flash_attn_unpadded_qkvpacked_func(
qkv,
cu_seqlens,
max_s,
self.dropout_p if self.training else 0.0,
softmax_scale=self.softmax_scale,
causal=causal,
)
output = rearrange(output, "(b s) ... -> b s ...", b=batch_size)
else:
nheads = qkv.shape[-2]
x = rearrange(qkv, "b s three h d -> b s (three h d)")
x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
x_unpad = rearrange(x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads)
output_unpad = flash_attn_unpadded_qkvpacked_func(
x_unpad,
cu_seqlens,
max_s,
self.dropout_p if self.training else 0.0,
softmax_scale=self.softmax_scale,
causal=causal,
)
output = rearrange(
pad_input(
rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices, batch_size, seqlen
),
"b s (h d) -> b s h d",
h=nheads,
)
else:
assert max_s is not None
output = flash_attn_unpadded_qkvpacked_func(
qkv,
cu_seqlens,
max_s,
self.dropout_p if self.training else 0.0,
softmax_scale=self.softmax_scale,
causal=causal,
)
return output, None
class FlashMHA(nn.Module):
def __init__(
self,
embed_dim,
num_heads,
bias=True,
batch_first=True,
attention_dropout=0.0,
causal=False,
device=None,
dtype=None,
):
assert batch_first
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__()
self.embed_dim = embed_dim
self.causal = causal
self.num_heads = num_heads
assert self.embed_dim % num_heads == 0, "self.kdim must be divisible by num_heads"
self.head_dim = self.embed_dim // num_heads
assert (
self.head_dim % 8 == 0 and self.head_dim <= 128
), "Only support head_dim <= 128 and divisible by 8"
self.Wqkv = nn.Linear(embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs)
self.inner_attn = FlashAttention(attention_dropout=attention_dropout)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias, **factory_kwargs)
def forward(self, x, key_padding_mask=None, need_weights=False):
qkv = self.Wqkv(x)
qkv = rearrange(qkv, "b s (three h d) -> b s three h d", three=3, h=self.num_heads)
context, attn_weights = self.inner_attn(
qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=self.causal
)
return self.out_proj(rearrange(context, "b s h d -> b s (h d)")), attn_weights
# Disabling autotune for now, set num_warps=4 if headdim=64 and num_warps=8 if headdim=128
# @triton.autotune(
# configs=[
# triton.Config({"BLOCK_M": 128, "BLOCK_N": 128}, num_warps=4, num_stages=1),
# # This config has a race condition when EVEN_M == False, disabling it for now.
# # triton.Config({"BLOCK_M": 64, "BLOCK_N": 64}, num_warps=4, num_stages=1),
# ],
# key=['CACHE_KEY_SEQLEN_Q', 'CACHE_KEY_SEQLEN_K', 'BIAS_TYPE', 'IS_CAUSAL', 'BLOCK_HEADDIM']
# )
@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"],
}
)
@triton.jit
def _fwd_kernel(
Q,
K,
V,
Bias,
Out,
Lse,
TMP, # NOTE: TMP is a scratchpad buffer to workaround a compiler bug
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,
):
start_m = tl.program_id(0)
off_hb = tl.program_id(1)
off_b = off_hb // nheads
off_h = off_hb % nheads
# off_b = tl.program_id(1)
# off_h = tl.program_id(2)
# off_hb = off_b * nheads + off_h
# initialize offsets
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
offs_n = tl.arange(0, BLOCK_N)
offs_d = tl.arange(0, BLOCK_HEADDIM)
# Initialize pointers to Q, K, V
# Adding parenthesis around indexing might use int32 math instead of int64 math?
# https://github.com/openai/triton/issues/741
# I'm seeing a tiny bit of difference (5-7us)
q_ptrs = (
Q + off_b * stride_qb + off_h * stride_qh + (offs_m[:, None] * stride_qm + offs_d[None, :])
)
k_ptrs = (
K + off_b * stride_kb + off_h * stride_kh + (offs_n[:, None] * stride_kn + offs_d[None, :])
)
v_ptrs = (
V + off_b * stride_vb + off_h * stride_vh + (offs_n[:, None] * stride_vn + offs_d[None, :])
)
if BIAS_TYPE == "vector":
b_ptrs = Bias + off_b * stride_bb + off_h * stride_bh + offs_n
elif BIAS_TYPE == "matrix":
b_ptrs = (
Bias
+ off_b * stride_bb
+ off_h * stride_bh
+ (offs_m[:, None] * stride_bm + offs_n[None, :])
)
# initialize pointer to m and l
t_ptrs = TMP + off_hb * seqlen_q_rounded + offs_m
lse_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
acc_o = tl.zeros([BLOCK_M, BLOCK_HEADDIM], dtype=tl.float32)
# load q: it will stay in SRAM throughout
# [2022-10-30] TD: Triton bug - in the case of EVEN_M=True and EVEN_N=False, if we just call
# tl.load(q_ptrs), we get the wrong output!
if EVEN_M & EVEN_N:
if EVEN_HEADDIM:
q = tl.load(q_ptrs)
else:
q = tl.load(q_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
else:
if EVEN_HEADDIM:
q = tl.load(q_ptrs, mask=offs_m[:, None] < seqlen_q, other=0.0)
else:
q = tl.load(
q_ptrs, mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0
)
# loop over k, v and update accumulator
end_n = seqlen_k if not IS_CAUSAL else tl.minimum((start_m + 1) * BLOCK_M, seqlen_k)
for start_n in range(0, end_n, BLOCK_N):
start_n = tl.multiple_of(start_n, BLOCK_N)
# -- compute qk ----
if EVEN_N & EVEN_M: # If we just do "if EVEN_N", there seems to be some race condition
if EVEN_HEADDIM:
k = tl.load(k_ptrs + start_n * stride_kn)
else:
k = tl.load(k_ptrs + start_n * stride_kn, mask=offs_d[None, :] < headdim, other=0.0)
else:
if EVEN_HEADDIM:
k = tl.load(
k_ptrs + start_n * stride_kn,
mask=(start_n + offs_n)[:, None] < seqlen_k,
other=0.0,
)
else:
k = tl.load(
k_ptrs + start_n * stride_kn,
mask=((start_n + offs_n)[:, None] < seqlen_k) & (offs_d[None, :] < headdim),
other=0.0,
)
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
qk += tl.dot(q, k, trans_b=True)
# Trying to combine the two masks seem to make the result wrong
if not EVEN_N: # Need to mask out otherwise the softmax is wrong
qk += tl.where((start_n + offs_n)[None, :] < seqlen_k, 0, float("-inf"))
if IS_CAUSAL:
qk += tl.where(offs_m[:, None] >= (start_n + offs_n)[None, :], 0, float("-inf"))
if BIAS_TYPE != "none":
if BIAS_TYPE == "vector":
if EVEN_N:
bias = tl.load(b_ptrs + start_n).to(tl.float32)
else:
bias = tl.load(
b_ptrs + start_n, mask=(start_n + offs_n) < seqlen_k, other=0.0
).to(tl.float32)
bias = bias[None, :]
elif BIAS_TYPE == "matrix":
if EVEN_M & EVEN_N:
bias = tl.load(b_ptrs + start_n).to(tl.float32)
else:
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)
# Slightly faster to multiply the softmax_scale in the tl.exp below since the compiler
# can then fuse the mult and add into an fma instruction. But if we have bias we need to
# to multiply with softmax_scale here.
qk = qk * softmax_scale + bias
m_ij = tl.maximum(tl.max(qk, 1), lse_i)
p = tl.exp(qk - m_ij[:, None])
else:
m_ij = tl.maximum(tl.max(qk, 1) * softmax_scale, lse_i)
p = tl.exp(qk * softmax_scale - m_ij[:, None])
l_ij = tl.sum(p, 1)
# scale acc_o
acc_o_scale = tl.exp(m_i - m_ij)
# # -- update output accumulator --
# BUG: have to store and immediately load
tl.store(t_ptrs, acc_o_scale)
acc_o_scale = tl.load(t_ptrs)
acc_o = acc_o * acc_o_scale[:, None]
# update acc_o
if EVEN_N & EVEN_M: # If we just do "if EVEN_N", there seems to be some race condition
if EVEN_HEADDIM:
v = tl.load(v_ptrs + start_n * stride_vn)
else:
v = tl.load(v_ptrs + start_n * stride_vn, mask=offs_d[None, :] < headdim, other=0.0)
else:
if EVEN_HEADDIM:
v = tl.load(
v_ptrs + start_n * stride_vn,
mask=(start_n + offs_n)[:, None] < seqlen_k,
other=0.0,
)
else:
v = tl.load(
v_ptrs + start_n * stride_vn,
mask=((start_n + offs_n)[:, None] < seqlen_k) & (offs_d[None, :] < headdim),
other=0.0,
)
p = p.to(v.dtype)
acc_o += tl.dot(p, v)
# -- update statistics
m_i = m_ij
l_i_new = tl.exp(lse_i - m_ij) + l_ij
lse_i = m_ij + tl.log(l_i_new)
o_scale = tl.exp(m_i - lse_i)
# BUG: have to store and immediately load
tl.store(t_ptrs, o_scale)
o_scale = tl.load(t_ptrs)
acc_o = acc_o * o_scale[:, None]
# rematerialize offsets to save registers
start_m = tl.program_id(0)
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
# write back l and m
lse_ptrs = Lse + off_hb * seqlen_q_rounded + offs_m
tl.store(lse_ptrs, lse_i)
# initialize pointers to output
offs_d = tl.arange(0, BLOCK_HEADDIM)
out_ptrs = (
Out
+ off_b * stride_ob
+ off_h * stride_oh
+ (offs_m[:, None] * stride_om + offs_d[None, :])
)
if EVEN_M:
if EVEN_HEADDIM:
tl.store(out_ptrs, acc_o)
else:
tl.store(out_ptrs, acc_o, mask=offs_d[None, :] < headdim)
else:
if EVEN_HEADDIM:
tl.store(out_ptrs, acc_o, mask=offs_m[:, None] < seqlen_q)
else:
tl.store(
out_ptrs, acc_o, mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim)
)
@triton.jit
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,
):
start_m = tl.program_id(0)
off_hb = tl.program_id(1)
off_b = off_hb // nheads
off_h = off_hb % nheads
# initialize offsets
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
offs_d = tl.arange(0, BLOCK_HEADDIM)
# load
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)
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)
delta = tl.sum(o * do, axis=1)
# write-back
tl.store(Delta + off_hb * seqlen_q_rounded + offs_m, delta)
@triton.jit
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,
):
# [2022-11-01] TD: Same bug. In the case of EVEN_N=True and EVEN_M=False,
# if we just call tl.store(dv_ptrs), there's a race condition
if EVEN_N & EVEN_M:
if EVEN_HEADDIM:
tl.store(dv_ptrs, dv)
tl.store(dk_ptrs, dk)
else:
tl.store(dv_ptrs, dv, mask=offs_d[None, :] < headdim)
tl.store(dk_ptrs, dk, mask=offs_d[None, :] < headdim)
else:
if EVEN_HEADDIM:
tl.store(dv_ptrs, dv, mask=offs_n[:, None] < seqlen_k)
tl.store(dk_ptrs, dk, mask=offs_n[:, None] < seqlen_k)
else:
tl.store(dv_ptrs, dv, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim))
tl.store(dk_ptrs, dk, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim))
@triton.jit
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,
):
# We need to make sure begin_m is a multiple of BLOCK_M (not BLOCK_N)
begin_m = 0 if not IS_CAUSAL else ((start_n * BLOCK_N) // BLOCK_M) * BLOCK_M
# initialize row/col offsets
offs_qm = begin_m + tl.arange(0, BLOCK_M)
offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N)
offs_m = tl.arange(0, BLOCK_M)
offs_d = tl.arange(0, BLOCK_HEADDIM)
# initialize pointers to value-like data
q_ptrs = Q + (offs_qm[:, None] * stride_qm + offs_d[None, :])
k_ptrs = K + (offs_n[:, None] * stride_kn + offs_d[None, :])
v_ptrs = V + (offs_n[:, None] * stride_vn + offs_d[None, :])
do_ptrs = DO + (offs_qm[:, None] * stride_dom + offs_d[None, :])
dq_ptrs = DQ + (offs_qm[:, None] * stride_dqm + offs_d[None, :])
if BIAS_TYPE == "vector":
b_ptrs = Bias + offs_n
elif BIAS_TYPE == "matrix":
b_ptrs = Bias + (offs_qm[:, None] * stride_bm + offs_n[None, :])
# initialize dv and dk
dv = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32)
dk = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32)
# There seems to be some problem with Triton pipelining that makes results wrong for
# headdim=64, seqlen=(113, 255), bias_type='matrix'. In this case the for loop
# may have zero step, and pipelining with the bias matrix could screw it up.
# So we just exit early.
if begin_m >= seqlen_q:
dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_d[None, :])
dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_d[None, :])
_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,
)
return
# k and v stay in SRAM throughout
# [2022-10-30] TD: Same bug as the fwd. In the case of EVEN_N=True and EVEN_M=False,
# if we just call tl.load(k_ptrs), we get the wrong output!
if EVEN_N & EVEN_M:
if EVEN_HEADDIM:
k = tl.load(k_ptrs)
v = tl.load(v_ptrs)
else:
k = tl.load(k_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
v = tl.load(v_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
else:
if EVEN_HEADDIM:
k = tl.load(k_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0)
v = tl.load(v_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0)
else:
k = tl.load(
k_ptrs, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0
)
v = tl.load(
v_ptrs, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0
)
# loop over rows
num_block_m = tl.cdiv(seqlen_q, BLOCK_M)
for start_m in range(begin_m, num_block_m * BLOCK_M, BLOCK_M):
start_m = tl.multiple_of(start_m, BLOCK_M)
offs_m_curr = start_m + offs_m
# load q, k, v, do on-chip
# Same bug as below. Otherwise gives wrong result for headdim=40, seqlen=(128, 117)
if EVEN_M & EVEN_HEADDIM:
q = tl.load(q_ptrs)
else:
if EVEN_HEADDIM:
q = tl.load(q_ptrs, mask=offs_m_curr[:, None] < seqlen_q, other=0.0)
else:
q = tl.load(
q_ptrs,
mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim),
other=0.0,
)
# recompute p = softmax(qk, dim=-1).T
qk = tl.dot(q, k, trans_b=True)
# Trying to combine the two masks seem to make the result wrong
if not EVEN_N: # Need to mask out otherwise the softmax is wrong
qk = tl.where(offs_n[None, :] < seqlen_k, qk, float("-inf"))
if IS_CAUSAL:
qk = tl.where(offs_m_curr[:, None] >= (offs_n[None, :]), qk, float("-inf"))
if BIAS_TYPE != "none":
tl.debug_barrier() # Race condition otherwise
if BIAS_TYPE == "vector":
if EVEN_N:
bias = tl.load(b_ptrs).to(tl.float32)
else:
bias = tl.load(b_ptrs, mask=offs_n < seqlen_k, other=0.0).to(tl.float32)
bias = bias[None, :]
elif BIAS_TYPE == "matrix":
if EVEN_M & EVEN_N:
bias = tl.load(b_ptrs).to(tl.float32)
else:
bias = tl.load(
b_ptrs,
mask=(offs_m_curr[:, None] < seqlen_q) & (offs_n[None, :] < seqlen_k),
other=0.0,
).to(tl.float32)
qk = qk * softmax_scale + bias
# There seems to be a race condition when headdim=48/96, and dq, dk, dv are wrong.
# Also wrong for headdim=64.
if not (EVEN_M & EVEN_HEADDIM):
tl.debug_barrier()
lse_i = tl.load(LSE + offs_m_curr)
if BIAS_TYPE == "none":
p = tl.exp(qk * softmax_scale - lse_i[:, None])
else:
p = tl.exp(qk - lse_i[:, None])
# compute dv
# [2022-10-30] TD: A Triton bug: if EVEN_M=True and EVEN_HEADDIM=False, if we call
# do = tl.load(do_ptrs, mask=offs_d[None, :] < headdim, other=0.0), we get wrong outputs
# in the case of headdim=48/96, seqlen_q & seqlen_k >= 512. If headdim=40 or seqlen < 512,
# the output is correct.
if EVEN_M & EVEN_HEADDIM:
do = tl.load(do_ptrs)
else:
# [2022-11-01] TD: Triton bug, there's a race condition if we just use m_mask and not d_mask.
do = tl.load(
do_ptrs,
mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim),
other=0.0,
)
# if EVEN_M:
# if EVEN_HEADDIM:
# do = tl.load(do_ptrs)
# else:
# do = tl.load(do_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
# else:
# if EVEN_HEADDIM:
# do = tl.load(do_ptrs, mask=offs_m_curr[:, None] < seqlen_q, other=0.0)
# else:
# do = tl.load(do_ptrs, mask=(offs_m_curr[:, None] < seqlen_q)
# & (offs_d[None, :] < headdim), other=0.0)
dv += tl.dot(p.to(do.dtype), do, trans_a=True)
# compute dp = dot(v, do)
# There seems to be a race condition when headdim=48/96, and dq, dk are wrong.
# Also wrong for headdim=128, seqlen=(108, 256), and ATOMIC_ADD=True
# Also wrong for headdim=64, seqlen=(1023, 1024), and ATOMIC_ADD=False
if not (EVEN_M & EVEN_HEADDIM):
tl.debug_barrier()
dp = tl.dot(do, v, trans_b=True)
# There's a race condition for headdim=48
if not EVEN_HEADDIM:
tl.debug_barrier()
# compute ds = p * (dp - delta[:, None])
# Putting the subtraction after the dp matmul (instead of before) is slightly faster
Di = tl.load(D + offs_m_curr)
# Converting ds to q.dtype here reduces register pressure and makes it much faster
# for BLOCK_HEADDIM=128
ds = (p * (dp - Di[:, None]) * softmax_scale).to(q.dtype)
# compute dk = dot(ds.T, q)
dk += tl.dot(ds, q, trans_a=True)
# compute dq
if not (
EVEN_M & EVEN_HEADDIM
): # Otherewise there's a race condition when BIAS_TYPE='matrix'
tl.debug_barrier()
if not ATOMIC_ADD:
if EVEN_M & EVEN_HEADDIM: # Race condition if we just do EVEN_M
dq = tl.load(dq_ptrs, eviction_policy="evict_last")
dq += tl.dot(ds, k)
tl.store(dq_ptrs, dq, eviction_policy="evict_last")
else:
if EVEN_HEADDIM:
dq = tl.load(
dq_ptrs,
mask=offs_m_curr[:, None] < seqlen_q,
other=0.0,
eviction_policy="evict_last",
)
dq += tl.dot(ds, k)
tl.store(
dq_ptrs,
dq,
mask=offs_m_curr[:, None] < seqlen_q,
eviction_policy="evict_last",
)
else:
dq = tl.load(
dq_ptrs,
mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim),
other=0.0,
eviction_policy="evict_last",
)
dq += tl.dot(ds, k)
tl.store(
dq_ptrs,
dq,
mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim),
eviction_policy="evict_last",
)
else: # If we're parallelizing across the seqlen_k dimension
dq = tl.dot(ds, k)
if EVEN_M & EVEN_HEADDIM: # Race condition if we just do EVEN_M
tl.atomic_add(dq_ptrs, dq)
else:
if EVEN_HEADDIM:
tl.atomic_add(dq_ptrs, dq, mask=offs_m_curr[:, None] < seqlen_q)
else:
tl.atomic_add(
dq_ptrs,
dq,
mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim),
)
# increment pointers
dq_ptrs += BLOCK_M * stride_dqm
q_ptrs += BLOCK_M * stride_qm
do_ptrs += BLOCK_M * stride_dom
if BIAS_TYPE == "matrix":
b_ptrs += BLOCK_M * stride_bm
# write-back
dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_d[None, :])
dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_d[None, :])
_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,
)
def init_to_zero(name):
return lambda nargs: nargs[name].zero_()
@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"),
),
# Other configs seem to give wrong results when seqlen_q % 128 != 0, disabling them for now
# # Kernel is buggy (give wrong result) if we set BLOCK_m=128, BLOCK_n=64, num_warps=*4*
# triton.Config({"BLOCK_M": 128, "BLOCK_N": 64, "SEQUENCE_PARALLEL": False}, num_warps=8, num_stages=1, pre_hook=init_to_zero('DQ')),
# triton.Config({"BLOCK_M": 128, "BLOCK_N": 64, "SEQUENCE_PARALLEL": True}, num_warps=8, num_stages=1, pre_hook=init_to_zero('DQ')),
# triton.Config({"BLOCK_M": 64, "BLOCK_N": 64, "SEQUENCE_PARALLEL": False}, num_warps=4, num_stages=1, pre_hook=init_to_zero('DQ')),
# triton.Config({"BLOCK_M": 64, "BLOCK_N": 64, "SEQUENCE_PARALLEL": True}, num_warps=4, num_stages=1, pre_hook=init_to_zero('DQ')),
],
key=["CACHE_KEY_SEQLEN_Q", "CACHE_KEY_SEQLEN_K", "BIAS_TYPE", "IS_CAUSAL", "BLOCK_HEADDIM"],
)
@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"],
}
)
@triton.jit
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,
):
off_hb = tl.program_id(1)
off_b = off_hb // nheads
off_h = off_hb % nheads
# offset pointers for batch/head
Q += off_b * stride_qb + off_h * stride_qh
K += off_b * stride_kb + off_h * stride_kh
V += off_b * stride_vb + off_h * stride_vh
DO += off_b * stride_dob + off_h * stride_doh
DQ += off_b * stride_dqb + off_h * stride_dqh
DK += off_b * stride_dkb + off_h * stride_dkh
DV += off_b * stride_dvb + off_h * stride_dvh
if BIAS_TYPE != "none":
Bias += off_b * stride_bb + off_h * stride_bh
# pointer to row-wise quantities in value-like data
D += off_hb * seqlen_q_rounded
LSE += off_hb * seqlen_q_rounded
if not SEQUENCE_PARALLEL:
num_block_n = tl.cdiv(seqlen_k, BLOCK_N)
for start_n in range(0, num_block_n):
_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,
)
else:
start_n = tl.program_id(0)
_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,
)
def _flash_attn_forward(q, k, v, bias=None, causal=False, softmax_scale=None):
# shape constraints
batch, seqlen_q, nheads, d = q.shape
_, seqlen_k, _, _ = k.shape
assert k.shape == (batch, seqlen_k, nheads, d)
assert v.shape == (batch, seqlen_k, nheads, d)
assert d <= 128, "FlashAttention only support head dimensions up to 128"
assert q.dtype == k.dtype == v.dtype, "All tensors must have the same type"
assert q.dtype in [torch.float16, torch.bfloat16], "Only support fp16 and bf16"
assert q.is_cuda and k.is_cuda and v.is_cuda
softmax_scale = softmax_scale or 1.0 / math.sqrt(d)
has_bias = bias is not None
bias_type = "none"
if has_bias:
assert bias.dtype in [q.dtype, torch.float]
assert bias.is_cuda
assert bias.dim() == 4
if bias.stride(-1) != 1:
bias = bias.contiguous()
if bias.shape[2:] == (1, seqlen_k):
bias_type = "vector"
elif bias.shape[2:] == (seqlen_q, seqlen_k):
bias_type = "matrix"
else:
raise RuntimeError(
"Last 2 dimensions of bias must be (1, seqlen_k)" " or (seqlen_q, seqlen_k)"
)
bias = bias.expand(batch, nheads, seqlen_q, seqlen_k)
bias_strides = (bias.stride(0), bias.stride(1), bias.stride(2)) if has_bias else (0, 0, 0)
seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128
lse = torch.empty((batch, nheads, seqlen_q_rounded), device=q.device, dtype=torch.float32)
tmp = torch.empty((batch, nheads, seqlen_q_rounded), device=q.device, dtype=torch.float32)
o = torch.empty_like(q)
BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16)
BLOCK = 128
num_warps = 4 if d <= 64 else 8
grid = lambda META: (triton.cdiv(seqlen_q, META["BLOCK_M"]), batch * nheads)
_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, # key for triton cache (limit number of compilations)
# Can't use kwargs here because triton autotune expects key to be args, not kwargs
# IS_CAUSAL=causal, BLOCK_HEADDIM=d,
bias_type,
causal,
BLOCK_HEADDIM,
BLOCK_M=BLOCK,
BLOCK_N=BLOCK,
num_warps=num_warps,
num_stages=1,
)
return o, lse, softmax_scale # softmax_scale could have been updated
def _flash_attn_backward(
do, q, k, v, o, lse, dq, dk, dv, bias=None, causal=False, softmax_scale=None
):
# Make sure that the last dimension is contiguous
if do.stride(-1) != 1:
do = do.contiguous()
batch, seqlen_q, nheads, d = q.shape
_, seqlen_k, _, _ = k.shape
# assert d in {16, 32, 64, 128}
assert d <= 128
seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128
assert lse.shape == (batch, nheads, seqlen_q_rounded)
assert q.stride(-1) == k.stride(-1) == v.stride(-1) == o.stride(-1) == 1
assert dq.stride(-1) == dk.stride(-1) == dv.stride(-1) == 1
softmax_scale = softmax_scale or 1.0 / math.sqrt(d)
# dq_accum = torch.zeros_like(q, dtype=torch.float32)
dq_accum = torch.empty_like(q, dtype=torch.float32)
delta = torch.empty_like(lse)
# delta = torch.zeros_like(lse)
BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16)
grid = lambda META: (triton.cdiv(seqlen_q, META["BLOCK_M"]), batch * nheads)
_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,
)
has_bias = bias is not None
bias_type = "none"
if has_bias:
assert bias.dtype in [q.dtype, torch.float]
assert bias.is_cuda
assert bias.dim() == 4
assert bias.stride(-1) == 1
if bias.shape[2:] == (1, seqlen_k):
bias_type = "vector"
elif bias.shape[2:] == (seqlen_q, seqlen_k):
bias_type = "matrix"
else:
raise RuntimeError(
"Last 2 dimensions of bias must be (1, seqlen_k)" " or (seqlen_q, seqlen_k)"
)
bias = bias.expand(batch, nheads, seqlen_q, seqlen_k)
bias_strides = (bias.stride(0), bias.stride(1), bias.stride(2)) if has_bias else (0, 0, 0)
# BLOCK_M = 128
# BLOCK_N = 64
# num_warps = 4
grid = lambda META: (
triton.cdiv(seqlen_k, META["BLOCK_N"]) if META["SEQUENCE_PARALLEL"] else 1,
batch * nheads,
)
_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, # key for triton cache (limit number of compilations)
# Can't use kwargs here because triton autotune expects key to be args, not kwargs
# IS_CAUSAL=causal, BLOCK_HEADDIM=d,
bias_type,
causal,
BLOCK_HEADDIM,
# SEQUENCE_PARALLEL=False,
# BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N,
# num_warps=num_warps,
# num_stages=1,
)
dq.copy_(dq_accum)
class FlashAttnQKVPackedFunc(torch.autograd.Function):
@staticmethod
def forward(ctx, qkv, bias=None, causal=False, softmax_scale=None):
"""
qkv: (batch, seqlen, 3, nheads, headdim)
bias: optional, shape broadcastible to (batch, nheads, seqlen, seqlen).
For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen).
ALiBi mask for non-causal would have shape (1, nheads, seqlen, seqlen)
"""
# Make sure that the last dimension is contiguous
if qkv.stride(-1) != 1:
qkv = qkv.contiguous()
o, lse, ctx.softmax_scale = _flash_attn_forward(
qkv[:, :, 0],
qkv[:, :, 1],
qkv[:, :, 2],
bias=bias,
causal=causal,
softmax_scale=softmax_scale,
)
ctx.save_for_backward(qkv, o, lse, bias)
ctx.causal = causal
return o
@staticmethod
def backward(ctx, do):
qkv, o, lse, bias = ctx.saved_tensors
assert not ctx.needs_input_grad[1], "FlashAttention does not support bias gradient yet"
# Triton's autotune causes the Tensor._version to change, and so Pytorch autograd
# does a memcpy. To avoid this we run in inference_mode, which doesn't track the version.
with torch.inference_mode():
dqkv = torch.empty_like(qkv)
_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,
)
return dqkv, None, None, None
flash_attn_qkvpacked_func = FlashAttnQKVPackedFunc.apply
class FlashAttnKVPackedFunc(torch.autograd.Function):
@staticmethod
def forward(ctx, q, kv, bias=None, causal=False, softmax_scale=None):
"""
q: (batch, seqlen_q, nheads, headdim)
kv: (batch, seqlen_k, 2, nheads, headdim)
bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k).
For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k).
ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k)
"""
# Make sure that the last dimension is contiguous
q, kv = [x if x.stride(-1) == 1 else x.contiguous() for x in [q, kv]]
o, lse, ctx.softmax_scale = _flash_attn_forward(
q, kv[:, :, 0], kv[:, :, 1], bias=bias, causal=causal, softmax_scale=softmax_scale
)
ctx.save_for_backward(q, kv, o, lse, bias)
ctx.causal = causal
return o
@staticmethod
def backward(ctx, do):
q, kv, o, lse, bias = ctx.saved_tensors
if len(ctx.needs_input_grad) >= 3:
assert not ctx.needs_input_grad[2], "FlashAttention does not support bias gradient yet"
# Triton's autotune causes the Tensor._version to change, and so Pytorch autograd
# does a memcpy. To avoid this we run in inference_mode, which doesn't track the version.
with torch.inference_mode():
dq = torch.empty_like(q)
dkv = torch.empty_like(kv)
_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,
)
return dq, dkv, None, None, None
flash_attn_kvpacked_func = FlashAttnKVPackedFunc.apply
class FlashAttnFunc(torch.autograd.Function):
@staticmethod
def forward(ctx, q, k, v, bias=None, causal=False, softmax_scale=None):
"""
q: (batch_size, seqlen_q, nheads, headdim)
k, v: (batch_size, seqlen_k, nheads, headdim)
bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k).
For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k).
ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k)
"""
# Make sure that the last dimension is contiguous
q, k, v = [x if x.stride(-1) == 1 else x.contiguous() for x in [q, k, v]]
o, lse, ctx.softmax_scale = _flash_attn_forward(
q, k, v, bias=bias, causal=causal, softmax_scale=softmax_scale
)
ctx.save_for_backward(q, k, v, o, lse, bias)
ctx.causal = causal
return o
@staticmethod
def backward(ctx, do):
q, k, v, o, lse, bias = ctx.saved_tensors
assert not ctx.needs_input_grad[3], "FlashAttention does not support bias gradient yet"
# Triton's autotune causes the Tensor._version to change, and so Pytorch autograd
# does a memcpy. To avoid this we run in inference_mode, which doesn't track the version.
with torch.inference_mode():
dq = torch.empty_like(q)
dk = torch.empty_like(k)
dv = torch.empty_like(v)
_flash_attn_backward(
do,
q,
k,
v,
o,
lse,
dq,
dk,
dv,
bias=bias,
causal=ctx.causal,
softmax_scale=ctx.softmax_scale,
)
return dq, dk, dv, None, None, None
flash_attn_func = FlashAttnFunc.apply
def flash_attn_unpadded_unpacked_func_triton(q, k, v, bias=None, causal=False, softmax_scale=None):
return flash_attn_triton.flash_attn_func(q, k, v, bias, causal, softmax_scale)
def _flash_attn_forward_cuda(
q,
k,
v,
out,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
causal,
return_softmax,
num_splits=0,
generator=None,
):
"""
num_splits: how much to parallelize over the seqlen_q dimension. num_splits=0 means
it will be set by an internal heuristic. We're exposing num_splits mostly for benchmarking.
Don't change it unless you know what you're doing.
"""
softmax_lse, *rest = flash_attn_cuda.fwd(
q,
k,
v,
out,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
False,
causal,
return_softmax,
num_splits,
generator,
)
# if out.isnan().any() or softmax_lse.isnan().any():
# breakpoint()
S_dmask = rest[0] if return_softmax else None
return out, softmax_lse, S_dmask
def _flash_attn_backward_cuda(
dout,
q,
k,
v,
out,
softmax_lse,
dq,
dk,
dv,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
causal,
num_splits=0,
generator=None,
):
"""
num_splits: whether to parallelize over the seqlen_k dimension (num_splits > 1) or
not (num_splits = 1). num_splits=0 means it will be set by an internal heuristic.
Any value above 1 will call the same kernel (i.e. num_splits=2 would call the same kernel
as num_splits=3), so effectively the choices are 0, 1, and 2.
This hyperparameter can be tuned for performance, but default value (heuristic) should work fine.
"""
_, _, _, softmax_d = flash_attn_cuda.bwd(
dout,
q,
k,
v,
out,
softmax_lse,
dq,
dk,
dv,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
False,
causal,
num_splits,
generator,
)
# if dk.isnan().any() or dk.isnan().any() or dv.isnan().any() or softmax_d.isnan().any():
# breakpoint()
return dq, dk, dv, softmax_d
class FlashAttnQKVPackedFunc(torch.autograd.Function):
@staticmethod
def forward(
ctx,
qkv,
cu_seqlens,
max_seqlen,
dropout_p,
softmax_scale,
causal,
return_softmax,
):
# Save rng_state because the backward pass will regenerate the dropout mask
rng_state = torch.cuda.get_rng_state() if dropout_p > 0 else None
if softmax_scale is None:
softmax_scale = qkv.shape[-1] ** (-0.5)
out, softmax_lse, S_dmask = _flash_attn_forward_cuda(
qkv[:, 0],
qkv[:, 1],
qkv[:, 2],
torch.empty_like(qkv[:, 0]),
cu_seqlens,
cu_seqlens,
max_seqlen,
max_seqlen,
dropout_p,
softmax_scale,
causal=causal,
return_softmax=return_softmax,
)
ctx.save_for_backward(qkv, out, softmax_lse, cu_seqlens, rng_state)
ctx.dropout_p = dropout_p
ctx.max_seqlen = max_seqlen
ctx.softmax_scale = softmax_scale
ctx.causal = causal
return out if not return_softmax else (out, softmax_lse, S_dmask)
@staticmethod
def backward(ctx, dout, *args):
qkv, out, softmax_lse, cu_seqlens, rng_state = ctx.saved_tensors
if rng_state is not None:
cur_rng_state = torch.cuda.get_rng_state()
torch.cuda.set_rng_state(rng_state)
dqkv = torch.empty_like(qkv)
_flash_attn_backward_cuda(
dout,
qkv[:, 0],
qkv[:, 1],
qkv[:, 2],
out,
softmax_lse,
dqkv[:, 0],
dqkv[:, 1],
dqkv[:, 2],
cu_seqlens,
cu_seqlens,
ctx.max_seqlen,
ctx.max_seqlen,
ctx.dropout_p,
ctx.softmax_scale,
ctx.causal,
)
if rng_state is not None:
torch.cuda.set_rng_state(cur_rng_state)
return dqkv, None, None, None, None, None, None
def flash_attn_unpadded_qkvpacked_func_cuda(
qkv,
cu_seqlens,
max_seqlen,
dropout_p,
softmax_scale=None,
causal=False,
return_attn_probs=False,
):
return FlashAttnQKVPackedFunc.apply(
qkv, cu_seqlens, max_seqlen, dropout_p, softmax_scale, causal, return_attn_probs
)
class FlashAttnKVPackedFunc(torch.autograd.Function):
@staticmethod
def forward(
ctx,
q,
kv,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
causal,
return_softmax,
):
# Save rng_state because the backward pass will regenerate the dropout mask
rng_state = torch.cuda.get_rng_state() if dropout_p > 0 else None
if softmax_scale is None:
softmax_scale = q.shape[-1] ** (-0.5)
out, softmax_lse, S_dmask = _flash_attn_forward_cuda(
q,
kv[:, 0],
kv[:, 1],
torch.empty_like(q),
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
causal=causal,
return_softmax=return_softmax,
)
ctx.save_for_backward(q, kv, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state)
ctx.dropout_p = dropout_p
ctx.max_seqlen_q = max_seqlen_q
ctx.max_seqlen_k = max_seqlen_k
ctx.softmax_scale = softmax_scale
ctx.causal = causal
return out if not return_softmax else (out, softmax_lse, S_dmask)
@staticmethod
def backward(ctx, dout, *args):
(
q,
kv,
out,
softmax_lse,
cu_seqlens_q,
cu_seqlens_k,
rng_state,
) = ctx.saved_tensors
if rng_state is not None:
cur_rng_state = torch.cuda.get_rng_state()
torch.cuda.set_rng_state(rng_state)
dq = torch.empty_like(q)
dkv = torch.empty_like(kv)
_flash_attn_backward_cuda(
dout,
q,
kv[:, 0],
kv[:, 1],
out,
softmax_lse,
dq,
dkv[:, 0],
dkv[:, 1],
cu_seqlens_q,
cu_seqlens_k,
ctx.max_seqlen_q,
ctx.max_seqlen_k,
ctx.dropout_p,
ctx.softmax_scale,
ctx.causal,
)
if rng_state is not None:
torch.cuda.set_rng_state(cur_rng_state)
return dq, dkv, None, None, None, None, None, None, None, None
def flash_attn_unpadded_kvpacked_func_cuda(
q,
kv,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale=None,
causal=False,
return_attn_probs=False,
):
"""dropout_p should be set to 0.0 during evaluation
Arguments:
q: (total_q, nheads, headdim), where total_q = total number of query tokens in the batch.
kv: (total_k, 2, nheads, headdim), where total_k = total number of key tokens in the batch.
cu_seqlens_q: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
of the sequences in the batch, used to index into q.
cu_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
of the sequences in the batch, used to index into kv.
max_seqlen_q: int. Maximum query sequence length in the batch.
max_seqlen_k: int. Maximum key sequence length in the batch.
dropout_p: float. Dropout probability.
softmax_scale: float. The scaling of QK^T before applying softmax.
Default to 1 / sqrt(headdim).
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
return_attn_probs: bool. Whether to return the attention probabilities. This option is for
testing only. The returned probabilities are not guaranteed to be correct
(they might not have the right scaling).
Return:
out: (total, nheads, headdim).
softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The
logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
normalization factor).
S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen).
The output of softmax (possibly with different scaling). It also encodes the dropout
pattern (negative means that location was dropped, nonnegative means it was kept).
"""
return FlashAttnKVPackedFunc.apply(
q,
kv,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
causal,
return_attn_probs,
)
class FlashAttnFunc(torch.autograd.Function):
@staticmethod
def forward(
ctx,
q,
k,
v,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
causal,
return_softmax,
):
# Save rng_state because the backward pass will regenerate the dropout mask
rng_state = torch.cuda.get_rng_state() if dropout_p > 0 else None
if softmax_scale is None:
softmax_scale = q.shape[-1] ** (-0.5)
out, softmax_lse, S_dmask = _flash_attn_forward_cuda(
q,
k,
v,
torch.empty_like(q),
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
causal=causal,
return_softmax=return_softmax,
)
ctx.save_for_backward(q, k, v, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state)
ctx.dropout_p = dropout_p
ctx.max_seqlen_q = max_seqlen_q
ctx.max_seqlen_k = max_seqlen_k
ctx.softmax_scale = softmax_scale
ctx.causal = causal
return out if not return_softmax else (out, softmax_lse, S_dmask)
@staticmethod
def backward(ctx, dout, *args):
(
q,
k,
v,
out,
softmax_lse,
cu_seqlens_q,
cu_seqlens_k,
rng_state,
) = ctx.saved_tensors
if rng_state is not None:
cur_rng_state = torch.cuda.get_rng_state()
torch.cuda.set_rng_state(rng_state)
dq, dk, dv = torch.empty_like(q), torch.empty_like(k), torch.empty_like(v)
_flash_attn_backward_cuda(
dout,
q,
k,
v,
out,
softmax_lse,
dq,
dk,
dv,
cu_seqlens_q,
cu_seqlens_k,
ctx.max_seqlen_q,
ctx.max_seqlen_k,
ctx.dropout_p,
ctx.softmax_scale,
ctx.causal,
)
if rng_state is not None:
torch.cuda.set_rng_state(cur_rng_state)
return dq, dk, dv, None, None, None, None, None, None, None, None
def flash_attn_unpadded_func_cuda(
q,
k,
v,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale=None,
causal=False,
return_attn_probs=False,
):
"""dropout_p should be set to 0.0 during evaluation
Arguments:
q: (total_q, nheads, headdim), where total_q = total number of query tokens in the batch.
k: (total_k, nheads, headdim), where total_k = total number of key tokens in the batch.
v: (total_k, nheads, headdim), where total_k = total number of key tokens in the batch.
cu_seqlens_q: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
of the sequences in the batch, used to index into q.
cu_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
of the sequences in the batch, used to index into kv.
max_seqlen_q: int. Maximum query sequence length in the batch.
max_seqlen_k: int. Maximum key sequence length in the batch.
dropout_p: float. Dropout probability.
softmax_scale: float. The scaling of QK^T before applying softmax.
Default to 1 / sqrt(headdim).
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
return_attn_probs: bool. Whether to return the attention probabilities. This option is for
testing only. The returned probabilities are not guaranteed to be correct
(they might not have the right scaling).
Return:
out: (total, nheads, headdim).
softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The
logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
normalization factor).
S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen).
The output of softmax (possibly with different scaling). It also encodes the dropout
pattern (negative means that location was dropped, nonnegative means it was kept).
"""
return FlashAttnFunc.apply(
q,
k,
v,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
causal,
return_attn_probs,
)
class IndexFirstAxis(torch.autograd.Function):
@staticmethod
def forward(ctx, input, indices):
ctx.save_for_backward(indices)
assert input.ndim >= 2
ctx.first_axis_dim, other_shape = input.shape[0], input.shape[1:]
second_dim = other_shape.numel()
# TD [2022-03-04] For some reason torch.gather is a bit faster than indexing.
# return input[indices]
return torch.gather(
rearrange(input, "b ... -> b (...)"), 0, repeat(indices, "z -> z d", d=second_dim)
).reshape(-1, *other_shape)
@staticmethod
def backward(ctx, grad_output):
(indices,) = ctx.saved_tensors
assert grad_output.ndim >= 2
other_shape = grad_output.shape[1:]
grad_output = rearrange(grad_output, "b ... -> b (...)")
grad_input = torch.zeros(
[ctx.first_axis_dim, grad_output.shape[1]],
device=grad_output.device,
dtype=grad_output.dtype,
)
# TD [2022-03-04] For some reason torch.scatter is a bit faster than indexing.
# grad_input[indices] = grad_output
grad_input.scatter_(0, repeat(indices, "z -> z d", d=grad_output.shape[1]), grad_output)
return grad_input.reshape(ctx.first_axis_dim, *other_shape), None
index_first_axis = IndexFirstAxis.apply
class IndexPutFirstAxis(torch.autograd.Function):
@staticmethod
def forward(ctx, values, indices, first_axis_dim):
ctx.save_for_backward(indices)
assert indices.ndim == 1
assert values.ndim >= 2
output = torch.zeros(
first_axis_dim, *values.shape[1:], device=values.device, dtype=values.dtype
)
# TD [2022-03-04] For some reason torch.scatter is a bit faster than indexing.
output[indices] = values
# output.scatter_(0, repeat(indices, 'z -> z d', d=values.shape[1]), values)
return output
@staticmethod
def backward(ctx, grad_output):
(indices,) = ctx.saved_tensors
# TD [2022-03-04] For some reason torch.gather is a bit faster than indexing.
grad_values = grad_output[indices]
# grad_values = torch.gather(grad_output, 0, repeat(indices, 'z -> z d', d=grad_output.shape[1]))
return grad_values, None, None
index_put_first_axis = IndexPutFirstAxis.apply
class IndexFirstAxisResidual(torch.autograd.Function):
@staticmethod
def forward(ctx, input, indices):
ctx.save_for_backward(indices)
assert input.ndim >= 2
ctx.first_axis_dim, other_shape = input.shape[0], input.shape[1:]
second_dim = other_shape.numel()
# TD [2022-03-04] For some reason torch.gather is a bit faster than indexing.
output = input[indices]
# We don't want to reshape input (b ... -> b (...)) since it could change the channel_last
# memory format to channel_first. In other words, input might not be contiguous.
# If we don't detach, Pytorch complains about output being a view and is being modified inplace
return output, input.detach()
@staticmethod
def backward(ctx, grad_output, grad_residual):
(indices,) = ctx.saved_tensors
assert grad_output.ndim >= 2
other_shape = grad_output.shape[1:]
assert grad_residual.shape[1:] == other_shape
grad_input = grad_residual
# grad_input[indices] += grad_output
indices = indices.reshape(indices.shape[0], *((1,) * (grad_output.ndim - 1)))
indices = indices.expand_as(grad_output)
grad_input.scatter_add_(0, indices, grad_output)
return grad_input.reshape(ctx.first_axis_dim, *other_shape), None
index_first_axis_residual = IndexFirstAxisResidual.apply
def unpad_input(hidden_states, attention_mask):
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
max_seqlen_in_batch = seqlens_in_batch.max().item()
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
# TD [2022-03-04] We don't want to index with a bool mask, because Pytorch will expand the
# bool mask, then call nonzero to get the indices, then index with those. The indices is @dim
# times larger than it needs to be, wasting memory. It's faster and more memory-efficient to
# index with integer indices. Moreover, torch's index is a bit slower than it needs to be,
# so we write custom forward and backward to make it a bit faster.
return (
index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices),
indices,
cu_seqlens,
max_seqlen_in_batch,
)
def pad_input(hidden_states, indices, batch, seqlen):
dim = hidden_states.shape[-1]
# output = torch.zeros((batch * seqlen), dim, device=hidden_states.device, dtype=hidden_states.dtype)
# output[indices] = hidden_states
output = index_put_first_axis(hidden_states, indices, batch * seqlen)
return rearrange(output, "(b s) ... -> b s ...", b=batch)
|
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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,451
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/record.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
# from difflib import unified_diff
from .junk import Junk
from .log import Logger
from .reader import Reader
from .exporter import Exporter
from .sanitizer import sanitize
from .error import ExcludeException
from .header import DocFields, EmlFields, Header
from .base import config, Hdr, Record, LnkProximity, LnkAudience
from .nominals import compare, para_make, para_split, para_join, quoter
from .header import TxtFields, ScrFields, InlFields, FwdFields, MixFields
log = Logger(__name__)
class Record(Record, Exporter):
raw = None
junk = None
source = None
no_date = False
label = 'record'
_text = None
@classmethod
def filterer(cls, path, ctxt, cntr, **kw):
kw.update(ctxt=ctxt, cntr=cntr)
rs = iter(cls.reader(Reader(path), **kw))
r = False
while True:
r = r or next(rs)
try:
src, raw = r
fs = cls.fields.extract(raw, **kw)
if fs:
fs, txt = fs
yield raw, Header(vars(fs), **kw), txt, src
except ExcludeException as e:
ctxt.filters.flog.append(ctxt.current, vars(fs))
cntr.incr('-')
r = rs.throw(e)
continue
except ValueError as e2:
log.warning('Failed reading record {}', e2)
# assert False
cntr.incr('F')
r = False
@classmethod
def importer(cls, path, ctxt, **kw):
kw.update(ctxt=ctxt, sort_mbox=True)
for raw, hdr, txt, src in cls.filterer(path, **kw):
if txt is None:
txt = '\n'.join(ctxt.extract(hdr.record_id, raw, **kw))
if 'UnicodeError' not in txt:
yield cls(hdr, src, txt)
@classmethod
def create_from(cls, src, quote, **kw):
fs = cls.fields.extract(quote, **kw)
if fs:
fs, txt = fs
return cls(Header(vars(fs), **kw), src, txt)
def __init__(self, hdr, source=None, raw=None):
self.hdr = hdr
if source is not None:
self.source = source
if raw is not None:
self.raw = raw
def __eq__(self, other):
if isinstance(other, type(self)):
return self.hdr == other.hdr
return NotImplemented
def __repr__(self):
s = self.source
if s:
return '{}({!r}, {!r})'.format(type(self).__name__, self.hdr, s)
return '{}({!r})'.format(type(self).__name__, self.hdr)
@property
def name(self):
return self.hdr.name
@property
def slug(self):
return self.hdr.slug
@property
def audience(self):
ls = (getattr(self.hdr, f, ()) for f in ('from_', 'to', 'cc', 'bcc'))
ns = sorted(set(e for s in ls if s is not None for e in s))
return ', '.join(ns)
@property
def zero_secs(self):
return self.hdr.name,
def text(self, ctxt=None, **_):
if self._text is None:
r = self.raw
if r is None:
self._text = para_join(ctxt.texts.get(self.name, ()))
else:
if self.junk:
r = sanitize(r)
self.raw = self.junk.dejunk_text(r)
self.junk = False
return self.raw
elif self.raw is not None:
del self.raw
return self._text
def topic(self, ctxt=None, **_):
if self._topic is None:
t = ctxt.topics.resolve_all(self.name, self.subject(ctxt),
self.audience, self.hdr.topic)
self._topic = t or config.TBD
return self._topic
def subject(self, ctxt=None, **_):
if self._subject is None:
ss = ctxt.subjects
self._subject = ss.resolve_all(self.name, self.hdr.subject)
return self._subject
def reducer(self, **kw):
ts = []
for lv, qs in quoter(self.text().splitlines()):
if lv == 0:
qs = '\n'.join(qs).strip()
if qs:
ts.append(qs)
else:
es = []
for m in (InlRec.create_from, FwdRec.create_from):
try:
yield lv, m(self.source, qs, **kw, raise_exclude=False)
break
except Exception as e:
es.append(e)
# import traceback as tb
# tb.print_tb(e.__traceback__)
else:
f = 'Quoting failed {}\n{!r}\n{!r}'
log.warning(f, self.name, qs, es)
assert False
self._text = para_make('\n'.join(ts))
def expand(self, txt, ctxt):
self._text = txt
ctxt.texts.expand(self.hdr.name, para_split(txt))
ctxt.nominals.append(txt)
def consolidate(self, others, ctxt, cntr, **kw):
ds = []
old = None
h = self.hdr
t = self.text()
for o in sorted(others, key=lambda m: m.name):
oh = o.hdr
hc = h.compare(oh)
ot = o.text(ctxt)
tc = compare(t, ot)
if tc:
if hc in (config.EQ, config.LT):
assert not old
if tc is config.GT:
# o.expand(t, ctxt)
pass
cntr.incr('=' if hc is config.EQ else '<')
return
elif hc is config.GT:
if tc is config.GT:
# o.expand(t, ctxt)
pass
elif tc is config.LT:
t = ot
assert not old
old, o.hdr = oh.name, h
if self.source is not None:
o.source = self.source
self = o
continue
ds.append(oh.date)
if h.name == oh.name:
h.date.after(ds)
if not old:
return self
ctxt.rename_msg(old, self.name)
self.register(ctxt)
cntr.incr('>')
def rename(self, old, new):
h = self.hdr
if h.replying == old:
h.replying = new
q = h.quoting
if q and old in q:
q = tuple(new if e == old else e for e in q)
if q:
h.quoting = q
else:
del h.quoting
def rectify(self, ctxt, force=False, **_):
h = self.hdr
r = h.replying
if r:
try:
h.replying = r = ctxt.mids[r]
except KeyError:
if not force:
return
q = h.quoting
if q and r in q:
q = tuple(e for e in q if e != r)
if q:
h.quoting = q
else:
del h.quoting
def register(self, ctxt, **_):
h = self.hdr
n, m = h.name, h.record_id
if m:
ctxt.mids[m] = n
del h.record_id
self.rectify(ctxt, force=True)
s = self.subject(ctxt)
if s:
ctxt.subjects[s] = n
t = self.topic(ctxt)
if t:
ctxt.topics[t] = n
s = self.source
if s:
ctxt.sources[s] = n
t = self.text()
ctxt.texts.register(n, para_split(t))
ctxt.nominals.append(t)
def undirected(self, links=(), **_):
if links is not None:
h, n = self.hdr, self.name
ls = (l for l in Hdr.links
if not l.directed and (not links or l in links))
for l in ls:
o = getattr(h, l.label)
if isinstance(o, tuple):
for i in o:
if i:
yield i, n, l
elif o:
yield o, n, l
def edger(self, links=(), directed=True, **kw):
if links is not None:
h, n = self.hdr, self.name
ls = (l for l in Hdr.links
if l.directed and (not links or l in links))
for l in ls:
o = getattr(h, l.label)
if isinstance(o, tuple):
for i in o:
if i and '|' in i:
yield i, n, l
elif o and '|' in o:
yield o, n, l
a = LnkAudience
if not links or a in links:
yield self.name, self.audience, a
if not directed:
yield from self.undirected(links, **kw)
class TxtRec(Record):
reader = Reader.from_tbox
fields = TxtFields
def edger(self, links=(), directed=True, **kw):
if links is not None:
yield from super().edger(links, directed, **kw)
p = LnkProximity
if not links or p in links:
h = self.hdr
yield h.name, h.date.proximity, p
class MixRec(Record):
reader = Reader.from_bbox
fields = MixFields
junk = Junk()
@property
def zero_secs(self):
return self.hdr.date.zero_secs
class ScrRec(TxtRec):
no_date = True
reader = Reader.from_sbox
fields = ScrFields
class DocRec(Record):
reader = Reader.from_docs
fields = DocFields
class PicRec(DocRec):
pass
class BlogRec(DocRec):
pass
class StoryRec(DocRec):
reader = Reader.from_main
class EmlRec(Record):
reader = Reader.from_mbox
fields = EmlFields
junk = Junk()
@property
def zero_secs(self):
return self.hdr.date.zero_secs
class InlRec(EmlRec):
fields = InlFields
junk = None
class FwdRec(EmlRec):
fields = FwdFields
junk = None
|
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["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,452
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/base/doc/dispatch.py
|
# Copyright 2019 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
from .blog import Blog
from .base import config
from .mboxes import Mboxes
from .meta import converter
from .context import Context
from .counter import counters
from .analyzer import Analyzer
from .log import Logger, start_stop_log
from .resource import Resource, resource
from .realm import Realm, realm_as, Agent
from .edit import protect, redact, obfuscate
log = Logger(__name__)
class Dispatch(Resource):
_res_path = config.qnar_dst + 'dispatch.qnr'
_blog = 'blog'
_ctxt = None
@classmethod
def globals(cls):
return globals()
@property
def ctxt(self):
if self._ctxt is None:
self._ctxt = Context.create(self.base, self.realm)
return self._ctxt
def filt_mbox(self, pool=None, **kw):
with resource(self.ctxt) as ctxt:
kw.update(ctxt=ctxt)
with start_stop_log(log, 'Filtering '):
if pool:
Mboxes(self.base).pool_filt(**kw)
else:
Mboxes(self.base).filt_mbox(**kw)
def merge_mbox(self, pool=None, wdir=None, **kw):
wdir = wdir or config.ARCH
wdir = config.recs_src + '/' + wdir + '/' + config.MBOX
with resource(self.ctxt) as ctxt:
kw.update(ctxt=ctxt, wdir=wdir)
with start_stop_log(log, 'Merging '):
if pool:
Mboxes(self.base).pool_merge(**kw)
else:
Mboxes(self.base).merge_mbox(**kw)
def strip_mbox(self, pool=None, wdir=None, **kw):
wdir = wdir or config.ARCH
wdir = config.recs_src + '/' + wdir + '/' + config.MBOX
with resource(self.ctxt) as ctxt:
kw.update(ctxt=ctxt, wdir=wdir)
with start_stop_log(log, 'Stripping '):
if pool:
Mboxes(self.base).pool_strip(**kw)
else:
Mboxes(self.base).strip_mbox(**kw)
def import_from(self, src, **kw):
with resource(self.ctxt) as ctxt:
kw.update(ctxt=ctxt)
with start_stop_log(log, 'Importing from ' + src):
ctxt.recs.import_from(src, **kw)
def protect(self, **kw):
with resource(self.ctxt) as ctxt:
for n, t in config.bridge_aliases[self.realm]:
ctxt.add_alias(n, t)
r = config.PRIV
with start_stop_log(log, 'Protecting from ' + r):
s = Context.create(self.base, r)
ctxt.recs.copy_from(s, protect, **kw, ctxt=ctxt)
def redact(self, **kw):
with resource(self.ctxt) as ctxt:
for n, t in config.bridge_aliases[self.realm]:
ctxt.add_alias(n, t)
for r in (config.PROT, config.PRIV):
with start_stop_log(log, 'Redacting from ' + r):
s = Context.create(self.base, r)
ctxt.recs.copy_from(s, redact, **kw, ctxt=ctxt)
def obfuscate(self, **kw):
with resource(self.ctxt) as ctxt:
for n, t in config.bridge_aliases[self.realm]:
ctxt.add_alias(n, t)
for r in (config.PUBL, config.PROT, config.PRIV):
with start_stop_log(log, 'Obfuscating from ' + r):
s = Context.create(self.base, r)
ctxt.recs.copy_from(s, obfuscate, **kw, ctxt=ctxt)
def check_recs(self, **kw):
a = Analyzer()
with resource(self.ctxt) as ctxt:
kw.update(ctxt=ctxt)
with start_stop_log(log, 'Checking '):
ms = ctxt.recs
a.check_sanity(ms.grapher(**kw, links=None), **kw)
a.check_coherence(ms.grapher(**kw, links=None), **kw)
def graph_recs(self, **kw):
pass
# with resource(self.ctxt) as ctxt:
# q = config.qnar_dst
# with graph(ctxt.base / (q + '/qnarre.dot'), **kw) as g:
# for c in TxtChains.creator(ctxt.recs):
# for n in c:
# print(n)
def export_all(self, kind, **kw):
with resource(self.ctxt) as ctxt:
kw.update(ctxt=ctxt)
with start_stop_log(log, 'Exporting ' + kind):
src = self.base / (config.SRC + self.realm)
dst = self.base / (config.DST + self.realm)
if kind is config.ORGS:
from .images import Orgs
Orgs(src, dst).export_all(**kw)
elif kind is config.IMGS:
from .images import Pngs
Pngs(src, dst).export_all(**kw)
elif kind is config.PICS:
from .images import Jpgs
Jpgs(src, dst).export_all(**kw)
elif kind is config.MBOX:
Mboxes(self.base).export_to(dst, **kw)
elif kind is config.BLOG:
Blog(self.base).populate(dst, **kw)
convert_args = ((('converted', '.'), ('failed', 'F')), 'Converting:')
def convert(self, regy, **kw):
with start_stop_log(log, 'Converting ' + self.realm.upper()):
ctxt = self.ctxt
with counters(self.convert_args, kw) as cs:
with realm_as(Realm.realms[self.realm]):
for c in ctxt.contacts:
Agent.convert(c, regy=regy)
cs.incr('.')
for _, rs in ctxt.recs.chainer(**kw, ctxt=ctxt):
for r in rs:
converter.convert(r, regy=regy, ctxt=ctxt)
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/deberta.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/fast/splinter.py": ["/qnarre/tokens/fast.py"], "/qnarre/models/old/trafo.py": ["/qnarre/core/attention.py", "/qnarre/core/base.py", "/qnarre/core/mlp.py", "/qnarre/core/deduce.py", "/qnarre/core/norm.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/led.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/realm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/convbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/data2vec.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/tools/triton/python/triton/language/math.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/run/beam.py": ["/qnarre/run/qa.py"], "/tools/triton/python/triton/compiler/compiler.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/tools/disasm.py", "/tools/triton/python/triton/compiler/code_generator.py", "/tools/triton/python/triton/compiler/make_launcher.py"], "/qnarre/base/doc/graph.py": ["/qnarre/base/doc/base.py"], "/qnarre/base/activism.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/judgment.py"], "/qnarre/base/doc/exporter.py": ["/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/fnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/roformer.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/megatron.py": ["/qnarre/prep/config/megatron.py", "/qnarre/models/megatron.py"], "/qnarre/prep/tokens/fast/roformer.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/roformer.py"], "/qnarre/core/runner.py": ["/qnarre/core/params.py"], "/qnarre/run/seq2seq.py": ["/qnarre/run/qa.py"], "/qnarre/prep/tokens/luke.py": ["/qnarre/tokens/utils.py"], 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"/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/models/plbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/reformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/images.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/base.py"], "/qnarre/models/yoso.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/canine.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/blog.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/counter.py"], "/qnarre/models/longformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/fast/pegasus.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/pegasus.py"], "/qnarre/base/judgment.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,453
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/models/big_bird.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import numpy as np
import torch
from dataclasses import dataclass
from torch import nn
from torch.nn import functional as F
from transformers.utils import logging
from torch.utils.checkpoint import checkpoint
from .. import core as qc
from ..core import utils as qu
from ..core import output as qo
from ..core import forward as qf
from ..core import attention as qa
from ..core.embed import Embed
from ..core.mlp import Classifier, MLP, Predictor, Pool
from ..prep.config.big_bird import PreTrained
from . import bert
log = logging.get_logger(__name__)
class ForCausal(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Predictor(**kw)
def forward(self, x, labels=None, **kw):
cfg = self.cfg
ys = self.model(x, **kw)
y = self.proj(ys[0])
loss = None
if labels is not None:
sl = y[:, :-1, :].contiguous()
ls = labels[:, 1:].contiguous()
loss = nn.CrossEntropyLoss()(sl.view(-1, cfg.s_vocab), ls.view(-1))
ys = (y,) + ys[2:] + (loss,)
return qo.LossCrosses(*ys)
class ForChoice(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(**kw)
self.drop = qc.Dropout(cfg.drop, **kw)
self.proj = qc.Linear(cfg.d_model, 1, **kw)
forward = bert.ForChoice.forward
class ForMasked(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Predictor(**kw)
forward = qf.forward_masked
class ForPreTraining(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(add_pool=True, **kw)
self.proj = Predictor(**kw)
self.seq = qc.Linear(cfg.d_model, 2, **kw)
def forward(self, x, labels=None, ns_labels=None, **kw):
cfg = self.cfg
ys = self.model(x, **kw)
y = self.proj(ys[0])
orders = self.seq(ys[1])
loss = None
if labels is not None:
f = nn.CrossEntropyLoss()
loss = f(y.view(-1, cfg.s_vocab), labels.view(-1))
if loss is not None:
loss = loss + f(orders.view(-1, 2), ns_labels.view(-1))
ys = (y, orders) + ys[2:] + (loss,)
return bert.LossSeq(*ys)
def prep_q_mask(q_lens, n):
y = torch.arange(0, n).to(q_lens.device)
y.unsqueeze_(0)
y = y < q_lens
return y
class ForQA(PreTrained):
def __init__(self, add_pool=False, **kw):
kw.update(n_labels=2)
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(add_pool=add_pool, **kw)
self.drop = qc.Dropout(cfg.drop)
self.ff = MLP(cfg.act, cfg.drop, cfg.eps, cfg)
self.proj = qc.Linear(cfg.d_model, cfg.n_labels)
def forward(self, x, beg=None, end=None, q_lens=None, typ=None, x_emb=None, **kw):
n = x.size(1) if x is not None else x_emb.size(1)
if q_lens is None and x is not None:
q_lens = torch.argmax(x.eq(self.SEP).int(), dim=-1) + 1
q_lens.unsqueeze_(1)
y_m = None
if q_lens is not None:
y_m = prep_q_mask(q_lens, n)
if typ is None:
typ = (~y_m).long()
y_m[:, 0] = False
y_m.unsqueeze_(2)
ys = self.model(x, typ=typ, x_emb=x_emb, **kw)
y = self.proj(self.ff(self.drop(ys[0])))
if y_m is not None:
y = y - y_m * 1e6
b, e = y.split(1, dim=-1)
b = b.squeeze(-1).contiguous()
e = e.squeeze(-1).contiguous()
loss = None
if beg is not None and end is not None:
if len(beg.size()) > 1:
beg = beg.squeeze(-1)
if len(end.size()) > 1:
end = end.squeeze(-1)
i = b.size(1)
f = nn.CrossEntropyLoss(ignore_index=i)
beg = beg.clamp(0, i)
end = end.clamp(0, i)
loss = (f(b, beg) + f(e, end)) / 2
ys = (b, e) + ys[2:] + (loss,)
return qo.LossQAPools(*ys)
class ForSeqClass(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Classifier(cfg.d_model, cfg.act, **kw)
forward = qf.forward_seq # y = self.proj(ys[0][:, 0, :])
class ForTokClass(PreTrained):
def __init__(self, **kw):
super().__init__(**kw)
self.get_cfg(kw)
self.model = Model(**kw)
self.proj = Classifier(**kw)
forward = qf.forward_tok
class Model(PreTrained):
def __init__(self, add_pool=True, **kw):
super().__init__(**kw)
cfg = self.get_cfg(kw)
self.emb = Embed(cfg.d_model, **kw)
self.enc = Encoder(**kw)
self.pool = Pool(**kw) if add_pool else None
if cfg.attn_type != "original_full" and cfg.add_cross:
self.set_attn_type("original_full")
def set_attn_type(self, x):
assert x in ["original_full", "block_sparse"]
cfg = self.cfg
if x == cfg.attn_type:
return
cfg.attn_type = x
self.enc.set_attention_type(x)
def forward(
self,
x,
cache=None,
enc_m=None,
enc=None,
head_m=None,
mask=None,
pos=None,
typ=None,
x_emb=None,
**kw,
):
cfg = self.cfg
if x is not None:
assert x_emb is None
s, d = x.size(), x.device
else:
s, d = x_emb.size()[:-1], x_emb.device
b, n = s
c_len = cache[0][0].shape[2] if cache is not None else 0
if mask is None:
mask = torch.ones(((b, n + c_len)), device=d)
if typ is None:
if hasattr(self.emb, "typ_ids"):
typ = self.emb.typ_ids[:, :n].expand(b, n)
else:
typ = torch.zeros(s, dtype=torch.long, device=d)
max_tokens_to_attend = (5 + 2 * cfg.n_rand_blocks) * cfg.block_size
if cfg.attn_type == "block_sparse" and n <= max_tokens_to_attend:
n = x.size(1) if x is not None else x_emb.size(1)
self.set_attn_type("original_full")
if cfg.attn_type == "block_sparse":
(p_len, x, mask, typ, pos, x_emb) = self.pad_to_block(
x, mask=mask, pos=pos, typ=typ, x_emb=x_emb
)
else:
p_len = 0
if cfg.attn_type == "block_sparse":
(blocked_enc_m, band_m, from_m, to_m) = self.create_masks_for_block(
mask, self.block_size
)
mask = None
else:
assert cfg.attn_type == "original_full"
blocked_enc_m = None
band_m = None
from_m = None
to_m = None
mask = self.get_mask(mask, s, d)
if cfg.is_dec and enc is not None:
if enc_m is None:
enc_m = torch.ones(enc.size()[:2], device=d)
enc_m = self.invert_mask(enc_m)
else:
enc_m = None
head_m = self.get_head_m(head_m, cfg.n_lays)
ys = self.emb(x, c_len=c_len, pos=pos, typ=typ, x_emb=x_emb)
ys = self.enc(
ys,
band_m=band_m,
blocked_enc_m=blocked_enc_m,
cache=cache,
enc_m=enc_m,
enc=enc,
from_m=from_m,
head_m=head_m,
mask=mask,
to_m=to_m,
)
y = ys[0]
pools = self.pool(y[:, 0, :]) if self.pool is not None else None
if p_len > 0:
y = y[:, :-p_len]
ys = (y,) + ys[1:] + (pools,)
return qo.PoolsCrosses(*ys)
@staticmethod
def create_masks_for_block(mask, block):
b, n = mask.size()
assert n % block == 0
def create_band_m(from_m, to_m):
to_pad = torch.cat([to_m[:, 1:-3], to_m[:, 2:-2], to_m[:, 3:-1]], dim=2)
y = torch.einsum("blq,blk->blqk", from_m[:, 2:-2], to_pad)
y.unsqueeze_(1)
return y
enc_m = mask.view(b, n // block, block)
band_m = create_band_m(enc_m, enc_m)
from_m = mask.view(b, 1, n, 1)
to_m = mask.view(b, 1, 1, n)
return enc_m, band_m, from_m, to_m
def pad_to_block(self, x, mask, typ, pos, x_emb, PAD):
cfg = self.cfg
block_size = cfg.block_size
shape = x.shape if x is not None else x_emb.shape
b, n = shape[:2]
p_len = (block_size - n % block_size) % block_size
if p_len > 0:
if x is not None:
x = F.pad(x, (0, p_len), value=PAD)
if pos is not None:
pos = F.pad(pos, (0, p_len), value=PAD)
if x_emb is not None:
p = x_emb.new_full((b, p_len), cfg.PAD, dtype=torch.long)
x_emb = torch.cat([x_emb, self.emb(p)], dim=-2)
mask = F.pad(mask, (0, p_len), value=False)
typ = F.pad(typ, (0, p_len), value=0)
return p_len, x, mask, typ, pos, x_emb
class Encoder(qc.Module):
hs = qc.Hypers({"d_model", "n_heads", "n_pos", "eps"}, {"drop_attn": 0.0, "is_dec": False})
def __init__(self, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
self.lays = qc.Stack([Layer(seed=i, **kw) for i in range(cfg.n_lays)])
self.grad_checkpoint = False
def set_attention_type(self, x):
assert x in ["original_full", "block_sparse"]
cfg = self.cfg
if x == cfg.attn_type:
return
cfg.attn_type = x
for lay in self.lays:
lay.set_attention_type(x)
def forward(self, x, cache=None, head_m=None, **kw):
cfg = self.cfg
y = x
attns = ()
caches = ()
crosses = ()
hiddens = ()
for i, lay in enumerate(self.lays):
hiddens += (y,)
h = head_m[i] if head_m is not None else None
c = cache[i] if cache is not None else None
if self.grad_checkpoint and self.training:
def create_forward(x):
def forward(*xs):
return x(*xs, cache=c)
return forward
ys = checkpoint(create_forward(lay), y, **kw, head_m=h)
else:
ys = lay(y, **kw, cache=c, head_m=h)
y = ys[0]
attns += (ys[1],)
if cfg.add_cross:
crosses += (ys[2],)
caches += (ys[-1],)
hiddens += (y,)
return qo.CachesCrosses(y, attns, caches, crosses, hiddens)
class Layer(qc.Module):
hs = qc.Hypers({"d_model", "n_heads", "n_pos", "eps"}, {"drop_attn": 0.0, "is_dec": False})
def __init__(self, seed=None, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
cfg.attn_type = cfg.attn_type
self.attn = Attention(cfg, seed=seed)
self.is_dec = cfg.is_dec
self.add_cross = cfg.add_cross
if self.add_cross:
assert self.is_dec
self.cross = Attention(cfg)
self.ffnet = MLP(cfg.act, cfg.drop, cfg.eps, **kw)
def set_attention_type(self, x):
assert x in ["original_full", "block_sparse"]
cfg = self.cfg
if x == cfg.attn_type:
return
cfg.attn_type = x
self.attn.set_attention_type(x)
if self.add_cross:
self.cross.set_attention_type(x)
def forward(
self,
x,
mask=None,
head_m=None,
enc=None,
enc_m=None,
band_m=None,
from_m=None,
to_m=None,
blocked_encoder_mask=None,
prev_kv=None,
**kw,
):
self_attn_past_key_value = prev_kv[:2] if prev_kv is not None else None
self_attention_outputs = self.attn(
x,
mask,
head_m,
enc=enc,
enc_m=enc_m,
prev_kv=self_attn_past_key_value,
band_m=band_m,
from_m=from_m,
to_m=to_m,
from_blocked_mask=blocked_encoder_mask,
to_blocked_mask=blocked_encoder_mask,
)
attention_output = self_attention_outputs[0]
if self.is_dec:
y = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
y = self_attention_outputs[1:]
cross_attn_present_key_value = None
if self.is_dec and enc is not None:
assert hasattr(self, "crossattention")
cross_attn_past_key_value = prev_kv[-2:] if prev_kv is not None else None
cross_attention_outputs = self.cross(
attention_output,
mask,
head_m,
enc,
enc_m,
cross_attn_past_key_value,
)
attention_output = cross_attention_outputs[0]
y = y + cross_attention_outputs[1:-1]
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
layer_output = self.ffnet(
attention_output,
)
y = (layer_output,) + y
if self.is_dec:
y = y + (present_key_value,)
return y
class Attention(qc.Module):
hs = qc.Hypers({"d_embed", "d_model", "n_heads", "use_bias", "attn_type"}, {"drop_attn": 0.0})
def __init__(self, seed=None, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
cfg.seed = seed
if cfg.attn_type == "original_full":
self.attn = FullAttn(**kw)
else:
assert cfg.attn_type == "block_sparse"
self.attn = SparseAttn(seed, **kw)
m = cfg.d_model
self.proj = qc.Linear(m, m, **kw)
self.drop = qc.Dropout(cfg.drop, **kw)
self.norm = qc.LayerNorm(m, **kw)
def set_attention_type(self, x):
cfg = self.cfg
assert x in ["original_full", "block_sparse"]
if x == cfg.attn_type:
return
cfg.attn_type = x
if x == "original_full":
a = FullAttn(**kw)
else:
a = SparseAttn(cfg.seed, **kw)
a.query = self.attn.query
a.value = self.attn.value
a.key = self.attn.key
self.attn = a
cfg.attn_type = x
if not self.training:
self.attn.eval()
def forward(self, x, enc=None, **kw):
if self.cfg.attn_type == "original_full":
ys = self.attn(x, **kw, enc=enc)
else:
assert enc is None
ys = self.attn(x, **kw)
y = self.norm(x + self.drop(self.proj(ys[0])))
y = (y,) + ys[1:]
return y
class FullAttn(qc.Module):
hs = qc.Hypers(
{"d_embed", "d_model", "n_heads", "use_bias"},
{"drop_attn": 0.0},
)
def __init__(self, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
m, n = cfg.d_model, cfg.n_heads
assert m % n == 0 or cfg.d_embed is not None
cfg.d_head = h = m // n
cfg.scale = 1 / (h**0.5)
self.query = qc.Linear(m, m, bias=cfg.use_bias, **kw)
self.key = qc.Linear(m, m, bias=cfg.use_bias, **kw)
self.value = qc.Linear(m, m, bias=cfg.use_bias, **kw)
self.drop = qc.Dropout(cfg.drop_attn, **kw)
split_heads = qa.split_heads
def forward(self, x, cache=None, enc_m=None, enc=None, head_m=None, mask=None, **kw):
cfg = self.cfg
q = self.split_heads(self.query(x))
if enc is None:
k = self.split_heads(self.key(x))
v = self.split_heads(self.value(x))
if cache is not None:
k = torch.cat([cache[0], k], dim=2)
v = torch.cat([cache[1], v], dim=2)
else:
mask = enc_m
if cache is None:
k = self.split_heads(self.key(enc))
v = self.split_heads(self.value(enc))
else:
k = cache[0]
v = cache[1]
a = torch.matmul(q, k.transpose(-1, -2))
a.mul_(cfg.scale)
if mask is not None:
a += mask
a = self.drop(F.softmax(a, dim=-1))
if head_m is not None:
a *= head_m
y = torch.matmul(a, v).permute(0, 2, 1, 3).contiguous()
y = y.view(y.size()[:-2] + (cfg.n_heads * cfg.d_head,))
return y, a, (k, v)
class SparseAttn(qc.Module):
hs = qc.Hypers(
{"d_embed", "d_model", "n_heads", "n_pos", "use_bias", "n_rand_blocks", "block_size"},
{"drop_attn": 0.0},
)
def __init__(self, seed=None, ps={}, hs=[], **kw):
super().__init__(ps, [self.hs] + hs, **kw)
cfg = self.get_cfg(kw)
cfg.seed = seed
m, n = cfg.d_model, cfg.n_heads
assert m % n == 0
cfg.d_head = int(m / n)
cfg.s_all_head = h = n * cfg.d_head
self.query = qc.Linear(m, h, bias=cfg.use_bias, **kw)
self.key = qc.Linear(m, h, bias=cfg.use_bias, **kw)
self.value = qc.Linear(m, h, bias=cfg.use_bias, **kw)
split_heads = qa.split_heads
def forward(self, x, **kw):
b, seqlen, _ = x.size()
to_seq_length = from_seq_length = seqlen
from_block_size = to_block_size = self.block_size
assert from_seq_length % from_block_size == 0
assert to_seq_length % to_block_size == 0
q = self.split_heads(self.query(x))
k = self.split_heads(self.key(x))
v = self.split_heads(self.value(x))
ctx, y = self.bigbird_block_sparse_attention(
q,
k,
v,
band_m,
from_m,
to_m,
from_blocked_mask,
to_blocked_mask,
d_head,
from_block_size,
to_block_size,
b,
from_seq_length,
to_seq_length,
plan_from_length=None,
plan_num_rand_blocks=None,
**kw,
)
ctx = ctx.contiguous().view(b, from_seq_length, -1)
return ctx, y
@staticmethod
def torch_bmm_nd(x1, x2, ndim=None):
s1, s2 = x1.shape, x2.shape
return torch.bmm(x1.reshape((-1,) + s1[-2:]), x2.reshape((-1,) + s2[-2:])).view(
s1[: ndim - 2] + (s1[ndim - 2], s2[ndim - 1]),
)
@staticmethod
def torch_bmm_nd_transpose(x1, x2, ndim=None):
s1, s2 = x1.shape, x2.shape
return torch.bmm(
x1.reshape((-1,) + s1[-2:]),
x2.reshape((-1,) + s2[-2:]).transpose(1, 2),
).view(s1[: ndim - 2] + (s1[ndim - 2], s2[ndim - 2]))
def bigbird_block_sparse_attention(
self,
q,
k,
v,
band_m,
from_m,
to_m,
from_blocked_mask,
to_blocked_mask,
d_head,
from_block_size,
to_block_size,
batch_size,
from_seq_len,
to_seq_len,
plan_from_length,
plan_num_rand_blocks,
**kw,
):
cfg = self.cfg
assert from_seq_len // from_block_size == to_seq_len // to_block_size
rsqrt_d = 1 / (d_head**0.5)
bsz = batch_size
attn_mask_penalty = -10000.0
np.random.seed(cfg.seed)
if from_seq_len in [1024, 3072, 4096]:
rand_attn = [
self._bigbird_block_rand_mask(
cfg.n_pos,
cfg.n_pos,
from_block_size,
to_block_size,
last_idx=1024,
)[: (from_seq_len // from_block_size - 2)]
for _ in range(cfg.n_heads)
]
else:
if plan_from_length is None:
plan_from_length, plan_num_rand_blocks = self._get_rand_attn_plan(
from_seq_len, from_block_size
)
rand_attn = self._bigbird_block_rand_mask_with_head(
from_seq_length=from_seq_len,
to_seq_length=to_seq_len,
from_block_size=from_block_size,
to_block_size=to_block_size,
plan_from_length=plan_from_length,
plan_num_rand_blocks=plan_num_rand_blocks,
)
rand_attn = np.stack(rand_attn, axis=0)
rand_attn = torch.tensor(rand_attn, device=q.device, dtype=torch.long)
rand_attn.unsqueeze_(0)
rand_attn = torch.cat([rand_attn for _ in range(batch_size)], dim=0)
rand_mask = self._create_rand_mask_from_inputs(
from_blocked_mask,
to_blocked_mask,
rand_attn,
bsz,
from_seq_len,
from_block_size,
)
q = q.view(bsz, cfg.n_heads, from_seq_len // from_block_size, from_block_size, -1)
blocked_key_matrix = k.view(
bsz, cfg.n_heads, to_seq_len // to_block_size, to_block_size, -1
)
blocked_value_matrix = v.view(
bsz, cfg.n_heads, to_seq_len // to_block_size, to_block_size, -1
)
gathered_key = self.torch_gather_b2(blocked_key_matrix, rand_attn)
gathered_key = gathered_key.view(
bsz, cfg.n_heads, to_seq_len // to_block_size - 2, cfg.n_rand_blocks * to_block_size, -1
)
gathered_value = self.torch_gather_b2(blocked_value_matrix, rand_attn)
gathered_value = gathered_value.view(
bsz, cfg.n_heads, to_seq_len // to_block_size - 2, cfg.n_rand_blocks * to_block_size, -1
)
first_product = self.torch_bmm_nd_transpose(q[:, :, 0], k, ndim=4)
first_product = first_product * rsqrt_d
first_product += (1.0 - to_m) * attn_mask_penalty
first_attn_weights = F.softmax(first_product, dim=-1)
first_context_layer = self.torch_bmm_nd(first_attn_weights, v, ndim=4)
first_context_layer.unsqueeze_(2)
second_key_mat = torch.cat(
[
blocked_key_matrix[:, :, 0],
blocked_key_matrix[:, :, 1],
blocked_key_matrix[:, :, 2],
blocked_key_matrix[:, :, -1],
gathered_key[:, :, 0],
],
dim=2,
)
second_value_mat = torch.cat(
[
blocked_value_matrix[:, :, 0],
blocked_value_matrix[:, :, 1],
blocked_value_matrix[:, :, 2],
blocked_value_matrix[:, :, -1],
gathered_value[:, :, 0],
],
dim=2,
)
second_product = self.torch_bmm_nd_transpose(q[:, :, 1], second_key_mat, ndim=4)
second_seq_pad = torch.cat(
[
to_m[:, :, :, : 3 * to_block_size],
to_m[:, :, :, -to_block_size:],
to_m.new_ones([bsz, 1, 1, cfg.n_rand_blocks * to_block_size]),
],
dim=3,
)
second_rand_pad = torch.cat(
[
rand_mask.new_ones([bsz, cfg.n_heads, from_block_size, 4 * to_block_size]),
rand_mask[:, :, 0],
],
dim=3,
)
second_product = second_product * rsqrt_d
second_product += (1.0 - torch.minimum(second_seq_pad, second_rand_pad)) * attn_mask_penalty
second_attn_weights = F.softmax(second_product, dim=-1)
second_context_layer = self.torch_bmm_nd(second_attn_weights, second_value_mat, ndim=4)
second_context_layer.unsqueeze_(2)
exp_blocked_key_matrix = torch.cat(
[
blocked_key_matrix[:, :, 1:-3],
blocked_key_matrix[:, :, 2:-2],
blocked_key_matrix[:, :, 3:-1],
],
dim=3,
)
exp_blocked_value_matrix = torch.cat(
[
blocked_value_matrix[:, :, 1:-3],
blocked_value_matrix[:, :, 2:-2],
blocked_value_matrix[:, :, 3:-1],
],
dim=3,
)
middle_query_matrix = q[:, :, 2:-2]
inner_band_product = self.torch_bmm_nd_transpose(
middle_query_matrix, exp_blocked_key_matrix, ndim=5
)
inner_band_product = inner_band_product * rsqrt_d
rand_band_product = self.torch_bmm_nd_transpose(
middle_query_matrix, gathered_key[:, :, 1:-1], ndim=5
)
rand_band_product = rand_band_product * rsqrt_d
first_band_product = torch.einsum(
"bhlqd,bhkd->bhlqk", middle_query_matrix, blocked_key_matrix[:, :, 0]
)
first_band_product = first_band_product * rsqrt_d
last_band_product = torch.einsum(
"bhlqd,bhkd->bhlqk", middle_query_matrix, blocked_key_matrix[:, :, -1]
)
last_band_product = last_band_product * rsqrt_d
inner_band_product += (1.0 - band_m) * attn_mask_penalty
first_band_product += (1.0 - to_m[:, :, :, :to_block_size].unsqueeze(3)) * attn_mask_penalty
last_band_product += (1.0 - to_m[:, :, :, -to_block_size:].unsqueeze(3)) * attn_mask_penalty
rand_band_product += (1.0 - rand_mask[:, :, 1:-1]) * attn_mask_penalty
band_product = torch.cat(
[first_band_product, inner_band_product, rand_band_product, last_band_product], dim=-1
)
attn_weights = F.softmax(band_product, dim=-1)
ctx = self.torch_bmm_nd(
attn_weights[:, :, :, :, to_block_size : 4 * to_block_size],
exp_blocked_value_matrix,
ndim=5,
)
ctx += self.torch_bmm_nd(
attn_weights[:, :, :, :, 4 * to_block_size : -to_block_size],
gathered_value[:, :, 1:-1],
ndim=5,
)
ctx += torch.einsum(
"bhlqk,bhkd->bhlqd",
attn_weights[:, :, :, :, :to_block_size],
blocked_value_matrix[:, :, 0],
)
ctx += torch.einsum(
"bhlqk,bhkd->bhlqd",
attn_weights[:, :, :, :, -to_block_size:],
blocked_value_matrix[:, :, -1],
)
second_last_key_mat = torch.cat(
[
blocked_key_matrix[:, :, 0],
blocked_key_matrix[:, :, -3],
blocked_key_matrix[:, :, -2],
blocked_key_matrix[:, :, -1],
gathered_key[:, :, -1],
],
dim=2,
)
second_last_value_mat = torch.cat(
[
blocked_value_matrix[:, :, 0],
blocked_value_matrix[:, :, -3],
blocked_value_matrix[:, :, -2],
blocked_value_matrix[:, :, -1],
gathered_value[:, :, -1],
],
dim=2,
)
second_last_product = self.torch_bmm_nd_transpose(q[:, :, -2], second_last_key_mat, ndim=4)
second_last_seq_pad = torch.cat(
[
to_m[:, :, :, :to_block_size],
to_m[:, :, :, -3 * to_block_size :],
to_m.new_ones([bsz, 1, 1, cfg.n_rand_blocks * to_block_size]),
],
dim=3,
)
second_last_rand_pad = torch.cat(
[
rand_mask.new_ones([bsz, cfg.n_heads, from_block_size, 4 * to_block_size]),
rand_mask[:, :, -1],
],
dim=3,
)
second_last_product = second_last_product * rsqrt_d
second_last_product += (
1.0 - torch.minimum(second_last_seq_pad, second_last_rand_pad)
) * attn_mask_penalty
second_last_attn_weights = F.softmax(second_last_product, dim=-1)
second_last_context_layer = self.torch_bmm_nd(
second_last_attn_weights, second_last_value_mat, ndim=4
)
second_last_context_layer.unsqueeze_(2)
last_product = self.torch_bmm_nd_transpose(q[:, :, -1], k, ndim=4)
last_product = last_product * rsqrt_d
last_product += (1.0 - to_m) * attn_mask_penalty
last_attn_weights = F.softmax(last_product, dim=-1)
last_context_layer = self.torch_bmm_nd(last_attn_weights, v, ndim=4)
last_context_layer.unsqueeze_(2)
ctx = torch.cat(
[
first_context_layer,
second_context_layer,
ctx,
second_last_context_layer,
last_context_layer,
],
dim=2,
)
ctx = ctx.view((bsz, cfg.n_heads, from_seq_len, -1)) * from_m
ctx = torch.transpose(ctx, 1, 2)
y = torch.zeros(
bsz,
cfg.n_heads,
from_seq_len,
to_seq_len,
dtype=torch.float,
device=ctx.device,
)
y[:, :, :from_block_size, :] = first_attn_weights # all keys global
y[:, :, from_block_size : 2 * from_block_size, : 3 * to_block_size] = second_attn_weights[
:, :, :, : 3 * to_block_size
]
y[:, :, from_block_size : 2 * from_block_size, -to_block_size:] = second_attn_weights[
:, :, :, 3 * to_block_size : 4 * to_block_size
]
for p1, i1, w1 in zip(range(bsz), rand_attn, second_attn_weights):
for p2, i2, w2 in zip(range(cfg.n_heads), i1, w1):
attn_probs_view = y.view(
bsz,
cfg.n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)
right_slice = w2[:, 4 * to_block_size :]
attn_probs_view[p1, p2, 1, :, i2[0]] = right_slice.view(
from_block_size, cfg.n_rand_blocks, to_block_size
)
for i in range(from_seq_len // from_block_size - 4):
attn_probs_view = y.view(
bsz,
cfg.n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)[:, :, 2:-2, :, 1:-1, :]
right_slice = attn_weights[:, :, i, :, to_block_size : 4 * to_block_size]
attn_probs_view[:, :, i, :, i : i + 3, :] = right_slice.view(
bsz, cfg.n_heads, from_block_size, 3, to_block_size
)
y[:, :, 2 * from_block_size : -2 * from_block_size, :to_block_size] = attn_weights[
:, :, :, :, :to_block_size
].view(bsz, cfg.n_heads, -1, to_block_size)
y[:, :, 2 * from_block_size : -2 * from_block_size, -to_block_size:] = attn_weights[
:, :, :, :, -to_block_size:
].view(bsz, cfg.n_heads, -1, to_block_size)
for p1, i1, w1 in zip(range(bsz), rand_attn, attn_weights):
for p2, i2, w2 in zip(range(cfg.n_heads), i1, w1):
for i in range(1, len(i2) - 1):
attn_probs_view = y.view(
bsz,
cfg.n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)
right_slice = w2[i - 1, :, 4 * to_block_size : -to_block_size]
attn_probs_view[p1, p2, i + 1, :, i2[i]] = right_slice.view(
from_block_size, cfg.n_rand_blocks, to_block_size
)
y[:, :, -2 * from_block_size : -from_block_size, :to_block_size] = second_last_attn_weights[
:, :, :, :to_block_size
]
y[
:, :, -2 * from_block_size : -from_block_size, -3 * to_block_size :
] = second_last_attn_weights[:, :, :, to_block_size : 4 * to_block_size]
for p1, i1, w1 in zip(range(bsz), rand_attn, second_last_attn_weights):
for p2, i2, w2 in zip(range(cfg.n_heads), i1, w1):
attn_probs_view = y.view(
bsz,
cfg.n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)
right_slice = w2[:, 4 * to_block_size :]
attn_probs_view[p1, p2, -2, :, i2[-1]] = right_slice.view(
from_block_size, cfg.n_rand_blocks, to_block_size
)
y[:, :, -from_block_size:, :] = last_attn_weights
return ctx, y
@staticmethod
def torch_gather_b2(params, indices):
assert params.shape[:2] == indices.shape[:2]
num_indices_to_gather = indices.shape[-2] * indices.shape[-1]
num_indices_to_pick_from = params.shape[2]
indices_shift = (
torch.arange(
indices.shape[0] * indices.shape[1] * num_indices_to_gather, device=indices.device
)
// num_indices_to_gather
* num_indices_to_pick_from
)
flattened_indices = indices.view(-1) + indices_shift
flattened_params = params.reshape(-1, params.shape[-2], params.shape[-1])
y = flattened_params.index_select(0, flattened_indices)
y = y.reshape(params.shape[:2] + (num_indices_to_gather,) + params.shape[3:])
return y
@staticmethod
def _create_rand_mask_from_inputs(
from_blocked_mask,
to_blocked_mask,
rand_attn,
num_rand_blocks,
batch_size,
from_seq_length,
from_block_size,
):
num_windows = from_seq_length // from_block_size - 2
rand_mask = torch.stack([p1[i1.flatten()] for p1, i1 in zip(to_blocked_mask, rand_attn)])
rand_mask = rand_mask.view(
batch_size, n_heads, num_windows, num_rand_blocks * from_block_size
)
rand_mask = torch.einsum("blq,bhlk->bhlqk", from_blocked_mask[:, 1:-1], rand_mask)
return rand_mask
@staticmethod
def _get_rand_attn_plan(from_seq_length, from_block_size, num_rand_blocks):
plan_from_length = []
plan_num_rand_blocks = []
if (2 * num_rand_blocks + 5) < (from_seq_length // from_block_size):
plan_from_length.append(int((2 * num_rand_blocks + 5) * from_block_size))
plan_num_rand_blocks.append(num_rand_blocks)
plan_from_length.append(from_seq_length)
plan_num_rand_blocks.append(0)
elif (num_rand_blocks + 5) < (from_seq_length // from_block_size):
plan_from_length.append(int((num_rand_blocks + 5) * from_block_size))
plan_num_rand_blocks.append(num_rand_blocks // 2)
plan_from_length.append(from_seq_length)
plan_num_rand_blocks.append(num_rand_blocks - (num_rand_blocks // 2))
else:
plan_from_length.append(from_seq_length)
plan_num_rand_blocks.append(num_rand_blocks)
return plan_from_length, plan_num_rand_blocks
@staticmethod
def _bigbird_block_rand_mask(
from_seq_length, to_seq_length, from_block_size, to_block_size, num_rand_blocks, last_idx=-1
):
assert from_seq_length // from_block_size == to_seq_length // to_block_size
rand_attn = np.zeros(
(from_seq_length // from_block_size - 2, num_rand_blocks), dtype=np.int32
)
middle_seq = np.arange(1, to_seq_length // to_block_size - 1, dtype=np.int32)
last = to_seq_length // to_block_size - 1
if last_idx > (2 * to_block_size):
last = (last_idx // to_block_size) - 1
r = num_rand_blocks
for i in range(1, from_seq_length // from_block_size - 1):
start = i - 2
end = i
if i == 1:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[2:last])[:r]
elif i == 2:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[3:last])[:r]
elif i == from_seq_length // from_block_size - 3:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:last])[:r]
elif i == from_seq_length // from_block_size - 2:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:last])[:r]
else:
if start > last:
start = last
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:start])[:r]
elif (end + 1) == last:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:start])[:r]
else:
rand_attn[i - 1, :] = np.random.permutation(
np.concatenate((middle_seq[:start], middle_seq[end + 1 : last]))
)[:r]
return rand_attn
def _bigbird_block_rand_mask_with_head(
self,
from_seq_length,
to_seq_length,
from_block_size,
to_block_size,
plan_from_length,
plan_num_rand_blocks,
window_block_left=1,
window_block_right=1,
global_block_top=1,
global_block_bottom=1,
global_block_left=1,
global_block_right=1,
):
cfg = self.cfg
assert from_seq_length // from_block_size == to_seq_length // to_block_size
assert from_seq_length in plan_from_length
num_blocks = from_seq_length // from_block_size
plan_block_length = np.array(plan_from_length) // from_block_size
max_plan_idx = plan_from_length.index(from_seq_length)
rand_attn = [
np.zeros((num_blocks, np.sum(plan_num_rand_blocks[: max_plan_idx + 1])), dtype=np.int32)
for i in range(cfg.n_heads)
]
for plan_idx in range(max_plan_idx + 1):
rnd_r_cnt = 0
if plan_idx > 0:
if plan_num_rand_blocks[plan_idx] > 0:
rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:plan_idx]))
curr_r_cnt = int(np.sum(plan_num_rand_blocks[: plan_idx + 1]))
for blk_rw_idx in range(global_block_top, plan_block_length[plan_idx - 1]):
for h in range(cfg.n_heads):
rand_attn[h][
blk_rw_idx, rnd_r_cnt:curr_r_cnt
] = self._get_single_block_row_attention(
block_id=blk_rw_idx,
to_start_block_id=plan_block_length[plan_idx - 1],
to_end_block_id=plan_block_length[plan_idx],
num_rand_blocks=plan_num_rand_blocks[plan_idx],
window_block_left=window_block_left,
window_block_right=window_block_right,
global_block_left=global_block_left,
global_block_right=global_block_right,
)
for pl_id in range(plan_idx):
if plan_num_rand_blocks[pl_id] == 0:
continue
for blk_rw_idx in range(
plan_block_length[plan_idx - 1], plan_block_length[plan_idx]
):
rnd_r_cnt = 0
to_start_block_id = 0
if pl_id > 0:
rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:pl_id]))
to_start_block_id = plan_block_length[pl_id - 1]
curr_r_cnt = int(np.sum(plan_num_rand_blocks[: pl_id + 1]))
for h in range(cfg.n_heads):
rand_attn[h][
blk_rw_idx, rnd_r_cnt:curr_r_cnt
] = self._get_single_block_row_attention(
block_id=blk_rw_idx,
to_start_block_id=to_start_block_id,
to_end_block_id=plan_block_length[pl_id],
num_rand_blocks=plan_num_rand_blocks[pl_id],
window_block_left=window_block_left,
window_block_right=window_block_right,
global_block_left=global_block_left,
global_block_right=global_block_right,
)
if plan_num_rand_blocks[plan_idx] == 0:
continue
curr_r_cnt = int(np.sum(plan_num_rand_blocks[: plan_idx + 1]))
from_start_block_id = global_block_top
to_start_block_id = 0
if plan_idx > 0:
rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:plan_idx]))
from_start_block_id = plan_block_length[plan_idx - 1]
to_start_block_id = plan_block_length[plan_idx - 1]
for blk_rw_idx in range(from_start_block_id, plan_block_length[plan_idx]):
for h in range(cfg.n_heads):
rand_attn[h][
blk_rw_idx, rnd_r_cnt:curr_r_cnt
] = self._get_single_block_row_attention(
block_id=blk_rw_idx,
to_start_block_id=to_start_block_id,
to_end_block_id=plan_block_length[plan_idx],
num_rand_blocks=plan_num_rand_blocks[plan_idx],
window_block_left=window_block_left,
window_block_right=window_block_right,
global_block_left=global_block_left,
global_block_right=global_block_right,
)
for nh in range(cfg.n_heads):
rand_attn[nh] = rand_attn[nh][global_block_top : num_blocks - global_block_bottom, :]
return rand_attn
@staticmethod
def _get_single_block_row_attention(
block_id,
to_start_block_id,
to_end_block_id,
num_rand_blocks,
window_block_left=1,
window_block_right=1,
global_block_left=1,
global_block_right=1,
):
to_block_list = np.arange(to_start_block_id, to_end_block_id, dtype=np.int32)
perm_block = np.random.permutation(to_block_list)
illegal_blocks = list(
range(block_id - window_block_left, block_id + window_block_right + 1)
)
illegal_blocks.extend(list(range(global_block_left)))
illegal_blocks.extend(list(range(to_end_block_id - global_block_right, to_end_block_id)))
if block_id == 1:
illegal_blocks.append(to_end_block_id - 2)
if block_id == to_end_block_id - 2:
illegal_blocks.append(1)
selected_random_blokcs = []
for i in range(to_end_block_id - to_start_block_id):
if perm_block[i] not in illegal_blocks:
selected_random_blokcs.append(perm_block[i])
if len(selected_random_blokcs) == num_rand_blocks:
break
return np.array(selected_random_blokcs, dtype=np.int32)
|
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"/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], 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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,454
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/test/unit/language/print_helper.py
|
import sys
import torch
from torch.testing import assert_close
import triton
import triton.language as tl
@triton.jit
def kernel_device_print(X, Y, BLOCK: tl.constexpr):
x = tl.load(X + tl.arange(0, BLOCK))
tl.device_print("", x)
tl.store(Y + tl.arange(0, BLOCK), x)
@triton.jit
def kernel_print(X, Y, BLOCK: tl.constexpr):
x = tl.load(X + tl.arange(0, BLOCK))
print("", x)
tl.store(Y + tl.arange(0, BLOCK), x)
@triton.jit
def kernel_static_print(X, Y, BLOCK: tl.constexpr):
x = tl.load(X + tl.arange(0, BLOCK))
tl.static_print(x)
tl.store(Y + tl.arange(0, BLOCK), x)
def test_print(func: str, data_type: str):
shape = (128, )
# limit the range of integers so that the sum does not overflow
x = torch.arange(0, shape[0], dtype=torch.int32, device='cuda').to(getattr(torch, data_type))
y = torch.zeros(shape, dtype=x.dtype, device="cuda")
if func == "device_print":
kernel_device_print[(1,)](x, y, BLOCK=shape[0])
elif func == "print":
kernel_print[(1,)](x, y, BLOCK=shape[0])
elif func == "static_print":
kernel_static_print[(1,)](x, y, BLOCK=shape[0])
assert_close(y, x)
if __name__ == "__main__":
test_print(sys.argv[1], sys.argv[2])
|
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"/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,455
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/feature/segformer.py
|
import numpy as np
from PIL import Image
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ImageFeatureExtractionMixin,
ImageInput,
is_torch_tensor,
)
from ...utils import logging
logger = logging.get_logger(__name__)
class SegformerFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize=True,
size=512,
resample=Image.BILINEAR,
do_normalize=True,
image_mean=None,
image_std=None,
reduce_labels=False,
**kw,
):
super().__init__(**kw)
self.do_resize = do_resize
self.size = size
self.resample = resample
self.do_normalize = do_normalize
self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
self.reduce_labels = reduce_labels
def __call__(
self,
images: ImageInput,
segmentation_maps: ImageInput = None,
return_tensors=None,
**kw,
):
valid_images = False
valid_segmentation_maps = False
# Check that images has a valid type
if isinstance(images, (Image.Image, np.ndarray)) or is_torch_tensor(images):
valid_images = True
elif isinstance(images, (list, tuple)):
if (
len(images) == 0
or isinstance(images[0], (Image.Image, np.ndarray))
or is_torch_tensor(images[0])
):
valid_images = True
if not valid_images:
raise ValueError(
"Images must of type `PIL.Image.Image`, `np.ndarray` or `torch.Tensor` (single example),"
"`List[PIL.Image.Image]`, `List[np.ndarray]` or `List[torch.Tensor]` (batch of examples)."
)
# Check that segmentation maps has a valid type
if segmentation_maps is not None:
if isinstance(segmentation_maps, (Image.Image, np.ndarray)) or is_torch_tensor(
segmentation_maps
):
valid_segmentation_maps = True
elif isinstance(segmentation_maps, (list, tuple)):
if (
len(segmentation_maps) == 0
or isinstance(segmentation_maps[0], (Image.Image, np.ndarray))
or is_torch_tensor(segmentation_maps[0])
):
valid_segmentation_maps = True
if not valid_segmentation_maps:
raise ValueError(
"Segmentation maps must of type `PIL.Image.Image`, `np.ndarray` or `torch.Tensor` (single example),"
"`List[PIL.Image.Image]`, `List[np.ndarray]` or `List[torch.Tensor]` (batch of examples)."
)
is_batched = bool(
isinstance(images, (list, tuple))
and (isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]))
)
if not is_batched:
images = [images]
if segmentation_maps is not None:
segmentation_maps = [segmentation_maps]
# reduce zero label if needed
if self.reduce_labels:
if segmentation_maps is not None:
for idx, map in enumerate(segmentation_maps):
if not isinstance(map, np.ndarray):
map = np.array(map)
# avoid using underflow conversion
map[map == 0] = 255
map = map - 1
map[map == 254] = 255
segmentation_maps[idx] = Image.fromarray(map.astype(np.uint8))
# transformations (resizing + normalization)
if self.do_resize and self.size is not None:
images = [
self.resize(image=image, size=self.size, resample=self.resample) for image in images
]
if segmentation_maps is not None:
segmentation_maps = [
self.resize(map, size=self.size, resample=Image.NEAREST)
for map in segmentation_maps
]
if self.do_normalize:
images = [
self.normalize(image=image, mean=self.image_mean, std=self.image_std)
for image in images
]
# return as BatchFeature
data = {"pixel_values": images}
if segmentation_maps is not None:
labels = []
for map in segmentation_maps:
if not isinstance(map, np.ndarray):
map = np.array(map)
labels.append(map.astype(np.int64))
# cast to np.int64
data["labels"] = labels
encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
return encoded_inputs
|
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"/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,456
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/dataset/samsum.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import json
import py7zr
import datasets as ds
_URLS = "https://arxiv.org/src/1911.12237v2/anc/corpus.7z"
class Samsum(ds.GeneratorBasedBuilder):
BUILDER_CONFIGS = [ds.BuilderConfig(name="samsum", version=ds.Version("1.1.0"))]
def _info(self):
return ds.DatasetInfo(
description="",
citation="",
homepage="",
license="",
features=ds.Features(
{
"id": ds.Value("string"),
"dialogue": ds.Value("string"),
"summary": ds.Value("string"),
}
),
)
def _split_generators(self, mgr):
path = mgr.download_and_extract(_URLS)
return [
ds.SplitGenerator(
name=ds.Split.TRAIN,
gen_kw={"filepath": (path, "train.json"), "split": "train"},
),
ds.SplitGenerator(
name=ds.Split.TEST,
gen_kw={"filepath": (path, "test.json"), "split": "test"},
),
ds.SplitGenerator(
name=ds.Split.VALIDATION,
gen_kw={"filepath": (path, "val.json"), "split": "val"},
),
]
def _generate_examples(self, filepath, _):
path, fname = filepath
with py7zr.SevenZipFile(path, "r") as z:
for name, bio in z.readall().items():
if name == fname:
data = json.load(bio)
for e in data:
yield e["id"], e
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/dispatch.py": ["/qnarre/base/doc/blog.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/mboxes.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/context.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/analyzer.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/images.py"], "/qnarre/models/big_bird.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/big_bird.py"], "/tools/triton/python/triton/runtime/jit.py": ["/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/fast/roberta.py": ["/qnarre/tokens/base.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/bigbird.py": ["/qnarre/prep/config/big_bird.py", "/qnarre/models/big_bird.py"], "/qnarre/models/gpt_neo.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/gpt_neo.py"], "/qnarre/prep/convert/t5.py": ["/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/prep/tokens/fsmt.py": 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"/qnarre/base/author.py", "/qnarre/base/conflict.py", "/qnarre/base/conjecture.py"], "/qnarre/models/splinter.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/rag.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/convert/albert.py": ["/qnarre/prep/config/albert.py", "/qnarre/models/albert.py"], "/qnarre/prep/tokens/fast/gpt2.py": ["/qnarre/tokens/fast.py"], "/qnarre/prep/tokens/fast/mbart.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/prep/convert/roformer.py": ["/qnarre/models/roformer.py"], "/qnarre/base/doc/util/tree.py": ["/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py", "/qnarre/base/doc/util/utils.py"], "/qnarre/models/rembert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/t5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/tokens/xlm.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/bert_tf2.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/tools/triton/python/triton/ops/__init__.py": ["/tools/triton/python/triton/ops/cross_entropy.py", "/tools/triton/python/triton/ops/matmul.py"], "/qnarre/core/norm.py": ["/qnarre/core/base.py"], "/qnarre/models/mpnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/author.py": ["/qnarre/base/named.py"], "/qnarre/base/doc/args.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/log.py"], "/qnarre/prep/tokens/gpt.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/distilbert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/proof.py": ["/qnarre/base/claim.py", "/qnarre/base/narrative.py", "/qnarre/base/author.py"], "/qnarre/prep/tokens/byt5.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/convert/electra.py": ["/qnarre/prep/config/electra.py", "/qnarre/models/electra.py"], "/qnarre/models/luke.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,457
|
quantapix/qnarre
|
refs/heads/main
|
/tools/triton/python/triton/runtime/jit.py
|
from __future__ import annotations, division
import ast
import functools
import hashlib
import inspect
import os
import subprocess
import textwrap
from collections import defaultdict, namedtuple
from typing import Callable, Generic, Iterable, Optional, TypeVar, Union, cast, overload
import triton
def get_cuda_stream(idx=None):
if idx is None:
idx = get_current_device()
try:
from torch._C import _cuda_getCurrentRawStream
return _cuda_getCurrentRawStream(idx)
except ImportError:
import torch
return torch.cuda.current_stream(idx).cuda_stream
def get_current_device():
import torch
return torch.cuda.current_device()
def set_current_device(idx):
import torch
torch.cuda.set_device(idx)
def get_device_capability(idx):
import torch
return torch.cuda.get_device_capability(idx)
T = TypeVar('T')
# -----------------------------------------------------------------------------
# Dependencies Finder
# -----------------------------------------------------------------------------
class DependenciesFinder(ast.NodeVisitor):
"""
This AST visitor is used to find dependencies of a JITFunction. This can
be used to invalidate a JITFunction's hash when its source code -- or
that of its dependencies -- changes.
"""
def __init__(self, globals, src) -> None:
super().__init__()
self.ret = hashlib.md5(src.encode("utf-8")).hexdigest()
self.globals = globals
def visit_Name(self, node):
return self.globals.get(node.id, None)
def visit_Attribute(self, node):
lhs = self.visit(node.value)
while isinstance(lhs, ast.Attribute):
lhs = self.visit(lhs.value)
if lhs is None or lhs is triton:
return None
return getattr(lhs, node.attr)
def visit_Call(self, node):
func = self.visit(node.func)
if func is None:
return
if inspect.isbuiltin(func):
return
if func.__module__ and func.__module__.startswith('triton.'):
return
assert isinstance(func, JITFunction), f"Function \"{func.__name__}\" is being called from a Triton function but is not a Triton function itself. Decorate it with @triton.jit to fix this"
if func.hash is None:
tree = ast.parse(func.src)
finder = DependenciesFinder(func.__globals__, func.src)
finder.visit(tree)
func.hash = finder.ret
noinline = str(getattr(func, 'noinline', False))
self.ret = (self.ret + func.hash + noinline).encode("utf-8")
self.ret = hashlib.md5(self.ret).hexdigest()
# -----------------------------------------------------------------------------
# JITFunction
# -----------------------------------------------------------------------------
@functools.lru_cache()
def version_key():
import pkgutil
contents = []
# frontend
with open(__file__, "rb") as f:
contents += [hashlib.md5(f.read()).hexdigest()]
# compiler
compiler_path = os.path.join(*triton.__path__, 'compiler')
for lib in pkgutil.iter_modules([compiler_path]):
with open(lib.module_finder.find_spec(lib.name).origin, "rb") as f:
contents += [hashlib.md5(f.read()).hexdigest()]
# backend
with open(triton._C.libtriton.__file__, "rb") as f:
contents += [hashlib.md5(f.read()).hexdigest()]
# language
language_path = os.path.join(*triton.__path__, 'language')
for lib in pkgutil.iter_modules([language_path]):
with open(lib.module_finder.find_spec(lib.name).origin, "rb") as f:
contents += [hashlib.md5(f.read()).hexdigest()]
# ptxas version
try:
ptxas_version = hashlib.md5(subprocess.check_output(["ptxas", "--version"])).hexdigest()
except Exception:
ptxas_version = ''
return '-'.join(triton.__version__) + '-' + ptxas_version + '-' + '-'.join(contents)
class KernelInterface(Generic[T]):
run: T
def __getitem__(self, grid) -> T:
"""
A JIT function is launched with: fn[grid](*args, **kwargs).
Hence JITFunction.__getitem__ returns a callable proxy that
memorizes the grid.
"""
return cast(T, functools.partial(cast(Callable, self.run), grid=grid))
class JITFunction(KernelInterface[T]):
# Hook for inspecting compiled functions and modules
cache_hook = None
divisibility = 16
@staticmethod
def _key_of(arg):
if hasattr(arg, "dtype"):
return arg.dtype
elif isinstance(arg, bool):
return "i1"
elif isinstance(arg, int):
if -2**31 <= arg and arg <= 2**31 - 1:
return "i32"
elif 2**63 <= arg and arg <= 2**64 - 1:
return "u64"
else:
return "i64"
elif isinstance(arg, float):
return 'fp32'
elif arg is None:
return None
else:
raise TypeError(f'Unsupported type {type(arg)} for {arg}')
@staticmethod
def _spec_of(arg):
if hasattr(arg, "data_ptr"):
return (arg.data_ptr() % JITFunction.divisibility == 0)
elif isinstance(arg, int):
return (arg % 16 == 0, arg == 1)
return (arg is None, )
def _get_config(self, *args):
def is_divisible_by_16(x):
if hasattr(x, "data_ptr"):
return x.data_ptr() % JITFunction.divisibility == 0
elif isinstance(x, int):
return x % JITFunction.divisibility == 0
if x is None:
return True
return False
divisible_by_16 = {i for i, arg in enumerate(args) if is_divisible_by_16(arg) and i not in self.do_not_specialize}
equal_to_1 = {i for i, arg in enumerate(args) if not isinstance(arg, bool) and isinstance(arg, int) and arg == 1 and i not in self.do_not_specialize}
return namedtuple("instance_descriptor", ["divisible_by_16", "equal_to_1"])(tuple(divisible_by_16), tuple(equal_to_1))
# return _triton.code_gen.instance_descriptor(divisible_by_16, equal_to_1)
@staticmethod
def _type_of(key):
# None are nullptr -- implicitly converted to *i8
if key is None:
return '*i8'
dtype_str = str(key).split(".")[-1]
tys = {
"bool": "i1",
"float8e5": "fp8e5",
"float8e4": "fp8e4",
"float16": "fp16",
"bfloat16": "bf16",
"float32": "fp32",
"float64": "fp64",
"int8": "i8",
"int16": "i16",
"int32": "i32",
"int64": "i64",
"uint8": "u8",
"uint16": "u16",
"uint32": "u32",
"uint64": "u64",
}
# reinterpret can create triton type
for v in list(tys.values()):
tys[v] = v
return key if isinstance(key, str) else f"*{tys[dtype_str]}"
def _make_signature(self, sig_key):
signature = ",".join([self._type_of(k) for i, k in enumerate(sig_key)])
return signature
def _make_constants(self, constexpr_key):
constants = dict(zip(self.constexprs, constexpr_key))
return constants
def _call_hook(self, key, signature, device, constants, num_warps, num_stages, extern_libs, configs):
if JITFunction.cache_hook is None:
return False
name = self.fn.__name__
module = self.fn.__module__
arg_reprs = ', '.join([f'{name}: {ty}' for name, ty in zip(self.arg_names, key[1])])
repr = f"{name}[num_warps={num_warps}, num_stages={num_stages}]({arg_reprs})"
key = str(key)
class LegacyCompiler:
def __init__(self, module, name):
self.module = module
self.name = name
pass
kwargs = dict(signature=signature, device=device, constants=constants,
num_warps=num_warps, num_stages=num_stages, extern_libs=extern_libs,
configs=configs)
return JITFunction.cache_hook(key=key, repr=repr, fn=LegacyCompiler(module, name), compile={"key": key, **kwargs}, is_manual_warmup=False, already_compiled=False)
def _get_arg_specialization_key(self, arg) -> str:
arg_annotation = self.__annotations__.get(arg, '')
if arg_annotation == '':
return f'({arg}.data_ptr() % {JITFunction.divisibility} == 0) if hasattr({arg}, "data_ptr") \
else ({arg} % {JITFunction.divisibility} == 0, {arg} == 1) if isinstance({arg}, int) \
else (False,)'
elif 'Tensor' in arg_annotation:
return f'({arg}.data_ptr() % {JITFunction.divisibility} == 0)'
elif arg_annotation == 'int':
return f'({arg} % {JITFunction.divisibility} == 0, {arg} == 1)'
else:
return '(False,)'
def _get_arg_sig_key(self, arg) -> str:
arg_annotation = self.__annotations__.get(arg, '')
if 'Tensor' in arg_annotation:
return f'{arg}.dtype'
elif arg_annotation == 'bool':
return "i1"
elif arg_annotation == 'float':
return 'fp32'
else:
return f'_key_of({arg})'
def _make_launcher(self):
regular_args = [f'{arg}' for i, arg in enumerate(self.arg_names) if i not in self.constexprs]
constexpr_args = [f'{arg}' for i, arg in enumerate(self.arg_names) if i in self.constexprs]
args = ', '.join(regular_args)
# cache key for regular argument type
sig_keys = ', '.join([self._get_arg_sig_key(arg) for arg in regular_args])
# cache key for constexpr argument values
constexpr_keys = ', '.join(constexpr_args)
# cache key for argument specialization
specializations = []
for i, arg in enumerate(regular_args):
if i in self.do_not_specialize:
continue
specializations += [self._get_arg_specialization_key(arg)]
spec_keys = ', '.join(specializations)
grid_args = ','.join([f'"{arg}": {arg}' for arg in self.arg_names])
src = f"""
def {self.fn.__name__}({', '.join(self.arg_names)}, grid, num_warps=4, num_stages=3, extern_libs=None, stream=None, warmup=False, device=None):
sig_key = {sig_keys},
constexpr_key = {f'{constexpr_keys},' if len(constexpr_keys) > 0 else ()}
spec_key = {f'{spec_keys},' if len(spec_keys) > 0 else ()}
key = (version_key, sig_key, constexpr_key, spec_key, num_warps, num_stages, self.debug)
if not extern_libs is None:
key = (key, tuple(extern_libs.items()))
assert num_warps > 0 and (num_warps & (num_warps - 1)) == 0, "num_warps must be a power of 2"
if callable(grid):
grid = grid({{{grid_args}}})
grid_size = len(grid)
grid_0 = grid[0]
grid_1 = grid[1] if grid_size > 1 else 1
grid_2 = grid[2] if grid_size > 2 else 1
if device is None:
device = get_current_device()
set_current_device(device)
if stream is None and not warmup:
stream = get_cuda_stream(device)
bin = cache[device].get(key, None)
if bin is not None:
if not warmup:
bin.c_wrapper(grid_0, grid_1, grid_2, bin.num_warps, bin.shared, stream, bin.cu_function, triton.compiler.CompiledKernel.launch_enter_hook, triton.compiler.CompiledKernel.launch_exit_hook, bin, {args})
return bin
# kernel not cached -- compile
else:
# build dict of constant values
args = [{args}]
all_args = {', '.join([f'{arg}' for arg in self.arg_names])},
configs = self._get_config(*all_args),
constants = self._make_constants(constexpr_key)
constants.update({{i: None for i, arg in enumerate(all_args) if arg is None}})
constants.update({{i: 1 for i in configs[0].equal_to_1}})
# build kernel signature -- doesn't include specialized arguments
signature = {{ i: self._type_of(_key_of(arg)) for i, arg in enumerate(all_args) if i not in self.constexprs }}
# build stub signature -- includes arguments that are specialized
for i, arg in constants.items():
if callable(arg):
raise TypeError(f"Callable constexpr at index {{i}} is not supported")
if not self._call_hook(key, signature, device, constants, num_warps, num_stages, extern_libs, configs):
bin = triton.compile(self, signature=signature, device=device, constants=constants, num_warps=num_warps, num_stages=num_stages, extern_libs=extern_libs, configs=configs, debug=self.debug)
if not warmup:
bin.c_wrapper(grid_0, grid_1, grid_2, bin.num_warps, bin.shared, stream, bin.cu_function, triton.compiler.CompiledKernel.launch_enter_hook, triton.compiler.CompiledKernel.launch_exit_hook, bin, *args)
self.cache[device][key] = bin
return bin
return None
"""
scope = {"version_key": version_key(), "get_cuda_stream": get_cuda_stream,
"self": self, "_spec_of": self._spec_of, "_key_of": self._key_of,
"cache": self.cache, "triton": triton,
"get_current_device": get_current_device,
"set_current_device": set_current_device}
exec(src, scope)
return scope[self.fn.__name__]
def __init__(self, fn, version=None, do_not_specialize=None, debug=None, noinline=None):
self.fn = fn
self.module = fn.__module__
self.version = version
# function signature information
signature = inspect.signature(fn)
self.arg_names = [v.name for v in signature.parameters.values()]
self.has_defaults = any(v.default != inspect._empty for v in signature.parameters.values())
# specialization hints
self.do_not_specialize = [] if do_not_specialize is None else do_not_specialize
self.do_not_specialize = {self.arg_names.index(arg) if isinstance(arg, str) else arg for arg in self.do_not_specialize}
# function source code (without decorators)
self.src = textwrap.dedent(inspect.getsource(fn))
self.src = self.src[self.src.find("def"):]
# cache of just-in-time compiled kernels
self.cache = defaultdict(dict)
self.hash = None
# JITFunction can be instantiated as kernel
# when called with a grid using __getitem__
self.kernel_decorators = []
self.kernel = None
self.debug = os.environ.get("TRITON_DEBUG", "0") == "1" if debug is None else debug
self.noinline = noinline
# annotations
normalize_ty = lambda ty: ty.__name__ if isinstance(ty, type) else ty
self.__annotations__ = {name: normalize_ty(ty) for name, ty in fn.__annotations__.items()}
# index of constexprs
self.constexprs = [self.arg_names.index(name) for name, ty in self.__annotations__.items() if 'constexpr' in ty]
# launcher
self.run = self._make_launcher()
# re-use docs of wrapped function
self.__doc__ = fn.__doc__
self.__name__ = fn.__name__
self.__globals__ = fn.__globals__
self.__module__ = fn.__module__
@property
def cache_key(self):
# TODO : hash should be attribute of `self`
if self.hash is None:
dependencies_finder = DependenciesFinder(globals=self.__globals__, src=self.src)
dependencies_finder.visit(self.parse())
self.hash = dependencies_finder.ret + version_key()
return self.hash
def warmup(self, *args, **kwargs):
return self.run(*map(MockTensor.wrap_dtype, args), **kwargs, warmup=True)
# we do not parse `src` in the constructor because
# the user might want to monkey-patch self.src dynamically.
# Our unit tests do this, for example.
def parse(self):
tree = ast.parse(self.src)
assert isinstance(tree, ast.Module)
assert len(tree.body) == 1
assert isinstance(tree.body[0], ast.FunctionDef)
return tree
def __call__(self, *args, **kwargs):
raise RuntimeError("Cannot call @triton.jit'd outside of the scope of a kernel")
def __setattr__(self, name, value):
# - when kernel decorators change, cached kernel
# needs to be cleared
if name == 'kernel_decorators':
self.kernel = None
super(JITFunction, self).__setattr__(name, value)
# - when `.src` attribute is set, cache path needs
# to be reinitialized
if name == 'src':
self.hash = None
def __repr__(self):
return f"JITFunction({self.module}:{self.fn.__name__})"
# -----------------------------------------------------------------------------
# `jit` decorator
# -----------------------------------------------------------------------------
@overload
def jit(fn: T) -> JITFunction[T]:
...
@overload
def jit(
*,
version=None,
do_not_specialize: Optional[Iterable[int]] = None,
debug: Optional[bool] = None,
noinline: Optional[bool] = None,
) -> Callable[[T], JITFunction[T]]:
...
def jit(
fn: Optional[T] = None,
*,
version=None,
do_not_specialize: Optional[Iterable[int]] = None,
debug: Optional[bool] = None,
noinline: Optional[bool] = None,
interpret: Optional[bool] = None,
) -> Union[JITFunction[T], Callable[[T], JITFunction[T]]]:
"""
Decorator for JIT-compiling a function using the Triton compiler.
:note: When a jit'd function is called, arguments are
implicitly converted to pointers if they have a :code:`.data_ptr()` method
and a `.dtype` attribute.
:note: This function will be compiled and run on the GPU. It will only have access to:
* python primitives,
* builtins within the triton package,
* arguments to this function,
* other jit'd functions
:param fn: the function to be jit-compiled
:type fn: Callable
"""
def decorator(fn: T) -> JITFunction[T]:
assert callable(fn)
if interpret:
from ..debugger.debugger import GridSelector
return GridSelector(fn)
else:
return JITFunction(
fn,
version=version,
do_not_specialize=do_not_specialize,
debug=debug,
noinline=noinline,
)
if fn is not None:
return decorator(fn)
else:
return decorator
# -----------------------------------------------------------------------------
# Utilities for mocking tensors
# -----------------------------------------------------------------------------
class MockTensor:
"""
Can be used in place of real tensors when calling:
kernel.warmup(MockTensor(torch.float32), ...)
"""
@staticmethod
def wrap_dtype(arg):
if arg.__class__.__name__ == "dtype" and\
arg.__module__ == "torch":
return MockTensor(arg)
return arg
def __init__(self, dtype):
self.dtype = dtype
@staticmethod
def data_ptr():
return 0 # optimistically assumes multiple of 16
class TensorWrapper:
def __init__(self, base, dtype):
self.dtype = dtype
self.base = base
self.is_cuda = base.is_cuda
self.device = base.device
def data_ptr(self):
return self.base.data_ptr()
def __str__(self) -> str:
return f'TensorWrapper[{self.dtype}]({self.base})'
def reinterpret(tensor, dtype):
if isinstance(tensor, TensorWrapper):
if dtype == tensor.base.dtype:
# Reinterpreting to the original interpretation; return the base.
return tensor.base
else:
# Reinterpreting a wrapped tensor to a different type.
return TensorWrapper(tensor.base, dtype)
elif hasattr(tensor, "data_ptr"):
# A new wrapper is needed around an unwrapped tensor.
return TensorWrapper(tensor, dtype)
else:
raise TypeError(f'Cannot reinterpret a {type(tensor)}.')
|
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"/tools/triton/python/triton/ops/blocksparse/__init__.py": ["/tools/triton/python/triton/ops/blocksparse/softmax.py"], "/qnarre/base/doc/analyzer.py": ["/qnarre/base/doc/counter.py", "/qnarre/base/doc/contain.py"], "/qnarre/prep/tokens/mpnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/prophetnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/gpt2.py": ["/qnarre/core/mlp.py"], "/qnarre/models/old/convert.py": ["/qnarre/core/utils.py"], "/qnarre/prep/convert/mbart.py": ["/qnarre/prep/config/mbart.py"], "/tools/triton/python/triton/language/semantic.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/prep/tokens/dpr.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/junk.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], "/qnarre/base/doc/reader.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/mboxes.py"], "/qnarre/base/doc/context.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/part.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/recs.py", "/qnarre/base/doc/filters.py", "/qnarre/base/doc/content.py", "/qnarre/base/doc/category.py"], "/qnarre/prep/tokens/rembert.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/debugger/debugger.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py", "/tools/triton/python/triton/debugger/tl_lang.py"], "/qnarre/prep/convert/reformer.py": ["/qnarre/prep/config/reformer.py", "/qnarre/models/reformer.py"], "/qnarre/prep/tokens/perceiver.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/modeling_utils.py": ["/qnarre/core/utils.py"], "/qnarre/tokens/utils.py": ["/qnarre/tokens/base.py"], "/tools/triton/python/triton/language/__init__.py": ["/tools/triton/python/triton/language/standard.py", "/tools/triton/python/triton/language/core.py", "/tools/triton/python/triton/language/random.py"], "/qnarre/base/doc/contain.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/tokens/fast/t5.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/t5.py"], "/tools/triton/python/triton/language/core.py": ["/tools/triton/python/triton/language/__init__.py"], "/qnarre/models/megatron.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/megatron.py"], "/qnarre/models/fnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/doc/record.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py"], 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"/qnarre/prep/config/bert.py"], "/qnarre/base/doc/recs.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/chain.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/date.py", "/qnarre/base/doc/header.py"], "/tools/triton/python/triton/runtime/autotuner.py": ["/tools/triton/python/triton/testing.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/convert/roberta.py": ["/qnarre/prep/config/bert.py", "/qnarre/models/bert.py"], "/qnarre/core/test/deduce.py": ["/qnarre/core/utils.py", "/qnarre/core/embed.py"], "/qnarre/prep/tokens/fast/gpt_neox.py": ["/qnarre/tokens/fast.py"], "/qnarre/base/doc/nominals.py": ["/qnarre/base/doc/base.py"], "/qnarre/models/ibert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/resource.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/qnarre/core/search.py": ["/qnarre/core/base.py"], "/qnarre/prep/tokens/fast/gpt.py": ["/qnarre/tokens/fast.py", "/qnarre/prep/tokens/gpt.py"], "/qnarre/base/doc/header.py": ["/qnarre/base/doc/date.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/category.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/base.py"], "/qnarre/models/ctrl.py": ["/qnarre/core/embed.py", "/qnarre/prep/config/ctrl.py"], "/qnarre/prep/convert/byt5.py": ["/qnarre/prep/convert/t5.py", "/qnarre/prep/config/t5.py", "/qnarre/models/t5.py"], "/qnarre/base/doc/mboxes.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/reader.py", "/qnarre/base/doc/record.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/sanitizer.py", "/qnarre/base/doc/counter.py"], "/qnarre/prep/tokens/fast/deberta.py": ["/qnarre/tokens/base.py", "/qnarre/prep/tokens/fast/gpt2.py", "/qnarre/prep/tokens/deberta.py"], "/qnarre/core/test/attend.py": ["/qnarre/core/utils.py"], "/tools/triton/python/triton/compiler/code_generator.py": ["/tools/triton/python/triton/__init__.py", "/tools/triton/python/triton/language/__init__.py", "/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/models/old/bert2.py": ["/qnarre/core/norm.py"], "/qnarre/base/__init__.py": ["/qnarre/base/org.py", "/qnarre/base/proof.py", "/qnarre/base/net.py", "/qnarre/base/doc.py", "/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/named.py", "/qnarre/base/conflict.py", "/qnarre/base/judgment.py", "/qnarre/base/activism.py", "/qnarre/base/conjecture.py"], "/qnarre/models/t5.py": ["/qnarre/prep/config/t5.py"], "/qnarre/core/deduce.py": ["/qnarre/core/base.py", "/qnarre/core/search.py"], "/qnarre/models/old/bert.py": ["/qnarre/core/squad.py"], "/tools/triton/python/triton/compiler/__init__.py": ["/tools/triton/python/triton/compiler/compiler.py", "/tools/triton/python/triton/compiler/errors.py"], "/qnarre/base/doc/command.py": ["/qnarre/base/doc/qnn.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/args.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/dispatch.py"], "/qnarre/prep/convert/pegasus.py": ["/qnarre/prep/config/pegasus.py"], "/tools/triton/python/triton/ops/matmul.py": ["/tools/triton/python/triton/ops/matmul_perf_model.py"], "/tools/triton/python/triton/debugger/tl_lang.py": ["/tools/triton/python/triton/debugger/core.py", "/tools/triton/python/triton/debugger/memory_map.py"], "/qnarre/prep/tokens/marian.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/xlnet.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlnet.py"], "/qnarre/prep/tokens/deberta2.py": ["/qnarre/tokens/utils.py"], "/qnarre/core/pretrained.py": ["/qnarre/core/base.py"], "/qnarre/base/org.py": ["/qnarre/base/doc.py", "/qnarre/base/net.py", "/qnarre/base/stats.py", "/qnarre/base/named.py"], "/qnarre/models/transfo_xl.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/transfo_xl.py"], "/qnarre/models/roberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/roberta.py"], "/qnarre/base/doc/util/node.py": ["/qnarre/base/doc/util/row.py"], "/qnarre/models/dpr.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/prep/tokens/plbart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/part.py": ["/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py"], "/qnarre/prep/convert/bart.py": ["/qnarre/models/bart.py"], "/qnarre/models/albert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/albert.py"], "/qnarre/prep/tokens/fast/xlnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py"], "/qnarre/models/bart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/models/segformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/chain.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/header.py", "/qnarre/base/doc/counter.py", "/qnarre/base/doc/connect.py", "/qnarre/base/doc/exporter.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/date.py"], "/qnarre/base/conflict.py": ["/qnarre/base/claim.py", "/qnarre/base/author.py", "/qnarre/base/narrative.py", "/qnarre/base/conjecture.py"], "/qnarre/models/electra.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/electra.py"], "/qnarre/base/doc/util/utils.py": ["/qnarre/base/doc/util/item.py", "/qnarre/base/doc/util/row.py", "/qnarre/base/doc/util/node.py"], "/qnarre/base/doc/connect.py": ["/qnarre/base/doc/graph.py", "/qnarre/base/doc/base.py"], "/qnarre/prep/convert/gpt2.py": ["/qnarre/models/gpt2.py"], "/qnarre/base/doc/counter.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/fast/mpnet.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/fast.py", "/qnarre/prep/tokens/mpnet.py"], "/qnarre/base/doc/util/row.py": ["/qnarre/base/doc/util/item.py"], "/qnarre/models/gpt_neox.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py"], "/qnarre/base/claim.py": ["/qnarre/base/named.py"], "/tools/triton/python/triton/runtime/driver.py": ["/tools/triton/python/triton/common/build.py", "/tools/triton/python/triton/runtime/cache.py"], "/qnarre/models/deberta.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/models/xlm.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/xlm.py"], "/qnarre/base/conjecture.py": ["/qnarre/base/claim.py", "/qnarre/base/proof.py", "/qnarre/base/narrative.py"], "/tools/triton/python/triton/testing.py": ["/tools/triton/python/triton/runtime/__init__.py"], "/qnarre/tokens/fast.py": ["/qnarre/tokens/utils.py", "/qnarre/tokens/base.py"], "/qnarre/prep/tokens/bart.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/narrative.py": ["/qnarre/base/named.py"], "/qnarre/prep/convert/transfo_xl.py": ["/qnarre/prep/config/transfo_xl.py", "/qnarre/models/transfo_xl.py"], "/qnarre/base/doc/message.py": ["/qnarre/base/doc/nominals.py", "/qnarre/base/doc/justifier.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/realm.py", "/qnarre/base/doc/part.py"], "/qnarre/models/mbart.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/mbart.py"], "/qnarre/base/doc/content.py": ["/qnarre/base/doc/log.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/resource.py"], "/qnarre/prep/tokens/canine.py": ["/qnarre/tokens/utils.py"], "/qnarre/models/nystromformer.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/prep/tokens/pegasus.py": ["/qnarre/tokens/utils.py"], "/qnarre/base/doc/date.py": ["/qnarre/base/__init__.py"], "/tools/triton/python/triton/language/extra/cuda.py": ["/tools/triton/python/triton/language/__init__.py"], "/tools/triton/python/triton/__init__.py": ["/tools/triton/python/triton/runtime/__init__.py", "/tools/triton/python/triton/runtime/jit.py", "/tools/triton/python/triton/compiler/__init__.py", "/tools/triton/python/triton/debugger/debugger.py"], "/qnarre/prep/tokens/transfo_xl.py": ["/qnarre/tokens/utils.py"], "/tools/triton/python/triton/compiler/make_launcher.py": ["/tools/triton/python/triton/common/__init__.py", "/tools/triton/python/triton/runtime/cache.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/prep/tokens/prophetnet.py": ["/qnarre/tokens/utils.py"], "/qnarre/prep/config/roberta.py": ["/qnarre/prep/config/bert.py"], "/qnarre/base/doc/category.py": ["/qnarre/base/doc/junk.py", "/qnarre/base/doc/log.py", "/qnarre/base/doc/resource.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/part.py"], "/tools/triton/python/triton/runtime/__init__.py": ["/tools/triton/python/triton/runtime/autotuner.py", "/tools/triton/python/triton/runtime/driver.py", "/tools/triton/python/triton/runtime/jit.py"], "/qnarre/models/bert.py": ["/qnarre/core/embed.py", "/qnarre/core/mlp.py", "/qnarre/prep/config/bert.py"], "/qnarre/base/doc/realm.py": ["/qnarre/base/doc/exporter.py", "/qnarre/base/doc/nominals.py", "/qnarre/base/doc/base.py", "/qnarre/base/doc/meta.py", "/qnarre/base/doc/part.py"], "/qnarre/base/net.py": ["/qnarre/base/author.py", "/qnarre/base/claim.py", "/qnarre/base/named.py"], "/tools/triton/python/triton/language/random.py": ["/tools/triton/python/triton/language/__init__.py"]}
|
33,458
|
quantapix/qnarre
|
refs/heads/main
|
/qnarre/prep/tokens/fast/roberta.py
|
# Copyright 2022 Quantapix Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import json
from tokenizers import pre_tokenizers, processors
from ....tokens.base import AddedToken
from ....tokens.fast import PreTrainedTokenizerFast
from ..roberta import Tokenizer as Roberta
VOCAB_FS = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_file": "tokenizer.json",
}
VOCAB_MAP = {
"vocab_file": {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
"roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
"roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json",
},
"merges_file": {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
"roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
"roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt",
},
"tokenizer_file": {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
"roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json",
"roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json",
},
}
INPUT_CAPS = {
"roberta-base": 512,
"roberta-large": 512,
"roberta-large-mnli": 512,
"distilroberta-base": 512,
"roberta-base-openai-detector": 512,
"roberta-large-openai-detector": 512,
}
class Tokenizer(PreTrainedTokenizerFast):
vocab_fs = VOCAB_FS
vocab_map = VOCAB_MAP
input_caps = INPUT_CAPS
model_input_names = ["input_ids", "mask"]
slow_tokenizer_class = Roberta
def __init__(
self,
vocab_file=None,
merges_file=None,
tokenizer_file=None,
errors="replace",
bos="<s>",
eos="</s>",
sep="</s>",
cls="<s>",
unk="<unk>",
pad="<pad>",
msk="<mask>",
add_prefix_space=False,
trim_offsets=True,
**kw,
):
super().__init__(
vocab_file,
merges_file,
tokenizer_file=tokenizer_file,
errors=errors,
bos=bos,
eos=eos,
sep=sep,
cls=cls,
unk=unk,
pad=pad,
msk=msk,
add_prefix_space=add_prefix_space,
trim_offsets=trim_offsets,
**kw,
)
pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type"))
pre_tok_state["add_prefix_space"] = add_prefix_space
self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state)
self.add_prefix_space = add_prefix_space
tokenizer_component = "post_processor"
tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None)
if tokenizer_component_instance:
state = json.loads(tokenizer_component_instance.__getstate__())
if "sep" in state:
state["sep"] = tuple(state["sep"])
if "cls" in state:
state["cls"] = tuple(state["cls"])
changes_to_apply = False
if state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
state["add_prefix_space"] = add_prefix_space
changes_to_apply = True
if state.get("trim_offsets", trim_offsets) != trim_offsets:
state["trim_offsets"] = trim_offsets
changes_to_apply = True
if changes_to_apply:
component_class = getattr(processors, state.pop("type"))
new_value = component_class(**state)
setattr(self.backend_tokenizer, tokenizer_component, new_value)
@property
def msk(self):
if self._mask_token is None and self.verbose:
logger.error("Using msk, but it is not set yet.")
return None
return str(self._mask_token)
@msk.setter
def msk(self, value):
value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value
self._mask_token = value
def _batch_encode_plus(self, *args, **kw):
is_split_into_words = kw.get("is_split_into_words", False)
assert self.add_prefix_space or not is_split_into_words, (
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*args, **kw)
def _encode_plus(self, *args, **kw):
is_split_into_words = kw.get("is_split_into_words", False)
assert self.add_prefix_space or not is_split_into_words, (
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._encode_plus(*args, **kw)
def save_vocabulary(self, dir, pre=None):
return tuple(self._tokenizer.model.save(dir, name=pre))
def build_inputs_with_special_tokens(self, toks_0, toks_1=None):
y = [self.BOS] + toks_0 + [self.EOS]
if toks_1 is None:
return y
return y + [self.EOS] + toks_1 + [self.EOS]
def create_token_type_ids_from_sequences(self, toks_0, toks_1=None):
sep = [self.SEP]
cls = [self.cls_token_id]
if toks_1 is None:
return len(cls + toks_0 + sep) * [0]
return len(cls + toks_0 + sep + sep + toks_1 + sep) * [0]
|
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