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train · 3.29M rows

index int64	repo_name string	branch_name string	path string	content string	import_graph string
33,659	quantapix/qnarre	refs/heads/main	/qnarre/models/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. # ============================================================================= # https://arxiv.org/abs/1906.08237 # https://github.com/zihangdai/xlnet 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.embed import pos_enc from ..core.mlp import Classifier, MLP, PoolBeg, PoolEnd, PoolProj from ..prep.config.xlnet import PreTrained log = logging.get_logger(__name__) class Model(PreTrained): def __init__(self, kw): super().__init__(kw) cfg = self.get_cfg(kw) self.embed = qc.Embed(cfg.s_vocab, cfg.d_model, kw) self.mask = nn.Parameter(torch.FloatTensor(1, 1, cfg.d_model)) self.lays = qc.Stack([Layer(kw) for _ in range(cfg.n_lays)]) self.drop = qc.Dropout(cfg.drop, kw) def create_mask(self, qlen, mlen): mask = torch.ones([qlen, qlen]) up = torch.triu(mask, diagonal=1) pad = torch.zeros([qlen, mlen]) y = torch.cat([pad, up], dim=1) if self.same_length: lo = torch.tril(mask, diagonal=-1) y = torch.cat([y[:, :qlen] + lo, y[:, qlen:]], dim=1) y = y.to(self.device) return y def cache_mem(self, x, prev): if self.reuse_len is not None and self.reuse_len > 0: x = x[: self.reuse_len] if self.mem_len is None or self.mem_len == 0: cutoff = 0 else: cutoff = -self.mem_len if prev is None: y = x[cutoff:] else: y = torch.cat([prev, x], dim=0)[cutoff:] return y.detach() def forward( self, x, mask=None, mems=None, perm_mask=None, target_mapping=None, typ=None, x_m=None, head_m=None, x_emb=None, use_mems=None, kw, ): cfg = self.cfg if "y_cache" in kw: use_mems = kw["y_cache"] if self.training: use_mems = use_mems if use_mems is not None else cfg.use_mems_train else: use_mems = use_mems if use_mems is not None else cfg.use_mems_eval if x is not None: assert x_emb is None x = x.transpose(0, 1).contiguous() shape = x.size() else: x_emb = x_emb.transpose(0, 1).contiguous() shape = x_emb.size()[:-1] n, b = shape typ = typ.transpose(0, 1).contiguous() if typ is not None else None x_m = x_m.transpose(0, 1).contiguous() if x_m is not None else None mask = mask.transpose(0, 1).contiguous() if mask is not None else None perm_mask = perm_mask.permute(1, 2, 0).contiguous() if perm_mask is not None else None target_mapping = ( target_mapping.permute(1, 2, 0).contiguous() if target_mapping is not None else None ) mlen = mems[0].shape[0] if mems is not None and mems[0] is not None else 0 klen = mlen + n dtype_float = cfg.dtype device = cfg.device if cfg.attn_type == "uni": attn_mask = self.create_mask(n, mlen) attn_mask = attn_mask[:, :, None, None] else: assert cfg.attn_type == "bi" attn_mask = None assert x_m is None or mask is None if x_m is None and mask is not None: x_m = 1.0 - mask if x_m is not None and perm_mask is not None: data_mask = x_m[None] + perm_mask elif x_m is not None and perm_mask is None: data_mask = x_m[None] elif x_m is None and perm_mask is not None: data_mask = perm_mask else: data_mask = None if data_mask is not None: if mlen > 0: mems_mask = torch.zeros([data_mask.shape[0], mlen, b]).to(data_mask) data_mask = torch.cat([mems_mask, data_mask], dim=1) if attn_mask is None: attn_mask = data_mask[:, :, :, None] else: attn_mask += data_mask[:, :, :, None] if attn_mask is not None: attn_mask = (attn_mask > 0).to(dtype_float) if attn_mask is not None: non_tgt_mask = -torch.eye(n).to(attn_mask) if mlen > 0: non_tgt_mask = torch.cat( [torch.zeros([n, mlen]).to(attn_mask), non_tgt_mask], dim=-1 ) non_tgt_mask = ((attn_mask + non_tgt_mask[:, :, None, None]) > 0).to(attn_mask) else: non_tgt_mask = None if x_emb is not None: word_emb_k = x_emb else: word_emb_k = self.embed(x) output_h = self.drop(word_emb_k) if target_mapping is not None: word_emb_q = self.mask.expand(target_mapping.shape[0], b, -1) output_g = self.drop(word_emb_q) else: output_g = None if typ is not None: if mlen > 0: mem_pad = torch.zeros([mlen, b], dtype=torch.long, device=device) cat_ids = torch.cat([mem_pad, typ], dim=0) else: cat_ids = typ seg_mat = (typ[:, None] != cat_ids[None, :]).long() seg_mat = F.one_hot(seg_mat, num_classes=2).to(dtype_float) else: seg_mat = None pos = self.drop(pos_enc(n, klen, bsz=b)) if head_m is not None: if head_m.dim() == 1: head_m = head_m.unsqueeze(0).unsqueeze(0).unsqueeze(0).unsqueeze(0) head_m = head_m.expand(cfg.n_lays, -1, -1, -1, -1) elif head_m.dim() == 2: head_m = head_m.unsqueeze(1).unsqueeze(1).unsqueeze(1) head_m = head_m.to(dtype=next(self.parameters()).dtype) else: head_m = [None] * cfg.n_lays new_mems = () if mems is None: mems = [None] * len(self.lays) attns = hiddens = () for i, lay in enumerate(self.lays): if use_mems: new_mems = new_mems + (self.cache_mem(output_h, mems[i]),) hiddens += (output_h, output_g) if output_g is not None else (output_h,) ys = lay( output_h, output_g, attn_mask_h=non_tgt_mask, attn_mask_g=attn_mask, r=pos, seg_mat=seg_mat, mems=mems[i], target_mapping=target_mapping, head_m=head_m[i], kw, ) output_h, output_g = ys[:2] attns += (ys[2],) hiddens += (output_h, output_g) if output_g is not None else (output_h,) y = self.drop(output_g if output_g is not None else output_h) y = y.permute(1, 0, 2).contiguous() if not use_mems: new_mems = None if output_g is not None: hiddens = tuple(h.permute(1, 0, 2).contiguous() for hs in hiddens for h in hs) else: hiddens = tuple(hs.permute(1, 0, 2).contiguous() for hs in hiddens) if target_mapping is not None: attns = tuple( tuple(att_stream.permute(2, 3, 0, 1).contiguous() for att_stream in t) for t in attns ) else: attns = tuple(t.permute(2, 3, 0, 1).contiguous() for t in attns) return Output(y, attns, hiddens, new_mems) @dataclass class Output(qc.Output): attns: tuple = None hiddens: tuple = None mems: tuple = None class ForChoice(PreTrained): def __init__(self, kw): super().__init__(kw) cfg = self.get_cfg(kw) self.model = Model(kw) self.seqs = qc.SeqSummary(kw) self.proj = qc.Linear(cfg.d_model, 1, kw) def forward(self, x, typ=None, x_m=None, mask=None, x_emb=None, labels=None, kw): n = x.shape[1] if x is not None else x_emb.shape[1] x, typ, x_m, mask = qu.view_2D(x, typ, x_m, mask) x_emb = qu.view_3D(x_emb) ys = self.model(x, typ=typ, x_m=x_m, mask=mask, x_emb=x_emb, kw) y = self.proj(self.seqs(ys[0])).view(-1, n) loss = None if labels is not None: loss = nn.CrossEntropyLoss()(y, labels.view(-1)) ys = (y,) + ys[1:] + (loss,) return WithLoss(ys) @dataclass class WithLoss(qc.Output): logits: tuple = None attns: tuple = None hiddens: tuple = None mems: tuple = None loss: tuple = None class ForQASimple(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) def forward(self, x, beg_pos=None, end_pos=None, kw): ys = self.model(x, kw) b, e = self.proj(ys[0]).split(1, dim=-1) b = b.squeeze(-1).contiguous() e = e.squeeze(-1).contiguous() loss = None if beg_pos is not None and end_pos is not None: if len(beg_pos.size()) > 1: beg_pos = beg_pos.squeeze(-1) if len(end_pos.size()) > 1: end_pos = end_pos.squeeze(-1) i = b.size(1) f = nn.CrossEntropyLoss(ignore_index=i) beg_pos = beg_pos.clamp(0, i) end_pos = end_pos.clamp(0, i) loss = (f(b, beg_pos) + f(e, end_pos)) / 2 ys = (b, e) + ys[1:] + (loss,) return QA(ys) @dataclass class QA(qc.Output): logits_beg: tuple = None logits_end: tuple = None attns: tuple = None hiddens: tuple = None mems: tuple = None loss: tuple = None class ForQA(PreTrained): def __init__(self, kw): super().__init__(kw) self.get_cfg(kw) self.model = Model(kw) self.logits_beg = PoolBeg(kw) self.logits_end = PoolEnd(kw) self.proj = PoolProj(kw) def forward( self, x, beg_pos=None, end_pos=None, is_impossible=None, cls_index=None, p_mask=None, kw, ): cfg = self.cfg ys = self.model(x, kw) y = ys[0] s = self.logits_beg(y, p_mask=p_mask) if beg_pos is not None and end_pos is not None: for i in (beg_pos, end_pos, cls_index, is_impossible): if i is not None and i.dim() > 1: i.squeeze_(-1) e = self.logits_end(y, beg_pos=beg_pos, p_mask=p_mask) f = nn.CrossEntropyLoss() loss = (f(s, beg_pos) + f(e, end_pos)) / 2 if cls_index is not None and is_impossible is not None: y = self.proj(y, beg_pos=beg_pos, cls_index=cls_index) loss += nn.BCEWithLogitsLoss()(y, is_impossible) * 0.5 ys = (y,) + ys[1:] + (loss,) return QATop(ys) else: _, n, hsz = y.size() slps = F.softmax(s, dim=-1) top_beg, top_beg_i = torch.topk(slps, cfg.beg_n_top, dim=-1) x = top_beg_i.unsqueeze(-1).expand(-1, -1, hsz) ss = torch.gather(y, -2, x).unsqueeze(1).expand(-1, n, -1, -1) x = y.unsqueeze(2).expand_as(ss) p_mask = p_mask.unsqueeze(-1) if p_mask is not None else None e = self.logits_end(x, beg_states=ss, p_mask=p_mask) elps = F.softmax(e, dim=1) top_end, top_end_i = torch.topk(elps, cfg.end_n_top, dim=1) top_end = top_end.view(-1, cfg.beg_n_top cfg.end_n_top) top_end_i = top_end_i.view(-1, cfg.beg_n_top * cfg.end_n_top) ss = torch.einsum("blh,bl->bh", y, slps) y = self.proj(y, beg_states=ss, cls_index=cls_index) ys = (y,) + ys[1:] + (top_beg, top_beg_i, top_end, top_end_i) return QATop(ys) @dataclass class QATop(qc.Output): logits: tuple = None attns: tuple = None hiddens: tuple = None mems: tuple = None top_beg = None top_beg_i = None top_end = None top_end_i = None loss: tuple = None class ForSeqClass(PreTrained): def __init__(self, kw): super().__init__(kw) cfg = self.get_cfg(kw) self.model = Model(kw) self.seqs = qc.SeqSummary(kw) self.proj = qc.Linear(cfg.d_model, cfg.n_labels, kw) def forward(self, x, labels=None, kw): cfg = self.cfg ys = self.model(x, kw) y = self.proj(self.seqs(ys[0])) loss = None if labels is not None: if cfg.problem is None: dt = labels.dtype if cfg.n_labels == 1: cfg.problem = "regression" elif cfg.n_labels > 1 and (dt == torch.long or dt == torch.int): cfg.problem = "single_label" else: cfg.problem = "multi_label" if cfg.problem == "regression": if cfg.n_labels == 1: loss = nn.MSELoss()(y.squeeze(), labels.squeeze()) else: loss = nn.MSELoss()(y, labels) elif cfg.problem == "single_label": loss = nn.CrossEntropyLoss()(y.view(-1, cfg.n_labels), labels.view(-1)) elif cfg.problem == "multi_label": loss = nn.BCEWithLogitsLoss()(y, labels) ys = (y,) + ys[1:] + (loss,) return WithLoss(ys) 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 LMHead(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.s_vocab, bias=True, kw) def prep_inputs(self, x, prev=None, use_mems=None, kw): b = x.shape[0] dummy_token = torch.zeros((b, 1), dtype=torch.long, device=x.device) offset = 2 if prev: x = torch.cat([x[:, -offset:], dummy_token], dim=1) else: x = torch.cat([x, dummy_token], dim=1) n = x.shape[1] pm = torch.zeros((b, n, n), dtype=torch.float, device=x.device) pm[:, :, -1] = 1.0 tm = torch.zeros((b, 1, n), dtype=torch.float, device=x.device) tm[:, 0, -1] = 1.0 y = {"x": x, "perm_mask": pm, "target_mapping": tm, "use_mems": use_mems} if prev: y["mems"] = tuple(x[:-offset, :, :] for x in prev) return y def forward(self, x, labels=None, kw): ys = self.model(x, *kw) y = self.proj(ys[0]) loss = None if labels is not None: loss = nn.CrossEntropyLoss()(y.view(-1, y.size(-1)), labels.view(-1)) ys = (y,) + ys[1:] + (loss,) return WithLoss(ys) class Layer(qc.Module): def __init__(self, kw): super().__init__(kw) cfg = self.het_cfg(kw) self.rel_attn = Attention(cfg) self.ffnet = MLP(cfg.act_ffnet, cfg.drop, cfg.eps, kw) def forward( self, output_h, output_g, attn_mask_h, attn_mask_g, r, seg_mat, mems=None, target_mapping=None, head_m=None, kw, ): cfg = self.cfg y = self.rel_attn( output_h, output_g, attn_mask_h, attn_mask_g, r, seg_mat, mems=mems, target_mapping=target_mapping, head_m=head_m, ) output_h, output_g = y[:2] if output_g is not None: output_g = self.ffnet(output_g) output_h = self.ffnet(output_h) y = (output_h, output_g) + y[2:] return y class Attention(qc.Module): def __init__(self, cfg): super().__init__() assert cfg.d_model % cfg.n_heads == 0 cfg.n_heads = cfg.n_heads self.d_head = cfg.d_head cfg.d_model = cfg.d_model self.scale = 1 / (cfg.d_head0.5) self.q = nn.Parameter(torch.FloatTensor(cfg.d_model, cfg.n_heads, self.d_head)) self.k = nn.Parameter(torch.FloatTensor(cfg.d_model, cfg.n_heads, self.d_head)) self.v = nn.Parameter(torch.FloatTensor(cfg.d_model, cfg.n_heads, self.d_head)) self.o = nn.Parameter(torch.FloatTensor(cfg.d_model, cfg.n_heads, self.d_head)) self.r = nn.Parameter(torch.FloatTensor(cfg.d_model, cfg.n_heads, self.d_head)) self.r_r_bias = nn.Parameter(torch.FloatTensor(cfg.n_heads, self.d_head)) self.r_s_bias = nn.Parameter(torch.FloatTensor(cfg.n_heads, self.d_head)) self.r_w_bias = nn.Parameter(torch.FloatTensor(cfg.n_heads, self.d_head)) self.seg_embed = nn.Parameter(torch.FloatTensor(2, cfg.n_heads, self.d_head)) self.norm = qc.LayerNorm(cfg.d_model, cfg.eps) self.drop = qc.Dropout(cfg.drop) @staticmethod def rel_shift(x, klen=-1): s = x.shape y = x.reshape(s[1], s[0], s[2], s[3]) y = y[1:, ...] y = y.reshape(s[0], s[1] - 1, s[2], s[3]) # x = x[:, 0:klen, :, :] y = torch.index_select(y, 1, torch.arange(klen, device=y.device, dtype=torch.long)) return y @staticmethod def rel_shift_bnij(x, klen=-1): s = x.shape y = x.reshape(s[0], s[1], s[3], s[2]) y = y[:, :, 1:, :] y = y.reshape(s[0], s[1], s[2], s[3] - 1) y = torch.index_select(y, 3, torch.arange(klen, device=y.device, dtype=torch.long)) # x = x[:, :, :, :klen] return y def rel_attn_core( self, q_head, k_head_h, v_head_h, k_head_r, seg_mat=None, attn_mask=None, head_m=None, kw, ): ac = torch.einsum("ibnd,jbnd->bnij", q_head + self.r_w_bias, k_head_h) bd = torch.einsum("ibnd,jbnd->bnij", q_head + self.r_r_bias, k_head_r) bd = self.rel_shift_bnij(bd, klen=ac.shape[3]) if seg_mat is None: ef = 0 else: ef = torch.einsum("ibnd,snd->ibns", q_head + self.r_s_bias, self.seg_embed) ef = torch.einsum("ijbs,ibns->bnij", seg_mat, ef) y = (ac + bd + ef) * self.scale if attn_mask is not None: if attn_mask.dtype == torch.float16: y = y - 65500 * torch.einsum("ijbn->bnij", attn_mask) else: y = y - 1e30 * torch.einsum("ijbn->bnij", attn_mask) y = F.softmax(y, dim=3) y = self.drop(y) if head_m is not None: y = y * torch.einsum("ijbn->bnij", head_m) y = torch.einsum("bnij,jbnd->ibnd", y, v_head_h) return y, torch.einsum("bnij->ijbn", y) def post_attention(self, h, attn_vec, residual=True): y = torch.einsum("ibnd,hnd->ibh", attn_vec, self.o) y = self.drop(y) if residual: y = y + h y = self.norm(y) return y def forward( self, h, g, attn_mask_h, attn_mask_g, r, seg_mat, mems=None, target_mapping=None, head_m=None, **kw, ): if g is not None: if mems is not None and mems.dim() > 1: cat = torch.cat([mems, h], dim=0) else: cat = h k_head_h = torch.einsum("ibh,hnd->ibnd", cat, self.k) v_head_h = torch.einsum("ibh,hnd->ibnd", cat, self.v) k_head_r = torch.einsum("ibh,hnd->ibnd", r, self.r) q_head_h = torch.einsum("ibh,hnd->ibnd", h, self.q) attn_vec_h = self.rel_attn_core( q_head_h, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_h, head_m=head_m, ) attn_vec_h, attn_prob_h = attn_vec_h output_h = self.post_attention(h, attn_vec_h) q_head_g = torch.einsum("ibh,hnd->ibnd", g, self.q) if target_mapping is not None: q_head_g = torch.einsum("mbnd,mlb->lbnd", q_head_g, target_mapping) attn_vec_g = self.rel_attn_core( q_head_g, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_g, head_m=head_m, ) attn_vec_g, attn_prob_g = attn_vec_g attn_vec_g = torch.einsum("lbnd,mlb->mbnd", attn_vec_g, target_mapping) else: attn_vec_g = self.rel_attn_core( q_head_g, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_g, head_m=head_m, ) attn_vec_g, attn_prob_g = attn_vec_g output_g = self.post_attention(g, attn_vec_g) attn_prob = attn_prob_h, attn_prob_g else: if mems is not None and mems.dim() > 1: cat = torch.cat([mems, h], dim=0) else: cat = h q_head_h = torch.einsum("ibh,hnd->ibnd", h, self.q) k_head_h = torch.einsum("ibh,hnd->ibnd", cat, self.k) v_head_h = torch.einsum("ibh,hnd->ibnd", cat, self.v) k_head_r = torch.einsum("ibh,hnd->ibnd", r.type(self.r.dtype), self.r) attn_vec = self.rel_attn_core( q_head_h, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_h, head_m=head_m, ) attn_vec, attn_prob = attn_vec output_h = self.post_attention(h, attn_vec) output_g = None return output_h, output_g, attn_prob	{"/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"], 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33,660	quantapix/qnarre	refs/heads/main	/qnarre/base/stats.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 Stats: def __init__(self, proofs=0, realities=0, dissents=0, conflicts=0, judgments=0, activisms=0, **_): self.proofs = proofs self.realities = realities self.dissents = dissents self.conflicts = conflicts self.judgments = judgments self.activisms = activisms def __str__(self): f = 'Stats: proofs {}, realities {}, dissents {}, ' f += 'conflicts {}, judgments {}, activisms {}' return f.format( self.proofs, self.realities, self.dissents, self.conflicts, self.judgments, self.activisms, ) @property def as_tuple(self): return ( self.proofs, self.realities, self.dissents, self.conflicts, self.judgments, self.activisms, )	{"/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": 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33,661	quantapix/qnarre	refs/heads/main	/tools/triton/test/lit.cfg.py	# -- Python -- import os import platform import re import subprocess import tempfile import lit.formats import lit.util from lit.llvm import llvm_config from lit.llvm.subst import FindTool, ToolSubst # Configuration file for the 'lit' test runner # name: The name of this test suite config.name = 'TRITON' config.test_format = lit.formats.ShTest(not llvm_config.use_lit_shell) # suffixes: A list of file extensions to treat as test files. config.suffixes = ['.mlir'] # test_source_root: The root path where tests are located. config.test_source_root = os.path.dirname(__file__) # test_exec_root: The root path where tests should be run. config.test_exec_root = os.path.join(config.triton_obj_root, 'test') config.substitutions.append(('%PATH%', config.environment['PATH'])) config.substitutions.append(('%shlibext', config.llvm_shlib_ext)) llvm_config.with_system_environment( ['HOME', 'INCLUDE', 'LIB', 'TMP', 'TEMP']) # llvm_config.use_default_substitutions() # excludes: A list of directories to exclude from the testsuite. The 'Inputs' # subdirectories contain auxiliary inputs for various tests in their parent # directories. config.excludes = [ 'Inputs', 'Examples', 'CMakeLists.txt', 'README.txt', 'LICENSE.txt'] # test_source_root: The root path where tests are located. config.test_source_root = os.path.dirname(__file__) # test_exec_root: The root path where tests should be run. config.test_exec_root = os.path.join(config.triton_obj_root, 'test') config.triton_tools_dir = os.path.join(config.triton_obj_root, 'bin') config.filecheck_dir = os.path.join(config.triton_obj_root, 'bin', 'FileCheck') tool_dirs = [ config.triton_tools_dir, config.llvm_tools_dir, config.filecheck_dir] # Tweak the PATH to include the tools dir. for d in tool_dirs: llvm_config.with_environment('PATH', d, append_path=True) tools = [ 'triton-opt', ToolSubst('%PYTHON', config.python_executable, unresolved='ignore'), ] llvm_config.add_tool_substitutions(tools, tool_dirs) # TODO: what's this? llvm_config.with_environment('PYTHONPATH', [ 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33,662	quantapix/qnarre	refs/heads/main	/qnarre/prep/tokens/deberta2.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 import sentencepiece as sp import six from ...tokens.utils import PreTrainedTokenizer VOCAB_MAP = { "vocab_file": { "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model", "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model", "microsoft/deberta-v2-xlarge-mnli": "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model", "microsoft/deberta-v2-xxlarge-mnli": "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model", } } INPUT_CAPS = { "microsoft/deberta-v2-xlarge": 512, "microsoft/deberta-v2-xxlarge": 512, "microsoft/deberta-v2-xlarge-mnli": 512, "microsoft/deberta-v2-xxlarge-mnli": 512, } PRETRAINED_INIT_CONFIGURATION = { "microsoft/deberta-v2-xlarge": {"do_lower_case": False}, "microsoft/deberta-v2-xxlarge": {"do_lower_case": False}, "microsoft/deberta-v2-xlarge-mnli": {"do_lower_case": False}, "microsoft/deberta-v2-xxlarge-mnli": {"do_lower_case": False}, } VOCAB_FS = {"vocab_file": "spm.model"} class Tokenizer(PreTrainedTokenizer): vocab_fs = VOCAB_FS vocab_map = VOCAB_MAP pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION input_caps = INPUT_CAPS def __init__( self, vocab_file, do_lower_case=False, split_by_punct=False, bos="[CLS]", eos="[SEP]", unk="[UNK]", sep="[SEP]", pad="[PAD]", cls="[CLS]", msk="[MASK]", sp_model_kw=None, kw, ): self.sp_model_kw = {} if sp_model_kw is None else sp_model_kw super().__init__( do_lower_case=do_lower_case, bos=bos, eos=eos, unk=unk, sep=sep, pad=pad, cls=cls, msk=msk, split_by_punct=split_by_punct, sp_model_kw=self.sp_model_kw, 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.do_lower_case = do_lower_case self.split_by_punct = split_by_punct self._tokenizer = SPMTokenizer( vocab_file, split_by_punct=split_by_punct, sp_model_kw=self.sp_model_kw ) @property def s_vocab(self): return len(self.vocab) @property def vocab(self): return self._tokenizer.vocab def get_vocab(self): vocab = self.vocab.copy() vocab.update(self.get_added_vocab()) return vocab def _tokenize(self, text): if self.do_lower_case: text = text.lower() return self._tokenizer.tokenize(text) def _convert_token_to_id(self, token): return self._tokenizer.spm.PieceToId(token) def _convert_id_to_token(self, index): return self._tokenizer.spm.IdToPiece(index) if index < self.s_vocab else self.unk def convert_tokens_to_string(self, tokens): return self._tokenizer.decode(tokens) 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 + 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 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 prepare_for_tokenization(self, text, is_split_into_words=False, kw): add_prefix_space = kw.pop("add_prefix_space", False) if is_split_into_words or add_prefix_space: text = " " + text return (text, kw) def save_vocabulary(self, dir, pre=None): return self._tokenizer.save_pretrained(dir, pre=pre) class SPMTokenizer: def __init__(self, vocab_file, split_by_punct=False, sp_model_kw=None): self.split_by_punct = split_by_punct self.vocab_file = vocab_file self.sp_model_kw = {} if sp_model_kw is None else sp_model_kw spm = sp.SentencePieceProcessor(self.sp_model_kw) if not os.path.exists(vocab_file): raise FileNotFoundError(f"{vocab_file} does not exist!") spm.load(vocab_file) bpe_vocab_size = spm.GetPieceSize() # Token map # <unk> 0+1 # <s> 1+1 # </s> 2+1 self.vocab = {spm.IdToPiece(i): i for i in range(bpe_vocab_size)} self.ids_to_tokens = [spm.IdToPiece(i) for i in range(bpe_vocab_size)] # self.vocab['[PAD]'] = 0 # self.vocab['[CLS]'] = 1 # self.vocab['[SEP]'] = 2 # self.vocab['[UNK]'] = 3 self.spm = spm def __getstate__(self): state = self.__dict__.copy() state["spm"] = None return state def __setstate__(self, d): self.__dict__ = d if not hasattr(self, "sp_model_kw"): self.sp_model_kw = {} self.spm = sp.SentencePieceProcessor(*self.sp_model_kw) self.spm.Load(self.vocab_file) def tokenize(self, text): pieces = self._encode_as_pieces(text) def _norm(x): if x not in self.vocab or x == "<unk>": return "[UNK]" else: return x pieces = [_norm(p) for p in pieces] return pieces def convert_ids_to_tokens(self, ids): tokens = [] for i in ids: tokens.append(self.ids_to_tokens[i]) return tokens def decode(self, tokens, start=-1, end=-1, raw_text=None): if raw_text is None: return self.spm.decode_pieces([t for t in tokens]) else: words = self.split_to_words(raw_text) word_tokens = [self.tokenize(w) for w in words] token2words = [0] len(tokens) tid = 0 for i, w in enumerate(word_tokens): for k, t in enumerate(w): token2words[tid] = i tid += 1 word_start = token2words[start] word_end = token2words[end] if end < len(tokens) else len(words) text = "".join(words[word_start:word_end]) return text def add_special_token(self, token): if token not in self.special_tokens: self.special_tokens.append(token) if token not in self.vocab: self.vocab[token] = len(self.vocab) - 1 self.ids_to_tokens.append(token) return self.id(token) def part_of_whole_word(self, token, is_bos=False): if is_bos: return True if ( len(token) == 1 and ( _is_whitespace(list(token)[0]) or _is_control(list(token)[0]) or _is_punctuation(list(token)[0]) ) ) or token in self.special_tokens: return False word_start = b"\xe2\x96\x81".decode("utf-8") return not token.startswith(word_start) def pad(self): return "[PAD]" def bos(self): return "[CLS]" def eos(self): return "[SEP]" def unk(self): return "[UNK]" def mask(self): return "[MASK]" def sym(self, id): return self.ids_to_tokens[id] def id(self, sym): return self.vocab[sym] if sym in self.vocab else 1 def _encode_as_pieces(self, text): text = convert_to_unicode(text) if self.split_by_punct: words = self._run_split_on_punc(text) pieces = [self.spm.encode(w, out_type=str) for w in words] return [p for w in pieces for p in w] else: return self.spm.encode(text, out_type=str) def split_to_words(self, text): pieces = self._encode_as_pieces(text) word_start = b"\xe2\x96\x81".decode("utf-8") words = [] offset = 0 prev_end = 0 for i, p in enumerate(pieces): if p.startswith(word_start): if offset > prev_end: words.append(text[prev_end:offset]) prev_end = offset w = p.replace(word_start, "") else: w = p try: s = text.index(w, offset) pn = "" k = i + 1 while k < len(pieces): pn = pieces[k].replace(word_start, "") if len(pn) > 0: break k += 1 if len(pn) > 0 and pn in text[offset:s]: offset = offset + 1 else: offset = s + len(w) except Exception: offset = offset + 1 if prev_end < offset: words.append(text[prev_end:offset]) return words 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): 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 save_pretrained(self, path, pre=None): filename = VOCAB_FS[list(VOCAB_FS.keys())[0]] if pre is not None: filename = pre + "-" + filename full_path = os.path.join(path, filename) with open(full_path, "wb") as fs: fs.write(self.spm.serialized_model_proto()) return (full_path,) 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) 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 convert_to_unicode(text): if six.PY3: if isinstance(text, str): return text elif isinstance(text, bytes): return text.decode("utf-8", "ignore") else: raise ValueError(f"Unsupported string type: {type(text)}") elif six.PY2: if isinstance(text, str): return text.decode("utf-8", "ignore") else: raise ValueError(f"Unsupported string type: {type(text)}") else: raise ValueError("Not running on Python2 or Python 3?")	{"/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", 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["/tools/triton/python/triton/language/__init__.py"]}
33,663	quantapix/qnarre	refs/heads/main	/qnarre/core/pretrained.py	import copy import json import os import re import torch from requests import HTTPError from transformers import custom_object_save from transformers.utils import logging from transformers.file_utils import ( CONFIG_NAME, EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError, cached_path, hf_bucket_url, is_offline_mode, is_remote_url, ) from .base import Hypers, Module log = logging.get_logger(__name__) class PreTrained(Module): hs = Hypers( [ "activation", "archs", "bad_words_ids", "BOS", "cross_attention_hidden_size", "dec_START", "EOS", "finetune", "forced_BOS", "forced_EOS", "id2label", "label2id", "n_labels", "PAD", "prefix", "problem", "remove_invalid_values", "SEP", "task_params", "tokenizer_class", "torch_dtype", ], { "add_cross": False, "chunk_ff": 0, "d_model": 0, "diversity_penalty": 0.0, "do_sample": False, "drop": 0.0, "early_stop": False, "encoder_no_repeat_ngram_size": 0, "is_dec": False, "is_enc_dec": False, "len_penalty": 1.0, "max_len": 20, "min_len": 0, "min_len": 10, "n_beam_groups": 1, "n_beams": 1, "n_heads": 0, "n_lays": 0, "name_or_path": "", "num_return_sequences": 1, "out_dict_gen": False, "out_scores": False, "remove_invalid_values": False, "repetition_penalty": 1.0, "s_no_repeat_ngram": 0, "s_vocab": 0, "temperature": 1.0, "tie_encoder_decoder": False, "tie_word_embeds": True, "top_k": 50, "top_p": 1.0, "torchscript": False, "typical_p": 1.0, "use_bfloat16": False, "y_attn": False, "y_kw": True, "y_hidden": False, }, ) def __init__(self, ps={}, hs=[], kw): super().__init__(ps, [self.hs] + hs, kw) cfg = self.get_cfg(kw) if cfg.id2label is not None: cfg.id2label = dict((int(k), v) for k, v in cfg.id2label.items()) if cfg.torch_dtype is not None and isinstance(cfg.torch_dtype, str): cfg.torch_dtype = getattr(torch, cfg.torch_dtype) if cfg.problem is not None: assert cfg.problem in ("multi_label", "regression", "single_label") grad_checkpoint = True is_composition = False _auto_class = None @property def y_kw(self): return self.y_kw and not self.torchscript @property def n_labels(self): return len(self.id2label) @n_labels.setter def n_labels(self, x): if not hasattr(self, "id2label") or self.id2label is None or len(self.id2label) != x: self.id2label = {i: f"LABEL_{i}" for i in range(x)} self.label2id = dict(zip(self.id2label.values(), self.id2label.keys())) def save_pretrained(self, path, kw): assert not os.path.isfile(path) os.makedirs(path, exist_ok=True) if self._auto_class is not None: custom_object_save(self, path, config=self) y = os.path.join(path, CONFIG_NAME) self.to_json_file(y, use_diff=True) log.info(f"Config saved in {y}") @classmethod def from_pretrained(cls, path, kw): y, kw = cls.get_config_dict(path, kw) if "model_type" in y and hasattr(cls, "model_type") and y["model_type"] != cls.model_type: log.warning(f"Using {y['model_type']} to instantiate {cls.model_type}") return cls.from_dict(y, kw) @classmethod def get_config_dict(cls, path, kw): x = copy.deepcopy(kw) y, kw = cls._get_config_dict(path, kw) if "configuration_files" in y: f = get_configuration_file(y["configuration_files"]) y, kw = cls._get_config_dict(path, _configuration_file=f, x) return y, kw @classmethod def _get_config_dict(cls, path, kw): local_files_only = kw.pop("local_files_only", False) from_pipeline = kw.pop("_from_pipeline", None) user_agent = {"file_type": "config", "from_auto_class": kw.pop("_from_auto", False)} if from_pipeline is not None: user_agent["using_pipeline"] = from_pipeline if is_offline_mode() and not local_files_only: log.info("Offline mode: forcing local_files_only=True") local_files_only = True path = str(path) if os.path.isfile(path) or is_remote_url(path): x = path else: f = kw.pop("_configuration_file", CONFIG_NAME) if os.path.isdir(path): x = os.path.join(path, f) else: x = hf_bucket_url(path, filename=f, revision=kw.pop("revision", None), mirror=None) try: x2 = cached_path( x, cache_dir=kw.pop("cache_dir", None), force_download=kw.pop("force_download", False), proxies=kw.pop("proxies", None), resume_download=kw.pop("resume_download", False), local_files_only=local_files_only, use_auth_token=kw.pop("use_auth_token", None), user_agent=user_agent, ) except RepositoryNotFoundError as e: raise OSError() from e except RevisionNotFoundError as e: raise OSError() from e except EntryNotFoundError as e: raise OSError() from e except HTTPError as e: raise OSError() from e except OSError as e: raise e try: y = cls._dict_from_json_file(x2) except (json.JSONDecodeError, UnicodeDecodeError) as e: raise OSError() from e if x2 == x: log.info(f"loading {x}") else: log.info(f"loading {x} from cache at {x2}") return y, kw @classmethod def from_dict(cls, x, kw): return_unused_kw = kw.pop("return_unused_kw", False) y = cls(x) ks = [] for k, v in kw.items(): if hasattr(y, k): setattr(y, k, v) if k != "torch_dtype": ks.append(k) for k in ks: kw.pop(k, None) log.info(f"Model config {y}") if return_unused_kw: return y, kw else: return y @classmethod def from_json_file(cls, x): return cls(*cls._dict_from_json_file(x)) @classmethod def _dict_from_json_file(cls, x): with open(x, "r", encoding="utf-8") as r: y = r.read() return json.loads(y) def __eq__(self, x): return self.__dict__ == x.__dict__ def __repr__(self): return f"{self.__class__.__name__} {self.to_json_string()}" def to_dict(self): y = copy.deepcopy(self.__dict__) if hasattr(self.__class__, "model_type"): y["model_type"] = self.__class__.model_type if "_auto_class" in y: del y["_auto_class"] self.dict_torch_dtype_to_str(y) return y def to_diff_dict(self): d = PreTrained().to_dict() c = self.__class__().to_dict() if not self.is_composition else {} y = {} for k, v in self.to_dict().items(): if k not in d or v != d[k] or (k in c and v != c[k]): y[k] = v self.dict_torch_dtype_to_str(y) return y def to_json_string(self, use_diff=True): if use_diff is True: y = self.to_diff_dict() else: y = self.to_dict() return json.dumps(y, indent=2, sort_keys=True) + "\n" def to_json_file(self, path, use_diff=True): with open(path, "w", encoding="utf-8") as w: w.write(self.to_json_string(use_diff=use_diff)) def update(self, x): for k, v in x.items(): setattr(self, k, v) def get_mask(self, m, shape, device=None): if m.dim() == 3: m = m[:, None, :, :] else: assert m.dim() == 2 if self.cfg.is_dec: def for_dec(x): b, n = shape xs = torch.arange(n, device=device) y = xs[None, None, :].repeat(b, n, 1) <= xs[None, :, None] y = y.to(x.dtype) if y.shape[1] < x.shape[1]: d = x.shape[1] - y.shape[1] y = torch.cat( [torch.ones((b, n, d), device=device, dtype=y.dtype), y], axis=-1 ) y = y[:, None, :, :] x[:, None, None, :] return y m = for_dec(m) else: m = m[:, None, None, :] m = m.to(dtype=self.cfg.dtype) m = (1.0 - m) * -10000.0 return m def get_head_m(self, x, n_lays, is_chunked=False): if x is None: y = [None] * n_lays else: if x.dim() == 1: y = x.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1) y = y.expand(n_lays, -1, -1, -1, -1) elif x.dim() == 2: y = x.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) assert y.dim() == 5 y = y.to(dtype=self.cfg.dtype) if is_chunked is True: y = y.unsqueeze(-1) return y def get_head_m2(self, x, n_lays): if x is None: y = [None] * n_lays else: if x.dim() == 1: y = x.unsqueeze(0).unsqueeze(0).unsqueeze(0).unsqueeze(0) y = y.expand(n_lays, -1, -1, -1, -1) elif x.dim() == 2: y = x.unsqueeze(1).unsqueeze(1).unsqueeze(1) assert y.dim() == 5 y = y.to(dtype=self.cfg.dtype) return y _CFG_FILE = re.compile(r"config\.(.*)\.json") def get_configuration_file(xs): map = {} for x in xs: s = _CFG_FILE.search(x) if s is not None: map[s.groups()[0]] = x ks = sorted(map.keys()) y = CONFIG_NAME for k in ks: y = map[k] 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", 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33,664	quantapix/qnarre	refs/heads/main	/qnarre/base/org.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 pathlib as pth import importlib as imp from .. import load_from from .doc import Doc from .net import Net from .stats import Stats from .named import Named, Saved class Org(Saved, Named): _tags = None docs = None net = None def __init__(self, , root, docs=None, net=None, kw): super().__init__(root=root, kw) p = pth.Path('{}.preset'.format(self.name)) self.preset = load_from(root=root, path=p) if docs: self.docs = docs if net: self.net = net @property def tags(self): if self._tags is None: m = imp.import_module('qnarre.core') ps = [] for n in getattr(m, 'all_nodes'): if isinstance(n, tuple): ps.append((n[0].to_tag(), n[1])) else: n = n.to_tag() ps.append((n, n[0])) self._tags = ' '.join('{}({})'.format(p[0], p[1]) for p in ps) return self._tags def from_text(self, txt, , root, *_): ds, nds = [], {} for d in txt.split('\n ')[1:]: d = d.split('\n', 1)[1] dps, d = d.split('\n :END', 1) dps = _to_props(dps.split(':PROPERTIES:', 1)[1]) dps['pages'] = gs = [] ngs = [] for g in d.split('\n Page')[1:]: rs, nrs = [], [] for r in g.split('\n* Para')[1:]: r = r.split(':TEXT:', 1)[1] r, ns = r.split('\n :END', 1) rs.append(r.strip().splitlines()) nps = [] for n in ns.split('\n** ')[1:]: ps = n.split('\n', 1) ts = ps.pop(0) ps = ps[0].strip() if ps else '' if ps: ps = ps.split('\n :END', 1)[0] ps = _to_props(ps.split(':PROPERTIES:', 1)[1]) else: ps = {} ts = ts.split(':') ps['text'] = ts.pop(0).strip() if ts: ps['tag'] = ' '.join(ts).strip() nps.append(ps) nrs.append(nps) gs.append(rs) ngs.append(nrs) ds.append(Doc.create(dps, root=root)) nds[dps['name']] = ngs self.docs = ds self.net = Net.create(name=self.name, docs=nds, root=root) def to_text(self, *_): txt = [ '#+TITLE: {}\n'.format(self.name), '#+TAGS: {}'.format(self.tags), '#+COLUMNS: %ONE(No.) %TOPIC(Topic) %ITEM(Subject Summary)', '#+PROPERTY: ONE 1', '#+TODO: TODO \| DONE', '', ] pres = self.preset.props ss = Stats(self.net.docs.get('stats', ())) for d in self.docs: f = '* {} {}\n{}' txt.append(f.format(d.date, d.title, _to_text(d.props))) ngs = self.net.docs.get(d.name, ()) for gi, rs in enumerate(d.pages): txt.append(' Page {}'.format(gi + 1)) nrs = ngs[gi] if gi < len(ngs) else () for ri, ls in enumerate(rs): txt.append('* Para {}\n :TEXT:'.format(ri + 1)) txt.extend(ls) txt.append(' :END:') ns = nrs[ri] if ri < len(nrs) else _to_nodes(ls, ss, pres) for ps in ns: ps = ps.copy() t = ps.pop('text') ts = ps.pop('tag', '').strip().replace(' ', ':') ts = ' :{}:'.format(ts) if ts else '' ps = ('\n' + _to_text(ps)) if ps else '' txt.append('**** {}{}{}'.format(t, ts, ps)) self.net.docs['stats'] = ss.as_tuple print(ss) return '\n'.join(txt).strip() def _to_text(props): ls = [' :PROPERTIES:'] m = 1 + max(len(k) for k in props.keys()) for k, v in props.items(): if v is not None: ls.append(' :{}:{}{}'.format(k, ' ' * (m - len(k)), v)) ls.append(' :END:') return '\n'.join(ls) def _to_props(txt): ps = {} for ln in txt.strip().splitlines(): _, k, v = ln.split(':', 2) k, v = k.strip(), v.strip() if k and v: ps[k] = v return ps def _to_nodes(lines, stats, preset): for ln in lines: ps = {'text': ln} if ln.startswith('@'): cs = ln.split(':', 2) if len(cs) > 1: c, arg1, ln = cs ln = ln.strip() else: c, ln = ln.split(None, 1) arg1 = ' ' ps.update(text=ln) if c.startswith('@P'): ps.update( tag='proof', name='proof {:0>5d}'.format(stats.proofs), topic=arg1, authority=' ', ) stats.proofs += 1 elif c.startswith('@R'): ps.update( tag='reality', name='reality {:0>5d}'.format(stats.realities), topic=arg1, # proofs='proof 00000\|', ) stats.realities += 1 elif c.startswith('@D'): ps.update( tag='distortion', name='dissent {:0>5d}'.format(stats.dissents), topic=arg1, # proofs='proof 00000\|', ) stats.dissents += 1 elif c.startswith('@C'): ps.update( tag='fraud', name='conflict {:0>5d}'.format(stats.conflicts), narrative=arg1, # reality='reality 00000', agency=' ', ) stats.conflicts += 1 elif c.startswith('@N'): ps.update( tag='repeat', conflict=' ' # 'fraud:conflict 00000', ) elif c.startswith('@J'): ps.update( tag='bias', name='judgment {:0>5d}'.format(stats.judgments), narrative=arg1, # conflicts='fraud:conflict 00000\|', # dissents='distortion:dissent 00000\|', authority=' ', ) stats.judgments += 1 elif c.startswith('@A'): ps.update( tag='perpetuate', name='activism {:0>5d}'.format(stats.activisms), narrative=arg1, # judgments='bias:judgment 00000\|', authority=' ', ) stats.activisms += 1 else: assert c.startswith('@-') continue ps.update(preset.get(c, {})) else: print('*', ln) yield ps	{"/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", 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33,665	quantapix/qnarre	refs/heads/main	/notebooks/old/src/metrics.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. # ============================================================================= # !pip install -U tf-nightly-2.0-preview import tensorflow as tf import advanced_tf.dataset as qd import advanced_tf.custom as qc import advanced_tf.autograph as qa ks = tf.keras kl = ks.layers @tf.function def adapter(d): enc, dec, tgt = d['enc'], d['dec'], d['tgt'] return (( enc.flat_values, enc.row_splits, dec.flat_values, dec.row_splits, tgt.flat_values, tgt.row_splits, ), ( tgt.to_tensor(), tgt.to_tensor(), )) class ToRagged(qc.ToRagged): @tf.function(input_signature=[[ tf.TensorSpec(shape=[None], dtype=tf.int32), tf.TensorSpec(shape=[None], dtype=tf.int64) ] * 3]) def call(self, x): ys = [] for i in range(3): i = 2 fv, rs = x[i:i + 2] ys.append(tf.RaggedTensor.from_row_splits(fv, rs)) return ys def model_for(ps): x = [ks.Input(shape=(), dtype='int32'), ks.Input(shape=(), dtype='int64')] x += [ks.Input(shape=(), dtype='int32'), ks.Input(shape=(), dtype='int64')] x += [ks.Input(shape=(), dtype='int32'), ks.Input(shape=(), dtype='int64')] y = ToRagged()(x) y = qc.Frames(ps)(y) embed = qc.Embed(ps) ye = qc.Encode(ps)(embed(y[:2])) yd = qc.Decode(ps)(embed(y[2:]) + [ye[0]]) y = qc.Debed(ps)(yd) ys = qa.Probe(ps)(yd) m = ks.Model(inputs=x, outputs=[y, ys]) m.compile( optimizer=ps.optimizer, loss={'debed': ps.loss, 'probe': ps.loss}, metrics={'debed': [ps.metric], 'probe': [ps.metric]}, ) print(m.summary()) return m class Loss(ks.losses.Loss): @staticmethod def xent(tgt, out): tgt = tf.reshape(tf.cast(tgt, tf.int64), [-1]) s = tf.shape(out) out = tf.reshape(out, [-1, s[-1]]) y = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tgt, logits=out) return tf.reshape(y, s[:-1]) def __init__(self): super().__init__(name='loss') def call(self, tgt, out): return self.xent(tgt, out) class Metric(ks.metrics.Metric): def __init__(self): super().__init__(name='metric', dtype=tf.float32) self.total = self.add_weight('total', initializer='zeros') self.count = self.add_weight('count', initializer='zeros') def update_state(self, tgt, out, sample_weight=None): vs = Loss.xent(tgt, out) self.total.assign_add(tf.math.reduce_sum(vs)) return self.count.assign_add(tf.cast(tf.size(vs), dtype=tf.float32)) def result(self): return tf.math.divide_no_nan(self.total, self.count) params = qc.params params.update( loss=Loss(), metric=Metric(), ) if __name__ == '__main__': ps = qd.Params(*params) import advanced_tf.masking as qm qm.main_graph(ps, qc.dset_for(ps, adapter), model_for(ps))	{"/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": 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33,666	quantapix/qnarre	refs/heads/main	/qnarre/models/old/transformer.py	import copy import torch from torch.nn import functional as F from torch.nn.init import xavier_uniform_ from .base import Hypers, Module, Lazy, Stack, Linear, LayerNorm, Dropout from .attention import Attention class Transformer(Lazy): hs = Hypers( [], { "activation": F.relu, "batch_first": False, "d_ffnet": 2048, "d_hidden": 512, "depth_dec": 6, "depth_enc": 6, "drop": 0.1, "n_heads": 8, "norm_eps": 1e-5, "norm_first": False, }, ) def __init__(self, depth=None, hs=[], kw): if depth is not None: kw.update(depth_enc=depth, depth_dec=depth) super().__init__([self.hs] + hs, kw) cfg = self.cfg kw.update(ps=cfg) h, e, d = cfg.d_hidden, cfg.depth_enc, cfg.depth_dec n = LayerNorm(h, cfg.norm_eps, kw) self.e_stack = EncStack(self, e, Encoder(kw), n, kw) n = LayerNorm(h, cfg.norm_eps, kw) self.d_stack = DecStack(self, d, Decoder(kw), n, kw) def build(self, x): cfg = self.cfg if not self.is_built(): with torch.no_grad(): self.reset_params() def forward(self, x, mask, k_mask): cfg = self.cfg batched = src.dim() == 3 if not cfg.batch_first and src.size(1) != tgt.size(1) and batched: raise RuntimeError("batches mismatch") elif cfg.batch_first and src.size(0) != tgt.size(0) and batched: raise RuntimeError("batches mismatch") if src.size(-1) != cfg.d_hidden or tgt.size(-1) != cfg.d_hidden: raise RuntimeError("d_hidden mismatch") src, tgt = x ctx = self.e_stack([src], src_mask, src_k_mask) y = self.d_stack([tgt, ctx], [tgt_mask, ctx_mask], [tgt_k_mask, ctx_k_mask]) return y @staticmethod def generate_square_subsequent_mask(sz): return torch.triu(torch.full((sz, sz), float("-inf")), diagonal=1) def reset_params(self): for p in self.parameters(): if p.dim() > 1: xavier_uniform_(p) class Stack(Module): hs = Hypers([], {"depth": 2}) def __init__(self, owner, depth=None, hs=[], kw): if depth is not None: kw.update(depth=depth) super().__init__([self.hs] + hs, kw) self.pre = owner.pre self.post = owner.post class EncStack(Stack): def __init__(self, owner, depth, enc, norm=None, kw): super().__init__(owner, depth, kw) self.encs = _clones(enc, self.cfg.depth) self.norm = norm def forward(self, x, mask=None, k_mask=None): x = x[0] y = self.pre([x, x]) for e in self.encs: y = e([y], mask, k_mask) if self.norm is not None: y = self.norm(y) y = self.post([x, y]) return y class DecStack(Stack): def __init__(self, owner, depth, dec, norm=None, kw): super().__init__(owner, depth, kw) self.decs = _clones(dec, self.cfg.depth) self.norm = norm def forward(self, x, mask=None, k_mask=None): x, ctx = x y = self.pre([x, x]) for d in self.decs: y = d([y, ctx], mask, k_mask) if self.norm is not None: y = self.norm(y) y = self.post([x, y]) return y class Encoder(Module): hs = Hypers( [], { "activation": F.relu, "batch_first": False, "d_ffnet": 2048, "d_hidden": 512, "drop": 0.1, "norm_eps": 1e-5, "n_heads": 2, "norm_first": False, }, ) def __init__(self, hs=[], kw): super().__init__([self.hs] + hs, kw) cfg = self.cfg kw.update(ps=cfg) n, h = cfg.n_heads, cfg.d_hidden self.refl = Attention(n, h, kw) self.lin1 = Linear(cfg.d_ffnet, kw) self.active = cfg.activation self.drop = Dropout() self.lin2 = Linear(h, kw) self.norm1 = LayerNorm(h, cfg.norm_eps, kw) self.norm2 = LayerNorm(h, cfg.norm_eps, kw) self.drop1 = Dropout() self.drop2 = Dropout() def __setstate__(self, x): if "activation" not in x: x["activation"] = F.relu super(Encoder, self).__setstate__(x) def forward(self, src, mask=None, k_mask=None): x = src if self.cfg.norm_first: x = x + self.reflect(self.norm1(x), mask, k_mask) x = x + self.ffnet(self.norm2(x)) else: x = self.norm1(x + self.reflect(x, mask, k_mask)) x = self.norm2(x + self.ffnet(x)) return x def reflect(self, x, mask, k_mask): x = self.refl(x, x, x, mask, k_mask, need_weights=False)[0] return self.drop1(x) def ffnet(self, x): x = self.lin2(self.drop(self.activ(self.lin1(x)))) return self.drop2(x) class Decoder(Module): hs = Hypers( [], { "activation": F.relu, "batch_first": False, "d_ffnet": 2048, "d_hidden": 512, "drop": 0.1, "norm_eps": 1e-5, "n_heads": 2, "norm_first": False, }, ) def __init__(self, hs=[], kw): super().__init__([self.hs] + hs, kw) cfg = self.cfg kw.update(ps=cfg) n, h = cfg.n_heads, cfg.d_hidden self.refl = Attention(n, h, kw) self.attn = Attention(n, h, kw) self.lin1 = Linear(cfg.d_ffnet, kw) self.activ = cfg.activation self.drop = Dropout() self.lin2 = Linear(h, kw) self.norm1 = LayerNorm(h, cfg.norm_eps, kw) self.norm2 = LayerNorm(h, cfg.norm_eps, kw) self.norm3 = LayerNorm(h, cfg.norm_eps, kw) self.drop1 = Dropout() self.drop2 = Dropout() self.drop3 = Dropout() def __setstate__(self, x): if "activation" not in x: x["activation"] = F.relu super(Decoder, self).__setstate__(x) def forward(self, tgt, mem, mask=None, k_mask=None): x = tgt if self.cfg.norm_first: x = x + self.reflect(self.norm1(x), tgt_mask, tgt_k_mask) x = x + self.attention(self.norm2(x), mem, mem_mask, mem_k_mask) x = x + self.ffnet(self.norm3(x)) else: x = self.norm1(x + self.reflect(x, tgt_mask, tgt_k_mask)) x = self.norm2(x + self.attention(x, mem, mem_mask, mem_k_mask)) x = self.norm3(x + self.ffnet(x)) return x def reflect(self, x, mask, k_mask): x = self.refl(x, x, x, attn_mask=mask, k_mask=k_mask, need_weights=False)[0] return self.drop1(x) def attention(self, x, mem, mask, k_mask): x = self.attn(x, mem, mem, attn_mask=mask, k_mask=k_mask, need_weights=False)[0] return self.drop2(x) def ffnet(self, x): x = self.lin2(self.drop(self.activ(self.lin1(x)))) return self.drop3(x) def _clones(m, n): return Stack([copy.deepcopy(m) for _ in range(n)])	{"/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": 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"/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": 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["/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": 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"/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"]}
33,667	quantapix/qnarre	refs/heads/main	/notebooks/old/src/autograph.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. # ============================================================================= # !pip install -U tf-nightly-2.0-preview import numpy as np import tensorflow as tf import dataset as qd import custom as qc import layers as ql ks = tf.keras kl = ks.layers def pos_timing(width, depth): assert depth % 2 == 0 d = np.arange(depth)[np.newaxis, :] d = 1 / np.power(10000, (2 * (d // 2)) / np.float32(depth)) t = np.arange(width)[:, np.newaxis] * d t = [np.sin(t[:, 0::2]), np.cos(t[:, 1::2])] t = np.concatenate(t, axis=-1)[np.newaxis, ...] t = tf.constant(t, dtype=tf.float32) return t class Embed(qc.Embed): def __init__(self, ps): super().__init__(ps) self.enc_p = pos_timing(ps.width_enc, ps.dim_hidden) self.dec_p = pos_timing(ps.width_dec, ps.dim_hidden) @tf.function(input_signature=[[ tf.TensorSpec(shape=[None, None], dtype=tf.int32), tf.TensorSpec(shape=[None], dtype=tf.int32) ]]) def call(self, x): y, lens = x y = tf.nn.embedding_lookup(self.emb, y) s = tf.shape(y) if s[-2] == self.ps.width_enc: y += tf.broadcast_to(self.enc_p, s) elif s[-2] == self.ps.width_dec: y += tf.broadcast_to(self.dec_p, s) else: pass y = tf.cast(s[-1], tf.float32)0.5 return [y, lens] class Frames(qc.Frames): @tf.function def call(self, x): y = super().call.python_function(x) tf.print() def print_row(r): tf.print( tf.numpy_function( lambda ts: ''.join([qd.vocab[t] for t in ts]), [r], Tout=[tf.string], )) return r tf.map_fn(print_row, self.prev) return y class Probe(ql.Layer): def __init__(self, ps): super().__init__(ps) self.dbd = qc.Dense(self, 'dbd', [ps.dim_hidden, ps.dim_vocab]) @tf.function def call(self, x): y, lens = x s = tf.shape(y) y = tf.reshape(y, [s[0] s[1], -1]) y = self.dbd(y) y = tf.reshape(y, [s[0], s[1], -1]) y = y[:, :tf.math.reduce_max(lens), :] return y def model_for(ps): x = [ks.Input(shape=(), dtype='int32'), ks.Input(shape=(), dtype='int64')] x += [ks.Input(shape=(), dtype='int32'), ks.Input(shape=(), dtype='int64')] x += [ks.Input(shape=(), dtype='int32'), ks.Input(shape=(), dtype='int64')] y = qc.ToRagged()(x) y = Frames(ps)(y) embed = Embed(ps) ye = qc.Encode(ps)(embed(y[:2])) yd = qc.Decode(ps)(embed(y[2:]) + [ye[0]]) y = Probe(ps)(yd) m = ks.Model(inputs=x, outputs=y) m.compile(optimizer=ps.optimizer, loss=ps.loss, metrics=[ps.metric]) print(m.summary()) return m if __name__ == '__main__': ps = qd.Params(**qc.params) import advanced_tf.masking as qm qm.main_graph(ps, qc.dset_for(ps), model_for(ps)) # import advanced_tf.ragged as qr # qr.main_eager(ps, dset_for(ps), model_for(ps))	{"/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", 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33,668	quantapix/qnarre	refs/heads/main	/tools/triton/python/tutorials/08-experimental-block-pointer.py	""" Block Pointer (Experimental) ============================ This tutorial will guide you through writing a matrix multiplication algorithm that utilizes block pointer semantics. These semantics are more friendly for Triton to optimize and can result in better performance on specific hardware. Note that this feature is still experimental and may change in the future. """ # %% # Motivations # ----------- # In the previous matrix multiplication tutorial, we constructed blocks of values by de-referencing blocks of pointers, # i.e., :code:`load(block<pointer_type<element_type>>) -> block<element_type>`, which involved loading blocks of # elements from memory. This approach allowed for flexibility in using hardware-managed cache and implementing complex # data structures, such as tensors of trees or unstructured look-up tables. # # However, the drawback of this approach is that it relies heavily on complex optimization passes by the compiler to # optimize memory access patterns. This can result in brittle code that may suffer from performance degradation when the # optimizer fails to perform adequately. Additionally, as memory controllers specialize to accommodate dense spatial # data structures commonly used in machine learning workloads, this problem is likely to worsen. # # To address this issue, we will use block pointers :code:`pointer_type<block<element_type>>` and load them into # :code:`block<element_type>`, in which way gives better friendliness for the compiler to optimize memory access # patterns. # # Let's start with the previous matrix multiplication example and demonstrate how to rewrite it to utilize block pointer # semantics. # %% # Make a Block Pointer # -------------------- # A block pointer pointers to a block in a parent tensor and is constructed by :code:`make_block_ptr` function, # which takes the following information as arguments: # # * :code:`base`: the base pointer to the parent tensor; # # * :code:`shape`: the shape of the parent tensor; # # * :code:`strides`: the strides of the parent tensor, which means how much to increase the pointer by when moving by 1 element in a specific axis; # # * :code:`offsets`: the offsets of the block; # # * :code:`block_shape`: the shape of the block; # # * :code:`order`: the order of the block, which means how the block is laid out in memory. # # For example, to a block pointer to a :code:`BLOCK_SIZE_M * BLOCK_SIZE_K` block in a row-major 2D matrix A by # offsets :code:`(pid_m * BLOCK_SIZE_M, 0)` and strides :code:`(stride_am, stride_ak)`, we can use the following code # (exactly the same as the previous matrix multiplication tutorial): # # .. code-block:: python # # a_block_ptr = tl.make_block_ptr(base=a_ptr, shape=(M, K), strides=(stride_am, stride_ak), # offsets=(pid_m * BLOCK_SIZE_M, 0), block_shape=(BLOCK_SIZE_M, BLOCK_SIZE_K), # order=(1, 0)) # # Note that the :code:`order` argument is set to :code:`(1, 0)`, which means the second axis is the inner dimension in # terms of storage, and the first axis is the outer dimension. This information may sound redundant, but it is necessary # for some hardware backends to optimize for better performance. # %% # Load/Store a Block Pointer # -------------------------- # To load/store a block pointer, we can use :code:`load/store` function, which takes a block pointer as an argument, # de-references it, and loads/stores a block. You may mask some values in the block, here we have an extra argument # :code:`boundary_check` to specify whether to check the boundary of each axis for the block pointer. With check on, # out-of-bound values will be masked according to the :code:`padding_option` argument (load only), which can be # :code:`zero` or :code:`nan`. Temporarily, we do not support other values due to some hardware limitations. In this # mode of block pointer load/store does not support :code:`mask` or :code:`other` arguments in the legacy mode. # # So to load the block pointer of A in the previous section, we can simply write # :code:`a = tl.load(a_block_ptr, boundary_check=(0, 1))`. Boundary check may cost extra performance, so if you can # guarantee that the block pointer is always in-bound in some axis, you can turn off the check by not passing the index # into the :code:`boundary_check` argument. For example, if we know that :code:`M` is a multiple of # :code:`BLOCK_SIZE_M`, we can replace with :code:`a = tl.load(a_block_ptr, boundary_check=(1, ))`, since axis 0 is # always in bound. # %% # Advance a Block Pointer # ----------------------- # To advance a block pointer, we can use :code:`advance` function, which takes a block pointer and the increment for # each axis as arguments and returns a new block pointer with the same shape and strides as the original one, # but with the offsets advanced by the specified amount. # # For example, to advance the block pointer by :code:`BLOCK_SIZE_K` in the second axis # (no need to multiply with strides), we can write :code:`a_block_ptr = tl.advance(a_block_ptr, (0, BLOCK_SIZE_K))`. # %% # Final Result # ------------ import torch import triton import triton.language as tl @triton.autotune( configs=[ triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), 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_with_block_pointers( # 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 ): """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 the matrix multiplication tutorial 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 block pointers for the first blocks of A and B. # We will advance this pointer as we move in the K direction and accumulate. # See above `Make a Block Pointer` section for details. a_block_ptr = tl.make_block_ptr(base=a_ptr, shape=(M, K), strides=(stride_am, stride_ak), offsets=(pid_m * BLOCK_SIZE_M, 0), block_shape=(BLOCK_SIZE_M, BLOCK_SIZE_K), order=(1, 0)) b_block_ptr = tl.make_block_ptr(base=b_ptr, shape=(K, N), strides=(stride_bk, stride_bn), offsets=(0, pid_n * BLOCK_SIZE_N), block_shape=(BLOCK_SIZE_K, BLOCK_SIZE_N), order=(1, 0)) # ----------------------------------------------------------- # 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, K, BLOCK_SIZE_K): # Load with boundary checks, no need to calculate the mask manually. # For better performance, you may remove some axis from the boundary # check, if you can guarantee that the access is always in-bound in # that axis. # See above `Load/Store a Block Pointer` section for details. a = tl.load(a_block_ptr, boundary_check=(0, 1)) b = tl.load(b_block_ptr, boundary_check=(0, 1)) # We accumulate along the K dimension. accumulator += tl.dot(a, b) # Advance the block pointer to the next K block. # See above `Advance a Block Pointer` section for details. a_block_ptr = tl.advance(a_block_ptr, (0, BLOCK_SIZE_K)) b_block_ptr = tl.advance(b_block_ptr, (BLOCK_SIZE_K, 0)) c = accumulator.to(tl.float16) # ---------------------------------------------------------------- # Write back the block of the output matrix C with boundary checks. # See above `Load/Store a Block Pointer` section for details. c_block_ptr = tl.make_block_ptr(base=c_ptr, shape=(M, N), strides=(stride_cm, stride_cn), offsets=(pid_m * BLOCK_SIZE_M, pid_n * BLOCK_SIZE_N), block_shape=(BLOCK_SIZE_M, BLOCK_SIZE_N), order=(1, 0)) tl.store(c_block_ptr, c, boundary_check=(0, 1)) # 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): # 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_with_block_pointers[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), ) return c # %% # Unit Test # --------- # # Still we can test our matrix multiplication with block pointers 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, 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33,669	quantapix/qnarre	refs/heads/main	/qnarre/models/transfo_xl.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://arxiv.org/abs/1901.02860 # https://github.com/kimiyoung/transformer-xl import torch 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.embed import Adaptive, Positional from ..core.mlp import Positionwise from ..prep.config.transfo_xl 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 = Adaptive(cfg.cutoffs, div_val=cfg.div_val, kw) self.pos_emb = Positional(cfg.d_model, kw) if cfg.untie_r: q_bias = None r_bias = None else: q_bias = nn.Parameter(torch.FloatTensor(cfg.n_heads, cfg.d_head)) r_bias = nn.Parameter(torch.FloatTensor(cfg.n_heads, cfg.d_head)) self.lays = qc.Stack() for _ in range(cfg.n_lays): self.lays.append(Layer(q_bias=q_bias, r_bias=r_bias, kw)) self.drop = qc.Dropout(cfg.drop, kw) def init_mems(self, b): cfg = self.cfg if cfg.mem_len > 0: p = next(self.parameters()) kw = dict(dtype=p.dtype, device=p.device) return [torch.zeros(cfg.mem_len, b, cfg.d_model, kw) for _ in range(cfg.n_lays)] return None def update_mems(self, xs, ys, mlen, qlen): assert len(xs) == len(ys) e = mlen + max(0, qlen) b = max(0, e - self.cfg.mem_len) with torch.no_grad(): return [torch.cat([ys[i], xs[i]], dim=0)[b:e].detach() for i in range(len(xs))] def forward(self, x, mems=None, head_m=None, x_emb=None, kw): cfg = self.cfg if x is None: x_emb = x_emb.transpose(0, 1).contiguous() s = x_emb.size()[:-1] else: assert x_emb is None x = x.transpose(0, 1).contiguous() s = x.size() y = self.tok_emb(x) if x_emb is None else x_emb n, b = s if mems is None: mems = self.init_mems(b) mlen = mems[0].size(0) if mems is not None else 0 klen = mlen + n pos = torch.arange(klen - 1, -1, -1.0, device=y.device, dtype=y.dtype) if cfg.clamp_len > 0: pos.clamp_(max=cfg.clamp_len) pos = self.drop(self.pos_emb(pos)) ones = y.new_ones((n, klen), dtype=torch.uint8) if cfg.same_length: d = klen - cfg.mem_len shift = n - d if d > 0 else n dec_m = (torch.triu(ones, 1 + mlen) + torch.tril(ones, -shift))[:, :, None] else: dec_m = torch.triu(ones, diagonal=1 + mlen)[:, :, None] y = self.drop(y) attns = hiddens = () head_m = self.get_head_m2(head_m, cfg.n_lays) for i, lay in enumerate(self.lays): hiddens += (y,) m = None if mems is None else mems[i] ys = lay(y, pos, kw, dec_m=dec_m, head_m=head_m[i], mems=m) y = ys[0] attns += (ys[1],) y = self.drop(y) mems = None if mems is None else self.update_mems(hiddens, mems, mlen, n) attns = tuple(x.permute(2, 3, 0, 1).contiguous() for x in attns) hiddens += (y,) hiddens = tuple(x.transpose(0, 1).contiguous() for x in hiddens) y = y.transpose(0, 1).contiguous() return qo.WithMems(y, attns, hiddens, mems) class ForSeqClass(PreTrained): def __init__(self, kw): super().__init__(kw) cfg = self.get_cfg(kw) self.model = Model(kw) self.proj = qc.Linear(cfg.d_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 LLMHead(PreTrained): def __init__(self, kw): super().__init__(kw) cfg = self.get_cfg(kw) self.model = Model(kw) assert cfg.sample_softmax <= 0 self.proj = Projector( cfg.s_vocab, cfg.d_embed, cfg.d_model, cfg.cutoffs, div_val=cfg.div_val, kw ) def tie_weights(self): cfg = self.cfg if cfg.tie_word_embeds: for i in range(len(self.proj.out_layers)): self._tie_or_clone_weights(self.proj.out_layers[i], self.model.tok_emb.lays[i]) if cfg.tie_projs: for i, tie_proj in enumerate(cfg.tie_projs): if tie_proj and cfg.div_val == 1 and cfg.d_model != cfg.d_embed: if cfg.torchscript: self.proj.out_projs[i] = nn.Parameter(self.model.tok_emb.projs[0].clone()) else: self.proj.out_projs[i] = self.model.tok_emb.projs[0] elif tie_proj and cfg.div_val != 1: if cfg.torchscript: self.proj.out_projs[i] = nn.Parameter(self.model.tok_emb.projs[i].clone()) else: self.proj.out_projs[i] = self.model.tok_emb.projs[i] def init_mems(self, bsz): return self.model.init_mems(bsz) def forward(self, x, x_emb=None, labels=None, kw): if x is None: assert x_emb is not None b, tgt = x_emb.size(0), x_emb.size(1) else: b, tgt = x.size(0), x.size(1) ys = self.model(x, x_emb=x_emb, *kw) xs = self.proj(ys[0][:, -tgt:], labels) y = xs.view(b, tgt, -1) if labels is None else () loss = xs.view(b, tgt - 1) if labels is not None else None ys = (y,) + ys[1:] + (loss,) return qo.LossMems(ys) class Projector(qc.Module): def __init__(self, s_vocab, d_embed, d_proj, cutoffs, div_val=1, keep_order=False): super().__init__() self.s_vocab = s_vocab self.d_embed = d_embed self.d_proj = d_proj self.cutoffs = cutoffs + [s_vocab] self.cutoff_ends = [0] + self.cutoffs self.div_val = div_val self.shortlist_size = self.cutoffs[0] self.n_clusters = len(self.cutoffs) - 1 self.head_size = self.shortlist_size + self.n_clusters if self.n_clusters > 0: self.cluster_weight = nn.Parameter(torch.zeros(self.n_clusters, self.d_embed)) self.cluster_bias = nn.Parameter(torch.zeros(self.n_clusters)) self.out_layers = qc.Stack() self.out_projs = nn.ParameterList() if div_val == 1: for i in range(len(self.cutoffs)): if d_proj != d_embed: self.out_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_embed))) else: self.out_projs.append(None) self.out_layers.append(qc.Linear(d_embed, s_vocab)) else: for i in range(len(self.cutoffs)): l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] d_emb_i = d_embed // (div_vali) self.out_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_emb_i))) self.out_layers.append(qc.Linear(d_emb_i, r_idx - l_idx)) self.keep_order = keep_order def _compute_logit(self, x, weight, bias, proj): if proj is None: y = F.linear(x, weight, bias=bias) else: # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1: x = F.linear(x, proj.t().contiguous()) y = F.linear(x, weight, bias=bias) # else: # logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t())) # if bias is not None: # logit = logit + bias return y def forward(self, x, labels=None, keep_order=False): if labels is not None: x = x[..., :-1, :].contiguous() labels = labels[..., 1:].contiguous() x = x.view(-1, x.size(-1)) labels = labels.view(-1) assert x.size(0) == labels.size(0) else: x = x.view(-1, x.size(-1)) if self.n_clusters == 0: y = self._compute_logit( x, self.out_layers[0].weight, self.out_layers[0].bias, self.out_projs[0] ) if labels is not None: y = -F.log_softmax(y, dim=-1).gather(1, labels.unsqueeze(1)).squeeze(1) else: y = F.log_softmax(y, dim=-1) else: ws, bs = [], [] for i in range(len(self.cutoffs)): if self.div_val == 1: l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] weight_i = self.out_layers[0].weight[l_idx:r_idx] bias_i = self.out_layers[0].bias[l_idx:r_idx] else: weight_i = self.out_layers[i].weight bias_i = self.out_layers[i].bias if i == 0: weight_i = torch.cat([weight_i, self.cluster_weight], dim=0) bias_i = torch.cat([bias_i, self.cluster_bias], dim=0) ws.append(weight_i) bs.append(bias_i) head_weight, head_bias, head_proj = ws[0], bs[0], self.out_projs[0] head_logit = self._compute_logit(x, head_weight, head_bias, head_proj) head_logprob = F.log_softmax(head_logit, dim=1) if labels is None: y = x.new_empty((head_logit.size(0), self.s_vocab)) else: y = torch.zeros_like(labels, dtype=x.dtype, device=x.device) offset = 0 cutoff_values = [0] + self.cutoffs for i in range(len(cutoff_values) - 1): l_idx, r_idx = cutoff_values[i], cutoff_values[i + 1] if labels is not None: mask_i = (labels >= l_idx) & (labels < r_idx) indices_i = mask_i.nonzero().squeeze() if indices_i.numel() == 0: continue target_i = labels.index_select(0, indices_i) - l_idx head_logprob_i = head_logprob.index_select(0, indices_i) hidden_i = x.index_select(0, indices_i) else: hidden_i = x if i == 0: if labels is not None: logprob_i = head_logprob_i.gather(1, target_i[:, None]).squeeze(1) else: y[:, : self.cutoffs[0]] = head_logprob[:, : self.cutoffs[0]] else: weight_i, bias_i, proj_i = ws[i], bs[i], self.out_projs[i] tail_logit_i = self._compute_logit(hidden_i, weight_i, bias_i, proj_i) tail_logprob_i = F.log_softmax(tail_logit_i, dim=1) cluster_prob_idx = self.cutoffs[0] + i - 1 if labels is not None: logprob_i = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather( 1, target_i[:, None] ).squeeze(1) else: logprob_i = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i y[:, l_idx:r_idx] = logprob_i if labels is not None: if (hasattr(self, "keep_order") and self.keep_order) or keep_order: y.index_copy_(0, indices_i, -logprob_i) else: y[offset : offset + logprob_i.size(0)].copy_(-logprob_i) offset += logprob_i.size(0) return y def log_prob(self, x): if self.n_clusters == 0: y = self._compute_logit( x, self.out_layers[0].weight, self.out_layers[0].bias, self.out_projs[0] ) return F.log_softmax(y, dim=-1) else: ws, bs = [], [] for i in range(len(self.cutoffs)): if self.div_val == 1: l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] weight_i = self.out_layers[0].weight[l_idx:r_idx] bias_i = self.out_layers[0].bias[l_idx:r_idx] else: weight_i = self.out_layers[i].weight bias_i = self.out_layers[i].bias if i == 0: weight_i = torch.cat([weight_i, self.cluster_weight], dim=0) bias_i = torch.cat([bias_i, self.cluster_bias], dim=0) ws.append(weight_i) bs.append(bias_i) head_weight, head_bias, head_proj = ws[0], bs[0], self.out_projs[0] head_logit = self._compute_logit(x, head_weight, head_bias, head_proj) y = x.new_empty((head_logit.size(0), self.s_vocab)) head_logprob = F.log_softmax(head_logit, dim=1) cutoff_values = [0] + self.cutoffs for i in range(len(cutoff_values) - 1): beg_idx, stop_idx = cutoff_values[i], cutoff_values[i + 1] if i == 0: y[:, : self.cutoffs[0]] = head_logprob[:, : self.cutoffs[0]] else: weight_i, bias_i, proj_i = ws[i], bs[i], self.out_projs[i] tail_logit_i = self._compute_logit(x, weight_i, bias_i, proj_i) tail_logprob_i = F.log_softmax(tail_logit_i, dim=1) logprob_i = head_logprob[:, -i] + tail_logprob_i y[:, beg_idx, stop_idx] = logprob_i return y class Layer(qc.Module): def __init__(self, kw): super().__init__() self.attn = Attention(kw) self.ff = Positionwise(kw) def forward(self, x, r, dec_m=None, kw): ys = self.attn(x, r, mask=dec_m, kw) return (self.ff(ys[0]),) + ys[1:] class Attention(qc.Module): hs = qc.Hypers( {"d_head", "d_model", "drop", "n_heads"}, {"drop_attn": 0.0, "eps": 1e-5, "pre_norm": False}, ) def __init__(self, r_bias=None, q_bias=None, ps={}, hs=[], kw): super().__init__(ps, [self.hs] + hs, kw) cfg = self.get_cfg(kw) m, n, h = cfg.d_model, cfg.n_heads, cfg.d_head cfg.scale = 1 / (h*0.5) self.qkv = qc.Linear(m, 3 n * h, bias=False) self.r_net = qc.Linear(m, n * h, bias=False) if r_bias is None or q_bias is None: self.q_bias = nn.Parameter(torch.FloatTensor(n, h)) self.r_bias = nn.Parameter(torch.FloatTensor(n, h)) else: self.q_bias = q_bias self.r_bias = r_bias self.drop = qc.Dropout(cfg.drop, kw) self.drop_attn = qc.Dropout(cfg.drop_attn, kw) self.proj = qc.Linear(n * h, m, bias=False, kw) self.norm = qc.LayerNorm(m, kw) def rel_shift(self, x, zero_triu=False): s = (x.size(0), 1) + x.size()[2:] y = torch.zeros(s, device=x.device, dtype=x.dtype) y = torch.cat([y, x], dim=1) s = (x.size(1) + 1, x.size(0)) + x.size()[2:] y = y.view(s) y = y[1:].view_as(x) if zero_triu: ones = torch.ones((y.size(0), y.size(1))) y = y torch.tril(ones, y.size(1) - y.size(0))[:, :, None, None] return y def forward(self, x, r, mask=None, mems=None, head_m=None, *kw): cfg = self.cfg y = x if mems is None else torch.cat([mems, x], 0) y = self.qkv(self.norm(y) if cfg.pre_norm else y) r = self.r_net(r) q, k, v = torch.chunk(a, 3, dim=-1) qlen, klen, rlen = x.size(0), k.size(0), r.size(0) q = q if mems is None else q[-qlen:] b, n, h = x.size(1), cfg.n_heads, cfg.d_head q = q.view(qlen, b, n, h) k = k.view(klen, b, n, h) v = v.view(klen, b, n, h) r = r.view(rlen, n, h) AC = torch.einsum("ibnd,jbnd->ijbn", (q + self.q_bias, k)) BD = self.rel_shift(torch.einsum("ibnd,jnd->ijbn", (q + self.r_bias, r))) a = AC + BD a.mul_(cfg.scale) if mask is not None and torch.sum(mask).item(): mask = mask == 1 i = self.get_minus_inf() if mask.dim() == 2: a = a.float().masked_fill(mask[None, :, :, None], i).type_as(a) elif mask.dim() == 3: a = a.float().masked_fill(mask[:, :, :, None], i).type_as(a) a = self.drop_attn(F.softmax(a, dim=1)) if head_m is not None: a = a head_m y = torch.einsum("ijbn,jbnd->ibnd", (a, v)) y = y.contiguous().view(y.size(0), y.size(1), n * h) y = x + self.drop(self.proj(y)) return y if cfg.pre_norm else self.norm(y), 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", 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33,670	quantapix/qnarre	refs/heads/main	/qnarre/prep/config/nanogpt/eval_gpt2.py	# evaluate the base gpt2 # n_layer=12, n_head=12, n_hidden=768 # 124M parameters batch_size = 8 eval_iters = 500 # use more iterations to get good estimate eval_only = True wandb_log = False init_from = "gpt2" # evaluate the base gpt2 # n_layer=24, n_head=16, n_hidden=1024 # 350M parameters batch_size = 8 eval_iters = 500 # use more iterations to get good estimate eval_only = True wandb_log = False init_from = "gpt2-medium" # evaluate the base gpt2 # n_layer=36, n_head=20, n_hidden=1280 # 774M parameters batch_size = 8 eval_iters = 500 # use more iterations to get good estimate eval_only = True wandb_log = False init_from = "gpt2-large" # evaluate the base gpt2 # n_layer=48, n_head=25, n_hidden=1600 # 1558M parameters batch_size = 8 eval_iters = 500 # use more iterations to get good estimate eval_only = True wandb_log = False init_from = "gpt2-xl"	{"/qnarre/prep/convert/xlnet.py": 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33,671	quantapix/qnarre	refs/heads/main	/qnarre/models/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. # ============================================================================= # https://arxiv.org/abs/1907.11692 import torch from torch import nn 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.embed import Embed from ..core.mlp import MLP, Classifier, Predictor from ..prep.config.roberta import PreTrained from . import bert log = logging.get_logger(__name__) 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 = qu.Pool(kw) if add_pool else None def forward( self, x, cache=None, enc_m=None, enc=None, head_m=None, mask=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 c_len = cache[0][0].shape[2] if cache is not None else 0 if mask is None: b, n = s mask = torch.ones(((b, n + c_len)), device=d) xm = 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, kw, c_len=c_len, x_emb=x_emb) ys = self.enc(ys, kw, cache=cache, enc_m=enc_m, enc=enc, head_m=head_m, mask=xm) pools = self.pool(ys[0]) if self.pool is not None else None ys += (pools,) return qo.PoolsCrosses(ys) class ForCausal(PreTrained): def __init__(self, kw): super().__init__(kw) self.get_cfg(kw) self.model = Model(add_pool=False, kw) self.proj = bert.LMHead(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 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 ForChoice(PreTrained): def __init__(self, kw): super().__init__(kw) self.get_cfg(kw) self.model = Model(kw) self.proj = Classifier(n_labels=1, kw) def forward(self, x, typ=None, mask=None, labels=None, pos=None, x_emb=None, kw): n = x.shape[1] if x is not None else x_emb.shape[1] x, mask, typ, pos = qu.view_2D(x, mask, typ, pos) x_emb = qu.view_3D(x_emb) ys = self.model(x, pos=pos, typ=typ, mask=mask, x_emb=x_emb, kw) y = self.proj(ys[1]).view(-1, n) loss = None if labels is not None: loss = nn.CrossEntropyLoss()(y, labels) ys = (y,) + ys[2:] + (loss,) return qo.WithLoss(ys) 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 class ForSeqClass(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_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 Encoder(qc.Module): hs = qc.Hypers({"add_cross", "n_lays"}) def __init__(self, n_lays=None, ps={}, hs=[], kw): if n_lays is not None: kw.update(n_lays=n_lays) super().__init__(ps, [self.hs] + hs, kw) cfg = self.get_cfg(kw) self.lays = qc.Stack([Layer(kw) for _ in range(cfg.n_lays)]) self.grad_checkpoint = False def forward(self, x, head_m=None, cache=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, mask=h, kw) else: ys = lay(y, mask=h, cache=c, kw) 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({"add_cross", "act"}, {"is_dec": False}) def __init__(self, add_cross=None, ps={}, hs=[], kw): if add_cross is not None: kw.update(add_cross=add_cross) super().__init__(ps, [self.hs] + hs, kw) cfg = self.get_cfg(kw) self.attn = bert.Attention(kw) if cfg.add_cross: assert cfg.is_dec self.cross = bert.Attention(pos_type="absolute", kw) self.proj = MLP(cfg.act, cfg.drop, cfg.eps, kw) def forward(self, x, cache=None, enc=None, kw): cfg = self.cfg c = cache[:2] if cache is not None else None y, a, kv = self.attn(x, cache=c, kw) a2 = None if cfg.is_dec and enc is not None: c = cache[-2:] if cache is not None else None y, a2, kv2 = self.cross(y, cache=c, enc=enc, *kw) kv = kv + kv2 return self.proj(y), a, a2, kv	{"/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", 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["/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"]}
33,672	quantapix/qnarre	refs/heads/main	/qnarre/base/doc/util/node.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 collections as co import collections.abc as abc from .row import Row from .log import Logger from .base import num_to_name log = Logger(__name__) class Node(Row, abc.MutableSequence): def __init__(self, name, rows=None, kw): super().__init__(name, kw) self._rows = [Row(*kw)] if rows is None else [rows] def __eq__(self, other): if isinstance(other, type(self)): return (super().__eq__(other) and self._rows == other._rows) return NotImplemented def __len__(self): return len(self._rows) def __getitem__(self, i): if isinstance(i, str): return super()[i] else: return self._rows[i] def __setitem__(self, i, value): if isinstance(i, str): super()[i] = value else: self._rows[i] = value def __delitem__(self, i): if isinstance(i, str): del super()[i] else: del self._rows[i] def insert(self, i, value): return self._rows.insert(i, value) def __iadd__(self, other): if isinstance(other, Row): self._rows.append(other) elif other is not None: self._rows.extend(other) return self def __repr__(self): s = type(self).__name__ s += "({}, (".format(repr(self.name)) for r in self._rows: s += repr(r) + ", " s += "), cols={})".format(repr(self._cols)) return s def stringer(self, indent=0, *kw): yield (" " indent + self.name + ":") for r in self._rows: yield from r.stringer(indent + 2, kw) def walker(self, depth_first=False, _): def _breadth_first(): nodes = [] for r in self._rows: if isinstance(r, Node): nodes.append(r) else: yield r for r in nodes: yield r reject = yield self if reject is not True: for r in self._rows if depth_first else _breadth_first(): if isinstance(r, Node): yield from r.walker(depth_first) else: yield r yield None def appender(self, src, itr=None): rs = [] itr = itr or iter(src) r = False try: while r is not None: r = r or next(itr) if r is None: break reject = yield r if reject is True: r = itr.send(reject) continue rs.append(r) if isinstance(r, Node): yield from r.appender(src, itr) yield None r = False except StopIteration: pass finally: self._rows.extend(rs) def merge(self, other): assert isinstance(other, Node) super().merge(other) self._rows.extend(other._rows) rename = None def consolidate(self, col): rs = co.OrderedDict() for r in self._rows: d = r.digest(col) try: rs[d].merge(r) except KeyError: rs[d] = r self._rows = list(rs.values()) for r in self._rows: if isinstance(r, Node): r.consolidate(col) def normalize(self, rename): self._rows.sort(key=lambda x: x.name) dirty = False i = 0 for r in self._rows: if isinstance(r, Node): r.normalize(rename) elif rename: n = num_to_name(i) if r.name != n: r.rename(n) dirty = True i += 1 if dirty: self._rows.sort(key=lambda x: x.name) schedule = 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"], "/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", 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33,673	quantapix/qnarre	refs/heads/main	/qnarre/prep/metric/accuracy.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 from sklearn.metrics import accuracy_score class Accuracy(ds.Metric): def _info(self): return ds.MetricInfo( description="", citation="", inputs_description="", features=ds.Features( { "predictions": ds.Sequence(ds.Value("int32")), "references": ds.Sequence(ds.Value("int32")), } if self.config_name == "multilabel" else { "predictions": ds.Value("int32"), "references": ds.Value("int32"), } ), ) def _compute(self, preds, refs, normalize=True, sample_weight=None): return { "accuracy": float( accuracy_score(refs, preds, normalize=normalize, sample_weight=sample_weight) ) }	{"/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"], 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33,674	quantapix/qnarre	refs/heads/main	/qnarre/models/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 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.dpr import PreTrained log = logging.get_logger(__name__) from dataclasses import dataclass from ..bert.modeling_bert import BertModel from .configuration_dpr import DPRConfig DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST = [ "facebook/dpr-ctx_encoder-single-nq-base", "facebook/dpr-ctx_encoder-multiset-base", ] DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST = [ "facebook/dpr-question_encoder-single-nq-base", "facebook/dpr-question_encoder-multiset-base", ] DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST = [ "facebook/dpr-reader-single-nq-base", "facebook/dpr-reader-multiset-base", ] @dataclass class Output(qo.Output): logits_beg = None logits_end = None relevance_logits = None hiddens = None attns = None class Encoder(PreTrained): def __init__(self, config): super().__init__(config) self.bert_model = BertModel(config, add_pooling_layer=False) if self.bert_model.config.d_model <= 0: raise ValueError("Encoder d_model can't be zero") self.projection_dim = config.projection_dim if self.projection_dim > 0: self.encode_proj = qc.Linear(self.bert_model.config.d_model, config.projection_dim) self.post_init() def forward( self, input_ids, attention_mask=None, token_type_ids=None, inputs_embeds=None, output_attentions=False, output_hidden_states=False, return_dict=False, ): outputs = self.bert_model( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] pooled_output = sequence_output[:, 0, :] if self.projection_dim > 0: pooled_output = self.encode_proj(pooled_output) if not return_dict: return (sequence_output, pooled_output) + outputs[2:] return qo.BaseWithPooling( y=sequence_output, pools=pooled_output, hiddens=outputs.hiddens, attns=outputs.attns, ) @property def embeddings_size(self): if self.projection_dim > 0: return self.encode_proj.out_features return self.bert_model.config.d_model class SpanPredictor(PreTrained): base_model_prefix = "encoder" def __init__(self, config): super().__init__(config) self.encoder = Encoder(config) self.qa_outputs = qc.Linear(self.encoder.embeddings_size, 2) self.qa_classifier = qc.Linear(self.encoder.embeddings_size, 1) # Initialize weights and apply final processing self.post_init() def forward( self, input_ids, attention_mask, inputs_embeds=None, output_attentions=False, output_hidden_states=False, return_dict=False, ): # notations: N - number of questions in a batch, M - number of passages per questions, L - sequence length n_passages, sequence_length = ( input_ids.size() if input_ids is not None else inputs_embeds.size()[:2] ) # feed encoder outputs = self.encoder( input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] # compute logits logits = self.qa_outputs(sequence_output) logits_beg, logits_end = logits.split(1, dim=-1) logits_beg = logits_beg.squeeze(-1).contiguous() logits_end = logits_end.squeeze(-1).contiguous() relevance_logits = self.qa_classifier(sequence_output[:, 0, :]) # resize logits_beg = logits_beg.view(n_passages, sequence_length) logits_end = logits_end.view(n_passages, sequence_length) relevance_logits = relevance_logits.view(n_passages) if not return_dict: return (logits_beg, logits_end, relevance_logits) + outputs[2:] return Output( logits_beg=logits_beg, logits_end=logits_end, relevance_logits=relevance_logits, hiddens=outputs.hiddens, attns=outputs.attns, ) class DPRPretrainedContextEncoder(PreTrained): config_class = DPRConfig load_tf_weights = None base_model_prefix = "ctx_encoder" _keys_to_ignore_on_load_missing = [r"position_ids"] class DPRPretrainedQuestionEncoder(PreTrained): config_class = DPRConfig load_tf_weights = None base_model_prefix = "question_encoder" _keys_to_ignore_on_load_missing = [r"position_ids"] class DPRPretrainedReader(PreTrained): config_class = DPRConfig load_tf_weights = None base_model_prefix = "span_predictor" _keys_to_ignore_on_load_missing = [r"position_ids"] class DPRContextEncoder(DPRPretrainedContextEncoder): def __init__(self, config): super().__init__(config) self.config = config self.ctx_encoder = Encoder(config) self.post_init() def forward( self, input_ids=None, attention_mask=None, token_type_ids=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 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") device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = ( torch.ones(input_shape, device=device) if input_ids is None else (input_ids != self.config.PAD) ) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) outputs = self.ctx_encoder( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return outputs[1:] return qo.WithPools( pools=outputs.pools, hiddens=outputs.hiddens, attns=outputs.attns, ) class DPRQuestionEncoder(DPRPretrainedQuestionEncoder): def __init__(self, config): super().__init__(config) self.config = config self.question_encoder = Encoder(config) # Initialize weights and apply final processing self.post_init() def forward( self, input_ids=None, attention_mask=None, token_type_ids=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 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") device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = ( torch.ones(input_shape, device=device) if input_ids is None else (input_ids != self.config.PAD) ) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) outputs = self.question_encoder( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return outputs[1:] return qo.WithPools( pools=outputs.pools, hiddens=outputs.hiddens, attns=outputs.attns, ) class Reader(DPRPretrainedReader): def __init__(self, config): super().__init__(config) self.config = config self.span_predictor = SpanPredictor(config) # Initialize weights and apply final processing self.post_init() def forward( self, input_ids=None, attention_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 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") device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(input_shape, device=device) return self.span_predictor( input_ids, attention_mask, inputs_embeds=inputs_embeds, 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33,675	quantapix/qnarre	refs/heads/main	/qnarre/prep/tokens/plbart.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 contextlib import contextmanager from shutil import copyfile import sentencepiece as spm from ...tokens.utils import AddedToken, PreTrainedTokenizer SPIECE_UNDERLINE = "▁" VOCAB_FS = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"} VOCAB_MAP = { "vocab_file": { "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/sentencepiece.bpe.model", "uclanlp/plbart-c-cpp-defect-detection": "https://huggingface.co/uclanlp/plbart-c-cpp-defect-detection/resolve/main/sentencepiece.bpe.model", "uclanlp/plbart-cs-java": "https://huggingface.co/uclanlp/plbart-cs-java/resolve/main/sentencepiece.bpe.model", "uclanlp/plbart-en_XX-java": "https://huggingface.co/uclanlp/plbart-en_XX-java/resolve/main/sentencepiece.bpe.model", "uclanlp/plbart-go-en_XX": "https://huggingface.co/uclanlp/plbart-go-en_XX/resolve/main/sentencepiece.bpe.model", "uclanlp/plbart-java-clone-detection": "https://huggingface.co/uclanlp/plbart-java-clone-detection/resolve/main/sentencepiece.bpe.model", "uclanlp/plbart-java-cs": "https://huggingface.co/uclanlp/plbart-java-cs/resolve/main/sentencepiece.bpe.model", "uclanlp/plbart-java-en_XX": "https://huggingface.co/uclanlp/plbart-java-en_XX/resolve/main/sentencepiece.bpe.model", "uclanlp/plbart-javascript-en_XX": "https://huggingface.co/uclanlp/plbart-javascript-en_XX/resolve/main/sentencepiece.bpe.model", "uclanlp/plbart-php-en_XX": "https://huggingface.co/uclanlp/plbart-php-en_XX/resolve/main/sentencepiece.bpe.model", "uclanlp/plbart-python-en_XX": "https://huggingface.co/uclanlp/plbart-python-en_XX/resolve/main/sentencepiece.bpe.model", "uclanlp/plbart-refine-java-medium": "https://huggingface.co/uclanlp/plbart-refine-java-medium/resolve/main/sentencepiece.bpe.model", "uclanlp/plbart-refine-java-small": "https://huggingface.co/uclanlp/plbart-refine-java-small/resolve/main/sentencepiece.bpe.model", "uclanlp/plbart-ruby-en_XX": "https://huggingface.co/uclanlp/plbart-ruby-en_XX/resolve/main/sentencepiece.bpe.model", } } INPUT_CAPS = { "uclanlp/plbart-base": 1024, "uclanlp/plbart-c-cpp-defect-detection": 1024, "uclanlp/plbart-cs-java": 1024, "uclanlp/plbart-en_XX-java": 1024, "uclanlp/plbart-go-en_XX": 1024, "uclanlp/plbart-java-clone-detection": 1024, "uclanlp/plbart-java-cs": 1024, "uclanlp/plbart-java-en_XX": 1024, "uclanlp/plbart-javascript-en_XX": 1024, "uclanlp/plbart-php-en_XX": 1024, "uclanlp/plbart-python-en_XX": 1024, "uclanlp/plbart-refine-java-medium": 1024, "uclanlp/plbart-refine-java-small": 1024, "uclanlp/plbart-ruby-en_XX": 1024, } FAIRSEQ_LANGUAGE_CODES = { "base": ["java", "python", "en_XX"], "multi": ["java", "python", "en_XX", "javascript", "php", "ruby", "go"], } class Tokenizer(PreTrainedTokenizer): vocab_fs = VOCAB_FS input_caps = INPUT_CAPS vocab_map = VOCAB_MAP model_input_names = ["input_ids", "attention_mask"] prefix_tokens = [] suffix_tokens = [] def __init__( self, vocab_file, bos="<s>", eos="</s>", sep="</s>", cls="<s>", unk="<unk>", pad="<pad>", msk="<mask>", language_codes="base", tokenizer_file=None, src_lang=None, tgt_lang=None, sp_model_kw=None, additional_special_tokens=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__( bos=bos, eos=eos, unk=unk, sep=sep, cls=cls, pad=pad, msk=msk, language_codes=language_codes, tokenizer_file=tokenizer_file, src_lang=src_lang, tgt_lang=tgt_lang, additional_special_tokens=additional_special_tokens, sp_model_kw=self.sp_model_kw, kw, ) self.sp_model = spm.SentencePieceProcessor(self.sp_model_kw) self.sp_model.Load(str(vocab_file)) self.vocab_file = vocab_file self.language_codes = language_codes fairseq_language_codes = FAIRSEQ_LANGUAGE_CODES[self.language_codes] self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3} self.fairseq_offset = 1 self.sp_model_size = len(self.sp_model) self.lang_code_to_id = { code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(fairseq_language_codes) } self.id_to_lang_code = {v: k for k, v in self.lang_code_to_id.items()} if self.language_codes == "base": self.fairseq_tokens_to_ids["<mask>"] = ( len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset ) self.fairseq_tokens_to_ids.update(self.lang_code_to_id) self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()} self._additional_special_tokens = list(self.lang_code_to_id.keys()) if additional_special_tokens is not None: self._additional_special_tokens.extend( [t for t in additional_special_tokens if t not in self._additional_special_tokens] ) if self.language_codes == "base": self._src_lang = src_lang self.cur_lang_code_id = ( self.lang_code_to_id[self._src_lang] if self._src_lang is not None else self._src_lang ) else: self._src_lang = src_lang if src_lang is not None else "en_XX" self.cur_lang_code_id = self.lang_code_to_id[self._src_lang] self.tgt_lang = tgt_lang self.set_src_lang_special_tokens(self._src_lang) def __getstate__(self): state = self.__dict__.copy() state["sp_model"] = None state["sp_model_proto"] = self.sp_model.serialized_model_proto() 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.LoadFromSerializedProto(self.sp_model_proto) @property def s_vocab(self): if self.language_codes == "base": return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1 else: return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset @property def src_lang(self): return self._src_lang @src_lang.setter def src_lang(self, new_src_lang): self._src_lang = new_src_lang self.set_src_lang_special_tokens(self._src_lang) 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) prefix_ones = [1] * len(self.prefix_tokens) suffix_ones = [1] * len(self.suffix_tokens) if toks_1 is None: return prefix_ones + ([0] * len(toks_0)) + suffix_ones return prefix_ones + ([0] * len(toks_0)) + ([0] * len(toks_1)) + suffix_ones def build_inputs_with_special_tokens(self, toks_0, toks_1=None): if toks_1 is None: return self.prefix_tokens + toks_0 + self.suffix_tokens return self.prefix_tokens + toks_0 + toks_1 + self.suffix_tokens 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 _build_translation_inputs( self, raw_inputs, return_tensors, src_lang, tgt_lang, extra_kw, ): if src_lang is None or tgt_lang is None: raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model") self.src_lang = src_lang inputs = self( raw_inputs, add_special_tokens=True, return_tensors=return_tensors, extra_kw ) tgt_lang_id = self.convert_tokens_to_ids(tgt_lang) inputs["forced_bos_token_id"] = tgt_lang_id return inputs 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 _tokenize(self, text): return self.sp_model.encode(text, out_type=str) def _convert_token_to_id(self, token): if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] spm_id = self.sp_model.PieceToId(token) return spm_id + self.fairseq_offset if spm_id else self.unk_token_id def _convert_id_to_token(self, index): if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(index - self.fairseq_offset) def convert_tokens_to_string(self, tokens): out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip() return out_string 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,) def prepare_seq2seq_batch( self, src_texts, src_lang="en_XX", tgt_texts=None, tgt_lang="python", kw, ): self.src_lang = src_lang self.tgt_lang = tgt_lang return super().prepare_seq2seq_batch(src_texts, tgt_texts, kw) @contextmanager def as_target_tokenizer(self): self.set_tgt_lang_special_tokens(self.tgt_lang) yield self.set_src_lang_special_tokens(self.src_lang) def set_src_lang_special_tokens(self, src_lang): self.cur_lang_code = self.lang_code_to_id[src_lang] if src_lang is not None else None self.prefix_tokens = [] if self.cur_lang_code is not None: self.suffix_tokens = [self.EOS, self.cur_lang_code] else: self.suffix_tokens = [self.EOS] def set_tgt_lang_special_tokens(self, lang): self.cur_lang_code = self.lang_code_to_id[lang] if lang is not None else None self.prefix_tokens = [] if self.cur_lang_code is not None: self.suffix_tokens = [self.EOS, self.cur_lang_code] else: 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33,676	quantapix/qnarre	refs/heads/main	/qnarre/base/doc/part.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.abc as abc from unicodedata import normalize from .base import config from .meta import with_current, with_property def slugify(value): v = str(value) v = normalize("NFKD", v).encode("ascii", "ignore").decode("ascii") v = re.sub(r"[^\w\s-]", "", v).strip().lower() return re.sub(r"[-\s]+", "-", v) class Registry(abc.MutableMapping): def __init__(self, elems=None, kw): super().__init__(kw) self._elems = elems or {} def __bool__(self): return True def __len__(self): return len(self._elems) def __iter__(self): return iter(self._elems) def __getitem__(self, n): return self._elems[self.resolve_alias(n)] def __setitem__(self, n, e): self._elems[self.resolve_alias(n)] = e def __delitem__(self, n): del self._elems[self.resolve_alias(n)] def add_once(self, v): if v: try: v = self[v] except KeyError: self[v] = v return v def add_alias(self, name, tgt): return Alias(name, tgt, self) def resolve_alias(self, name): v = self._elems.get(slugify(name)) while isinstance(v, Alias): name = v.tgt v = self._elems.get(name) return name def resolve_all(self, names): for n in names: r = self.resolve_alias(n) if r != n: return r else: return n @with_current() class Labels(Registry): pass @with_current() class Texts(Registry): pass class Parts(Registry): pass class Defaults: parent = None tags = () sources = () def __init__(self, kw): if kw: print(type(self)) print(kw) super().__init__(kw) def convert_from(self, src, kw): pass class Base: @classmethod def create(cls, i, kw): return i if isinstance(i, cls) else cls(i, kw) @classmethod def creator(cls, ii, kw): for i in ii: yield cls.create(i, kw) def __init__(self, kw): super().__init__(kw) @property def html(self): return str(self) @with_property("text", Texts.current.add_once, "") class Textual(Base): def __init__(self, text="", kw): super().__init__(kw) self.text = text def __str__(self): return self.text class Title(Textual): pass class Summary(Textual): pass @with_property("title", Title.create) @with_property("summary", Summary.create) class Titled: def __init__(self, title=None, summary=None, kw): super().__init__(kw) self.title = title self.summary = summary def convert_from(self, rec, kw): t = rec.hdr.title or "" if self.title: assert str(self.title) == t else: self.title = t s = rec.hdr.summary or "" if self.summary: assert str(self.summary) == s else: self.summary = s super().convert_from(rec, kw) @with_property("label", Labels.current.add_once, "") class Part(Base): hide = False @classmethod def slugify(cls, label="", slug=""): return slug or slugify(label) @classmethod def create(cls, i, slug="", regy=None, kw): if not isinstance(i, cls): s = cls.slugify(i, slug) i = i if regy is None else regy.get(s, i) if not isinstance(i, cls): i = cls(i, kw, slug=s, regy=regy) return i @classmethod def get_template(cls): return "part" def __init__(self, label="", slug="", regy=None, default=False, kw): super().__init__(kw) self._slug = s = Labels.current.add_once(self.slugify(label, slug)) if label != s: self.label = label if regy: assert s not in regy, "{} duplicate".format(s) if default and len(regy) == 0: regy[config.DEFAULT] = self if isinstance(self, Alias): regy._elems[s] = self else: regy[s] = self def __hash__(self): return hash(self._slug) def __eq__(self, other): if isinstance(other, Part): return self._slug == other._slug return NotImplemented def __lt__(self, other): if isinstance(other, Part): return self._slug < other._slug return NotImplemented def __repr__(self): return "{}({!r})".format(type(self).__name__, self.name) def __str__(self): return self.name @property def slug(self): return self._slug @property def name(self): return self.label or self._slug def get_absolute_url(self): from django.urls import reverse return reverse("qnarre:part", kw={"slug": self._slug}) class Alias(Part): def __init__(self, name, tgt, regy=None): super().__init__(slugify(name), regy=regy) self.tgt = t = slugify(tgt) if regy: assert t in regy, "{} not found".format(t) def __repr__(self): return "{}, {!r})".format(super().__repr__()[:-1], self.tgt) class Dated(Part): @property def date(self): s = self.slug.split("_") if len(s) == 2: if "-" in s[1]: return s[0] + " at " + s[1].replace("-", ":") return s[0] class Tag(Part): pass @with_property("tags", Tag.creator) class Tagged: def __init__(self, tags=(), kw): super().__init__(kw) self.tags = tags class Role(Part): group = None class Topic(Part): @property def name(self): return (super().name or config.TBD).title() class Contact(Part): adrs = () _adr = None @classmethod def slugify(cls, name="", slug=""): if not slug: n = name.strip() if name else "" s = n.split() if len(s) == 2: f, la = s elif len(s) >= 3: f, _, la = s[:3] else: return slugify(n) assert f and la if f in ("Atty.", "Dr.", "Ms.", "Mrs.", "Mr.", "Sr.", "Hon."): return slugify("{}_{}".format(f[:-1], la)) return slugify("{}_{}".format(f[0], la)) return slug def __init__(self, label, adr=None, ctxt=None, regy=None, kw): super().__init__(label, kw, regy=regy or ctxt, default=True) if ctxt: self.append(adr, ctxt) else: self._adr = adr or () def __repr__(self): a = self._adr return "{}, {!r})".format(super().__repr__()[:-1], self.adrs if a is None else a) def map_by_adr(self, ctxt): if self._adr is not None: a = self._adr del self._adr self.append(a, ctxt) def append(self, adr, ctxt): if isinstance(adr, tuple): for a in adr: self.append(a, ctxt) elif adr and ctxt: adr = Labels.current.add_once(adr.lower()) try: assert self == ctxt.by_adr[adr] except KeyError: ctxt.by_adr[adr] = self self.adrs = (self.adrs, adr) def plainer(self, kw): yield ":Name: {}".format(self.name) yield ":Email: {}".format("xyz@aaa.com") yield ":Web site: {}".format("www.abc.com") yield ":Archived page: {}".format("page...") @with_property("address", Labels.current.add_once, "") class Place(Part): def __init__(self, name, address=None, ctxt=None): super().__init__(name, regy=ctxt) self.address = address def __repr__(self): return "{}, {!r})".format(super().__repr__()[:-1], self.address) @with_property("value", Textual.create) class Setting(Part): def __init__(self, name, value="", kw): super().__init__(name, **kw) self.value = value @property def html(self): return self.name + ": " + self.value	{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", 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33,677	quantapix/qnarre	refs/heads/main	/qnarre/prep/convert/bart.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 os.path import exists from pathlib import Path from torch import nn from transformers import ( BartModel, BartTokenizer, ) from transformers.utils import logging from ..config.bart import PreTrained from ...models.bart import Model, ForSeqClass, ForCondGen FAIRSEQ_MODELS = ["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"] extra_arch = {"bart.large": BartModel, "bart.large.mnli": ForSeqClass} logging.set_verbosity_info() log = logging.get_logger(__name__) SAMPLE_TEXT = " Hello world! cécé herlolip" mnli_rename_keys = [ ("model.classification_heads.mnli.dense.weight", "classification_head.dense.weight"), ("model.classification_heads.mnli.dense.bias", "classification_head.dense.bias"), ("model.classification_heads.mnli.out_proj.weight", "classification_head.out_proj.weight"), ("model.classification_heads.mnli.out_proj.bias", "classification_head.out_proj.bias"), ] def remove_ignore_keys_(state_dict): ignore_keys = [ "encoder.version", "decoder.version", "model.encoder.version", "model.decoder.version", "_float_tensor", ] for k in ignore_keys: state_dict.pop(k, None) def rename_key(dct, old, new): val = dct.pop(old) dct[new] = val def load_xsum_checkpoint(checkpoint_path): sd = torch.load(checkpoint_path, map_location="cpu") hub_interface = torch.hub.load("pytorch/fairseq", "bart.large.cnn").eval() hub_interface.model.load_state_dict(sd["model"]) return hub_interface def make_linear_from_emb(emb): s_vocab, emb_size = emb.weight.shape lin_layer = nn.Linear(s_vocab, emb_size, bias=False) lin_layer.weight.data = emb.weight.data return lin_layer @torch.no_grad() def convert_checkpoint(src_path, save_path, hf_checkpoint_name=None): if not exists(src_path): bart = torch.hub.load("pytorch/fairseq", src_path).eval() else: bart = load_xsum_checkpoint(src_path) bart.model.upgrade_state_dict(bart.model.state_dict()) if hf_checkpoint_name is None: hf_checkpoint_name = src_path.replace(".", "-") cfg = PreTrained.from_pretrained(hf_checkpoint_name) tokens = bart.encode(SAMPLE_TEXT).unsqueeze(0) tokens2 = ( BartTokenizer.from_pretrained(hf_checkpoint_name) .encode(SAMPLE_TEXT, return_tensors="pt") .unsqueeze(0) ) assert torch.eq(tokens, tokens2).all() if src_path == "bart.large.mnli": state_dict = bart.state_dict() remove_ignore_keys_(state_dict) state_dict["model.shared.weight"] = state_dict["model.decoder.embed_tokens.weight"] for src, dest in mnli_rename_keys: rename_key(state_dict, src, dest) m = ForSeqClass(cfg).eval() m.load_state_dict(state_dict) fairseq_output = bart.predict("mnli", tokens, return_logits=True) new_model_outputs = m(tokens)[0] else: state_dict = bart.model.state_dict() remove_ignore_keys_(state_dict) state_dict["shared.weight"] = state_dict["decoder.embed_tokens.weight"] fairseq_output = bart.extract_features(tokens) if hf_checkpoint_name == "facebook/bart-large": m = Model(cfg).eval() m.load_state_dict(state_dict) new_model_outputs = m(tokens).model[0] else: m = ForCondGen(cfg).eval() m.model.load_state_dict(state_dict) if hasattr(m, "lm_head"): m.lm_head = make_linear_from_emb(m.model.shared) new_model_outputs = m.model(tokens)[0] assert fairseq_output.shape == new_model_outputs.shape assert (fairseq_output == new_model_outputs).all().item() Path(save_path).mkdir(exist_ok=True) m.save_pretrained(save_path) if __name__ == "__main__": x = ArgumentParser() x.add_argument("--src_path", type=str) x.add_argument("--cfg_path", default=None, type=str) x.add_argument("--save_path", default=None, type=str) y = x.parse_args() convert_checkpoint(y.src_path, y.save_path, y.cfg_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", 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33,678	quantapix/qnarre	refs/heads/main	/qnarre/run/plm.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 permutation language modeling import logging from datasets import load_dataset from functools import partial from torch.utils.data import DataLoader from transformers import ( DataCollatorForPermutationLanguageModeling, XLNetConfig, XLNetLMHeadModel, ) from .mlm import Runner as Mlm from .params import TRAIN, EVAL, ALL, EACH from .runner import Runner as Base from .utils import group_texts log = logging.getLogger(__name__) class Runner(Base): AutoModel = XLNetLMHeadModel @property def dataset(self): if self._dataset is None: ps = self.params if ps.dataset_name is not None: y = load_dataset(ps.dataset_name, ps.dataset_config, cache_dir=ps.cache_dir) if EVAL not in y.keys(): y[EVAL] = load_dataset( ps.dataset_name, ps.dataset_config, split=f"train[:{ps.split_percent}%]", cache_dir=ps.cache_dir, ) y[TRAIN] = load_dataset( ps.dataset_name, ps.dataset_config, split=f"train[{ps.split_percent}%:]", cache_dir=ps.cache_dir, ) else: x, xs = None, {} if ps.eval_file is not None: xs[EVAL] = x = ps.eval_file if ps.train_file is not None: xs[TRAIN] = x = ps.train_file x = x.split(".")[-1] if x == "txt": x = "text" y = load_dataset(x, data_files=xs, cache_dir=ps.cache_dir) if EVAL not in y.keys(): y[EVAL] = load_dataset( x, data_files=xs, split=f"train[:{ps.split_percent}%]", cache_dir=ps.cache_dir, ) y[TRAIN] = load_dataset( x, data_files=xs, split=f"train[{ps.split_percent}%:]", cache_dir=ps.cache_dir, ) self._dataset = y return self._dataset @property def cols(self): if self._cols is None: ps = self.params cs = self.dataset[TRAIN if ps.do_train else EVAL].column_names t = "text" if "text" in cs else cs[0] self._cols = {ALL: cs, EACH: [t]} return self._cols @property def config(self): if self._config is None: ps = self.params x = ps.config_name if ps.config_name else ps.model_name if x: y = self.AutoConfig.from_pretrained( x, cache_dir=ps.cache_dir, revision=ps.model_version, use_auth_token=True if ps.use_auth_token else None, ) else: y = XLNetConfig() log.warning("Creating new config") if ps.config_overrides is not None: log.info(f"Overriding config: {ps.config_overrides}") y.update_from_string(ps.config_overrides) log.info(f"New config: {y}") self._config = y return self._config @property def tokenizer(self): if self._tokenizer is None: ps = self.params x = ps.tokenizer_name if ps.tokenizer_name else ps.model_name if not x: raise ValueError("Tokenizer from scratch is not supported") y = self.AutoTokenizer.from_pretrained( x, cache_dir=ps.cache_dir, use_fast=ps.use_fast_tokenizer, revision=ps.model_version, use_auth_token=True if ps.use_auth_token else None, ) self._tokenizer = y if ps.max_seq_length is None: b = y.model_max_length if b > 1024: log.warning(f"Using max_seq_length=1024") b = 1024 else: if ps.max_seq_length > y.model_max_length: log.warning(f"Using max_seq_length={y.model_max_length}") b = min(ps.max_seq_length, y.model_max_length) self.max_seq_length = b return self._tokenizer @property def model(self): if self._model is None: ps = self.params if ps.model_name: y = self.AutoModel.from_pretrained( ps.model_name, from_tf=bool(".ckpt" in ps.model_name), config=self.config, cache_dir=ps.cache_dir, revision=ps.model_version, use_auth_token=True if ps.use_auth_token else None, ) else: log.info("Training new model") y = self.AutoModel.from_config(self.config) self._model = y return self._model train_ds = Mlm.train_ds def prep_for_train(self, xs): ps, c = self.params, self.cols[EACH][0] if ps.line_by_line: xs[c] = [x for x in xs[c] if len(x) > 0 and not x.isspace()] return self.tokenizer( xs[c], padding=self.padding, truncation=True, max_len=self.max_seq_length ) else: return self.tokenizer(xs[c]) @property def loaders(self): if self._loaders is None: ps = self.params c = DataCollatorForPermutationLanguageModeling( self.tokenizer, plm_probability=ps.plm_probability, max_span_length=ps.max_span_length, ) 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 eval_epoch = Mlm.eval_epoch def main(): ps = [("--max_seq_length", {"type", "default": 512})] x = Runner(ps) 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()	{"/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": 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33,679	quantapix/qnarre	refs/heads/main	/qnarre/models/albert.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 from torch.nn import functional as F from transformers.utils import logging from .. import core as qc from ..core import attention as qa from ..core import forward as qf from ..core import output as qo from ..core import utils as qu from ..core.embed import Embed from ..core.mlp import Classifier, MLP, Predictor, Pool from ..prep.config.albert import PreTrained from . import bert log = logging.get_logger(__name__) class ForChoice(PreTrained): def __init__(self, kw): super().__init__(kw) self.get_cfg(kw) self.model = Model(kw) self.proj = Classifier(n_labels=1, kw) forward = bert.ForChoice.forward class ForMasked(PreTrained): def __init__(self, kw): super().__init__(kw) cfg = self.get_cfg(kw) self.model = Model(add_pool=False, kw) self.proj = Predictor(cfg.d_embed, kw) forward = qf.forward_masked class ForPreTraining(PreTrained): def __init__(self, kw): super().__init__(kw) cfg = self.get_cfg(kw) self.model = Model(kw) self.proj = Predictor(cfg.d_embed, kw) self.next = Classifier(n_labels=2, kw) forward = bert.ForPreTraining.forward 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 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 Model(PreTrained): def __init__(self, add_pool=True, kw): super().__init__(kw) self.get_cfg(kw) self.emb = Embed(kw) self.enc = Encoder(kw) self.pool = Pool(kw) if add_pool else None def forward(self, x, head_m=None, mask=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 if mask is None: mask = torch.ones(s, device=d) mask = self.get_mask(mask, s, d) head_m = self.get_head_m(head_m, cfg.n_lays) ys = self.emb(x, x_emb, kw) ys = self.enc(ys, mask=mask, head_m=head_m, *kw) if self.pool is not None: ys += (self.pool(ys[0]),) return qo.WithPools(ys) class Encoder(qc.Module): hs = qc.Hypers({"d_embed", "d_model", "n_groups"}) def __init__(self, ps={}, hs=[], kw): super().__init__(ps, [self.hs] + hs, kw) cfg = self.get_cfg(kw) self.proj = qc.Linear(cfg.d_embed, cfg.d_model, kw) self.groups = qc.Stack([Group(kw) for _ in range(cfg.n_groups)]) def forward(self, x, head_m=None, *kw): cfg = self.cfg y = self.proj(x) attns = () hiddens = () hm = [None] cfg.n_lays if head_m is None else head_m for i in range(cfg.n_lays): hiddens += (y,) n = int(cfg.n_lays / cfg.n_groups) g = int(i / (cfg.n_lays / cfg.n_groups)) ys = self.groups[g](y, head_m=hm[g * n : (g + 1) * n], kw) y = ys[0] attns += ys[1] hiddens += (y,) return qo.Base(y, attns, hiddens) class Group(qc.Module): hs = qc.Hypers({"s_group"}) def __init__(self, ps={}, hs=[], kw): super().__init__(ps, [self.hs] + hs, kw) cfg = self.get_cfg(kw) self.lays = qc.Stack([Layer(kw) for _ in range(cfg.s_group)]) def forward(self, x, head_m=None, kw): y = x attns = () hiddens = () for i, lay in enumerate(self.lays): hiddens += (y,) ys = lay(y, head_m=head_m[i], kw) y = ys[0] attns += (ys[1],) hiddens += (y,) return qo.Base(y, attns, hiddens) class Layer(qc.Module): hs = qc.Hypers({"act", "d_ff", "d_model", "drop", "eps"}) def __init__(self, ps={}, hs=[], kw): super().__init__(ps, [self.hs] + hs, kw) cfg = self.get_cfg(kw) self.attn = Attention(kw) self.ffnet = MLP(kw) self.norm = qc.LayerNorm(cfg.d_model, cfg.eps, kw) def forward(self, x, kw): ys = self.attn(x, kw) y = self.ffnet(ys[0]) y = self.norm(y + ys[0]) return (y,) + ys[1:] class Attention(qc.Module): hs = qc.Hypers( {"d_embed", "d_model", "n_heads", "n_pos"}, {"drop_attn": 0.0, "pos_type": "absolute"} ) def __init__(self, pos_type=None, ps={}, hs=[], kw): if pos_type is not None: kw.update(pos_type=pos_type) 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 / (h0.5) self.query = qc.Linear(m, m, kw) self.key = qc.Linear(m, m, kw) self.value = qc.Linear(m, m, *kw) if cfg.pos_type == "relative_key" or cfg.pos_type == "relative_key_query": self.pos_emb = qc.Embed(2 cfg.n_pos - 1, h, kw) self.attn_drop = qc.Dropout(cfg.drop_attn, kw) self.proj = qc.Linear(m, m, kw) self.drop = qc.Dropout(cfg.drop, kw) self.norm = qc.LayerNorm(m, cfg.eps, kw) split_heads = qa.split_heads def forward(self, x, mask=None, head_m=None, kw): cfg = self.cfg q = self.split_heads(self.query(x)) k = self.split_heads(self.key(x)) v = self.split_heads(self.value(x)) a = torch.matmul(q, k.transpose(-1, -2)) a.mul_(cfg.scale) if mask is not None: a = a + mask t = cfg.pos_type if t == "relative_key" or t == "relative_key_query": n = x.size()[1] kw = dict(device=x.device, dtype=torch.long) left, right = torch.arange(n, kw).view(-1, 1), torch.arange(n, kw).view(1, -1) pos = self.pos_emb(left - right + self.n_pos - 1).to(dtype=q.dtype) if t == "relative_key": a += torch.einsum("bhld,lrd->bhlr", q, pos) elif t == "relative_key_query": a += 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33,680	quantapix/qnarre	refs/heads/main	/qnarre/prep/config/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 torch from ... import core as qc class PreTrained(qc.PreTrained): hs = qc.Hypers( {"act_sum"}, dict( asm=False, BOS=0, causal=False, d_model=2048, drop_attn=0.1, drop_sum_first=0.1, drop=0.1, embed_init_std=2048-0.5, end_n_top=5, EOS=1, eps=1e-12, gelu_activation=True, init_std=0.02, is_enc=True, lang_embeds=True, LANG=0, model_type="xlm", MSK_TOK=0, MSK=5, n_heads=16, n_langs=1, n_lays=12, n_pos=512, PAD=2, s_vocab=30145, sin_embeds=False, start_n_top=5, sum_proj=True, sum_type="first", sum_use_proj=True, UNK=3, ), ) @property def dummy_inputs(self): inputs_list = torch.tensor([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]) attns_list = torch.tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]]) if self.cfg.lang_embeds and self.cfg.n_langs > 1: langs_list = torch.tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]]) else: langs_list = None return {"input_ids": inputs_list, "mask": attns_list, "langs": langs_list} def _init_weights(self, module): if isinstance(module, qc.Embedding): if self.cfg is not None and self.cfg.embed_init_std is not None: qc.init.normal_(module.weight, mean=0, std=self.cfg.embed_init_std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() if isinstance(module, qc.Linear): if self.cfg is not None and self.cfg.init_std is not None: qc.init.normal_(module.weight, mean=0, std=self.cfg.init_std) if module.bias is not None: qc.init.constant_(module.bias, 0.0) if isinstance(module, qc.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def __init__(self, kw): if "s_vocab" in kw: self.s_vocab = kw["s_vocab"] super().__init__(PAD=PAD, BOS=BOS, **kw) MAP = { "xlm-mlm-en-2048": dict( act_sum=None, archs=["LMHead"], embed_init_std=0.02209708691207961, ), "xlm-mlm-ende-1024": dict( act_sum=None, archs=["LMHead"], d_model=1024, id2lang={"0": "de", "1": "en"}, lang2id={"de": 0, "en": 1}, max_vocab=-1, min_count=0, n_heads=8, n_langs=2, n_lays=6, s_vocab=64699, same_enc_dec=True, share_inout_emb=True, ), "xlm-mlm-enro-1024": dict( act_sum=None, archs=["LMHead"], d_model=1024, id2lang={"0": "en", "1": "ro"}, lang2id={"en": 0, "ro": 1}, max_vocab=-1, min_count=0, n_heads=8, n_langs=2, n_lays=6, s_vocab=64592, same_enc_dec=True, share_inout_emb=True, ), "xlm-mlm-tlm-xnli15-1024": dict( act_sum=None, archs=["LMHead"], d_model=1024, id2lang={"2": "de", "4": "en"}, lang2id={"de": 2, "en": 4}, max_vocab=95000, min_count=0, n_heads=8, n_langs=15, s_vocab=95000, same_enc_dec=True, share_inout_emb=True, ), "xlm-mlm-xnli15-1024": dict( act_sum=None, archs=["LMHead"], d_model=1024, id2lang={"2": "de", "4": "en"}, lang2id={"de": 2, "en": 4}, max_vocab=95000, min_count=0, n_heads=8, n_langs=15, s_vocab=95000, same_enc_dec=True, share_inout_emb=True, ), "xlm-clm-ende-1024": dict( act_sum=None, archs=["LMHead"], d_model=1024, id2lang={"0": "de", "1": "en"}, lang2id={"de": 0, "en": 1}, max_vocab=-1, min_count=0, n_heads=8, n_langs=2, n_lays=6, s_vocab=64699, same_enc_dec=True, share_inout_emb=True, ), }	{"/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", 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33,681	quantapix/qnarre	refs/heads/main	/qnarre/prep/tokens/fast/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 from shutil import copyfile from ....tokens.utils import AddedToken from ....tokens.fast import PreTrainedTokenizerFast from .xlnet import Tokenizer as XLNet VOCAB_FS = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"} 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", }, "tokenizer_file": { "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json", "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json", }, } INPUT_CAPS = { "xlnet-base-cased": None, "xlnet-large-cased": None, } SPIECE_UNDERLINE = "▁" SEG_ID_A = 0 SEG_ID_B = 1 SEG_ID_CLS = 2 SEG_ID_SEP = 3 SEG_ID_PAD = 4 class Tokenizer(PreTrainedTokenizerFast): vocab_fs = VOCAB_FS vocab_map = VOCAB_MAP input_caps = INPUT_CAPS padding_side = "left" slow_tokenizer_class = XLNet def __init__( self, vocab_file=None, tokenizer_file=None, 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>"], kw, ): msk = AddedToken(msk, lstrip=True, rstrip=False) if isinstance(msk, str) else msk super().__init__( vocab_file=vocab_file, tokenizer_file=tokenizer_file, 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, 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.can_save_slow_tokenizer = False if not self.vocab_file else True 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 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): assert self.can_save_slow_tokenizer y = os.path.join(dir, (pre + "-" if pre else "") + VOCAB_FS["vocab_file"]) if os.path.abspath(self.vocab_file) != os.path.abspath(y): copyfile(self.vocab_file, y) 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"], 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"/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"]}
33,682	quantapix/qnarre	refs/heads/main	/qnarre/models/bart.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 math import random import torch from torch import nn from torch.nn import functional as F from torch.utils.checkpoint import checkpoint from transformers.utils import logging from .. import core as qc from ..core import attention as qa from ..core import forward as qf from ..core import output as qo from ..core import utils as qu from ..core.embed import PosEmbed from ..core.mlp import Classifier from ..prep.config.bart import PreTrained log = logging.get_logger(__name__) class ForCausal(PreTrained): def __init__(self, kw): kw.update(is_dec=True, is_enc_dec=False) super().__init__(kw) cfg = self.get_cfg(kw) self.model = Decoder(kw) self.proj = qc.Linear(cfg.d_model, cfg.s_vocab, bias=False, 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: loss = nn.CrossEntropyLoss()(y.view(-1, cfg.s_vocab), labels.view(-1)) ys = (y,) + ys[1:] + (loss,) return qo.LossCrosses(ys) class ForCondGen(PreTrained): def __init__(self, kw): super().__init__(kw) cfg = self.get_cfg(kw) self.model = Model(kw) n = self.model.emb.cfg.n_embed self.proj = qc.Linear(cfg.d_model, n, bias=False, kw) self.register_buffer("final_logits_bias", torch.zeros((1, n))) def forward(self, x, labels=None, x_dec_emb=None, x_dec=None, kw): cfg = self.cfg if labels is not None: if x_dec is None and x_dec_emb is None: x_dec = qu.shift_right(labels, cfg.PAD, cfg.dec_START) ys = self.model(x, x_dec=x_dec, x_dec_emb=x_dec_emb, kw) y = self.proj(ys[0]) + self.final_logits_bias loss = None if labels is not None: loss = nn.CrossEntropyLoss()(y.view(-1, cfg.s_vocab), labels.view(-1)) ys = (y,) + ys[1:] + (loss,) return qo.LossSeq2Seq(ys) class ForQA(PreTrained): def __init__(self, kw): kw.update(n_labels=2) 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 class ForSeqClass(PreTrained): def __init__(self, kw): kw.update(n_labels=2) super().__init__(kw) cfg = self.get_cfg(kw) self.model = Model(kw) self.proj = Classifier(cfg.d_model, kw) forward = qf.forward_seq def pre_proj(self, x, ys): y = ys[0] eos_m = x.eq(self.cfg.EOS) assert len(torch.unique_consecutive(eos_m.sum(1))) <= 1 y = y[eos_m, :].view(y.size(0), -1, y.size(-1)) return y[:, -1, :] class Model(PreTrained): def __init__(self, kw): super().__init__(kw) cfg = self.get_cfg(kw) self.emb = qc.Embed(cfg.s_vocab, cfg.d_model, kw) self.enc = Encoder(self.emb, kw) self.dec = Decoder(self.emb, kw) def forward( self, x, dec_head_m=None, dec_m=None, mask=None, x_dec_emb=None, x_dec=None, y_enc=None, kw, ): cfg = self.cfg if x_dec is None and x_dec_emb is None: assert x is not None x_dec = qu.shift_right(x, cfg.PAD, cfg.dec_START) if y_enc is None: y_enc = self.enc(x, kw, mask=mask) y = self.dec( x_dec, kw, enc_m=mask, enc=y_enc[0], head_m=dec_head_m, mask=dec_m, x_emb=x_dec_emb ) ys = y + y_enc return qo.Seq2Seq(ys) class Encoder(qc.Module): hs = qc.Hypers({"d_model", "n_heads", "n_pos", "eps"}, {"drop_attn": 0.0, "is_dec": False}) def __init__(self, tok_emb=None, ps={}, hs=[], kw): super().__init__(ps, [self.hs] + hs, kw) cfg = self.get_cfg(kw) d = cfg.d_model cfg.scale = d0.5 if cfg.scale else 1.0 self.tok_emb = qc.Embed(cfg.s_vocab, d, kw) if tok_emb is None else tok_emb self.pos_emb = PosEmbed(cfg.n_pos, d, kw) self.lays = qc.Stack([EncLayer(kw) for _ in range(cfg.n_enc_lays)]) self.norm = qc.LayerNorm(d, kw) self.drop = qc.Dropout(cfg.drop, kw) self.grad_checkpoint = False def forward(self, x, head_m=None, mask=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) cfg.scale y = x_emb + self.pos_emb(s) y = self.drop(self.norm(y)) attns = hiddens = () if mask is not None: mask = qu.expand_mask(mask, x_emb.dtype) assert head_m is None or (head_m.size()[0] == (len(self.lays))) for i, lay in enumerate(self.lays): hiddens += (y,) if self.training and (random.uniform(0, 1) < cfg.drop_enc): continue h = head_m[i] if head_m is not None else None if self.grad_checkpoint and self.training: def create_forward(x): def forward(xs): return x(xs) return forward ys = checkpoint(create_forward(lay), y, head_m=h, mask=mask, kw) else: ys = lay(y, head_m=h, mask=mask, kw) y = ys[0] attns += (ys[1],) hiddens += (y,) return qo.Base(y, attns, hiddens) class BartEncoder(PreTrained): def __init__(self, config: BartConfig, embed_tokens: Optional[nn.Embedding] = None): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.encoder_layerdrop embed_dim = config.d_model self.padding_idx = config.pad_token_id self.max_source_positions = config.max_position_embeddings self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx) if embed_tokens is not None: self.embed_tokens.weight = embed_tokens.weight self.embed_positions = qe.PosEmbed( config.max_position_embeddings, embed_dim, ) self.layers = nn.ModuleList( [BartEncoderLayer(config) for _ in range(config.encoder_layers)] ) self.layernorm_embedding = nn.LayerNorm(embed_dim) self.gradient_checkpointing = False def forward( self, input_ids=None, mask=None, head_m=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 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 = input_ids input_ids = input_ids.view(-1, input_ids.shape[-1]) elif inputs_embeds is not None: input = inputs_embeds[:, :, -1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale embed_pos = self.embed_positions(input) embed_pos = embed_pos.to(inputs_embeds.device) hidden_states = inputs_embeds + embed_pos hidden_states = self.layernorm_embedding(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) if mask is not None: mask = _expand_mask(mask, inputs_embeds.dtype) encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None if head_m is not None: if head_m.size()[0] != (len(self.layers)): raise ValueError( f"The head_m should be specified for {len(self.layers)} layers, but it is for" f" {head_m.size()[0]}." ) for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) dropout_probability = random.uniform(0, 1) if self.training and (dropout_probability < self.layerdrop): # skip the layer layer_outputs = (None, None) else: 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), hidden_states, mask, (head_m[idx] if head_m is not None else None), ) else: layer_outputs = encoder_layer( hidden_states, mask, head_m=(head_m[idx] if head_m is not None else None), output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple( v for v in [hidden_states, encoder_states, all_attentions] if v is not None ) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) class Decoder(qc.Module): hs = qc.Hypers({"d_model", "n_heads", "n_pos", "eps"}, {"drop_attn": 0.0, "is_dec": False}) def __init__(self, tok_emb=None, ps={}, hs=[], kw): super().__init__(ps, [self.hs] + hs, kw) cfg = self.get_cfg(kw) d = cfg.d_model cfg.scale = d0.5 if cfg.scale else 1.0 self.tok_emb = qc.Embed(cfg.s_vocab, d, kw) if tok_emb is None else tok_emb self.pos_emb = PosEmbed(cfg.n_pos, d, kw) self.lays = qc.Stack([DecLayer(kw) for _ in range(cfg.n_dec_lays)]) self.norm = qc.LayerNorm(d, kw) self.drop = qc.Dropout(cfg.drop, kw) self.grad_checkpoint = False def prep_dec_m(self, mask, shape, x_emb, c_len): y = None if shape[-1] > 1: y = qu.causal_mask(shape, x_emb.dtype, c_len=c_len).to(self.device) if mask is not None: m = qu.expand_mask(mask, x_emb.dtype, len=shape[-1]) y = m if y is None else m + y return y def forward( self, x, cache=None, cross_m=None, enc_m=None, enc=None, head_m=None, mask=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) cfg.scale c_len = cache[0][0].shape[2] if cache is not None else 0 y = x_emb + self.pos_emb(s, c_len) y = self.drop(self.norm(y)) attns = caches = crosses = hiddens = () mask = self.prep_dec_m(mask, s, x_emb, c_len) if enc is not None and enc_m is not None: enc_m = qu.expand_mask(enc_m, x_emb.dtype, len=s[-1]) for m in [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,) 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(cross_m=c, enc_m=enc_m, enc=enc, head_m=h, mask=mask) 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) else: ys = lay(y, cache=c, kw) y = ys[0] attns += (ys[1],) if enc is not None: crosses += (ys[2],) caches += (ys[-1],) hiddens += (y,) ys = (y, attns, caches, crosses, hiddens) return qo.CachesCrosses(y, attns, caches, crosses, hiddens) class BartDecoder(PreTrained): def __init__(self, config: BartConfig, embed_tokens: Optional[nn.Embedding] = None): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.decoder_layerdrop self.padding_idx = config.pad_token_id self.max_target_positions = config.max_position_embeddings self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx) if embed_tokens is not None: self.embed_tokens.weight = embed_tokens.weight self.embed_positions = qe.PosEmbed( config.max_position_embeddings, config.d_model, ) self.layers = nn.ModuleList( [BartDecoderLayer(config) for _ in range(config.decoder_layers)] ) self.layernorm_embedding = nn.LayerNorm(config.d_model) self.gradient_checkpointing = False def _prepare_dec_m(self, mask, input_shape, inputs_embeds, caches_length): combined_mask = None if input_shape[-1] > 1: combined_mask = qu.causal_mask( input_shape, inputs_embeds.dtype, device=inputs_embeds.device, caches_length=caches_length, ) if mask is not None: expanded_attn_mask = qu.expand_mask( mask, inputs_embeds.dtype, tgt_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, enc=None, enc_m=None, head_m=None, cross_m=None, caches=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 x_dec_emb at the same time" ) elif input_ids is not None: input = input_ids input_shape = input.shape input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] input = inputs_embeds[:, :, -1] else: raise ValueError("You have to specify either decoder_input_ids or x_dec_emb") caches_length = caches[0][0].shape[2] if caches is not None else 0 if inputs_embeds is None: inputs_embeds = self.embed_tokens(input) * self.embed_scale mask = self._prepare_dec_m(mask, input_shape, inputs_embeds, caches_length) if enc is not None and enc_m is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] enc_m = _expand_mask(enc_m, inputs_embeds.dtype, tgt_len=input_shape[-1]) positions = self.embed_positions(input, caches_length) positions = positions.to(inputs_embeds.device) hidden_states = inputs_embeds + positions hidden_states = self.layernorm_embedding(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) if self.gradient_checkpointing and self.training: if use_cache: logger.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_refls = () if output_attentions else None all_cross_attentions = () if (output_attentions and enc is not None) else None next_decoder_cache = () if use_cache else None for attn_mask, mask_name in zip([head_m, cross_m], ["head_m", "cross_m"]): if attn_mask is not None: if attn_mask.size()[0] != (len(self.layers)): raise ValueError( f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for" f" {head_m.size()[0]}." ) for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) if output_hidden_states: all_hidden_states += (hidden_states,) dropout_probability = random.uniform(0, 1) if self.training and (dropout_probability < self.layerdrop): continue cache = caches[idx] if caches is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(inputs): # None for cache return module(inputs, output_attentions, use_cache) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(decoder_layer), hidden_states, mask, enc, enc_m, head_m[idx] if head_m is not None else None, cross_m[idx] if cross_m is not None else None, None, ) else: layer_outputs = decoder_layer( hidden_states, mask=mask, enc=enc, enc_m=enc_m, head_m=(head_m[idx] if head_m is not None else None), cross_m=(cross_m[idx] if cross_m is not None else None), cache=cache, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[3 if output_attentions else 1],) if output_attentions: all_refls += (layer_outputs[1],) if enc is not None: all_cross_attentions += (layer_outputs[2],) # add hidden states from the last decoder layer 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_refls, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, caches=next_cache, hidden_states=all_hidden_states, attentions=all_refls, cross_attentions=all_cross_attentions, ) class EncLayer(qc.Module): hs = qc.Hypers( {"act", "d_enc_ff", "d_model", "drop_act", "n_enc_heads"}, {"drop": 0.0, "is_dec": False}, ) def __init__(self, ps={}, hs=[], kw): super().__init__(ps, [self.hs] + hs, kw) cfg = self.get_cfg(kw) d = cfg.d_model self.refl = Attention(n_heads=cfg.n_enc_heads, kw) self.norm_refl = qc.LayerNorm(d, kw) self.act = qu.activation(cfg.act) self.drop_act = qc.Dropout(cfg.drop_act, kw) self.ff = qc.Linear(d, cfg.d_enc_ff, kw) self.proj = qc.Linear(cfg.d_enc_ff, d, kw) self.norm = qc.LayerNorm(d, kw) self.drop = qc.Dropout(cfg.drop, kw) def forward(self, x, kw): y, a, _ = self.refl(x, kw) y = self.norm_refl(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) if y.dtype == torch.float16 and (torch.isinf(y).any() or torch.isnan(y).any()): clamp = torch.finfo(y.dtype).max - 1000 y = torch.clamp(y, min=-clamp, max=clamp) return y, a class DecLayer(qc.Module): hs = qc.Hypers( {"act", "d_dec_ff", "d_model", "drop_act", "n_dec_heads"}, {"drop": 0.0, "is_dec": False}, ) def __init__(self, ps={}, hs=[], kw): super().__init__(ps, [self.hs] + hs, kw) cfg = self.get_cfg(kw) d = cfg.d_model self.refl = Attention(n_heads=cfg.n_dec_heads, is_dec=True, kw) self.norm_refl = qc.LayerNorm(d, kw) self.act = qu.activation(cfg.act) self.drop_act = qc.Dropout(cfg.drop_act, kw) self.attn = Attention(n_heads=cfg.n_dec_heads, is_dec=True, kw) self.norm_attn = qc.LayerNorm(d, kw) self.ff = qc.Linear(d, cfg.d_dec_ff, kw) self.proj = qc.Linear(cfg.d_dec_ff, d, kw) self.norm = nn.LayerNorm(d, kw) self.drop = qc.Dropout(cfg.drop, kw) def forward(self, x, cache=None, cross_m=None, enc_m=None, enc=None, kw): c = cache[:2] if cache is not None else None y, a, kv = self.refl(x, cache=c, kw) y = self.norm_refl(x + self.drop(y)) a2 = None if enc is not None: x = y c = cache[-2:] if cache is not None else None y, a2, kv2 = self.attn(y, cache=c, enc=enc, head_m=cross_m, mask=enc_m, kw) y = self.norm_attn(x + self.drop(y)) kv = kv + kv2 x = y y = self.drop_act(self.act(self.ff(y))) y = self.proj(y) y = self.drop(y, p=self.dropout, training=self.training) return self.norm(x + y), a, a2, kv class Attention(qc.Module): hs = qc.Hypers({"d_model", "n_heads"}, {"drop_attn": 0.0}) def __init__(self, is_dec=False, ps={}, hs=[], kw): super().__init__(ps, [self.hs] + hs, kw) self.is_dec = is_dec cfg = self.get_cfg(kw) d, n = cfg.d_model, cfg.n_heads assert d % n == 0 cfg.s_head = s = int(d / n) cfg.scale = s-0.5 self.query = qc.Linear(d, d, kw) self.key = qc.Linear(d, d, kw) self.value = qc.Linear(d, d, kw) self.proj = qc.Linear(d, d, kw) self.drop = qc.Dropout(cfg.drop_attn, kw) split_heads = qa.split_heads def forward(self, x, cache=None, enc=None, head_m=None, mask=None, kw): cfg = self.cfg q = self.split_heads(self.query(x) * cfg.scale) 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: # is_cross if cache is None: k = self.split_heads(self.key(enc)) v = self.split_heads(self.value(enc)) else: k, v = cache n, s = cfg.n_heads, cfg.s_head b, tgt, _ = x.size() h = (b * n, -1, s) y = torch.bmm(q.view(h), k.view(h).transpose(1, 2)) src = k.view(h).size(1) assert y.size() == (b * n, tgt, src) if mask is not None: assert mask.size() == (b, 1, tgt, src) y = y.view(b, n, tgt, src) + mask 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, 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33,683	quantapix/qnarre	refs/heads/main	/qnarre/models/segformer.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 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 attention as qa from ..core.embed import Embed from ..core.mlp import Classifier, MLP, Predictor, Pool from ..prep.config.bert import PreTrained import math import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss log = logging.get_logger(__name__) LIST = [ "nvidia/segformer-b0-finetuned-ade-512-512", ] class SegFormerImageClassifierOutput(ImageClassifierOutput): loss = None logits = None hiddens = None attns = None # Copied from transformers.models.convnext.modeling_convnext.drop_path def drop_path(x, drop_prob: float = 0.0, training=False, scale_by_keep=True): if drop_prob == 0.0 or not training: return x keep_prob = 1 - drop_prob shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) random_tensor.floor_() # binarize output = x.div(keep_prob) * random_tensor return output # Copied from transformers.models.convnext.modeling_convnext.ConvNextDropPath with ConvNext->Segformer class SegformerDropPath(qc.Module): def __init__(self, drop_prob=None): super().__init__() self.drop_prob = drop_prob def forward(self, x): return drop_path(x, self.drop_prob, self.training) class SegformerOverlapPatchEmbeddings(qc.Module): def __init__(self, patch_size, stride, num_channels, d_model): super().__init__() self.proj = nn.Conv2d( num_channels, d_model, kernel_size=patch_size, stride=stride, padding=patch_size // 2, ) self.layer_norm = qc.LayerNorm(d_model) def forward(self, pixel_values): embeddings = self.proj(pixel_values) _, _, height, width = embeddings.shape embeddings = embeddings.flatten(2).transpose(1, 2) embeddings = self.layer_norm(embeddings) return embeddings, height, width class SegformerEfficientSelfAttention(qc.Module): def __init__(self, config, d_model, n_heads, sequence_reduction_ratio): super().__init__() self.d_model = d_model self.n_heads = n_heads if self.d_model % self.n_heads != 0: raise ValueError( f"The hidden size ({self.d_model}) is not a multiple of the number of attention " f"heads ({self.n_heads})" ) self.attention_head_size = int(self.d_model / self.n_heads) self.all_head_size = self.n_heads * self.attention_head_size self.query = qc.Linear(self.d_model, self.all_head_size) self.key = qc.Linear(self.d_model, self.all_head_size) self.value = qc.Linear(self.d_model, self.all_head_size) self.drop = qc.Dropout(config.drop_attn) self.sr_ratio = sequence_reduction_ratio if sequence_reduction_ratio > 1: self.sr = nn.Conv2d( d_model, d_model, kernel_size=sequence_reduction_ratio, stride=sequence_reduction_ratio, ) self.layer_norm = qc.LayerNorm(d_model) def transpose_for_scores(self, hiddens): new_shape = hiddens.size()[:-1] + (self.n_heads, self.attention_head_size) hiddens = hiddens.view(new_shape) return hiddens.permute(0, 2, 1, 3) def forward( self, hiddens, height, width, output_attentions=False, ): query_layer = self.transpose_for_scores(self.query(hiddens)) if self.sr_ratio > 1: batch_size, seq_len, num_channels = hiddens.shape # Reshape to (batch_size, num_channels, height, width) hiddens = hiddens.permute(0, 2, 1).reshape(batch_size, num_channels, height, width) # Apply sequence reduction hiddens = self.sr(hiddens) # Reshape back to (batch_size, seq_len, num_channels) hiddens = hiddens.reshape(batch_size, num_channels, -1).permute(0, 2, 1) hiddens = self.layer_norm(hiddens) key_layer = self.transpose_for_scores(self.key(hiddens)) value_layer = self.transpose_for_scores(self.value(hiddens)) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) # 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) 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,) return outputs class SegformerSelfOutput(qc.Module): def __init__(self, config, d_model): super().__init__() self.dense = qc.Linear(d_model, d_model) self.drop = qc.Dropout(config.drop) def forward(self, hiddens, input_tensor): hiddens = self.dense(hiddens) hiddens = self.drop(hiddens) return hiddens class Attention(qc.Module): def __init__(self, config, d_model, n_heads, sequence_reduction_ratio): super().__init__() self.self = SegformerEfficientSelfAttention( config=config, d_model=d_model, n_heads=n_heads, sequence_reduction_ratio=sequence_reduction_ratio, ) self.output = SegformerSelfOutput(config, d_model=d_model) class SegformerDWConv(qc.Module): def __init__(self, dim=768): super().__init__() self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim) def forward(self, hiddens, height, width): batch_size, seq_len, num_channels = hiddens.shape hiddens = hiddens.transpose(1, 2).view(batch_size, num_channels, height, width) hiddens = self.dwconv(hiddens) hiddens = hiddens.flatten(2).transpose(1, 2) return hiddens class SegformerMixFFN(qc.Module): def __init__(self, cfg, in_features, hidden_features=None, out_features=None): super().__init__() out_features = out_features or in_features self.dense1 = qc.Linear(in_features, hidden_features) self.dwconv = SegformerDWConv(hidden_features) self.act = qu.activation(cfg.act) self.dense2 = qc.Linear(hidden_features, out_features) self.drop = qc.Dropout(cfg.drop) def forward(self, x, height, width): y = self.dense1(x) y = self.dwconv(y, height, width) y = self.act(y) y = self.drop(y) y = self.dense2(y) y = self.drop(y) return y class Layer(qc.Module): def __init__( self, config, d_model, n_heads, drop_path, sequence_reduction_ratio, mlp_ratio, ): super().__init__() self.layer_norm_1 = qc.LayerNorm(d_model) self.attention = Attention( config, d_model=d_model, n_heads=n_heads, sequence_reduction_ratio=sequence_reduction_ratio, ) self.drop_path = SegformerDropPath(drop_path) if drop_path > 0.0 else nn.Identity() self.layer_norm_2 = qc.LayerNorm(d_model) mlp_hidden_size = int(d_model * mlp_ratio) self.mlp = SegformerMixFFN(config, in_features=d_model, hidden_features=mlp_hidden_size) def forward(self, hiddens, height, width, output_attentions=False): self_attention_outputs = self.attention( self.layer_norm_1(hiddens), # in Segformer, layernorm is applied before self-attention height, width, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attns if we output attention weights # first residual connection (with stochastic depth) attention_output = self.drop_path(attention_output) hiddens = attention_output + hiddens mlp_output = self.mlp(self.layer_norm_2(hiddens), height, width) # second residual connection (with stochastic depth) mlp_output = self.drop_path(mlp_output) layer_output = mlp_output + hiddens outputs = (layer_output,) + outputs return outputs class Encoder(qc.Module): def __init__(self, config): super().__init__() self.config = config # stochastic depth decay rule drop_path_decays = [ x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths)) ] # patch embeddings embeddings = [] for i in range(config.num_encoder_blocks): embeddings.append( SegformerOverlapPatchEmbeddings( patch_size=config.patch_sizes[i], stride=config.strides[i], num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1], d_model=config.hidden_sizes[i], ) ) self.patch_embeddings = nn.ModuleList(embeddings) # Transformer blocks blocks = [] cur = 0 for i in range(config.num_encoder_blocks): # each block consists of layers layers = [] if i != 0: cur += config.depths[i - 1] for j in range(config.depths[i]): layers.append( Layer( config, d_model=config.hidden_sizes[i], n_heads=config.n_heads[i], drop_path=drop_path_decays[cur + j], sequence_reduction_ratio=config.sr_ratios[i], mlp_ratio=config.mlp_ratios[i], ) ) blocks.append(nn.ModuleList(layers)) self.block = nn.ModuleList(blocks) # Layer norms self.layer_norm = nn.ModuleList( [qc.LayerNorm(config.hidden_sizes[i]) for i in range(config.num_encoder_blocks)] ) def forward( self, pixel_values, 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 batch_size = pixel_values.shape[0] hiddens = pixel_values for idx, x in enumerate(zip(self.patch_embeddings, self.block, self.layer_norm)): embedding_layer, block_layer, norm_layer = x # first, obtain patch embeddings hiddens, height, width = embedding_layer(hiddens) # second, send embeddings through blocks for i, blk in enumerate(block_layer): layer_outputs = blk(hiddens, height, width, output_attentions) hiddens = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) # third, apply layer norm hiddens = norm_layer(hiddens) # fourth, optionally reshape back to (batch_size, num_channels, height, width) if idx != len(self.patch_embeddings) - 1 or ( idx == len(self.patch_embeddings) - 1 and self.config.reshape_last_stage ): hiddens = ( hiddens.reshape(batch_size, height, width, -1).permute(0, 3, 1, 2).contiguous() ) 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, all_self_attentions] if v is not None ) return qo.Base( y=hiddens, hiddens=all_hidden_states, attns=all_self_attentions, ) class Model(PreTrained): def __init__(self, config): super().__init__(config) self.config = config self.encoder = Encoder(config) self.post_init() def forward( self, pixel_values, 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 encoder_outputs = self.encoder( pixel_values, 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.Base( y=sequence_output, hiddens=encoder_outputs.hiddens, attns=encoder_outputs.attns, ) class SegformerForImageClassification(PreTrained): def __init__(self, config): super().__init__(config) self.n_labels = config.n_labels self.segformer = Model(config) self.classifier = qc.Linear(config.hidden_sizes[-1], config.n_labels) def forward( self, pixel_values=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 outputs = self.segformer( pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] # convert last hidden states to (batch_size, heightwidth, d_model) batch_size = sequence_output.shape[0] if self.config.reshape_last_stage: sequence_output = sequence_output.permute(0, 2, 3, 1) sequence_output = sequence_output.reshape(batch_size, -1, self.config.hidden_sizes[-1]) # global average pooling sequence_output = sequence_output.mean(dim=1) logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.n_labels == 1: self.config.problem_type = "regression" elif self.n_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.n_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.n_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return SegFormerImageClassifierOutput( loss=loss, logits=logits, hiddens=outputs.hiddens, attns=outputs.attns, ) class SegformerMLP(qc.Module): def __init__(self, config, input_dim): super().__init__() self.proj = qc.Linear(input_dim, config.decoder_hidden_size) def forward(self, hiddens): hiddens = hiddens.flatten(2).transpose(1, 2) hiddens = self.proj(hiddens) return hiddens class SegformerDecodeHead(PreTrained): def __init__(self, config): super().__init__(config) # linear layers which will unify the channel dimension of each of the encoder blocks to the same config.decoder_hidden_size mlps = [] for i in range(config.num_encoder_blocks): mlp = SegformerMLP(config, input_dim=config.hidden_sizes[i]) mlps.append(mlp) self.linear_c = nn.ModuleList(mlps) # the following 3 layers implement the ConvModule of the original implementation self.linear_fuse = nn.Conv2d( in_channels=config.decoder_hidden_size config.num_encoder_blocks, out_channels=config.decoder_hidden_size, kernel_size=1, bias=False, ) self.batch_norm = nn.BatchNorm2d(config.decoder_hidden_size) self.activation = nn.ReLU() self.drop = qc.Dropout(config.classifier_dropout_prob) self.classifier = nn.Conv2d(config.decoder_hidden_size, config.n_labels, kernel_size=1) self.config = config def forward(self, enc_hiddens): batch_size = enc_hiddens[-1].shape[0] all_hidden_states = () for encoder_hidden_state, mlp in zip(enc_hiddens, self.linear_c): if self.config.reshape_last_stage is False and encoder_hidden_state.ndim == 3: height = width = int(math.sqrt(encoder_hidden_state.shape[-1])) encoder_hidden_state = ( encoder_hidden_state.reshape(batch_size, height, width, -1) .permute(0, 3, 1, 2) .contiguous() ) # unify channel dimension height, width = encoder_hidden_state.shape[2], encoder_hidden_state.shape[3] encoder_hidden_state = mlp(encoder_hidden_state) encoder_hidden_state = encoder_hidden_state.permute(0, 2, 1) encoder_hidden_state = encoder_hidden_state.reshape(batch_size, -1, height, width) # upsample encoder_hidden_state = F.interpolate( encoder_hidden_state, size=enc_hiddens[0].size()[2:], mode="bilinear", align_corners=False, ) all_hidden_states += (encoder_hidden_state,) hiddens = self.linear_fuse(torch.cat(all_hidden_states[::-1], dim=1)) hiddens = self.batch_norm(hiddens) hiddens = self.act(hiddens) hiddens = self.drop(hiddens) # logits are of shape (batch_size, n_labels, height/4, width/4) logits = self.classifier(hiddens) return logits class SegformerForSemanticSegmentation(PreTrained): def __init__(self, config): super().__init__(config) self.segformer = Model(config) self.decode_head = SegformerDecodeHead(config) # Initialize weights and apply final processing self.post_init() def forward( self, pixel_values, 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 output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) outputs = self.segformer( pixel_values, output_attentions=output_attentions, output_hidden_states=True, # we need the intermediate hidden states return_dict=return_dict, ) enc_hiddens = outputs.hiddens if return_dict else outputs[1] logits = self.decode_head(enc_hiddens) loss = None if labels is not None: if self.config.n_labels == 1: raise ValueError("The number of labels should be greater than one") else: # upsample logits to the images' original size upsampled_logits = F.interpolate( logits, size=labels.shape[-2:], mode="bilinear", align_corners=False ) loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index) loss = loss_fct(upsampled_logits, labels) if not return_dict: if output_hidden_states: output = (logits,) + outputs[1:] else: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SemanticSegmenterOutput( loss=loss, logits=logits, hiddens=outputs.hiddens if output_hidden_states else None, attns=outputs.attns, )	{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": 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"/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"]}
33,684	quantapix/qnarre	refs/heads/main	/qnarre/prep/tokens/electra.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": { "google/electra-small-generator": "https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt", "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt", "google/electra-large-generator": "https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt", "google/electra-small-discriminator": "https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt", "google/electra-base-discriminator": "https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt", "google/electra-large-discriminator": "https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt", } } INPUT_CAPS = { "google/electra-small-generator": 512, "google/electra-base-generator": 512, "google/electra-large-generator": 512, "google/electra-small-discriminator": 512, "google/electra-base-discriminator": 512, "google/electra-large-discriminator": 512, } PRETRAINED_INIT_CONFIGURATION = { "google/electra-small-generator": {"do_lower_case": True}, "google/electra-base-generator": {"do_lower_case": True}, "google/electra-large-generator": {"do_lower_case": True}, "google/electra-small-discriminator": {"do_lower_case": True}, "google/electra-base-discriminator": {"do_lower_case": True}, "google/electra-large-discriminator": {"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	{"/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"]}
33,685	quantapix/qnarre	refs/heads/main	/qnarre/prep/config/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. # ============================================================================= from ... import core as qc class PreTrained(qc.PreTrained): hs = qc.Hypers( [], dict( act="gelu", add_cross_attention=True, BOS=1, d_hidden=1024, decoder_ffn_dim=4096, decoder_start_token_id=0, disable_ngram_loss=False, drop_act=0.1, drop_attn=0.1, drop=0.1, encoder_ffn_dim=4096, EOS=2, eps=0.0, init_std=0.02, is_enc_dec=True, model_type="prophetnet", n_dec_lays=12, n_pos=512, ngram=2, num_buckets=32, num_decoder_attention_heads=16, num_encoder_attention_heads=16, num_encoder_layers=12, PAD=0, relative_max_distance=128, s_vocab=30522, y_cache=True, grad_checkpoint=True, ), ) def _init_weights(self, module): if isinstance(module, qc.Linear): module.weight.data.normal_(mean=0.0, std=self.config.init_std) 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_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, (ProphetNetDecoder, ProphetNetEncoder)): module.gradient_checkpointing = value def _shift_right(self, input_ids): decoder_start_token_id = self.config.decoder_start_token_id PAD = self.config.PAD assert decoder_start_token_id is not None shifted_input_ids = input_ids.new_zeros(input_ids.shape) shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() shifted_input_ids[..., 0] = decoder_start_token_id assert PAD is not None, "self.model.config.PAD has to be defined." shifted_input_ids.masked_fill_(shifted_input_ids == -100, PAD) assert torch.all(shifted_input_ids >= 0).item() return shifted_input_ids MAP = { "microsoft/prophetnet-large-uncased": "https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json", }	{"/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": 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33,686	quantapix/qnarre	refs/heads/main	/qnarre/core/fused.py	import torch import torch.nn as nn import enum import torch import torch.nn.functional as F 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, ) from ..fused_kernels import load_fused_kernels torch._C._jit_set_profiling_mode(False) torch._C._jit_set_profiling_executor(False) torch._C._jit_override_can_fuse_on_cpu(True) torch._C._jit_override_can_fuse_on_gpu(True) def bias_dropout_add(x, bias, residual, p, training): y = F.dropout(x + bias, p=p, training=training) if residual is not None: y = residual + y return y def get_bias_dropout_add(training): def _bias_dropout_add(x, bias, residual, p): return bias_dropout_add(x, bias, residual, p, training) return _bias_dropout_add @torch.jit.script def bias_dropout_add_fused_train(x, bias, residual, p): return bias_dropout_add(x, bias, residual, p, True) @torch.jit.script def bias_dropout_add_fused_inference(x, bias, residual, p): return bias_dropout_add(x, bias, residual, p, False) class ScaledUpperTriangMaskedSoftmax(torch.autograd.Function): @staticmethod def forward(ctx, x, scale): s = torch.tensor([scale]) y = scaled_upper_triang_masked_softmax_forward(x, s[0]) ctx.save_for_backward(y, s) return y @staticmethod def backward(ctx, x): y, s = ctx.saved_tensors y = scaled_upper_triang_masked_softmax_backward(x, y, s[0]) return y, 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, x, mask, scale): s = torch.tensor([scale]) y = scaled_masked_softmax_forward(x, mask, s[0]) ctx.save_for_backward(y, s) return y @staticmethod def backward(ctx, x): y, s = ctx.saved_tensors y = scaled_masked_softmax_backward(x, y, s[0]) return y, None, None def scaled_masked_softmax(inputs, mask, scale): 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, x_fp16, x_bf16, attn_mask_type, scaled_masked_softmax_fusion, mask_func, softmax_in_fp32, scale, ): super().__init__() self.x_fp16 = x_fp16 self.x_bf16 = x_bf16 if self.x_fp16 and self.x_bf16: raise RuntimeError("both fp16 and bf16 flags cannot be active at the same time.") self.x_float16 = self.x_fp16 or self.x_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, x, mask): # [b, np, sq, sk] assert x.dim() == 4 if self.is_kernel_available(mask, x.size()): return self.forward_fused_softmax(x, mask) else: return self.forward_torch_softmax(x, 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.x_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, x, mask): # input.shape = [b, np, sq, sk] s = self.scale if self.scale is not None else 1.0 return self.fused_softmax_func(x, mask, s) def forward_torch_softmax(self, x, mask): if self.x_float16 and self.softmax_in_fp32: x = x.float() if self.scale is not None: x = x * self.scale y = torch.nn.Softmax(dim=-1)(self.mask_func(x, mask) if mask is not None else x) if self.x_float16 and self.softmax_in_fp32: if self.x_fp16: y = y.half() else: y = y.bfloat16() return y @staticmethod def get_batch_per_block(sq, sk, b, np): return scaled_masked_softmax_get_batch_per_block(sq, sk, b, np) class ScaledUpperTriangMaskedSoftmax(torch.autograd.Function): @staticmethod def forward(ctx, x, scale): import scaled_upper_triang_masked_softmax_cuda s = torch.tensor([scale]) y = scaled_upper_triang_masked_softmax_cuda.forward(x, s[0]) ctx.save_for_backward(y, s) return y @staticmethod def backward(ctx, x): import scaled_upper_triang_masked_softmax_cuda y, s = ctx.saved_tensors y = scaled_upper_triang_masked_softmax_cuda.backward(x, y, s[0]) return y, None class ScaledMaskedSoftmax(torch.autograd.Function): @staticmethod def forward(ctx, x, mask, scale): import scaled_masked_softmax_cuda s = torch.tensor([scale]) y = scaled_masked_softmax_cuda.forward(x, mask, s[0]) ctx.save_for_backward(y, s) return y @staticmethod def backward(ctx, x): import scaled_masked_softmax_cuda y, s = ctx.saved_tensors y = scaled_masked_softmax_cuda.backward(x, y, s[0]) return y, None, None class SoftmaxFusionTypes(enum.Enum): upper_triang = 1 # causal mask general = 2 # general mask none = 3 # no fusion class FusedScaleMaskSoftmax(nn.Module): def __init__( self, x_fp16, x_bf16, fusion_type, mask_func, softmax_in_fp32, scale, ): super().__init__() self.x_fp16 = x_fp16 self.x_bf16 = x_bf16 self.x_float16 = self.x_fp16 or self.x_bf16 assert fusion_type in [ SoftmaxFusionTypes.upper_triang, SoftmaxFusionTypes.general, SoftmaxFusionTypes.none, ] if fusion_type != SoftmaxFusionTypes.none: load_fused_kernels() self.upper_triang_mask_fusion = fusion_type == SoftmaxFusionTypes.upper_triang self.general_mask_fusion = fusion_type == SoftmaxFusionTypes.general self.fusion = fusion_type != SoftmaxFusionTypes.none self.mask_func = mask_func self.softmax_in_fp32 = softmax_in_fp32 self.scale = scale assert self.scale is None or softmax_in_fp32, "softmax should be in fp32 when scaled" def forward(self, x, mask): # [b, np, sq, sk] assert x.dim() == 4 if self.is_kernel_available(mask, x.size()): return self.forward_fused_softmax(x, mask) else: return self.forward_torch_softmax(x, mask) def is_kernel_available(self, mask, b, np, sq, sk): attn_batches = b np if ( self.fusion # user wants to fuse and self.x_float16 # input must be fp16 and mask is not None # mask tensor must not be None and 16 < sk <= 2048 # sk must be 16 ~ 2048 and sq % 4 == 0 # sq must be divisor of 4 and attn_batches % 4 == 0 # np * b must be divisor of 4 ): if 0 <= sk <= 2048: batch_per_block = self.get_batch_per_block(sq, sk, b, np) if self.upper_triang_mask_fusion: 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, x, mask): b, np, sq, sk = x.size() s = self.scale if self.scale is not None else 1.0 if self.upper_triang_mask_fusion: assert sq == sk, "causal mask is only for self attention" y = ScaledUpperTriangMaskedSoftmax.apply(x.view(-1, sq, sk), s) return y.view(b, np, sq, sk) else: return ScaledMaskedSoftmax.apply(x, mask, s) def forward_torch_softmax(self, x, mask): if self.x_float16 and self.softmax_in_fp32: x = x.float() if self.scale is not None: x = x * self.scale y = torch.nn.Softmax(dim=-1)(self.mask_func(x, mask) if mask is not None else x) if self.x_float16 and self.softmax_in_fp32: if self.x_fp16: y = y.half() else: y = y.bfloat16() return y @staticmethod def get_batch_per_block(sq, sk, b, np): import scaled_masked_softmax_cuda return scaled_masked_softmax_cuda.get_batch_per_block(sq, sk, b, np)	{"/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", 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"/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"]}
33,687	quantapix/qnarre	refs/heads/main	/qnarre/prep/config/albert.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 collections import OrderedDict from ... import core as qc class PreTrained(qc.PreTrained): hs = qc.Hypers( [], dict( act="gelu_new", BOS=2, d_embed=128, d_ff=16384, d_model=4096, down_scale_factor=1, drop_attn=0, drop_proj=0.1, drop=0, EOS=3, init_range=0.02, model_type="albert", n_groups=1, n_heads=64, n_lays=12, n_mem_blocks=0, n_pos=512, n_typ=2, net_type=0, eps=1e-12, PAD=0, pos_type="absolute", s_gap=0, s_group=1, s_vocab=30000, ), ) def _init_weights(self, m): if isinstance(m, qc.Linear): m.weight.data.normal_(mean=0.0, std=self.cfg.init_range) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, qc.Embed): m.weight.data.normal_(mean=0.0, std=self.cfg.init_range) if m.padding_idx is not None: m.weight.data[m.padding_idx].zero_() elif isinstance(m, qc.LayerNorm): m.bias.data.zero_() m.weight.data.fill_(1.0) MAP = { "albert-base-v1": dict( act="gelu", archs=["ForMasked"], d_ff=3072, d_model=768, drop_attn=0.1, drop=0.1, n_heads=12, ), "albert-large-v1": dict( act="gelu", archs=["ForMasked"], d_ff=4096, d_model=1024, drop_attn=0.1, drop=0.1, n_heads=16, n_lays=24, ), "albert-xlarge-v1": dict( act="gelu", archs=["ForMasked"], d_ff=8192, d_model=2048, drop_attn=0.1, drop=0.1, n_heads=16, n_lays=24, ), "albert-xxlarge-v1": dict( act="gelu", archs=["ForMasked"], ), "albert-base-v2": dict( archs=["ForMasked"], d_ff=3072, d_model=768, n_heads=12, ), "albert-large-v2": dict( archs=["ForMasked"], d_ff=4096, d_model=1024, n_heads=16, n_lays=24, ), "albert-xlarge-v2": dict( archs=["ForMasked"], d_ff=8192, d_model=2048, n_heads=16, n_lays=24, ), "albert-xxlarge-v2": dict( archs=["ForMasked"], ), } class Onnx: @property def inputs(self): return OrderedDict( [ ("input_ids", {0: "batch", 1: "sequence"}), ("mask", {0: "batch", 1: "sequence"}), ("typ_ids", {0: "batch", 1: "sequence"}), ] )	{"/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": 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33,688	quantapix/qnarre	refs/heads/main	/qnarre/tokens/base.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 json import os import re import warnings from collections import OrderedDict, UserDict from contextlib import contextmanager from dataclasses import dataclass, field from typing import NamedTuple import numpy as np from requests import HTTPError # from .dynamic_module_utils import custom_object_save from ..core import ( EntryNotFoundError, ExplicitEnum, PaddingStrategy, RepositoryNotFoundError, RevisionNotFoundError, TensorType, _is_numpy, _is_tensorflow, _is_torch, _is_torch_device, cached_path, copy_func, get_file_from_repo, hf_bucket_url, is_offline_mode, is_remote_url, is_tf_available, is_tokenizers_available, is_torch_available, to_py_obj, torch_required, ) from transformers.utils import logging if is_tokenizers_available(): from tokenizers import AddedToken from tokenizers import Encoding as EncodingFast else: @dataclass(frozen=True, eq=True) class AddedToken: content = field(default_factory=str) single_word = False lstrip = False rstrip = False normalized = True def __getstate__(self): return self.__dict__ @dataclass class EncodingFast: pass log = logging.get_logger(__name__) VERY_LARGE_INTEGER = int(1e30) LARGE_INTEGER = int(1e20) SPECIAL_TOKENS_MAP_FILE = "special_tokens_map.json" ADDED_TOKENS_FILE = "added_tokens.json" TOKENIZER_CONFIG_FILE = "tokenizer_config.json" FULL_TOKENIZER_FILE = "tokenizer.json" _re_tokenizer_file = re.compile(r"tokenizer\.(.)\.json") class TruncationStrategy(ExplicitEnum): ONLY_FIRST = "only_first" ONLY_SECOND = "only_second" LONGEST_FIRST = "longest_first" DO_NOT_TRUNCATE = "do_not_truncate" class CharSpan(NamedTuple): start = None end = None class TokenSpan(NamedTuple): start = None end = None class BatchEncoding(UserDict): def __init__( self, data=None, encoding=None, tensor_type=None, prepend_batch_axis=False, n_sequences=None, ): super().__init__(data) if isinstance(encoding, EncodingFast): encoding = [encoding] self._encodings = encoding if n_sequences is None and encoding is not None and len(encoding): n_sequences = encoding[0].n_sequences self._n_sequences = n_sequences self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis) @property def n_sequences(self): return self._n_sequences @property def is_fast(self): return self._encodings is not None def __getitem__(self, item): if isinstance(item, str): return self.data[item] else: return self._encodings[item] def __getattr__(self, item): try: return self.data[item] except KeyError: raise AttributeError def __getstate__(self): return {"data": self.data, "encodings": self._encodings} def __setstate__(self, state): if "data" in state: self.data = state["data"] if "encodings" in state: self._encodings = state["encodings"] def keys(self): return self.data.keys() def values(self): return self.data.values() def items(self): return self.data.items() @property def encodings(self): return self._encodings def tokens(self, b=0): return self._encodings[b].tokens def sequence_ids(self, b=0): return self._encodings[b].sequence_ids def words(self, b=0): return self.word_ids(b) def word_ids(self, b=0): return self._encodings[b].word_ids def token_to_sequence(self, b_or_t, t=None): if t is not None: b = b_or_t else: b = 0 t = b_or_t if b < 0: b = self._batch_size + b if t < 0: t = self._seq_len + t return self._encodings[b].token_to_sequence(t) def token_to_word(self, b_or_t, t=None): if t is not None: b = b_or_t else: b = 0 t = b_or_t if b < 0: b = self._batch_size + b if t < 0: t = self._seq_len + t return self._encodings[b].token_to_word(t) def word_to_tokens(self, b_or_w, w=None, sequence_index=0): if w is not None: b = b_or_w else: b = 0 w = b_or_w if b < 0: b = self._batch_size + b if w < 0: w = self._seq_len + w span = self._encodings[b].word_to_tokens(w, sequence_index) return TokenSpan(span) if span is not None else None def token_to_chars(self, b_or_t, t=None): if t is not None: b = b_or_t else: b = 0 t = b_or_t return CharSpan((self._encodings[b].token_to_chars(t))) def char_to_token(self, b_or_c, c=None, sequence_index=0): if c is not None: b = b_or_c else: b = 0 c = b_or_c return self._encodings[b].char_to_token(c, sequence_index) def word_to_chars(self, b_or_w, w=None, sequence_index=0): if w is not None: b = b_or_w else: b = 0 w = b_or_w return CharSpan((self._encodings[b].word_to_chars(w, sequence_index))) def char_to_word(self, b_or_c, c=None, sequence_index=0): if c is not None: b = b_or_c else: b = 0 c = b_or_c return self._encodings[b].char_to_word(c, sequence_index) def convert_to_tensors(self, tensor_type=None, prepend_batch_axis=False): if tensor_type is None: return self if not isinstance(tensor_type, TensorType): tensor_type = TensorType(tensor_type) if tensor_type == TensorType.TENSORFLOW: import tensorflow as tf as_tensor = tf.constant is_tensor = tf.is_tensor elif tensor_type == TensorType.PYTORCH: import torch as_tensor = torch.tensor is_tensor = torch.is_tensor else: as_tensor = np.asarray is_tensor = _is_numpy for key, value in self.items(): try: if prepend_batch_axis: value = [value] if not is_tensor(value): tensor = as_tensor(value) self[key] = tensor except: # noqa E722 if key == "overflowing_tokens": raise ValueError( "Unable to create tensor returning overflowing tokens of different lengths. " "Please see if a fast version of this tokenizer is available to have this feature available." ) raise ValueError( "Unable to create tensor, you should probably activate truncation and/or padding " "with 'padding=True' 'truncation=True' to have batched tensors with the same length." ) return self @torch_required def to(self, device): if isinstance(device, str) or _is_torch_device(device) or isinstance(device, int): self.data = {k: v.to(device=device) for k, v in self.data.items()} else: log.warning( f"Attempting to cast a BatchEncoding to type {str(device)}. This is not supported." ) return self class SpecialTokensMixin: SPECIAL_TOKENS_ATTRIBUTES = [ "bos", "eos", "unk", "sep", "pad", "cls", "msk", "additional_special_tokens", ] def __init__(self, verbose=True, kw): self._bos_token = None self._eos_token = None self._unk_token = None self._sep_token = None self._pad_token = None self._cls_token = None self._mask_token = None self._pad_token_type_id = 0 self._additional_special_tokens = [] self.verbose = verbose # We directly set the hidden value to allow initialization with special tokens # which are not yet in the vocabulary. Necessary for serialization/de-serialization # TODO clean this up at some point (probably by switching to fast tokenizers) for key, value in kw.items(): if value is None: continue if key in self.SPECIAL_TOKENS_ATTRIBUTES: if key == "additional_special_tokens": assert isinstance(value, (list, tuple)), f"Value {value} is not a list or tuple" assert all( isinstance(t, (str, AddedToken)) for t in value ), "One of the tokens is not a string or an AddedToken" setattr(self, key, value) elif isinstance(value, (str, AddedToken)): setattr(self, key, value) else: raise TypeError( f"special token {key} has to be either str or AddedToken but got: {type(value)}" ) def sanitize_special_tokens(self): return self.add_tokens(self.all_special_tokens_extended, special_tokens=True) def add_special_tokens(self, special_tokens_dict): if not special_tokens_dict: return 0 added_tokens = 0 for key, value in special_tokens_dict.items(): assert key in self.SPECIAL_TOKENS_ATTRIBUTES, f"Key {key} is not a special token" if self.verbose: log.info(f"Assigning {value} to the {key} key of the tokenizer") setattr(self, key, value) if key == "additional_special_tokens": assert isinstance(value, (list, tuple)) and all( isinstance(t, (str, AddedToken)) for t in value ), f"Tokens {value} for key {key} should all be str or AddedToken instances" added_tokens += self.add_tokens(value, special_tokens=True) else: assert isinstance( value, (str, AddedToken) ), f"Token {value} for key {key} should be a str or an AddedToken instance" added_tokens += self.add_tokens([value], special_tokens=True) return added_tokens def add_tokens( self, new_tokens, special_tokens=False, ): if not new_tokens: return 0 if not isinstance(new_tokens, (list, tuple)): new_tokens = [new_tokens] return self._add_tokens(new_tokens, special_tokens=special_tokens) def _add_tokens(self, new_tokens, special_tokens=False): raise NotImplementedError @property def bos(self): if self._bos_token is None and self.verbose: log.error("Using bos, but it is not set yet.") return None return str(self._bos_token) @property def eos(self): if self._eos_token is None and self.verbose: log.error("Using eos, but it is not set yet.") return None return str(self._eos_token) @property def unk(self): if self._unk_token is None and self.verbose: log.error("Using unk, but it is not set yet.") return None return str(self._unk_token) @property def sep(self): if self._sep_token is None and self.verbose: log.error("Using sep, but it is not set yet.") return None return str(self._sep_token) @property def pad(self): if self._pad_token is None and self.verbose: log.error("Using pad, but it is not set yet.") return None return str(self._pad_token) @property def cls(self): if self._cls_token is None and self.verbose: log.error("Using cls, but it is not set yet.") return None return str(self._cls_token) @property def msk(self): if self._mask_token is None and self.verbose: log.error("Using msk, but it is not set yet.") return None return str(self._mask_token) @property def additional_special_tokens(self): if self._additional_special_tokens is None and self.verbose: log.error("Using additional_special_tokens, but it is not set yet.") return None return [str(tok) for tok in self._additional_special_tokens] @bos.setter def bos(self, value): self._bos_token = value @eos.setter def eos(self, value): self._eos_token = value @unk.setter def unk(self, value): self._unk_token = value @sep.setter def sep(self, value): self._sep_token = value @pad.setter def pad(self, value): self._pad_token = value @cls.setter def cls(self, value): self._cls_token = value @msk.setter def msk(self, value): self._mask_token = value @additional_special_tokens.setter def additional_special_tokens(self, value): self._additional_special_tokens = value @property def BOS(self): if self._bos_token is None: return None return self.convert_tokens_to_ids(self.bos) @property def EOS(self): if self._eos_token is None: return None return self.convert_tokens_to_ids(self.eos) @property def unk_token_id(self): if self._unk_token is None: return None return self.convert_tokens_to_ids(self.unk) @property def SEP(self): if self._sep_token is None: return None return self.convert_tokens_to_ids(self.sep) @property def PAD(self): if self._pad_token is None: return None return self.convert_tokens_to_ids(self.pad) @property def pad_token_type_id(self): return self._pad_token_type_id @property def cls_token_id(self): if self._cls_token is None: return None return self.convert_tokens_to_ids(self.cls) @property def mask_token_id(self): if self._mask_token is None: return None return self.convert_tokens_to_ids(self.msk) @property def additional_special_tokens_ids(self): return self.convert_tokens_to_ids(self.additional_special_tokens) @BOS.setter def BOS(self, value): self._bos_token = self.convert_tokens_to_ids(value) @EOS.setter def EOS(self, value): self._eos_token = self.convert_tokens_to_ids(value) @unk_token_id.setter def unk_token_id(self, value): self._unk_token = self.convert_tokens_to_ids(value) @SEP.setter def SEP(self, value): self._sep_token = self.convert_tokens_to_ids(value) @PAD.setter def PAD(self, value): self._pad_token = self.convert_tokens_to_ids(value) @cls_token_id.setter def cls_token_id(self, value): self._cls_token = self.convert_tokens_to_ids(value) @mask_token_id.setter def mask_token_id(self, value): self._mask_token = self.convert_tokens_to_ids(value) @additional_special_tokens_ids.setter def additional_special_tokens_ids(self, values): self._additional_special_tokens = [self.convert_tokens_to_ids(value) for value in values] @property def special_tokens_map(self): set_attr = {} for attr in self.SPECIAL_TOKENS_ATTRIBUTES: attr_value = getattr(self, "_" + attr) if attr_value: set_attr[attr] = ( type(attr_value)(str(attr_value_sub) for attr_value_sub in attr_value) if isinstance(attr_value, (list, tuple)) else str(attr_value) ) return set_attr @property def special_tokens_map_extended( self, ): set_attr = {} for attr in self.SPECIAL_TOKENS_ATTRIBUTES: attr_value = getattr(self, "_" + attr) if attr_value: set_attr[attr] = attr_value return set_attr @property def all_special_tokens(self): all_toks = [str(s) for s in self.all_special_tokens_extended] return all_toks @property def all_special_tokens_extended(self): all_toks = [] set_attr = self.special_tokens_map_extended for attr_value in set_attr.values(): all_toks = all_toks + ( list(attr_value) if isinstance(attr_value, (list, tuple)) else [attr_value] ) all_toks = list(OrderedDict.fromkeys(all_toks)) return all_toks @property def all_special_ids(self): all_toks = self.all_special_tokens all_ids = self.convert_tokens_to_ids(all_toks) return all_ids class PreTrainedTokenizerBase(SpecialTokensMixin): vocab_fs = {} vocab_map = {} pretrained_init_configuration = {} input_caps = {} _auto_class = None model_input_names = ["input_ids", "typ_ids", "mask"] padding_side = "right" truncation_side = "right" slow_tokenizer_class = None def __init__(self, kw): self.init_inputs = () self.init_kw = copy.deepcopy(kw) self.name_or_path = kw.pop("name_or_path", "") self._processor_class = kw.pop("processor_class", None) model_max_length = kw.pop("model_max_length", kw.pop("max_len", None)) self.model_max_length = ( model_max_length if model_max_length is not None else VERY_LARGE_INTEGER ) self.padding_side = kw.pop("padding_side", self.padding_side) assert self.padding_side in ["right", "left"] self.truncation_side = kw.pop("truncation_side", self.truncation_side) assert self.truncation_side in ["right", "left"] self.model_input_names = kw.pop("model_input_names", self.model_input_names) self.deprecation_warnings = {} super().__init__(*kw) @property def max_len_single_sentence(self): return self.model_max_length - self.num_special_tokens_to_add(pair=False) @property def max_len_sentences_pair(self): return self.model_max_length - self.num_special_tokens_to_add(pair=True) @max_len_single_sentence.setter def max_len_single_sentence(self, value): if ( value == self.model_max_length - self.num_special_tokens_to_add(pair=False) and self.verbose ): if not self.deprecation_warnings.get("max_len_single_sentence", False): log.warning( "Setting 'max_len_single_sentence' is now deprecated. " "This value is automatically set up." ) self.deprecation_warnings["max_len_single_sentence"] = True else: raise ValueError( "Setting 'max_len_single_sentence' is now deprecated. " "This value is automatically set up." ) @max_len_sentences_pair.setter def max_len_sentences_pair(self, value): # For backward compatibility, allow to try to setup 'max_len_sentences_pair'. if ( value == self.model_max_length - self.num_special_tokens_to_add(pair=True) and self.verbose ): if not self.deprecation_warnings.get("max_len_sentences_pair", False): log.warning( "Setting 'max_len_sentences_pair' is now deprecated. " "This value is automatically set up." ) self.deprecation_warnings["max_len_sentences_pair"] = True else: raise ValueError( "Setting 'max_len_sentences_pair' is now deprecated. " "This value is automatically set up." ) def _set_processor_class(self, processor_class): self._processor_class = processor_class def __repr__(self): return ( f"{'PreTrainedTokenizerFast' if self.is_fast else 'PreTrainedTokenizer'}(name_or_path='{self.name_or_path}', " f"s_vocab={self.s_vocab}, model_max_len={self.model_max_length}, is_fast={self.is_fast}, " f"padding_side='{self.padding_side}', truncation_side='{self.truncation_side}', special_tokens={self.special_tokens_map_extended})" ) def get_vocab(self): raise NotImplementedError() @classmethod def from_pretrained(cls, pretrained_model_name_or_path, init_inputs, kw): cache_dir = kw.pop("cache_dir", None) force_download = kw.pop("force_download", False) resume_download = kw.pop("resume_download", False) proxies = kw.pop("proxies", None) local_files_only = kw.pop("local_files_only", False) use_auth_token = kw.pop("use_auth_token", None) revision = kw.pop("revision", None) subfolder = kw.pop("subfolder", None) from_pipeline = kw.pop("_from_pipeline", None) from_auto_class = kw.pop("_from_auto", False) user_agent = { "file_type": "tokenizer", "from_auto_class": from_auto_class, "is_fast": "Fast" in cls.__name__, } if from_pipeline is not None: user_agent["using_pipeline"] = from_pipeline if is_offline_mode() and not local_files_only: log.info("Offline mode: forcing local_files_only=True") local_files_only = True pretrained_model_name_or_path = str(pretrained_model_name_or_path) vocab_files = {} init_configuration = {} if os.path.isfile(pretrained_model_name_or_path) or is_remote_url( pretrained_model_name_or_path ): if len(cls.vocab_fs) > 1: raise ValueError( f"Calling {cls.__name__}.from_pretrained() with the path to a single file or url is not " "supported for this tokenizer. Use a model identifier or the path to a directory instead." ) warnings.warn( f"Calling {cls.__name__}.from_pretrained() with the path to a single file or url is deprecated and " "won't be possible anymore in v5. Use a model identifier or the path to a directory instead.", FutureWarning, ) file_id = list(cls.vocab_fs.keys())[0] vocab_files[file_id] = pretrained_model_name_or_path else: # At this point pretrained_model_name_or_path is either a directory or a model identifier name additional_files_names = { "added_tokens_file": ADDED_TOKENS_FILE, "special_tokens_map_file": SPECIAL_TOKENS_MAP_FILE, "tokenizer_config_file": TOKENIZER_CONFIG_FILE, } vocab_files_target = {cls.vocab_fs, *additional_files_names} if "tokenizer_file" in vocab_files_target: # Try to get the tokenizer config to see if there are versioned tokenizer files. fast_tokenizer_file = FULL_TOKENIZER_FILE resolved_config_file = get_file_from_repo( pretrained_model_name_or_path, TOKENIZER_CONFIG_FILE, cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, use_auth_token=use_auth_token, revision=revision, local_files_only=local_files_only, ) if resolved_config_file is not None: with open(resolved_config_file, encoding="utf-8") as reader: tokenizer_config = json.load(reader) if "fast_tokenizer_files" in tokenizer_config: fast_tokenizer_file = get_fast_tokenizer_file( tokenizer_config["fast_tokenizer_files"] ) vocab_files_target["tokenizer_file"] = fast_tokenizer_file for file_id, file_name in vocab_files_target.items(): if os.path.isdir(pretrained_model_name_or_path): if subfolder is not None: full_file_name = os.path.join( pretrained_model_name_or_path, subfolder, file_name ) else: full_file_name = os.path.join(pretrained_model_name_or_path, file_name) if not os.path.exists(full_file_name): log.info(f"Didn't find file {full_file_name}. We won't load it.") full_file_name = None else: full_file_name = hf_bucket_url( pretrained_model_name_or_path, filename=file_name, subfolder=subfolder, revision=revision, mirror=None, ) vocab_files[file_id] = full_file_name resolved_vocab_files = {} unresolved_files = [] for file_id, file_path in vocab_files.items(): if file_path is None: resolved_vocab_files[file_id] = None else: try: resolved_vocab_files[file_id] = cached_path( file_path, 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 FileNotFoundError as error: if local_files_only: unresolved_files.append(file_id) else: raise error 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: log.debug( f"{pretrained_model_name_or_path} does not contain a file named {file_path}." ) resolved_vocab_files[file_id] = None except HTTPError as err: if "404 Client Error" in str(err): log.debug(f"Connection problem to access {file_path}.") resolved_vocab_files[file_id] = None else: raise err if len(unresolved_files) > 0: log.info( f"Can't load following files from cache: {unresolved_files} and cannot check if these " "files are necessary for the tokenizer to operate." ) if all(full_file_name is None for full_file_name in resolved_vocab_files.values()): raise EnvironmentError( f"Can't load tokenizer 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 all relevant files for a {cls.__name__} tokenizer." ) for file_id, file_path in vocab_files.items(): if file_id not in resolved_vocab_files: continue if file_path == resolved_vocab_files[file_id]: log.info(f"loading file {file_path}") else: log.info(f"loading file {file_path} from cache at {resolved_vocab_files[file_id]}") return cls._from_pretrained( resolved_vocab_files, pretrained_model_name_or_path, init_configuration, init_inputs, use_auth_token=use_auth_token, cache_dir=cache_dir, *kw, ) @classmethod def _from_pretrained( cls, resolved_vocab_files, pretrained_model_name_or_path, init_configuration, init_inputs, use_auth_token=None, cache_dir=None, *kw, ): from_slow = kw.get("from_slow", False) has_tokenizer_file = resolved_vocab_files.get("tokenizer_file", None) is not None if (from_slow or not has_tokenizer_file) and cls.slow_tokenizer_class is not None: slow_tokenizer = (cls.slow_tokenizer_class)._from_pretrained( copy.deepcopy(resolved_vocab_files), pretrained_model_name_or_path, copy.deepcopy(init_configuration), init_inputs, (copy.deepcopy(kw)), ) else: slow_tokenizer = None tokenizer_config_file = resolved_vocab_files.pop("tokenizer_config_file", None) if tokenizer_config_file is not None: with open(tokenizer_config_file, encoding="utf-8") as tokenizer_config_handle: init_kw = json.load(tokenizer_config_handle) config_tokenizer_class = init_kw.get("tokenizer_class") init_kw.pop("tokenizer_class", None) init_kw.pop("auto_map", None) saved_init_inputs = init_kw.pop("init_inputs", ()) if not init_inputs: init_inputs = saved_init_inputs else: config_tokenizer_class = None init_kw = init_configuration if config_tokenizer_class is None: from .models.auto.configuration_auto import AutoConfig # tests_ignore try: config = AutoConfig.from_pretrained( pretrained_model_name_or_path, use_auth_token=use_auth_token, cache_dir=cache_dir, ) config_tokenizer_class = config.tokenizer_class except (OSError, ValueError, KeyError): config = None if config_tokenizer_class is None: from .models.auto.tokenization_auto import TOKENIZER_MAPPING_NAMES # tests_ignore if hasattr(config, "model_type"): model_type = config.model_type else: # Fallback: use pattern matching on the string. model_type = None for pattern in TOKENIZER_MAPPING_NAMES.keys(): if pattern in str(pretrained_model_name_or_path): model_type = pattern break if model_type is not None: ( config_tokenizer_class, config_tokenizer_class_fast, ) = TOKENIZER_MAPPING_NAMES.get(model_type, (None, None)) if config_tokenizer_class is None: config_tokenizer_class = config_tokenizer_class_fast if config_tokenizer_class is not None: if cls.__name__.replace("Fast", "") != config_tokenizer_class.replace("Fast", ""): log.warning( "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. " "It may result in unexpected tokenization. \n" f"The tokenizer class you load from this checkpoint is '{config_tokenizer_class}'. \n" f"The class this function is called from is '{cls.__name__}'." ) init_kw.update(kw) def convert_added_tokens(obj): if isinstance(obj, dict) and "__type" in obj and obj["__type"] == "AddedToken": obj.pop("__type") return AddedToken(obj) elif isinstance(obj, (list, tuple)): return list(convert_added_tokens(o) for o in obj) elif isinstance(obj, dict): return {k: convert_added_tokens(v) for k, v in obj.items()} return obj init_kw = convert_added_tokens(init_kw) if pretrained_model_name_or_path in cls.input_caps: # if we're using a pretrained model, ensure the tokenizer # wont index sequences longer than the number of positional embeddings model_max_length = cls.input_caps[pretrained_model_name_or_path] if model_max_length is not None and isinstance(model_max_length, (int, float)): init_kw["model_max_length"] = min( init_kw.get("model_max_length", int(1e30)), model_max_length ) # Merge resolved_vocab_files arguments in init_kw. added_tokens_file = resolved_vocab_files.pop("added_tokens_file", None) for args_name, file_path in resolved_vocab_files.items(): if args_name not in init_kw: init_kw[args_name] = file_path if slow_tokenizer is not None: init_kw["__slow_tokenizer"] = slow_tokenizer init_kw["name_or_path"] = pretrained_model_name_or_path # Instantiate tokenizer. try: tokenizer = cls(init_inputs, init_kw) except OSError: raise OSError( "Unable to load vocabulary from file. " "Please check that the provided vocabulary is accessible and not corrupted." ) # Save inputs and kw for saving and re-loading with ``save_pretrained`` # Removed: Now done at the base class level # tokenizer.init_inputs = init_inputs # tokenizer.init_kw = init_kw # If there is a complementary special token map, load it special_tokens_map_file = resolved_vocab_files.pop("special_tokens_map_file", None) if special_tokens_map_file is not None: with open(special_tokens_map_file, encoding="utf-8") as special_tokens_map_handle: special_tokens_map = json.load(special_tokens_map_handle) for key, value in special_tokens_map.items(): if key in kw and kw[key]: # This value has already been redefined by the kw # We keep this new value and ignore the one stored in the special_tokens_map_file continue if isinstance(value, dict): value = AddedToken(value) elif isinstance(value, list): value = [ AddedToken(token) if isinstance(token, dict) else token for token in value ] setattr(tokenizer, key, value) # Add supplementary tokens. special_tokens = tokenizer.all_special_tokens if added_tokens_file is not None: with open(added_tokens_file, encoding="utf-8") as added_tokens_handle: added_tok_encoder = json.load(added_tokens_handle) # Sort added tokens by index added_tok_encoder_sorted = list(sorted(added_tok_encoder.items(), key=lambda x: x[1])) for token, index in added_tok_encoder_sorted: if ( has_tokenizer_file and index != len(tokenizer) and tokenizer.convert_tokens_to_ids(token) != index ): # Tokenizer fast: added token needs to either be in the vocabulary with the proper index or the # index is the current length of the tokenizer (not in vocabulary) raise ValueError( f"Wrong index found for {token}: should be {tokenizer.convert_tokens_to_ids(token)} but found " f"{index}." ) elif not has_tokenizer_file and index != len(tokenizer): # Tokenizer slow: added token cannot already be in the vocabulary so its index needs to be the # current length of the tokenizer. raise ValueError( f"Non-consecutive added token '{token}' found. " f"Should have index {len(tokenizer)} but has index {index} in saved vocabulary." ) # Safe to call on a tokenizer fast even if token already there. tokenizer.add_tokens(token, special_tokens=bool(token in special_tokens)) # Check all our special tokens are registered as "no split" token (we don't cut them) and are in the vocab added_tokens = tokenizer.sanitize_special_tokens() if added_tokens: log.warning_advice( "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained." ) return tokenizer def save_pretrained( self, save_directory, legacy_format=None, filename_prefix=None, push_to_hub=False, kw, ): if os.path.isfile(save_directory): log.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) special_tokens_map_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + SPECIAL_TOKENS_MAP_FILE, ) tokenizer_config_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + TOKENIZER_CONFIG_FILE, ) tokenizer_config = copy.deepcopy(self.init_kw) if len(self.init_inputs) > 0: tokenizer_config["init_inputs"] = copy.deepcopy(self.init_inputs) for file_id in self.vocab_fs.keys(): tokenizer_config.pop(file_id, None) # Sanitize AddedTokens def convert_added_tokens(obj, add_type_field=True): if isinstance(obj, AddedToken): out = obj.__getstate__() if add_type_field: out["__type"] = "AddedToken" return out elif isinstance(obj, (list, tuple)): return list(convert_added_tokens(o, add_type_field=add_type_field) for o in obj) elif isinstance(obj, dict): return { k: convert_added_tokens(v, add_type_field=add_type_field) for k, v in obj.items() } return obj # add_type_field=True to allow dicts in the kw / differentiate from AddedToken serialization tokenizer_config = convert_added_tokens(tokenizer_config, add_type_field=True) # Add tokenizer class to the tokenizer config to be able to reload it with from_pretrained tokenizer_class = self.__class__.__name__ # Remove the Fast at the end unless we have a special `PreTrainedTokenizerFast` if tokenizer_class.endswith("Fast") and tokenizer_class != "PreTrainedTokenizerFast": tokenizer_class = tokenizer_class[:-4] tokenizer_config["tokenizer_class"] = tokenizer_class if getattr(self, "_auto_map", None) is not None: tokenizer_config["auto_map"] = self._auto_map if getattr(self, "_processor_class", None) is not None: tokenizer_config["processor_class"] = self._processor_class # 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=tokenizer_config) with open(tokenizer_config_file, "w", encoding="utf-8") as f: f.write(json.dumps(tokenizer_config, ensure_ascii=False)) log.info(f"tokenizer config file saved in {tokenizer_config_file}") # Sanitize AddedTokens in special_tokens_map write_dict = convert_added_tokens(self.special_tokens_map_extended, add_type_field=False) with open(special_tokens_map_file, "w", encoding="utf-8") as f: f.write(json.dumps(write_dict, ensure_ascii=False)) log.info(f"Special tokens file saved in {special_tokens_map_file}") file_names = (tokenizer_config_file, special_tokens_map_file) save_files = self._save_pretrained( save_directory=save_directory, file_names=file_names, legacy_format=legacy_format, filename_prefix=filename_prefix, ) if push_to_hub: url = self._push_to_hub(repo, commit_message=commit_message) log.info(f"Tokenizer pushed to the hub in this commit: {url}") return save_files def _save_pretrained( self, save_directory, file_names, legacy_format=None, filename_prefix=None, ): if legacy_format is False: raise ValueError( "Only fast tokenizers (instances of PreTrainedTokenizerFast) can be saved in non legacy format." ) save_directory = str(save_directory) added_tokens_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + ADDED_TOKENS_FILE ) added_vocab = self.get_added_vocab() if added_vocab: with open(added_tokens_file, "w", encoding="utf-8") as f: out_str = json.dumps(added_vocab, ensure_ascii=False) f.write(out_str) log.info(f"added tokens file saved in {added_tokens_file}") vocab_files = self.save_vocabulary(save_directory, filename_prefix=filename_prefix) return file_names + vocab_files + (added_tokens_file,) def save_vocabulary(self, save_directory, filename_prefix=None): raise NotImplementedError def tokenize(self, text, pair=None, add_special_tokens=False, kw): raise NotImplementedError def encode( self, text, text_pair=None, add_special_tokens=True, padding=False, truncation=False, max_len=None, stride=0, return_tensors=None, kw, ): encoded_inputs = self.encode_plus( text, text_pair=text_pair, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_len=max_len, stride=stride, return_tensors=return_tensors, kw, ) return encoded_inputs["input_ids"] def num_special_tokens_to_add(self, pair=False): raise NotImplementedError def _get_padding_truncation_strategies( self, padding=False, truncation=False, max_len=None, pad_to_multiple_of=None, verbose=True, kw, ): old_truncation_strategy = kw.pop("truncation_strategy", "do_not_truncate") old_pad_to_max_length = kw.pop("pad_to_max_length", False) # Backward compatibility for previous behavior, maybe we should deprecate it: # If you only set max_len, it activates truncation for max_len if max_len is not None and padding is False and truncation is False: if verbose: if not self.deprecation_warnings.get("Truncation-not-explicitly-activated", False): log.warning( "Truncation was not explicitly activated but `max_len` is provided a specific value, " "please use `truncation=True` to explicitly truncate examples to max length. " "Defaulting to 'longest_first' truncation strategy. " "If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy " "more precisely by providing a specific strategy to `truncation`." ) self.deprecation_warnings["Truncation-not-explicitly-activated"] = True truncation = "longest_first" # Get padding strategy if padding is False and old_pad_to_max_length: if verbose: warnings.warn( "The `pad_to_max_length` argument is deprecated and will be removed in a future version, " "use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or " "use `padding='max_len'` to pad to a max length. In this case, you can give a specific " "length with `max_len` (e.g. `max_len=45`) or leave max_len to None to pad to the " "maximal input size of the model (e.g. 512 for Bert).", FutureWarning, ) if max_len is None: padding_strategy = PaddingStrategy.LONGEST else: padding_strategy = PaddingStrategy.MAX_LENGTH elif padding is not False: if padding is True: if verbose: if max_len is not None and ( truncation is False or truncation == "do_not_truncate" ): warnings.warn( "`max_len` is ignored when `padding`=`True` and there is no truncation strategy. " "To pad to max length, use `padding='max_len'`." ) if old_pad_to_max_length is not False: warnings.warn( "Though `pad_to_max_length` = `True`, it is ignored because `padding`=`True`." ) padding_strategy = ( PaddingStrategy.LONGEST ) # Default to pad to the longest sequence in the batch elif not isinstance(padding, PaddingStrategy): padding_strategy = PaddingStrategy(padding) elif isinstance(padding, PaddingStrategy): padding_strategy = padding else: padding_strategy = PaddingStrategy.DO_NOT_PAD # Get truncation strategy if truncation is False and old_truncation_strategy != "do_not_truncate": if verbose: warnings.warn( "The `truncation_strategy` argument is deprecated and will be removed in a future version, " "use `truncation=True` to truncate examples to a max length. You can give a specific " "length with `max_len` (e.g. `max_len=45`) or leave max_len to None to truncate to the " "maximal input size of the model (e.g. 512 for Bert). " " If you have pairs of inputs, you can give a specific truncation strategy selected among " "`truncation='only_first'` (will only truncate the first sentence in the pairs) " "`truncation='only_second'` (will only truncate the second sentence in the pairs) " "or `truncation='longest_first'` (will iteratively remove tokens from the longest sentence in the pairs).", FutureWarning, ) truncation_strategy = TruncationStrategy(old_truncation_strategy) elif truncation is not False: if truncation is True: truncation_strategy = ( TruncationStrategy.LONGEST_FIRST ) # Default to truncate the longest sequences in pairs of inputs elif not isinstance(truncation, TruncationStrategy): truncation_strategy = TruncationStrategy(truncation) elif isinstance(truncation, TruncationStrategy): truncation_strategy = truncation else: truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE # Set max length if needed if max_len is None: if padding_strategy == PaddingStrategy.MAX_LENGTH: if self.model_max_length > LARGE_INTEGER: if verbose: if not self.deprecation_warnings.get("Asking-to-pad-to-max_len", False): log.warning( "Asking to pad to max_len but no maximum length is provided and the model has no predefined maximum length. " "Default to no padding." ) self.deprecation_warnings["Asking-to-pad-to-max_len"] = True padding_strategy = PaddingStrategy.DO_NOT_PAD else: max_len = self.model_max_length if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE: if self.model_max_length > LARGE_INTEGER: if verbose: if not self.deprecation_warnings.get( "Asking-to-truncate-to-max_len", False ): log.warning( "Asking to truncate to max_len but no maximum length is provided and the model has no predefined maximum length. " "Default to no truncation." ) self.deprecation_warnings["Asking-to-truncate-to-max_len"] = True truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE else: max_len = self.model_max_length # Test if we have a padding token if padding_strategy != PaddingStrategy.DO_NOT_PAD and (not self.pad or self.PAD < 0): raise ValueError( "Asking to pad but the tokenizer does not have a padding token. " "Please select a token to use as `pad` `(tokenizer.pad = tokenizer.eos e.g.)` " "or add a new pad token via `tokenizer.add_special_tokens({'pad': '[PAD]'})`." ) # Check that we will truncate to a multiple of pad_to_multiple_of if both are provided if ( truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and padding_strategy != PaddingStrategy.DO_NOT_PAD and pad_to_multiple_of is not None and max_len is not None and (max_len % pad_to_multiple_of != 0) ): raise ValueError( f"Truncation and padding are both activated but " f"truncation length ({max_len}) is not a multiple of pad_to_multiple_of ({pad_to_multiple_of})." ) return padding_strategy, truncation_strategy, max_len, kw def __call__( self, text, text_pair=None, add_special_tokens=True, padding=False, truncation=False, 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 _is_valid_text_input(t): if isinstance(t, str): return True elif isinstance(t, (list, tuple)): if len(t) == 0: return True elif isinstance(t[0], str): return True elif isinstance(t[0], (list, tuple)): return len(t[0]) == 0 or isinstance(t[0][0], str) return False if not _is_valid_text_input(text): raise ValueError( "text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) " "or `List[List[str]]` (batch of pretokenized examples)." ) if text_pair is not None and not _is_valid_text_input(text_pair): raise ValueError( "text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) " "or `List[List[str]]` (batch of pretokenized examples)." ) if is_split_into_words: is_batched = ( isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple)) ) else: is_batched = isinstance(text, (list, tuple)) if is_batched: if isinstance(text_pair, str): raise TypeError( "when tokenizing batches of text, `text_pair` must be a list or tuple with the same length as `text`." ) if text_pair is not None and len(text) != len(text_pair): raise ValueError( f"batch length of `text`: {len(text)} does not match batch length of `text_pair`: {len(text_pair)}." ) batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text return self.batch_encode_plus( batch_text_or_text_pairs=batch_text_or_text_pairs, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_len=max_len, stride=stride, is_split_into_words=is_split_into_words, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, kw, ) else: return self.encode_plus( text=text, text_pair=text_pair, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_len=max_len, stride=stride, is_split_into_words=is_split_into_words, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, kw, ) def encode_plus( self, text, text_pair=None, add_special_tokens=True, padding=False, truncation=False, 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, ): ( padding_strategy, truncation_strategy, max_len, kw, ) = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_len=max_len, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, kw, ) return self._encode_plus( text=text, text_pair=text_pair, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_len=max_len, stride=stride, is_split_into_words=is_split_into_words, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, kw, ) 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, ): raise NotImplementedError def batch_encode_plus( self, batch_text_or_text_pairs, add_special_tokens=True, padding=False, truncation=False, 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, ): ( padding_strategy, truncation_strategy, max_len, kw, ) = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_len=max_len, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, kw, ) return self._batch_encode_plus( batch_text_or_text_pairs=batch_text_or_text_pairs, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_len=max_len, stride=stride, is_split_into_words=is_split_into_words, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, kw, ) 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, ): raise NotImplementedError def pad( self, encoded_inputs, padding=True, max_len=None, pad_to_multiple_of=None, return_attention_mask=None, return_tensors=None, verbose=True, ): if isinstance(encoded_inputs, (list, tuple)) and isinstance( encoded_inputs[0], (dict, BatchEncoding) ): encoded_inputs = { key: [example[key] for example in encoded_inputs] for key in encoded_inputs[0].keys() } if self.model_input_names[0] not in encoded_inputs: raise ValueError( "You should supply an encoding or a list of encodings to this method " f"that includes {self.model_input_names[0]}, but you provided {list(encoded_inputs.keys())}" ) required_input = encoded_inputs[self.model_input_names[0]] if not required_input: if return_attention_mask: encoded_inputs["mask"] = [] return encoded_inputs first_element = required_input[0] if isinstance(first_element, (list, tuple)): # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element. for item in required_input: if len(item) != 0: first_element = item[0] break # At this state, if `first_element` is still a list/tuple, it's an empty one so there is nothing to do. if not isinstance(first_element, (int, list, tuple)): if is_tf_available() and _is_tensorflow(first_element): return_tensors = "tf" if return_tensors is None else return_tensors elif is_torch_available() and _is_torch(first_element): return_tensors = "pt" if return_tensors is None else return_tensors elif isinstance(first_element, np.ndarray): return_tensors = "np" if return_tensors is None else return_tensors else: raise ValueError( f"type of {first_element} unknown: {type(first_element)}. " f"Should be one of a python, numpy, pytorch or tensorflow object." ) for key, value in encoded_inputs.items(): encoded_inputs[key] = to_py_obj(value) # Convert padding_strategy in PaddingStrategy padding_strategy, _, max_len, _ = self._get_padding_truncation_strategies( padding=padding, max_len=max_len, verbose=verbose ) required_input = encoded_inputs[self.model_input_names[0]] if required_input and not isinstance(required_input[0], (list, tuple)): encoded_inputs = self._pad( encoded_inputs, max_len=max_len, padding_strategy=padding_strategy, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) return BatchEncoding(encoded_inputs, tensor_type=return_tensors) batch_size = len(required_input) assert all( len(v) == batch_size for v in encoded_inputs.values() ), "Some items in the output dictionary have a different batch size than others." if padding_strategy == PaddingStrategy.LONGEST: max_len = max(len(inputs) for inputs in required_input) padding_strategy = PaddingStrategy.MAX_LENGTH batch_outputs = {} for i in range(batch_size): inputs = dict((k, v[i]) for k, v in encoded_inputs.items()) outputs = self._pad( inputs, max_len=max_len, padding_strategy=padding_strategy, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) for key, value in outputs.items(): if key not in batch_outputs: batch_outputs[key] = [] batch_outputs[key].append(value) return BatchEncoding(batch_outputs, tensor_type=return_tensors) def create_token_type_ids_from_sequences(self, toks_0, toks_1=None): if toks_1 is None: return len(toks_0) [0] return [0] * len(toks_0) + [1] * len(toks_1) def build_inputs_with_special_tokens(self, toks_0, toks_1=None): if toks_1 is None: return toks_0 return toks_0 + toks_1 def prepare_for_model( self, ids, pair_ids=None, add_special_tokens=True, padding=False, truncation=False, 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_offsets_mapping=False, return_length=False, verbose=True, prepend_batch_axis=False, kw, ): ( padding_strategy, truncation_strategy, max_len, kw, ) = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_len=max_len, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, kw, ) pair = bool(pair_ids is not None) len_ids = len(ids) len_pair_ids = len(pair_ids) if pair else 0 if return_token_type_ids and not add_special_tokens: raise ValueError( "Asking to return typ_ids while setting add_special_tokens to False " "results in an undefined behavior. Please set add_special_tokens to True or " "set return_token_type_ids to None." ) if ( return_overflowing_tokens and truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is not None ): raise ValueError( "Not possible to return overflowing tokens for pair of sequences with the " "`longest_first`. Please select another truncation strategy than `longest_first`, " "for instance `only_second` or `only_first`." ) # Load from model defaults if return_token_type_ids is None: return_token_type_ids = "typ_ids" in self.model_input_names if return_attention_mask is None: return_attention_mask = "mask" in self.model_input_names encoded_inputs = {} # Compute the total size of the returned encodings total_len = ( len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0) ) # Truncation: Handle max sequence length overflowing_tokens = [] if ( truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_len and total_len > max_len ): ids, pair_ids, overflowing_tokens = self.truncate_sequences( ids, pair_ids=pair_ids, num_tokens_to_remove=total_len - max_len, truncation_strategy=truncation_strategy, stride=stride, ) if return_overflowing_tokens: encoded_inputs["overflowing_tokens"] = overflowing_tokens encoded_inputs["num_truncated_tokens"] = total_len - max_len # Add special tokens if add_special_tokens: sequence = self.build_inputs_with_special_tokens(ids, pair_ids) typ_ids = self.create_token_type_ids_from_sequences(ids, pair_ids) else: sequence = ids + pair_ids if pair else ids typ_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else []) # Build output dictionary encoded_inputs["input_ids"] = sequence if return_token_type_ids: encoded_inputs["typ_ids"] = typ_ids if return_special_tokens_mask: if add_special_tokens: encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids) else: encoded_inputs["special_tokens_mask"] = [0] * len(sequence) # Check lengths self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_len, verbose) # Padding if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask: encoded_inputs = self.pad( encoded_inputs, max_len=max_len, padding=padding_strategy.value, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) if return_length: encoded_inputs["length"] = len(encoded_inputs["input_ids"]) batch_outputs = BatchEncoding( encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis ) return batch_outputs def truncate_sequences( self, ids, pair_ids=None, num_tokens_to_remove=0, truncation_strategy="longest_first", stride=0, ): if num_tokens_to_remove <= 0: return ids, pair_ids, [] if not isinstance(truncation_strategy, TruncationStrategy): truncation_strategy = TruncationStrategy(truncation_strategy) overflowing_tokens = [] if truncation_strategy == TruncationStrategy.ONLY_FIRST or ( truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is None ): if len(ids) > num_tokens_to_remove: window_len = min(len(ids), stride + num_tokens_to_remove) if self.truncation_side == "left": overflowing_tokens = ids[:window_len] ids = ids[num_tokens_to_remove:] elif self.truncation_side == "right": overflowing_tokens = ids[-window_len:] ids = ids[:-num_tokens_to_remove] else: raise ValueError( f"invalid truncation strategy: {self.truncation_side}, use 'left' or 'right'." ) else: error_msg = ( f"We need to remove {num_tokens_to_remove} to truncate the input " f"but the first sequence has a length {len(ids)}. " ) if truncation_strategy == TruncationStrategy.ONLY_FIRST: error_msg = ( error_msg + "Please select another truncation strategy than " f"{truncation_strategy}, for instance 'longest_first' or 'only_second'." ) log.error(error_msg) elif truncation_strategy == TruncationStrategy.LONGEST_FIRST: log.warning( f"Be aware, overflowing tokens are not returned for the setting you have chosen," f" i.e. sequence pairs with the '{TruncationStrategy.LONGEST_FIRST.value}' " f"truncation strategy. So the returned list will always be empty even if some " f"tokens have been removed." ) for _ in range(num_tokens_to_remove): if pair_ids is None or len(ids) > len(pair_ids): if self.truncation_side == "right": ids = ids[:-1] elif self.truncation_side == "left": ids = ids[1:] else: raise ValueError("invalid truncation strategy:" + str(self.truncation_side)) else: if self.truncation_side == "right": pair_ids = pair_ids[:-1] elif self.truncation_side == "left": pair_ids = pair_ids[1:] else: raise ValueError("invalid truncation strategy:" + str(self.truncation_side)) elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None: if len(pair_ids) > num_tokens_to_remove: window_len = min(len(pair_ids), stride + num_tokens_to_remove) if self.truncation_side == "right": overflowing_tokens = pair_ids[-window_len:] pair_ids = pair_ids[:-num_tokens_to_remove] elif self.truncation_side == "left": overflowing_tokens = pair_ids[:window_len] pair_ids = pair_ids[num_tokens_to_remove:] else: raise ValueError("invalid truncation strategy:" + str(self.truncation_side)) else: log.error( f"We need to remove {num_tokens_to_remove} to truncate the input " f"but the second sequence has a length {len(pair_ids)}. " f"Please select another truncation strategy than {truncation_strategy}, " f"for instance 'longest_first' or 'only_first'." ) return (ids, pair_ids, overflowing_tokens) def _pad( self, encoded_inputs, max_len=None, padding_strategy=PaddingStrategy.DO_NOT_PAD, pad_to_multiple_of=None, return_attention_mask=None, ): if return_attention_mask is None: return_attention_mask = "mask" in self.model_input_names required_input = encoded_inputs[self.model_input_names[0]] if padding_strategy == PaddingStrategy.LONGEST: max_len = len(required_input) if ( max_len is not None and pad_to_multiple_of is not None and (max_len % pad_to_multiple_of != 0) ): max_len = ((max_len // pad_to_multiple_of) + 1) * pad_to_multiple_of needs_to_be_padded = ( padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_len ) # Initialize attention mask if not present. if return_attention_mask and "mask" not in encoded_inputs: encoded_inputs["mask"] = [1] * len(required_input) if needs_to_be_padded: difference = max_len - len(required_input) if self.padding_side == "right": if return_attention_mask: encoded_inputs["mask"] = encoded_inputs["mask"] + [0] * difference if "typ_ids" in encoded_inputs: encoded_inputs["typ_ids"] = ( encoded_inputs["typ_ids"] + [self.pad_token_type_id] * difference ) if "special_tokens_mask" in encoded_inputs: encoded_inputs["special_tokens_mask"] = ( encoded_inputs["special_tokens_mask"] + [1] * difference ) encoded_inputs[self.model_input_names[0]] = required_input + [self.PAD] * difference elif self.padding_side == "left": if return_attention_mask: encoded_inputs["mask"] = [0] * difference + encoded_inputs["mask"] if "typ_ids" in encoded_inputs: encoded_inputs["typ_ids"] = [ self.pad_token_type_id ] * difference + encoded_inputs["typ_ids"] if "special_tokens_mask" in encoded_inputs: encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs[ "special_tokens_mask" ] encoded_inputs[self.model_input_names[0]] = [self.PAD] * difference + required_input else: raise ValueError("Invalid padding strategy:" + str(self.padding_side)) return encoded_inputs def convert_tokens_to_string(self, tokens): raise NotImplementedError def batch_decode( self, sequences, skip_special_tokens=False, clean_up_tokenization_spaces=True, kw, ): return [ self.decode( seq, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, kw, ) for seq in sequences ] def decode( self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True, kw, ): token_ids = to_py_obj(token_ids) return self._decode( token_ids=token_ids, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, kw, ) def _decode( self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True, kw, ): raise NotImplementedError def get_special_tokens_mask( self, toks_0, toks_1=None, has_specials=False, ): assert has_specials and toks_1 is None, ( "You cannot use ``has_specials=False`` with this tokenizer. " "Please use a slow (full python) tokenizer to activate this argument. " "Or set `return_special_tokens_mask=True` when calling the encoding method " "to get the special tokens mask in any tokenizer. " ) all_special_ids = self.all_special_ids # cache the property special_tokens_mask = [1 if token in all_special_ids else 0 for token in toks_0] return special_tokens_mask @staticmethod def clean_up_tokenization(out_string): out_string = ( out_string.replace(" .", ".") .replace(" ?", "?") .replace(" !", "!") .replace(" ,", ",") .replace(" ' ", "'") .replace(" n't", "n't") .replace(" 'm", "'m") .replace(" 's", "'s") .replace(" 've", "'ve") .replace(" 're", "'re") ) return out_string def _eventual_warn_about_too_long_sequence(self, ids, max_len, verbose): if max_len is None and len(ids) > self.model_max_length and verbose: if not self.deprecation_warnings.get( "sequence-length-is-longer-than-the-specified-maximum", False ): log.warning( "Token indices sequence length is longer than the specified maximum sequence length " f"for this model ({len(ids)} > {self.model_max_length}). Running this sequence through the model " "will result in indexing errors" ) self.deprecation_warnings["sequence-length-is-longer-than-the-specified-maximum"] = True @contextmanager def as_target_tokenizer(self): yield @classmethod def register_for_auto_class(cls, auto_class="AutoTokenizer"): 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 def prepare_seq2seq_batch( self, src_texts, tgt_texts=None, max_len=None, max_target_length=None, padding="longest", return_tensors=None, truncation=True, kw, ): kw.pop("src_lang", None) kw.pop("tgt_lang", None) if max_len is None: max_len = self.model_max_length model_inputs = self( src_texts, add_special_tokens=True, return_tensors=return_tensors, max_len=max_len, padding=padding, truncation=truncation, kw, ) if tgt_texts is None: return model_inputs # Process tgt_texts if max_target_length is None: max_target_length = max_len with self.as_target_tokenizer(): labels = self( tgt_texts, add_special_tokens=True, return_tensors=return_tensors, padding=padding, max_len=max_target_length, truncation=truncation, kw, ) model_inputs["labels"] = labels["input_ids"] return model_inputs def get_fast_tokenizer_file(tokenization_files): tokenizer_files_map = {} for file_name in tokenization_files: search = _re_tokenizer_file.search(file_name) if search is not None: v = search.groups()[0] tokenizer_files_map[v] = file_name available_versions = sorted(tokenizer_files_map.keys()) tokenizer_file = FULL_TOKENIZER_FILE for v in available_versions: tokenizer_file = tokenizer_files_map[v] return tokenizer_file PreTrainedTokenizerBase.push_to_hub = copy_func(PreTrainedTokenizerBase.push_to_hub) PreTrainedTokenizerBase.push_to_hub.__doc__ = PreTrainedTokenizerBase.push_to_hub.__doc__.format( object="tokenizer", object_class="AutoTokenizer", 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33,689	quantapix/qnarre	refs/heads/main	/qnarre/base/doc/chain.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 pprint as pp from .log import Logger from .base import config from .header import Header from .counter import counters from .connect import Connects from .exporter import Exporter from .resource import Resource from .justifier import Justifier from .base import Record, LnkQuoting, LnkReplying, traits_for from .base import LnkTopic, LnkSubject, LnkProximity, LnkAudience, LnkSource from .date import Date # needed for repr reading log = Logger(__name__) class ChnHeader(Header, Justifier): def __init__(self, hdr): super().__init__(vars(hdr)) class Chain(Exporter): def __init__(self, names): self.names = tuple(sorted(set(names))) def __repr__(self): return '{}({!r})'.format(type(self).__name__, self.names) @property def name(self): return 'Chain ' + self.names[-1] def topic(self, _): return self._topic def subject(self, _): return self._subject def collapse(self, ctxt): r = ctxt.recs[self.names[-1]] h = r.hdr src = h.source or r.source or '' self.hdr = hdr = ChnHeader(h) ps = set() tp = sb = '' f = hdr.from_ for n in self.names[:-1]: r = ctxt.recs[n] h = r.hdr s = h.source or r.source or '' if src and s and src != s: src = None src = None if src is None else (src or s) hdr.merge(h) rt, rs = r.topic(ctxt), r.subject(ctxt) assert not tp or not rt or tp == rt tp = rt or tp assert not sb or not rs or sb == rs sb = rs or sb ps.add(h.date.proximity) hdr.init_justs(traits_for(f).justify for f in hdr.from_) hdr.from_ = f self._topic = tp if not sb: if len(ps) == 1: hdr.title = 'On {}'.format(hdr.date.short) sb = src.split('.')[0] if src else hdr.title else: sb = 'Chained' self._subject = sb def plainer(self, ctxt, kw): for m in self.names: r = ctxt.recs[m] yield from r.hdr.plainer(ctxt, kw) yield from r.plainer(kw, ctxt=ctxt) yield '\n' def htmer(self, frame=None, ctxt=None, kw): yield frame[0] yield frame[1] for m in self.names: r = ctxt.recs[m] yield from r.hdr.htmer(self.hdr, frame, ctxt, kw) yield from r.htmer(kw, ctxt=ctxt) yield frame[-3] yield frame[-2] yield frame[-1] def blogger(self, kw): yield from self.hdr.blogger(kw) yield '\n'.join(self.plainer(kw)) yield from self.hdr.footer(kw) fields = (Record, LnkQuoting, LnkReplying, LnkTopic, LnkSubject, LnkProximity, LnkAudience, LnkSource) fields = tuple((f.label, '') for f in fields) class Chains(Resource): _res_path = config.qnar_dst + 'chains.qnr' _graphs = None @classmethod def globals(cls): return globals() def __init__(self, data=None, kw): self._seed, self._adjs, elems = data or ((), (), ()) elems = {c.name: c for c in elems} super().__init__(elems, kw) def __repr__(self): es = tuple(sorted(self.values(), key=lambda c: c.name)) es = pp.pformat(es, indent=4) return '{}(({!r}, {!r}, {}))'.format( type(self).__name__, self._seed, self._adjs, es) @property def graphs(self): if self._graphs is None: self._graphs = Connects(self._seed) return self._graphs graph_args = (fields, 'Graphing:', '') collapse_args = ((('purged', 'd'), ('quoting', 'q'), ('replying', 'r'), ('subject', 's'), ('proximity', 'p'), ('audience', 'a'), ('failed', 'F')), 'Collapsing:', '') def chainer(self, src, ctxt, kw): gs = self.graphs with counters(self.graph_args, kw) as cs: cs.retitle() gs.grow_from(src, self._adjs, kw) del kw['cntr'] with counters(self.collapse_args, kw) as cs: cs.retitle() gs.collapse_all(*kw) rg = gs.record for r in sorted(rg.nodes()): try: c = Chain((r, rg.node[r][config.CHAIN])) except KeyError: yield ctxt.recs[r] else: c.collapse(ctxt) self[c.name] = c yield c	{"/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"], 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["/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"]}
33,690	quantapix/qnarre	refs/heads/main	/qnarre/run/squad_old.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 math import pathlib as pth import collections as co from datetime import datetime import tensorflow as tf from google.protobuf import struct_pb2 from tensorboard.plugins.hparams import api_pb2 from tensorboard.plugins.hparams import summary as hparams from qnarre.core.models import bert from qnarre.core import Squad from qnarre.feeds.dset.squad_ds import dataset as squad_ds ks = tf.keras kls = ks.layers kcb = ks.callbacks def model_for(params): PS = params sh = (PS.max_seq_len,) toks = kls.Input(shape=sh, dtype="int32", name="tokens") segs = kls.Input(shape=sh, dtype="int32", name="segments") opts = kls.Input(shape=sh, dtype="int32", name="optimals") spans = kls.Input(shape=(2,), dtype="int32", name="optimals") ins = [toks, segs, opts, spans] y = Squad(PS)(ins) return ks.Model(inputs=ins, outputs=[y]) def dataset_for(kind, params): ds = squad_ds(kind, params) if kind == "train": ds = ds.shuffle(buffer_size=50000) ds = ds.batch(params.batch_size) # ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE) else: ds = ds ds = ds.batch(params.batch_size) return ds RawResult = co.namedtuple("RawResult", ["unique_id", "start_logits", "end_logits"]) def write_predictions( PS, all_examples, all_features, all_results, n_best_size, max_answer_length, do_lower_case, output_prediction_file, output_nbest_file, output_null_log_odds_file, ): tf.logging.info("Writing predictions to: %s" % (output_prediction_file)) tf.logging.info("Writing nbest to: %s" % (output_nbest_file)) example_index_to_features = co.defaultdict(list) for feature in all_features: example_index_to_features[feature.example_index].append(feature) unique_id_to_result = {} for result in all_results: unique_id_to_result[result.unique_id] = result _PrelimPrediction = co.namedtuple( # pylint: disable=invalid-name "PrelimPrediction", ["feature_index", "start_index", "end_index", "start_logit", "end_logit"], ) all_predictions = co.OrderedDict() all_nbest_json = co.OrderedDict() scores_diff_json = co.OrderedDict() for (example_index, example) in enumerate(all_examples): features = example_index_to_features[example_index] prelim_predictions = [] # keep track of the minimum score of null start+end of position 0 score_null = 1000000 # large and positive min_null_feature_index = 0 # the paragraph slice with min mull score null_start_logit = 0 # the start logit at the slice with min null score null_end_logit = 0 # the end logit at the slice with min null score for (feature_index, feature) in enumerate(features): result = unique_id_to_result[feature.unique_id] start_indexes = _get_best_indexes(result.start_logits, n_best_size) end_indexes = _get_best_indexes(result.end_logits, n_best_size) # if we could have irrelevant answers, get the min score of irrelevant if PS.version_2_with_negative: feature_null_score = result.start_logits[0] + result.end_logits[0] if feature_null_score < score_null: score_null = feature_null_score min_null_feature_index = feature_index null_start_logit = result.start_logits[0] null_end_logit = result.end_logits[0] for start_index in start_indexes: for end_index in end_indexes: # We could hypothetically create invalid predictions, e.g., predict # that the start of the span is in the question. We throw out all # invalid predictions. if start_index >= len(feature.tokens): continue if end_index >= len(feature.tokens): continue if start_index not in feature.token_to_orig_map: continue if end_index not in feature.token_to_orig_map: continue if not feature.token_is_max_context.get(start_index, False): continue if end_index < start_index: continue length = end_index - start_index + 1 if length > max_answer_length: continue prelim_predictions.append( _PrelimPrediction( feature_index=feature_index, start_index=start_index, end_index=end_index, start_logit=result.start_logits[start_index], end_logit=result.end_logits[end_index], ) ) if PS.version_2_with_negative: prelim_predictions.append( _PrelimPrediction( feature_index=min_null_feature_index, start_index=0, end_index=0, start_logit=null_start_logit, end_logit=null_end_logit, ) ) prelim_predictions = sorted( prelim_predictions, key=lambda x: (x.start_logit + x.end_logit), reverse=True, ) _NbestPrediction = co.namedtuple( # pylint: disable=invalid-name "NbestPrediction", ["text", "start_logit", "end_logit"] ) seen_predictions = {} nbest = [] for pred in prelim_predictions: if len(nbest) >= n_best_size: break feature = features[pred.feature_index] if pred.start_index > 0: # this is a non-null prediction tok_tokens = feature.tokens[pred.start_index : (pred.end_index + 1)] orig_doc_start = feature.token_to_orig_map[pred.start_index] orig_doc_end = feature.token_to_orig_map[pred.end_index] orig_tokens = example.doc_tokens[orig_doc_start : (orig_doc_end + 1)] tok_text = " ".join(tok_tokens) # De-tokenize WordPieces that have been split off. tok_text = tok_text.replace(" ##", "") tok_text = tok_text.replace("##", "") # Clean whitespace tok_text = tok_text.strip() tok_text = " ".join(tok_text.split()) orig_text = " ".join(orig_tokens) final_text = get_final_text(tok_text, orig_text, do_lower_case) if final_text in seen_predictions: continue seen_predictions[final_text] = True else: final_text = "" seen_predictions[final_text] = True nbest.append( _NbestPrediction( text=final_text, start_logit=pred.start_logit, end_logit=pred.end_logit, ) ) # if we didn't inlude the empty option in the n-best, inlcude it if PS.version_2_with_negative: if "" not in seen_predictions: nbest.append( _NbestPrediction( text="", start_logit=null_start_logit, end_logit=null_end_logit ) ) # In very rare edge cases we could have no valid predictions. So we # just create a nonce prediction in this case to avoid failure. if not nbest: nbest.append(_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0)) assert len(nbest) >= 1 total_scores = [] best_non_null_entry = None for entry in nbest: total_scores.append(entry.start_logit + entry.end_logit) if not best_non_null_entry: if entry.text: best_non_null_entry = entry probs = _compute_softmax(total_scores) nbest_json = [] for (i, entry) in enumerate(nbest): output = co.OrderedDict() output["text"] = entry.text output["probability"] = probs[i] output["start_logit"] = entry.start_logit output["end_logit"] = entry.end_logit nbest_json.append(output) assert len(nbest_json) >= 1 if not PS.version_2_with_negative: all_predictions[example.qas_id] = nbest_json[0]["text"] else: # predict '' iff the null score - the score of best non-null > threshold score_diff = ( score_null - best_non_null_entry.start_logit - (best_non_null_entry.end_logit) ) scores_diff_json[example.qas_id] = score_diff if score_diff > PS.null_score_diff_threshold: all_predictions[example.qas_id] = "" else: all_predictions[example.qas_id] = best_non_null_entry.text all_nbest_json[example.qas_id] = nbest_json with tf.gfile.GFile(output_prediction_file, "w") as writer: writer.write(json.dumps(all_predictions, indent=4) + "\n") with tf.gfile.GFile(output_nbest_file, "w") as writer: writer.write(json.dumps(all_nbest_json, indent=4) + "\n") if PS.version_2_with_negative: with tf.gfile.GFile(output_null_log_odds_file, "w") as writer: writer.write(json.dumps(scores_diff_json, indent=4) + "\n") def get_final_text(PS, pred_text, orig_text, do_lower_case): def _strip_spaces(text): ns_chars = [] ns_to_s_map = co.OrderedDict() for (i, c) in enumerate(text): if c == " ": continue ns_to_s_map[len(ns_chars)] = i ns_chars.append(c) ns_text = "".join(ns_chars) return (ns_text, ns_to_s_map) tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case) tok_text = " ".join(tokenizer.tokenize(orig_text)) start_position = tok_text.find(pred_text) if start_position == -1: if PS.verbose_logging: tf.logging.info("Unable to find text: '%s' in '%s'" % (pred_text, orig_text)) return orig_text end_position = start_position + len(pred_text) - 1 (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text) (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text) if len(orig_ns_text) != len(tok_ns_text): if PS.verbose_logging: tf.logging.info( "Length not equal after stripping spaces: '%s' vs '%s'", orig_ns_text, tok_ns_text, ) return orig_text # We then project the characters in `pred_text` back to `orig_text` using # the character-to-character alignment. tok_s_to_ns_map = {} for (i, tok_index) in tok_ns_to_s_map.items(): tok_s_to_ns_map[tok_index] = i orig_start_position = None if start_position in tok_s_to_ns_map: ns_start_position = tok_s_to_ns_map[start_position] if ns_start_position in orig_ns_to_s_map: orig_start_position = orig_ns_to_s_map[ns_start_position] if orig_start_position is None: if PS.verbose_logging: tf.logging.info("Couldn't map start position") return orig_text orig_end_position = None if end_position in tok_s_to_ns_map: ns_end_position = tok_s_to_ns_map[end_position] if ns_end_position in orig_ns_to_s_map: orig_end_position = orig_ns_to_s_map[ns_end_position] if orig_end_position is None: if PS.verbose_logging: tf.logging.info("Couldn't map end position") return orig_text output_text = orig_text[orig_start_position : (orig_end_position + 1)] return output_text def _get_best_indexes(logits, n_best_size): index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True) best_indexes = [] for i in range(len(index_and_score)): if i >= n_best_size: break best_indexes.append(index_and_score[i][0]) return best_indexes def _compute_softmax(scores): if not scores: return [] max_score = None for score in scores: if max_score is None or score > max_score: max_score = score exp_scores = [] total_sum = 0.0 for score in scores: x = math.exp(score - max_score) exp_scores.append(x) total_sum += x probs = [] for score in exp_scores: probs.append(score / total_sum) return probs def run_squad(sess, params): # with tf.distribute.MirroredStrategy().scope(): model = model_for(params) model.compile( optimizer=params.optimizer, loss="sparse_categorical_crossentropy", metrics=["accuracy"], ) ds_train = dataset_for("train", params) ds_test = dataset_for("test", params) save_p = pth.Path(params.dir_save) if save_p.exists(): model.train_on_batch(ds_train[:1]) model.load_weights(save_p) model.summary() p = params.log_dir + "/train/" + sess writer = tf.summary.create_file_writer(p) sum_s = hparams.session_start_pb(hparams=params.hparams) cbacks = [ kcb.TensorBoard(log_dir=p, histogram_freq=1, embeddings_freq=0, update_freq="epoch"), # kcb.EarlyStopping( # monitor='val_loss', min_delta=1e-2, patience=2, verbose=True), ] if save_p.exists(): cbacks.append( kcb.ModelCheckpoint( model_save_path=save_p, save_best_only=True, monitor="val_loss", verbose=True, ) ) hist = model.fit( ds_train, callbacks=cbacks, epochs=params.train_epochs, validation_data=ds_test ) print(f"History: {hist.history}") if save_p.exists(): model.save_weights(save_p, save_format="tf") loss, acc = model.evaluate(ds_test) print(f"\nTest loss, acc: {loss}, {acc}") with writer.as_default(): e = tf.compat.v1.Event(summary=sum_s).SerializeToString() tf.summary.import_event(e) tf.summary.scalar("accuracy", acc, step=1, description="Accuracy") sum_e = hparams.session_end_pb(api_pb2.STATUS_SUCCESS) e = tf.compat.v1.Event(summary=sum_e).SerializeToString() tf.summary.import_event(e) _params = dict( seq_stride=128, max_ans_len=30, max_qry_len=64, max_seq_len=384, n_best_size=20, null_score_diff_threshold=0.0, train_epochs=2.0, use_fp16=False, use_xla=False, warmup_split=0.1, batch_size=8, learn_rate=5e-6, ) _params.update( dir_data=".data/squad", log_dir=".model/squad/logs", dir_model=".model/squad", dir_save=".model/squad/save", ) def main(_): PS = bert.load_params().override(_params) nus = [16, 32, 512] drs = [0.1, 0.2] writer = tf.summary.create_file_writer(PS.log_dir + "/train") with writer.as_default(): s = _to_summary_pb(nus, drs, "") e = tf.compat.v1.Event(summary=s).SerializeToString() tf.summary.import_event(e) for nu in nus: for dr in drs: kw = {"num_units": nu, "drop_rate": dr} sess = datetime.now().strftime("%Y%m%d-%H%M%S") print(f"--- Running session {sess}:", kw) PS.update(**kw) run_squad(sess, PS) 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.experiment_pb( hparam_infos=[ api_pb2.HParamInfo( name="num_units", display_name="Number of units", type=api_pb2.DATA_TYPE_FLOAT64, domain_discrete=nus_val, ), api_pb2.HParamInfo( name="drop_rate", display_name="Dropout rate", type=api_pb2.DATA_TYPE_FLOAT64, domain_discrete=drs_val, ), api_pb2.HParamInfo( name="optimizer", display_name="Optimizer", type=api_pb2.DATA_TYPE_STRING, domain_discrete=opts_val, ), ], metric_infos=[ api_pb2.MetricInfo(name=api_pb2.MetricName(tag="accuracy"), display_name="Accuracy"), ], ) if __name__ == "__main__": # tf.logging.set_verbosity(tf.logging.INFO) bert.load_flags() from absl import flags flags.DEFINE_float("null_score_diff_threshold", None, "") flags.DEFINE_float("warmup_split", None, "") flags.DEFINE_integer("max_ans_len", None, "") flags.DEFINE_integer("max_qry_len", None, "") flags.DEFINE_integer("n_best_size", None, "") flags.DEFINE_integer("seq_stride", None, "") from absl import app app.run(main) """ python $SQUAD_DIR/evaluate-v1.1.py $SQUAD_DIR/dev-v1.1.json /results/predictions.json """	{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", 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33,691	quantapix/qnarre	refs/heads/main	/qnarre/base/conflict.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 .claim import Claim from .author import Agent from .narrative import Node from .conjecture import Reality class Conflict(Node): agency = None count = 1 def __init__(self, text=None, reality=None, agency=None, kw): super().__init__(kw) if text: for k in ('factor', 'bias', 'weight'): kw.pop(k, None) self.claim = Claim(text=text, *kw) if reality: self.reality = Reality.create(name=reality) if agency: self.agency = Agent.create(name=agency) @property def sign(self): return ('?' if self.agency else '') + self._sign @property def topic(self): return super().topic or self.reality.topic @property def narrative(self): return super().narrative or self.reality.narrative @property def factor(self): return super().factor self.count * (2 if self.agency else 1) @property def weight(self): return self.partial(self.reality.weight) + self.bias @property def fragment(self): return self.weight @property def value(self): v = super().value n = self.reality.name f = self.fragment return '{} {} <-> {} F{}'.format(v, self.claim.value, n, f) @property def fields(self): fs = {'Reality': self.reality.name, 'Fragment': self.fragment} fs.update(self.claim.fields) fs.update(super().fields) if self.agency: fs['Agency'] = self.agency.value return fs class Inherent(Conflict): _factor = 0 _sign = '?h' class Conceal(Conflict): _factor = 0.25 _sign = '?c' class Deceive(Conflict): _factor = 0.5 _sign = '?d' class Fraud(Conflict): _factor = 0.75 _sign = '?u' class Extort(Conflict): _factor = 1 _sign = '?e' class Repeat(Claim): sign = 'n' def __init__(self, conflict, kw): super().__init__(*kw) assert ':' in conflict self.conflict = Conflict.create(name=conflict) self.conflict.count += 1 @property def value(self): return '{} +{}'.format(super().value, self.conflict.name) @property def fields(self): fs = self.conflict.fields fs.update(super().fields) fs['Name'] = self.conflict.name fs['Type'] = self.tag return fs	{"/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"], 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33,692	quantapix/qnarre	refs/heads/main	/qnarre/models/electra.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://openreview.net/pdf?id=r1xMH1BtvB # https://github.com/google-research/electra import torch import torch.utils.checkpoint from torch import nn 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.embed import Embed from ..core.mlp import Classifier, Predictor from ..prep.config.electra import PreTrained from . import bert log = logging.get_logger(__name__) class Model(PreTrained): def __init__(self, kw): super().__init__(kw) cfg = self.get_cfg(kw) self.emb = Embed(kw) if cfg.d_embed != cfg.d_model: self.proj = qc.Linear(cfg.d_embed, cfg.d_model, kw) self.enc = Encoder(kw) def forward( self, x, x_emb=None, mask=None, head_m=None, enc=None, enc_m=None, cache=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 c_len = cache[0][0].shape[2] if cache is not None else 0 if mask is None: mask = torch.ones(s, device=d) 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, x_emb=x_emb, c_len=c_len, kw) if hasattr(self, "proj"): ys = self.proj(ys) ys = self.enc(ys, mask=mask, head_m=head_m, enc=enc, enc_m=enc_m, cache=cache, kw) return ys class Generator(qc.Module): hs = qc.Hypers({"d_embed", "d_model", "drop", "eps", "s_vocab"}, {"act": "gelu"}) def __init__(self, ps={}, hs=[], kw): super().__init__(ps, [self.hs] + hs, kw) cfg = self.get_cfg(kw) self.lin = qc.Linear(cfg.d_model, cfg.d_embed, kw) self.act = qu.activation(cfg.act) self.norm = qc.LayerNorm(cfg.d_embed, cfg.eps, kw) self.proj = qc.Linear(cfg.d_embed, cfg.s_vocab, kw) def forward(self, x): y = self.lin(x) y = self.act(y) y = self.norm(y) y = self.proj(y) return y class ForCausal(PreTrained): def __init__(self, kw): super().__init__(kw) cfg = self.get_cfg(kw) self.model = Model(kw) self.proj = Generator(kw) self.init_weights() 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[1:] + (loss,) return qo.LossCrosses(ys) class ForMasked(PreTrained): def __init__(self, kw): super().__init__(kw) cfg = self.get_cfg(kw) self.model = Model(add_pool=False, kw) self.proj = Predictor(cfg.d_embed, kw) forward = qf.forward_masked class ForChoice(PreTrained): def __init__(self, kw): super().__init__(kw) cfg = self.get_cfg(kw) self.model = Model(kw) self.seqs = qc.SeqSummary(kw) self.proj = qc.Linear(cfg.d_model, 1, kw) def forward(self, x, x_emb=None, mask=None, typ=None, pos=None, labels=None, kw): n = x.shape[1] if x is not None else x_emb.shape[1] x, mask, typ, pos = qu.view_2D(x, mask, typ, pos) x_emb = qu.view_3D(x_emb) ys = self.model(x, x_emb, mask=mask, typ=typ, pos=pos, *kw) y = self.proj(self.seqs(ys[0])).view(-1, n) loss = None if labels is not None: loss = nn.CrossEntropyLoss()(y, labels) ys = (y,) + ys[1:] + (loss,) return qo.WithLoss(ys) class ForPreTraining(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, n_labels=1, drop_proj=0.0, kw) def forward(self, x, mask=None, labels=None, kw): ys = self.model(x, mask=mask, kw) y = self.proj(ys[0]).squeeze(-1) loss = None if labels is not None: f = nn.BCEWithLogitsLoss() if mask is not None: a = mask.view(-1, ys[0].shape[1]) == 1 loss = f(y.view(-1, ys[0].shape[1])[a], labels[a].float()) else: loss = f(y.view(-1, ys[0].shape[1]), labels.float()) ys = (y,) + ys[1:] + (loss,) return qo.WithLoss(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, "gelu", 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 class Encoder(qc.Module): hs = qc.Hypers({"add_cross", "n_lays"}) def __init__(self, n_lays=None, ps={}, hs=[], kw): if n_lays is not None: kw.update(n_lays=n_lays) super().__init__(ps, [self.hs] + hs, kw) cfg = self.get_cfg(kw) self.lays = qc.Stack([bert.Layer(kw) for _ in range(cfg.n_lays)]) self.grad_checkpoint = False def forward(self, x, head_m=None, cache=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 = torch.utils.checkpoint.checkpoint(create_forward(lay), y, head_m=h, kw) else: ys = lay(y, head_m=h, cache=c, *kw) 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)	{"/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"], 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"/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"]}
33,693	quantapix/qnarre	refs/heads/main	/qnarre/prep/config/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. # ============================================================================= from ... import core as qc class PreTrained(qc.PreTrained): hs = qc.Hypers( [], dict( act_dim=4, act="relu", action_tanh=True, BOS=50256, d_hidden=128, drop_attn=0.1, drop_embed=0.1, drop_resid=0.1, EOS=50256, init_range=0.02, eps=1e-5, max_ep_len=4096, model_type="decision_transformer", n_embd=768, n_heads=1, n_inner=None, n_lays=3, n_pos=1024, reorder_and_upcast_attn=False, s_vocab=1, scale_attn_by_inverse_layer_idx=False, scale_attn_weights=True, state_dim=17, sum_act=None, drop_sum_first=0.1, sum_proj=True, sum_type="cls_index", sum_use_proj=True, y_cache=True, gradient_checkpoint=True, ), ) def _init_weights(self, module): if isinstance(module, (qc.Linear, Conv1D)): 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) for name, p in module.named_parameters(): if "c_proj" in name and "weight" in name: p.data.normal_( mean=0.0, std=(self.config.init_range / math.sqrt(2 * self.config.n_lays)), ) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, DecisionTransformerGPT2Model): module.gradient_checkpointing = value MAP = { "edbeeching/decision-transformer-gym-hopper-medium": "https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json", }	{"/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": 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33,694	quantapix/qnarre	refs/heads/main	/qnarre/base/doc/util/utils.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 enum import pathlib as pth import contextlib as cl from .log import Logger from .item import Item from .row import Row from .node import Node log = Logger(__name__) def scanner(path, col): rs = [] with os.scandir(path) as es: items = {} for e in es: p = pth.Path(e.path) n = p.stem if e.is_dir(follow_symlinks=False): rs.append(Node(n)) elif e.is_file(follow_symlinks=False): assert p.suffix i = items.get(n) if i is None: items[n] = i = Item(path=p) rs.append(Row(n, {col: i})) else: i[p.suffix] = p else: log.warning('Ignoring dir entry {}', p) rs.sort(key=lambda x: x.name) for r in rs: reject = yield r if isinstance(r, Node): if reject is not True: yield from scanner(path / r.name, col) yield None @cl.contextmanager def sinker(sink): def gen(): try: while True: key, entry = yield es = sink.setdefault(key, []) if entry: es.append(entry) except GeneratorExit: pass g = gen() g.send(None) yield g class NoValue(enum.Enum): def __repr__(self): return "{}.{}".format(type(self).__name__, self.name) class Sinks(NoValue): excluded = enum.auto() duplicate = enum.auto() synonym = enum.auto() class Index(dict): def __init__(self, tree, kw): super().__init__() for e in tree.separator(kw, uniques=super()): pass class XNames(): def __init__(self, tree, kw): super().__init__() self._tree = tree def walker(self, kw): indent = 0 for e in self._tree.filterer(kw): _, row = e if row is None: indent -= 2 else: yield ' ' * indent + row.name if isinstance(row, Node): indent += 2 assert indent == 0	{"/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": 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33,695	quantapix/qnarre	refs/heads/main	/qnarre/prep/dataset/glue.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 csv import os import numpy as np import datasets as ds _MRPC_DEV_IDS = "https://dl.fbaipublicfiles.com/glue/data/mrpc_dev_ids.tsv" _MRPC_TRAIN = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt" _MRPC_TEST = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt" _MNLI_BASE_kw = dict( text_features={ "premise": "sentence1", "hypothesis": "sentence2", }, label_classes=["entailment", "neutral", "contradiction"], label_column="gold_label", data_url="https://dl.fbaipublicfiles.com/glue/data/MNLI.zip", data_dir="MNLI", ) class Config(ds.BuilderConfig): def __init__( self, text_features, label_column, data_url, data_dir, label_classes=None, process_label=lambda x: x, kw, ): super(Config, self).__init__(version=ds.Version("1.0.0", ""), kw) self.text_features = text_features self.label_column = label_column self.label_classes = label_classes self.data_url = data_url self.data_dir = data_dir self.process_label = process_label class Glue(ds.GeneratorBasedBuilder): BUILDER_CONFIGS = [ Config( name="cola", text_features={"sentence": "sentence"}, label_classes=["unacceptable", "acceptable"], label_column="is_acceptable", data_url="https://dl.fbaipublicfiles.com/glue/data/CoLA.zip", data_dir="CoLA", ), Config( name="sst2", text_features={"sentence": "sentence"}, label_classes=["negative", "positive"], label_column="label", data_url="https://dl.fbaipublicfiles.com/glue/data/SST-2.zip", data_dir="SST-2", ), Config( name="mrpc", text_features={"sentence1": "", "sentence2": ""}, label_classes=["not_equivalent", "equivalent"], label_column="Quality", data_url="", data_dir="MRPC", ), Config( name="qqp", text_features={ "question1": "question1", "question2": "question2", }, label_classes=["not_duplicate", "duplicate"], label_column="is_duplicate", data_url="https://dl.fbaipublicfiles.com/glue/data/QQP-clean.zip", data_dir="QQP", ), Config( name="stsb", text_features={ "sentence1": "sentence1", "sentence2": "sentence2", }, label_column="score", data_url="https://dl.fbaipublicfiles.com/glue/data/STS-B.zip", data_dir="STS-B", process_label=np.float32, ), Config( name="mnli", _MNLI_BASE_kw, ), Config( name="mnli_mismatched", _MNLI_BASE_kw, ), Config( name="mnli_matched", **_MNLI_BASE_kw, ), Config( name="qnli", text_features={ "question": "question", "sentence": "sentence", }, label_classes=["entailment", "not_entailment"], label_column="label", data_url="https://dl.fbaipublicfiles.com/glue/data/QNLIv2.zip", data_dir="QNLI", ), Config( name="rte", text_features={ "sentence1": "sentence1", "sentence2": "sentence2", }, label_classes=["entailment", "not_entailment"], label_column="label", data_url="https://dl.fbaipublicfiles.com/glue/data/RTE.zip", data_dir="RTE", ), Config( name="wnli", text_features={ "sentence1": "sentence1", "sentence2": "sentence2", }, label_classes=["not_entailment", "entailment"], label_column="label", data_url="https://dl.fbaipublicfiles.com/glue/data/WNLI.zip", data_dir="WNLI", ), Config( name="ax", text_features={ "premise": "sentence1", "hypothesis": "sentence2", }, label_classes=["entailment", "neutral", "contradiction"], label_column="", data_url="https://dl.fbaipublicfiles.com/glue/data/AX.tsv", data_dir="", ), ] def _info(self): fs = {k: ds.Value("string") for k in self.config.text_features.keys()} if self.config.label_classes: fs["label"] = ds.features.ClassLabel(names=self.config.label_classes) else: fs["label"] = ds.Value("float32") fs["idx"] = ds.Value("int32") return ds.DatasetInfo(description="", features=ds.Features(fs), homepage="", citation="") def _split_generators(self, mgr): if self.config.name == "ax": data_file = mgr.download(self.config.data_url) return [ ds.SplitGenerator( name=ds.Split.TEST, gen_kw={ "data_file": data_file, "split": "test", }, ) ] if self.config.name == "mrpc": data_dir = None mrpc_files = mgr.download( { "dev_ids": _MRPC_DEV_IDS, "train": _MRPC_TRAIN, "test": _MRPC_TEST, } ) else: dl_dir = mgr.download_and_extract(self.config.data_url) data_dir = os.path.join(dl_dir, self.config.data_dir) mrpc_files = None train_split = ds.SplitGenerator( name=ds.Split.TRAIN, gen_kw={ "data_file": os.path.join(data_dir or "", "train.tsv"), "split": "train", "mrpc_files": mrpc_files, }, ) if self.config.name == "mnli": return [ train_split, _mnli_split_generator("validation_matched", data_dir, "dev", matched=True), _mnli_split_generator("validation_mismatched", data_dir, "dev", matched=False), _mnli_split_generator("test_matched", data_dir, "test", matched=True), _mnli_split_generator("test_mismatched", data_dir, "test", matched=False), ] elif self.config.name == "mnli_matched": return [ _mnli_split_generator("validation", data_dir, "dev", matched=True), _mnli_split_generator("test", data_dir, "test", matched=True), ] elif self.config.name == "mnli_mismatched": return [ _mnli_split_generator("validation", data_dir, "dev", matched=False), _mnli_split_generator("test", data_dir, "test", matched=False), ] else: return [ train_split, ds.SplitGenerator( name=ds.Split.VALIDATION, gen_kw={ "data_file": os.path.join(data_dir or "", "dev.tsv"), "split": "dev", "mrpc_files": mrpc_files, }, ), ds.SplitGenerator( name=ds.Split.TEST, gen_kw={ "data_file": os.path.join(data_dir or "", "test.tsv"), "split": "test", "mrpc_files": mrpc_files, }, ), ] def _generate_examples(self, data_file, split, mrpc_files=None): if self.config.name == "mrpc": examples = self._generate_example_mrpc_files(mrpc_files=mrpc_files, split=split) for example in examples: yield example["idx"], example else: process_label = self.config.process_label label_classes = self.config.label_classes is_cola_non_test = self.config.name == "cola" and split != "test" with open(data_file, encoding="utf8") as f: reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) if is_cola_non_test: reader = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE) for n, row in enumerate(reader): if is_cola_non_test: row = { "sentence": row[3], "is_acceptable": row[1], } example = {feat: row[col] for feat, col in self.config.text_features.items()} example["idx"] = n if self.config.label_column in row: label = row[self.config.label_column] if label_classes and label not in label_classes: label = int(label) if label else None example["label"] = process_label(label) else: example["label"] = process_label(-1) for value in example.values(): if value is None: break else: yield example["idx"], example def _generate_example_mrpc_files(self, mrpc_files, split): if split == "test": with open(mrpc_files["test"], encoding="utf8") as f: f.seek(3) reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) for n, row in enumerate(reader): yield { "sentence1": row["#1 String"], "sentence2": row["#2 String"], "label"(row["Quality"]), "idx": n, } else: with open(mrpc_files["dev_ids"], encoding="utf8") as f: reader = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE) dev_ids = [[row[0], row[1]] for row in reader] with open(mrpc_files["train"], encoding="utf8") as f: f.seek(3) reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) for n, row in enumerate(reader): is_row_in_dev = [row["#1 ID"], row["#2 ID"]] in dev_ids if is_row_in_dev == (split == "dev"): yield { "sentence1": row["#1 String"], "sentence2": row["#2 String"], "label"(row["Quality"]), "idx": n, } def _mnli_split_generator(name, data_dir, split, matched): return ds.SplitGenerator( name=name, gen_kw={ "data_file": os.path.join( data_dir, "%s_%s.tsv" % (split, "matched" if matched else "mismatched") ), "split": split, "mrpc_files": 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"], 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33,696	quantapix/qnarre	refs/heads/main	/qnarre/base/doc/connect.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 feeder, sinker, bridger, Graphs from .base import config, LnkQuoting, LnkReplying, Record from .base import LnkTopic, LnkSubject, LnkProximity, LnkAudience, LnkSource class Connects(Graphs): _graphs = tuple( a.label for a in (Record, LnkQuoting, LnkReplying, LnkTopic, LnkSubject, LnkProximity, LnkAudience, LnkSource)) def check(self): mn = set(self.record.nodes()) if mn: for g in (self.quoting, self.replying): assert set(g.nodes()) <= mn for g in (self.topic, self.subject, self.proximity, self.audience, self.source): for e in g.edges(): assert e[0] in mn def collapse_quoting(self, cntr, _): qg, rg = self.quoting, self.replying dirty = None while True: for k in (feeder, sinker, bridger): for p, m, s in qg.linked_recs(k): if p: rg.add_edge(p, m) if s: rg.add_edge(m, s) qg.remove_node(m) cntr.incr('q') dirty = True if not dirty: return dirty is False dirty = False def roll_up(self, msg, chn): mg = self.record c = mg.node[chn].setdefault(config.CHAIN, []) c.append(msg) c.extend(mg.node[msg].get(config.CHAIN, ())) for g in (self.proximity, self.audience): ss = [g.successors(m)[0] for m in (msg, chn) if m in g] if len(ss) != 2 or ss[0] != ss[1]: g.remove_msg(chn) for g in self.graphs: g.remove_msg(msg) def collapse(self, ms, cntr, _): tg = self.topic ts = set(tg.successors(m)[0] for m in ms if m in tg) if len(ts) < 2: sg = self.subject ss = set(sg.successors(m)[0] for m in ms if m in sg) if len(ss) < 2: rg = self.replying p, m, s = ms if p and s: c = p if rg.degree(p) < rg.degree(s) else s else: c = p if p else s if ts: tg.add_edge(c, ts.pop()) if ss: sg.add_edge(c, ss.pop()) self.roll_up(m, c) cntr.incr('r') return True def collapse_replying(self, kw): rg = self.replying dirty = None while True: for k in (feeder, sinker, bridger): for ms in rg.linked_recs(k): if not self.collapse(ms, kw): rg.remove_node(ms[1]) continue dirty = True if not dirty: self.check() return dirty is False dirty = False def collapse_loop(self, kw): dirty = None while True: if self.collapse_quoting(kw): dirty = True if self.collapse_replying(kw): dirty = True if not dirty: self.check() return dirty is False dirty = False def collapse_proximity(self, cntr, _): pg, rg = self.proximity, self.replying dirty = None for c in nx.weakly_connected_components(pg): p = None for m in sorted(m for m in c if not pg.in_degree(m)): if p: rg.add_edge(p, m) cntr.incr('p') dirty = True p = m return dirty def collapse_audience(self, cntr, _): ag, mg, pg = self.audience, self.record, self.proximity rg = self.replying dirty = None for c in nx.weakly_connected_components(ag): p = None for m in sorted(m for m in c if not ag.in_degree(m) and ':' in m): if m in pg and len(mg.node[m].get(config.CHAIN, ())) > 5: p = None continue if p: rg.add_edge(p, m) cntr.incr('a') dirty = True p = m return dirty def collapse_subject(self, cntr, _): sg, rg = self.subject, self.replying dirty = None for c in nx.weakly_connected_components(sg): p = None for m in sorted(m for m in c if not sg.in_degree(m) and ':' in m): if p: rg.add_edge(p, m) cntr.incr('s') dirty = True p = m return dirty def collapse_all(self, kw): self.purge_empty(kw) self.collapse_loop(kw) if self.collapse_proximity(kw): self.collapse_loop(kw) if self.collapse_audience(kw): self.collapse_loop(kw) if self.collapse_subject(kw): self.collapse_loop(**kw) Connects.init_class()	{"/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": 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"/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", 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"/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": 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33,697	quantapix/qnarre	refs/heads/main	/qnarre/prep/convert/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. # ============================================================================= import re import tensorflow as tf import torch from argparse import ArgumentParser from os.path import abspath from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME from transformers.utils import logging from ..config.gpt2 import PreTrained from ...models.gpt2 import Model logging.set_verbosity_info() log = logging.get_logger(__name__) def load_src_weights(model, path): path = abspath(path) log.info(f"Loading from: {path}") xs = tf.train.list_variables(path) assert len(xs) > 0 ns, ws = _load_weights(xs, path) for n, w in zip(ns, ws): ss = n[6:].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] == "w" or scopes[0] == "g": p = getattr(p, "weight") elif scopes[0] == "b": p = getattr(p, "bias") elif scopes[0] == "wpe" or scopes[0] == "wte": p = getattr(p, scopes[0]) 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 _load_weights(xs, 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(path, n).squeeze() return ns, ws 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"Saveing 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"], 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"/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"], 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"/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"]}
33,698	quantapix/qnarre	refs/heads/main	/qnarre/base/doc/counter.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 curses import asyncio as aio import contextlib as cl import multiprocessing as mp import multiprocessing.pool as pl import multiprocessing.queues as qs from queue import Empty from .log import Logger from .resource import Resource log = Logger(__name__) cpu_count = os.cpu_count() PID = 'pid' START = 'start:' STOP = 'stop:' CNTR = 'cntr' class Queue(qs.Queue): _pid = None def __init__(self): super().__init__(ctx=mp.get_context()) q = mp.Queue() for i in range(cpu_count): q.put((PID, i + 1)) self.cmdq = q @property def pid(self): if self._pid is None: self._pid = 1 k, self._pid = self.cmdq.get() assert k == PID return self._pid def put(self, src, msg): super().put((src, (self.pid, msg))) class Counters(Resource): queue = None title = None stats = None done = None _lines = None def __init__(self, fields, title='Starting:', stats='Stats:', done='done', kw): super().__init__(kw) self.fields = fields if title is not None: self.title = title if stats is not None: self.stats = stats if done is not None: self.done = done @property def name(self): return CNTR @property def lines(self): if self._lines is None: self._lines = {} return self._lines def post(self, args): m = ' '.join(a for a in args if a is not None) if self.queue is not None: self.queue.put(self.name, m) else: s = '\n' if args[0] in (START, STOP) else '' m = s + m print(m, end='', flush=True) def start(self): self._elems = {(k if k else n): 0 for n, k in self.fields} # self.post(START, self.title) self.count = 0 def retitle(self, txt=''): self.post(START, ' '.join((self.title, txt))) def incr(self, key=None, amount=1): k = self.fields[0][1] if key is None else key self[k] += amount if len(k) == 1: m = k if self.count % 100 else '\n{:0>6d} {}'.format(self.count, k) self.count += 1 self.post(m) return True def stop(self): if self.stats is not None: f = '{}: {}' s = (f.format(n, self[k if k else n]) for n, k in self.fields) s = ', '.join(s) self.post(STOP, self.stats, s) else: self.post(STOP, self.done) def track(self, scr, pid, msg): r = False assert pid > 0 i = 3 (pid - 1) if msg.startswith(START): ln = msg[len(START) + 1:] ln += ' ' * (100 - len(ln)) elif msg.startswith(STOP): i += 2 ln = msg[len(STOP) + 1:] ln += ' ' * (100 - len(ln)) r = True else: i += 1 if msg.startswith('\n'): ln = self.lines[i] = msg[1:] ln += ' ' * 100 else: self.lines[i] += msg ln = self.lines[i] my, mx = scr.getmaxyx() my = (my // 3) * 3 y = i % my x = 110 * (i // my) if x < mx: scr.addstr(y, x, ln[:mx - x]) scr.refresh() return r @cl.contextmanager def counters(args, kw): try: cs = kw[CNTR] except KeyError: kw[CNTR] = cs = Counters(args) cs.start() yield cs cs.stop() class Worker: def __init__(self, queue, args): self.queue = queue for a in args: if a is not None: setattr(self, a.name, a) def activate(self): for v in vars(self): if v != 'queue': getattr(self, v).queue = self.queue return self def run(self, mth, tgt, args, *kw): kw.update({v: getattr(self, v) for v in vars(self) if v != 'queue'}) with counters(None, kw): return mth(tgt, args, *kw) worker = None def init_worker(wrk): global worker worker = wrk.activate() def run_worker(args, *kw): global worker return worker.run(args, *kw) class Pool(pl.Pool): def __init__(self, cntr=None, lgr=None, processes=None, initializer=None, initargs=None, args, loop=None, *kw): processes = processes or cpu_count initializer = initializer or init_worker self.tracker = t = Worker(Queue(), cntr, lgr) initargs = initargs or t, super().__init__(processes, initializer, initargs, args, kw) self.loop = loop or aio.get_event_loop() self.futs = [] def call_worker(self, mth, tgt, args, kw): f = self.loop.create_future() def _cb(res): def safe_cb(res): f.set_result(res) self.loop.call_soon_threadsafe(safe_cb, res) def _ecb(exc): def safe_ecb(exc): f.set_exception(exc) self.loop.call_soon_threadsafe(safe_ecb, exc) self.apply_async(run_worker, (mth, tgt, args), kw, _cb, _ecb) self.futs.append(f) async def track_workers(self): scr = curses.initscr() curses.noecho() curses.cbreak() t = self.tracker try: scr.erase() for i in range(len(self.futs)): while True: try: s, m = t.queue.get_nowait() await aio.sleep(.001) except Empty: await aio.sleep(.01) else: if getattr(t, s).track(scr, m): break finally: curses.echo() curses.nocbreak() curses.endwin() def run(self): f = aio.gather(self.futs, self.track_workers(), loop=self.loop) self.loop.run_until_complete(f) if __name__ == "__main__": class aa: def aaa(self, tgt, limit, , cntr): for i in range(limit[0]): cntr.incr('.') fields = ((('scanned', '.'), ('excluded', '-'), ('imported', '+'), ('failed', 'F')), 'Testing:') ax = aa() p = Pool(Counters(fields), processes=10) for a in ((500, ), (600, ), (700, ), (800, ), (900, ), (1000, )): p.call_worker(aa.aaa, ax, (None, a)) p.run()	{"/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": 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33,699	quantapix/qnarre	refs/heads/main	/tools/triton/python/test/unit/runtime/test_autotuner.py	import torch import triton import triton.language as tl def test_kwargs(): N = 1024 src = torch.empty(N, device='cuda') dst = torch.empty(N, device='cuda') configs = [triton.Config(kwargs={'BLOCK_SIZE': 32}), triton.Config(kwargs={'BLOCK_SIZE': 128})] @triton.autotune(configs=configs, key=['N']) @triton.jit def _kernel(dst, src, N, BLOCK_SIZE: tl.constexpr): offsets = tl.program_id(0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) x = tl.load(src + offsets, mask=offsets < N) tl.store(dst + offsets, x, mask=offsets < N) grid = lambda META: (triton.cdiv(N, META['BLOCK_SIZE']),) _kernel[grid](dst, src, N) _kernel[grid](dst=dst, src=src, N=N)	{"/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", 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"/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"]}
33,700	quantapix/qnarre	refs/heads/main	/qnarre/prep/tokens/fast/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 json from tokenizers import normalizers from ....tokens.utils import AddedToken from ....tokens.fast import PreTrainedTokenizerFast from ..mpnet import Tokenizer as MPNet VOCAB_FS = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"} VOCAB_MAP = { "vocab_file": { "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt", }, "tokenizer_file": { "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json", }, } INPUT_CAPS = { "microsoft/mpnet-base": 512, } PRETRAINED_INIT_CONFIGURATION = { "microsoft/mpnet-base": {"do_lower_case": True}, } class MPNetTokenizerFast(PreTrainedTokenizerFast): vocab_fs = VOCAB_FS vocab_map = VOCAB_MAP pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION input_caps = INPUT_CAPS slow_tokenizer_class = MPNet model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file=None, tokenizer_file=None, do_lower_case=True, bos="<s>", eos="</s>", sep="</s>", cls="<s>", unk="[UNK]", pad="<pad>", msk="<mask>", tokenize_chinese_chars=True, strip_accents=None, kw, ): super().__init__( vocab_file, tokenizer_file=tokenizer_file, do_lower_case=do_lower_case, bos=bos, eos=eos, sep=sep, cls=cls, unk=unk, pad=pad, msk=msk, tokenize_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, kw, ) pre_tok_state = json.loads(self.backend_tokenizer.normalizer.__getstate__()) if ( pre_tok_state.get("lowercase", do_lower_case) != do_lower_case or pre_tok_state.get("strip_accents", strip_accents) != strip_accents ): pre_tok_class = getattr(normalizers, pre_tok_state.pop("type")) pre_tok_state["lowercase"] = do_lower_case pre_tok_state["strip_accents"] = strip_accents self.backend_tokenizer.normalizer = pre_tok_class(*pre_tok_state) self.do_lower_case = do_lower_case @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 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_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): return tuple(self._tokenizer.model.save(dir, name=pre))	{"/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": 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33,701	quantapix/qnarre	refs/heads/main	/qnarre/prep/convert/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 os import re import numpy as np import tensorflow as tf from huggingface_hub import hf_hub_download from transformers import AutoTokenizer from ...utils import logging _REALM_BLOCK_RECORDS_FILENAME = "block_records.npy" log = logging.get_logger(__name__) def load_tf_weights_in_realm(model, config, tf_checkpoint_path): tf_path = abspath(tf_checkpoint_path) log.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model 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) for name, array in zip(names, arrays): if isinstance(model, RealmReader) and "reader" not in name: log.info(f"Skipping {name} as it is not {model.__class__.__name__}'s parameter") continue # For pretrained openqa reader if (name.startswith("bert") or name.startswith("cls")) and isinstance( model, RealmForOpenQA ): name = name.replace("bert/", "reader/realm/") name = name.replace("cls/", "reader/cls/") # For pretrained encoder if (name.startswith("bert") or name.startswith("cls")) and isinstance( model, RealmKnowledgeAugEncoder ): name = name.replace("bert/", "realm/") # For finetuned reader if name.startswith("reader"): reader_prefix = "" if isinstance(model, RealmReader) else "reader/" name = name.replace("reader/module/bert/", f"{reader_prefix}realm/") name = name.replace("reader/module/cls/", f"{reader_prefix}cls/") name = name.replace("reader/dense/", f"{reader_prefix}qa_outputs/dense_intermediate/") name = name.replace("reader/dense_1/", f"{reader_prefix}qa_outputs/dense_output/") name = name.replace( "reader/layer_normalization", f"{reader_prefix}qa_outputs/layer_normalization" ) # For embedder and scorer if name.startswith("module/module/module/"): # finetuned embedder_prefix = "" if isinstance(model, RealmEmbedder) else "embedder/" name = name.replace("module/module/module/module/bert/", f"{embedder_prefix}realm/") name = name.replace( "module/module/module/LayerNorm/", f"{embedder_prefix}cls/LayerNorm/" ) name = name.replace("module/module/module/dense/", f"{embedder_prefix}cls/dense/") name = name.replace( "module/module/module/module/cls/predictions/", f"{embedder_prefix}cls/predictions/" ) name = name.replace("module/module/module/bert/", f"{embedder_prefix}realm/") name = name.replace( "module/module/module/cls/predictions/", f"{embedder_prefix}cls/predictions/" ) elif name.startswith("module/module/"): # pretrained embedder_prefix = "" if isinstance(model, RealmEmbedder) else "embedder/" name = name.replace("module/module/LayerNorm/", f"{embedder_prefix}cls/LayerNorm/") name = name.replace("module/module/dense/", f"{embedder_prefix}cls/dense/") 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 pointer = model for m_name in name: if re.fullmatch(r"[A-Za-z]+_\d+", m_name): scope_names = re.split(r"_(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] == "kernel" or scope_names[0] == "gamma": pointer = getattr(pointer, "weight") elif scope_names[0] == "output_bias" or scope_names[0] == "beta": pointer = getattr(pointer, "bias") else: try: pointer = getattr(pointer, scope_names[0]) except AttributeError: log.info(f"Skipping {'/'.join(name)}") continue if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] if m_name[-11:] == "_embeddings": pointer = getattr(pointer, "weight") elif m_name == "kernel": array = np.transpose(array) try: assert ( pointer.shape == array.shape ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched" except AssertionError as e: e.args += (pointer.shape, array.shape) raise log.info(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) return model def convert_tfrecord_to_np(block_records_path, num_block_records): import tensorflow.compat.v1 as tf blocks_dataset = tf.data.TFRecordDataset(block_records_path, buffer_size=512 * 1024 * 1024) blocks_dataset = blocks_dataset.batch(num_block_records, drop_remainder=True) np_record = next(blocks_dataset.take(1).as_numpy_iterator()) return np_record class ScaNNSearcher: def __init__( self, db, num_neighbors, dimensions_per_block=2, num_leaves=1000, num_leaves_to_search=100, training_sample_size=100000, ): from scann.scann_ops.py.scann_ops_pybind import builder as Builder builder = Builder(db=db, num_neighbors=num_neighbors, distance_measure="dot_product") builder = builder.tree( num_leaves=num_leaves, num_leaves_to_search=num_leaves_to_search, training_sample_size=training_sample_size, ) builder = builder.score_ah(dimensions_per_block=dimensions_per_block) self.searcher = builder.build() def search_batched(self, question_projection): retrieved_block_ids, _ = self.searcher.search_batched(question_projection.detach().cpu()) return retrieved_block_ids.astype("int64") class RealmRetriever: def __init__(self, block_records, tokenizer): super().__init__() self.block_records = block_records self.tokenizer = tokenizer def __call__( self, retrieved_block_ids, question_input_ids, answer_ids, max_length=None, return_tensors="pt", ): retrieved_blocks = np.take(self.block_records, indices=retrieved_block_ids, axis=0) question = self.tokenizer.decode(question_input_ids[0], skip_special_tokens=True) text = [] text_pair = [] for retrieved_block in retrieved_blocks: text.append(question) text_pair.append(retrieved_block.decode()) concat_inputs = self.tokenizer( text, text_pair, padding=True, truncation=True, return_special_tokens_mask=True, max_length=max_length, ) concat_inputs_tensors = concat_inputs.convert_to_tensors(return_tensors) if answer_ids is not None: return self.block_has_answer(concat_inputs, answer_ids) + (concat_inputs_tensors,) else: return (None, None, None, concat_inputs_tensors) @classmethod def from_pretrained( cls, pretrained_model_name_or_path, init_inputs, kw, ): if os.path.isdir(pretrained_model_name_or_path): block_records_path = os.path.join( pretrained_model_name_or_path, _REALM_BLOCK_RECORDS_FILENAME ) else: block_records_path = hf_hub_download( repo_id=pretrained_model_name_or_path, filename=_REALM_BLOCK_RECORDS_FILENAME, kw, ) block_records = np.load(block_records_path, allow_pickle=True) tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, init_inputs, *kw) return cls(block_records, tokenizer) def save_pretrained(self, save_directory): # save block records np.save(os.path.join(save_directory, _REALM_BLOCK_RECORDS_FILENAME), self.block_records) # save tokenizer self.tokenizer.save_pretrained(save_directory) def block_has_answer(self, concat_inputs, answer_ids): has_answers = [] start_pos = [] end_pos = [] max_answers = 0 for input_id in concat_inputs.input_ids: input_id_list = input_id.tolist() # Check answers between two [SEP] tokens first_sep_idx = input_id_list.index(self.tokenizer.sep_token_id) second_sep_idx = ( first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id) ) start_pos.append([]) end_pos.append([]) for answer in answer_ids: for idx in range(first_sep_idx + 1, second_sep_idx): if answer[0] == input_id_list[idx]: if input_id_list[idx : idx + len(answer)] == answer: start_pos[-1].append(idx) end_pos[-1].append(idx + len(answer) - 1) if len(start_pos[-1]) == 0: has_answers.append(False) else: has_answers.append(True) if len(start_pos[-1]) > max_answers: max_answers = len(start_pos[-1]) # Pad -1 to max_answers for start_pos_, end_pos_ in zip(start_pos, end_pos): if len(start_pos_) < max_answers: padded = [-1] (max_answers - len(start_pos_)) start_pos_ += padded end_pos_ += padded return has_answers, start_pos, end_pos	{"/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"], 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["/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"], 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"/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": 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"/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"]}
33,702	quantapix/qnarre	refs/heads/main	/qnarre/base/doc/merge.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 pathlib as pth from qnarre import load_from, load_docs def merge(root, genres=None, authors=None, kw): kw.update(root=root, preset={}) print('Loading from {}...'.format(str(root))) ds = [d for d in load_docs(genres=genres, authors=authors, kw)] n = load_from(pth.Path('merged.py'), kw) print('...done') print('Merging ({} + 1)...'.format(len(ds))) n.org.docs = sorted(ds, key=lambda d: d.date) n.org.save() print('...done') if __name__ == '__main__': from argparse import ArgumentParser args = ArgumentParser() args.add_argument('-r', '--root', help='Path to root', default=None) args.add_argument('-g', '--genre', help='Genre to load', default=None) args.add_argument('-a', '--author', help='Author to load', default=None) args = args.parse_args() kw = {} if args.genre: kw['genres'] = args.genre, if args.author: kw['authors'] = args.author, merge(pth.Path.cwd() / (args.root or 'sample'), 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", 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33,703	quantapix/qnarre	refs/heads/main	/qnarre/run/run_swag_no_trainer.py	import logging import math import os import random from dataclasses import dataclass from itertools import chain from pathlib import Path import datasets import torch from datasets import load_dataset, load_metric from torch.utils.data import DataLoader from tqdm.auto import tqdm import transformers from accelerate import Accelerator from huggingface_hub import Repository from transformers import ( CONFIG_MAPPING, MODEL_MAPPING, AdamW, AutoConfig, AutoModelForChoicepleChoice, AutoTokenizer, PreTrainedTokenizerBase, default_data_collator, get_scheduler, set_seed, ) from transformers.file_utils import get_full_repo_name logger = logging.getLogger(__name__) # You should update this to your particular problem to have better documentation of `model_type` MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) @dataclass class DataCollatorForChoicepleChoice: tokenizer: PreTrainedTokenizerBase padding = True max_len = None pad_to_multiple_of = None def __call__(self, features): label_name = "label" if "label" in features[0].keys() else "labels" labels = [feature.pop(label_name) for feature in features] batch_size = len(features) num_choices = len(features[0]["input_ids"]) flattened_features = [ [{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features ] flattened_features = list(chain(flattened_features)) batch = self.tokenizer.pad( flattened_features, padding=self.padding, max_len=self.max_len, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors="pt", ) # Un-flatten batch = {k: v.view(batch_size, num_choices, -1) for k, v in batch.items()} # Add back labels batch["labels"] = torch.tensor(labels, dtype=torch.int64) return batch def main(): args = parse_args() # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. accelerator = Accelerator() # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state) # Setup logging, we only want one process per machine to log things on the screen. # accelerator.is_local_main_process is only True for one process per machine. logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.out_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.out_dir, clone_from=repo_name) elif args.out_dir is not None: os.makedirs(args.out_dir, exist_ok=True) accelerator.wait_for_everyone() # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if args.dataset_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset(args.dataset_name, args.dataset_config) else: data_files = {} if args.train_file is not None: data_files["train"] = args.train_file if args.eval_file is not None: data_files["validation"] = args.eval_file extension = args.train_file.split(".")[-1] raw_datasets = load_dataset(extension, data_files=data_files) # Trim a number of training examples if args.debug: for split in raw_datasets.keys(): raw_datasets[split] = raw_datasets[split].select(range(100)) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. if raw_datasets["train"] is not None: column_names = raw_datasets["train"].column_names else: column_names = raw_datasets["validation"].column_names # When using your own dataset or a different dataset from swag, you will probably need to change this. ending_names = [f"ending{i}" for i in range(4)] context_name = "sent1" question_header_name = "sent2" label_column = "label" if "label" in column_names else "labels" # Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. if args.config_name: config = AutoConfig.from_pretrained(args.model_name) elif args.model_name: config = AutoConfig.from_pretrained(args.model_name) else: config = CONFIG_MAPPING[args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained( args.tokenizer_name, use_fast=not args.use_slow_tokenizer ) elif args.model_name: tokenizer = AutoTokenizer.from_pretrained( args.model_name, use_fast=not args.use_slow_tokenizer ) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if args.model_name: model = AutoModelForChoicepleChoice.from_pretrained( args.model_name, from_tf=bool(".ckpt" in args.model_name), config=config, ) else: logger.info("Training new model") model = AutoModelForChoicepleChoice.from_config(config) model.resize_token_embeddings(len(tokenizer)) # Preprocessing the datasets. # First we tokenize all the texts. padding = "max_len" if args.pad_to_max_length else False def preprocess_function(examples): first_sentences = [[context] 4 for context in examples[context_name]] question_headers = examples[question_header_name] second_sentences = [ [f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(question_headers) ] labels = examples[label_column] # Flatten out first_sentences = list(chain(first_sentences)) second_sentences = list(chain(second_sentences)) # Tokenize tokenized_examples = tokenizer( first_sentences, second_sentences, max_len=args.max_len, padding=padding, truncation=True, ) # Un-flatten tokenized_inputs = { k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items() } tokenized_inputs["labels"] = labels return tokenized_inputs with accelerator.main_process_first(): processed_datasets = raw_datasets.map( preprocess_function, batched=True, remove_columns=raw_datasets["train"].column_names ) train_dataset = processed_datasets["train"] eval_dataset = processed_datasets["validation"] # Log a few random samples from the training set: for index in random.sample(range(len(train_dataset)), 3): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") # DataLoaders creation: if args.pad_to_max_length: # If padding was already done ot max length, we use the default data collator that will just convert everything # to tensors. data_collator = default_data_collator else: # Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of # the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple # of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). data_collator = DataCollatorForChoicepleChoice( tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None) ) train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.train_batch_size, ) eval_dataloader = DataLoader( eval_dataset, collate_fn=data_collator, batch_size=args.eval_batch_size ) # Optimizer # Split weights in two groups, one with weight decay and the other not. no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [ p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay) ], "weight_decay": args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = AdamW(optimizer_grouped_parameters, lr=args.lr) # Use the device given by the `accelerator` object. device = accelerator.device model.to(device) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader ) num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.grad_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.train_epochs * num_update_steps_per_epoch else: args.train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) lr_scheduler = get_scheduler( name=args.lr_scheduler, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps, num_training_steps=args.max_train_steps, ) # Metrics metric = load_metric("accuracy") # Train! total_batch_size = ( args.train_batch_size * accelerator.num_processes * args.grad_accumulation_steps ) logger.info("*** Running training *") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.train_epochs}") logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") logger.info( f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}" ) logger.info(f" Gradient Accumulation steps = {args.grad_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 for epoch in range(args.train_epochs): model.train() for step, batch in enumerate(train_dataloader): outputs = model(batch) loss = outputs.loss loss = loss / args.grad_accumulation_steps accelerator.backward(loss) if step % args.grad_accumulation_steps == 0 or step == len(train_dataloader) - 1: optimizer.step() lr_scheduler.step() optimizer.zero_grad() progress_bar.update(1) completed_steps += 1 if completed_steps >= args.max_train_steps: break model.eval() for step, batch in enumerate(eval_dataloader): with torch.no_grad(): outputs = model(**batch) predictions = outputs.logits.argmax(dim=-1) metric.add_batch( predictions=accelerator.gather(predictions), references=accelerator.gather(batch["labels"]), ) eval_metric = metric.compute() accelerator.print(f"epoch {epoch}: {eval_metric}") if args.push_to_hub and epoch < args.train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained(args.out_dir, save_function=accelerator.save) if accelerator.is_main_process: tokenizer.save_pretrained(args.out_dir) repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False ) if args.out_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained(args.out_dir, save_function=accelerator.save) if accelerator.is_main_process: tokenizer.save_pretrained(args.out_dir) if args.push_to_hub: 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33,704	quantapix/qnarre	refs/heads/main	/tools/triton/python/test/unit/language/test_random.py	import numpy as np import pytest import scipy.stats import torch import triton import triton.language as tl ##################################### # Reference Philox Implementation ##################################### class PhiloxConfig: def __init__(self, PHILOX_ROUND_A, PHILOX_ROUND_B, PHILOX_KEY_A, PHILOX_KEY_B, DTYPE): self.PHILOX_ROUND_A = np.array(PHILOX_ROUND_A, dtype=DTYPE) self.PHILOX_ROUND_B = np.array(PHILOX_ROUND_B, dtype=DTYPE) self.PHILOX_KEY_A = np.array(PHILOX_KEY_A, dtype=DTYPE) self.PHILOX_KEY_B = np.array(PHILOX_KEY_B, dtype=DTYPE) self.DTYPE = DTYPE # This is better for GPU PHILOX_32 = PhiloxConfig( PHILOX_KEY_A=0x9E3779B9, PHILOX_KEY_B=0xBB67AE85, PHILOX_ROUND_A=0xD2511F53, PHILOX_ROUND_B=0xCD9E8D57, DTYPE=np.uint32, ) # This is what numpy implements PHILOX_64 = PhiloxConfig( PHILOX_KEY_A=0x9E3779B97F4A7C15, PHILOX_KEY_B=0xBB67AE8584CAA73B, PHILOX_ROUND_A=0xD2E7470EE14C6C93, PHILOX_ROUND_B=0xCA5A826395121157, DTYPE=np.uint64, ) class CustomPhilox4x: def __init__(self, seed, config): self._config = config seed = self._into_pieces(seed) self._key = np.array(seed[:2], dtype=self._dtype) self._counter = np.array((0, 0) + seed[2:], dtype=self._dtype) @property def _dtype(self): return self._config.DTYPE def _into_pieces(self, n, pad=4): res = [] while len(res) < pad: res.append(np.array(n, dtype=self._dtype)) n >>= (np.dtype(self._dtype).itemsize * 8) assert n == 0 return tuple(res) def _multiply_low_high(self, a, b): low = a * b high = int(a) * int(b) high = np.array(high >> (np.dtype(self._dtype).itemsize * 8), dtype=self._dtype) return low, high def _single_round(self, counter, key): lo0, hi0 = self._multiply_low_high(self._config.PHILOX_ROUND_A, counter[0]) lo1, hi1 = self._multiply_low_high(self._config.PHILOX_ROUND_B, counter[2]) ret0 = hi1 ^ counter[1] ^ key[0] ret1 = lo1 ret2 = hi0 ^ counter[3] ^ key[1] ret3 = lo0 return np.array([ret0, ret1, ret2, ret3], dtype=self._dtype) def _raise_key(self, key): pk = [self._config.PHILOX_KEY_A, self._config.PHILOX_KEY_B] return key + np.array(pk, dtype=self._dtype) def random_raw(self): counter = self._counter key = self._key for _ in range(10): counter = self._single_round(counter, key) key = self._raise_key(key) self.advance(1) return counter def advance(self, n_steps): self._counter[0] += n_steps assert self._counter[0] < 2*32, "FIXME: doesn't work for large offsets" class CustomPhilox(CustomPhilox4x): def __init__(self, args, *kwargs): super().__init__(args, *kwargs) self.buffer = [] def random_raw(self): if len(self.buffer) == 0: self.buffer = list(super().random_raw())[::-1] return int(self.buffer.pop()) ##################################### # Unit Tests ##################################### BLOCK = 1024 # test generation of random uint32 @pytest.mark.parametrize('size, seed', [(size, seed) for size in ['10', '4,53', '10000'] for seed in [0, 42, 124, 54, 0xffffffff, 0xdeadbeefcafeb0ba]] ) def test_randint(size, seed, device='cuda'): size = list(map(int, size.split(','))) @triton.jit def kernel(X, N, seed): offset = tl.program_id(0) BLOCK + tl.arange(0, BLOCK) rand = tl.randint(seed, offset) tl.store(X + offset, rand, mask=offset < N) # triton result x = torch.empty(size, dtype=torch.int32, device=device) N = x.numel() grid = (triton.cdiv(N, BLOCK),) kernel[grid](x, N, seed) out_tri = x.cpu().numpy().astype(np.uint32).flatten().tolist() # reference result gen = CustomPhilox4x(seed, config=PHILOX_32) out_ref = [gen.random_raw()[0] for _ in out_tri] assert out_tri == out_ref # test uniform PRNG @pytest.mark.parametrize('size, seed', [(size, seed) for size in [1000000] for seed in [0, 42, 124, 54]] ) def test_rand(size, seed, device='cuda'): @triton.jit def kernel(X, N, seed): offset = tl.program_id(0) * BLOCK + tl.arange(0, BLOCK) rand = tl.rand(seed, offset) tl.store(X + offset, rand, mask=offset < N) # triton result x = torch.empty(size, dtype=torch.float32, device=device) N = x.numel() grid = (triton.cdiv(N, BLOCK),) kernel[grid](x, N, seed) assert all((x >= 0) & (x <= 1)) assert scipy.stats.kstest(x.tolist(), 'uniform', args=(0, 1)).statistic < 0.01 # test normal PRNG @pytest.mark.parametrize('size, seed', [(size, seed) for size in [1000000] for seed in [0, 42, 124, 54]] ) def test_randn(size, seed, device='cuda'): @triton.jit def kernel(X, N, seed): offset = tl.program_id(0) * BLOCK + tl.arange(0, BLOCK) rand = tl.randn(seed, offset) tl.store(X + offset, rand, mask=offset < N) # triton result x = torch.empty(size, dtype=torch.float32, device=device) N = x.numel() grid = (triton.cdiv(N, BLOCK),) kernel[grid](x, N, seed) assert abs(x.mean()) < 1e-2 assert abs(x.std() - 1) < 1e-2 # tl.rand() should never produce >=1.0 def test_rand_limits(): @triton.jit def kernel(input, output, n: tl.constexpr): idx = tl.arange(0, n) x = tl.load(input + idx) y = tl.random.uint32_to_uniform_float(x) tl.store(output + 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33,705	quantapix/qnarre	refs/heads/main	/qnarre/base/doc/util/row.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 asyncio as aio import collections.abc as abc from .log import Logger from .item import Item from .error import ExtractWarning log = Logger(__name__) class Row(abc.MutableMapping): _000 = '000' _old_name = '' def __init__(self, name=_000, cols=None, kw): super().__init__() assert not name < self._000 self.name = name self._cols = cols or {} self._cols.update(kw) def __bool__(self): return True __hash__ = None def __eq__(self, other): if isinstance(other, type(self)): return (self.name == other.name and self._cols == other._cols) return NotImplemented def __len__(self): return len(self._cols) def __iter__(self): return iter(self._cols) def __getitem__(self, c): return self._cols[c] def __setitem__(self, c, value): self._cols[c] = value def __delitem__(self, c): del self._cols[c] def __repr__(self): s = type(self).__name__ s += "({}".format(repr(self.name)) s += ", {})".format(repr(self._cols)) return s def stringer(self, indent=0, kw): s = '{} ('.format(self.name) for n, e in enumerate(self._cols.items()): c, i = e gap = ', ' if n else '' s += '{}{}: {}'.format(gap, c, i.stringer(*kw) if i else None) s += ')' yield (" " indent + s) def digest(self, col): i = self._cols.get(col) return i.digest if i else self.name def merge(self, other): name = self.name if self.name == other.name else None for c, oi in other._cols.items(): if c in self._cols: self._cols[c].merge(oi, name) else: self._cols[c] = oi def rename(self, name): self._old_name = self.name self.name = name log.info('Renaming {} to {}', self._old_name, self.name) def extract(self, path, , base, src_cols=(), ref_cols=(), kw): def _cols(): for c in ref_cols: yield (True, c) for c in src_cols: yield (False, c) i = Item(kw) for ref, c in _cols(): try: if i.expand(self._cols[c], base / c / path, ref): return i except KeyError: continue raise ExtractWarning() def touch_item(self, col, path, suff): try: i = self._cols[col] except KeyError: self._cols[col] = i = Item() i.touch(path, suff) def rename_item(self, col, path, to_tmp=False): if self._old_name: i = self._cols.get(col) if i: tag = '_qld' if to_tmp: path.mkdir(parents=True, exist_ok=True) o = self._old_name i.rename(path, o, o + tag) else: i.rename(path, self._old_name + tag, self.name) del self._old_name def copy_item(self, col, path, , base, touch=(), out=None, _): i = self._cols.get(col) if i: path = path / self.name if out: i.copy(frm=base / col / path, to=out / col / path) elif i.extern_path: def _touch_f(suff): for c in touch: self.touch_item(c, base / c / path, suff) touch_f = _touch_f if touch else None i.copy(to=base / col / path, touch_f=touch_f) def clear_col(self, col, _): self._cols.pop(col, None) def schedule(self, meth, args, pool, loop, col=None, kw): tgt = self._cols.get(col) if col else self if tgt: loop = loop or aio.get_event_loop() fut = loop.create_future() def _cb(res): def safe_cb(res): res = meth(tgt, args, result=res, *kw) fut.set_result(res) loop.call_soon_threadsafe(safe_cb, res) def _ecb(exc): def safe_ecb(exc): fut.set_exception(exc) loop.call_soon_threadsafe(safe_ecb, exc) pool.apply_async(meth, (tgt, args), kw, _cb, _ecb) return fut return 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"], 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"/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", 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"/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"]}
33,706	quantapix/qnarre	refs/heads/main	/qnarre/core/base.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 itertools import math import numbers import operator import torch from collections import OrderedDict, abc from dataclasses import dataclass from itertools import chain from torch import nn from torch.nn import functional as F from torch.nn.parameter import is_lazy, Parameter, UninitializedParameter from . import utils as qu class Hypers: @staticmethod def merge(hs): y = Hypers() for h in hs: y.ks.update(h.ks) y.kw.update(h.kw) return y def __init__(self, ks=None, kw=None): self.ks = ks or set() self.kw = kw or dict() def __repr__(self): return f"Hypers({self.__dict__})" class Config: @staticmethod def merge(ps={}, h=Hypers(), kw): ps = ps.__dict__ if isinstance(ps, Config) else ps assert isinstance(ps, dict) for k in h.ks: yield k, ps.get(k), for k, v in h.kw.items(): yield k, ps.get(k, v) for k, v in kw.items(): yield k, v def __init__(self, ps={}, h=Hypers(), kw): super().__init__() for k, v in Config.merge(ps, h, kw): setattr(self, k, v) def __repr__(self): return f"Config({self.__dict__})" class Module(nn.Module): hs = Hypers({"dtype", "device"}) def __init__(self, ps={}, hs=[], kw): super().__init__() self.cfg = Config(ps, Hypers.merge([self.hs] + hs), *kw) def get_cfg(self, kw=None): y = self.cfg if kw is not None: kw.update(ps=y) return y def invert_mask(self, x): if x.dim() == 3: y = x[:, None, :, :] if x.dim() == 2: y = x[:, None, None, :] dt = self.cfg.dtype y = y.to(dtype=dt) if dt == torch.float16: y = (1.0 - y) -1e4 else: assert dt in [torch.bfloat16, torch.float32] y = (1.0 - y) * -1e9 return y def get_param_dtype(self): try: return next(self.parameters()).dtype except StopIteration: def fn(x): return [(k, v) for k, v in x.__dict__.items() if torch.is_tensor(v)] y = next(self._named_members(get_members_fn=fn)) return y[1].dtype def get_minus_inf(self): return -65000 if self.get_param_dtype() == torch.float16 else -1e30 class Identity(Module): def __init__(self, xs, kw): super().__init__(xs, kw) def forward(self, x, _): return x class Linear(Module): hs = Hypers({"d_in", "d_out"}, {"bias": True}) def __init__(self, d_in=None, d_out=None, ps={}, hs=[], kw): if d_in is not None: kw.update(d_in=d_in) if d_out is not None: kw.update(d_out=d_out) super().__init__(ps, [self.hs] + hs, kw) cfg = self.cfg kw = {"dtype": cfg.dtype, "device": cfg.device} self.weight = Parameter(torch.empty((cfg.d_out, cfg.d_in), kw)) if cfg.bias: self.bias = Parameter(torch.empty(cfg.d_out, kw)) else: self.register_parameter("bias", None) self.reset_params() def reset_params(self): cfg = self.cfg b = 1 / math.sqrt(cfg.d_in) # nn.init.uniform_(self.weight, -b, b) nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5)) if self.bias is not None: nn.init.uniform_(self.bias, -b, b) def forward(self, x): return F.linear(x, self.weight, self.bias) class Bilinear(Module): hs = Hypers({"d_in1", "d_in2", "d_out"}, {"bias": True}) def __init__(self, ps={}, hs=[], kw): super().__init__(ps, [self.hs] + hs, kw) cfg = self.cfg kw = {"dtype": cfg.dtype, "device": cfg.device} self.weight = Parameter(torch.empty((cfg.d_out, cfg.d_in1, cfg.d_in2), kw)) if cfg.bias: self.bias = Parameter(torch.empty(cfg.d_out, kw)) else: self.register_parameter("bias", None) self.reset_params() def reset_params(self): cfg = self.cfg b = 1 / math.sqrt(cfg.d_in1) nn.init.uniform_(self.weight, -b, b) if self.bias is not None: nn.init.uniform_(self.bias, -b, b) def forward(self, x1, x2): return F.linear(x1, x2, self.weight, self.bias) class Embed(Module): hs = Hypers( {"d_embed", "max_norm", "n_embed", "PAD"}, {"norm_type": 2.0, "scale_grad": False, "sparse": False}, ) def __init__(self, n_embed=None, d_embed=None, ps={}, hs=[], kw): if n_embed is not None: kw.update(n_embed=n_embed) if d_embed is not None: kw.update(d_embed=d_embed) super().__init__(ps, [self.hs] + hs, kw) cfg = self.cfg if cfg.PAD is not None: if cfg.PAD > 0: assert cfg.PAD < cfg.n_embed elif cfg.PAD < 0: assert cfg.PAD >= -cfg.n_embed cfg.PAD = self.n_embed + cfg.PAD kw = {"device": cfg.device, "dtype": cfg.dtype} self.weight = Parameter(torch.empty((cfg.n_embed, cfg.d_embed), kw)) self.reset_params() def reset_params(self): nn.init.normal_(self.weight) cfg = self.cfg if cfg.PAD is not None: with torch.no_grad(): self.weight[cfg.PAD].fill_(0) def forward(self, x): c = self.cfg return F.embedding(x, self.weight, c.PAD, c.max_norm, c.norm_type, c.scale_grad, c.sparse) @classmethod def from_data(cls, x, freeze=True, kw): y = cls(kw) y.weight.data = x y.weight.requires_grad = not freeze return y class LayerNorm(Module): hs = Hypers(kw={"eps": 1e-5, "elemwise_affine": True}) def __init__(self, shape, eps=None, ps={}, hs=[], kw): if eps is not None: kw.update(eps=eps) super().__init__(ps, [self.hs] + hs, kw) cfg = self.cfg if isinstance(shape, numbers.Integral): shape = (shape,) self.shape = tuple(shape) kw = {"device": cfg.device, "dtype": cfg.dtype} if cfg.elemwise_affine: self.weight = Parameter(torch.empty(self.shape, kw)) self.bias = Parameter(torch.empty(self.shape, kw)) else: self.register_parameter("weight", None) self.register_parameter("bias", None) self.reset_params() def reset_params(self): if self.cfg.elemwise_affine: nn.init.ones_(self.weight) nn.init.zeros_(self.bias) def forward(self, x): return F.layer_norm(x, self.shape, self.weight, self.bias, self.cfg.eps) class Dropout(Module): hs = Hypers(kw={"p": 0.5, "inplace": False}) def __init__(self, p=None, ps={}, hs=[], kw): if p is not None: kw.update(p=p) super().__init__(ps, [self.hs] + hs, kw) cfg = self.cfg assert cfg.p >= 0 and cfg.p <= 1 def forward(self, x): cfg = self.cfg return F.drop(x, cfg.p, self.training, cfg.inplace) class Stack(Module): _modules = OrderedDict() def __init__(self, xs=None, kw): super().__init__(kw) if xs is not None: self += xs def _get_abs_string_index(self, i): i = operator.index(i) if not (-len(self) <= i < len(self)): raise IndexError("index {} is out of range".format(i)) if i < 0: i += len(self) return str(i) def __getitem__(self, i): if isinstance(i, slice): return self.__class__(list(self._modules.values())[i]) else: return self._modules[self._get_abs_string_index(i)] def __setitem__(self, i, x): i = self._get_abs_string_index(i) return setattr(self, str(i), x) def __delitem__(self, i): if isinstance(i, slice): for k in range(len(self._modules))[i]: delattr(self, str(k)) else: delattr(self, self._get_abs_string_index(i)) x = [str(i) for i in range(len(self._modules))] self._modules = OrderedDict(list(zip(x, self._modules.values()))) def __len__(self): return len(self._modules) def __iter__(self): return iter(self._modules.values()) def __iadd__(self, xs): return self.extend(xs) def __add__(self, x): y = Stack() for i, module in enumerate(chain(self, x)): y.add_module(str(i), module) return y def __dir__(self): y = super(Stack, self).__dir__() y = [k for k in y if not k.isdigit()] return y def insert(self, i, x): for j in range(len(self._modules), i, -1): self._modules[str(j)] = self._modules[str(j - 1)] self._modules[str(i)] = x def append(self, x): self.add_module(str(len(self)), x) return self def extend(self, xs): if not isinstance(xs, abc.Iterable): raise TypeError("extend needs to be called with an iterable") off = len(self) for i, x in enumerate(xs): self.add_module(str(off + i), x) return self class Lazy(Module): def __init__(self, kw): super().__init__(*kw) self._init_hook = self.register_forward_pre_hook(self._builder) self._load_hook = self._register_load_state_dict_pre_hook(self._loader) def _builder(self, m, x): m.build(x) if not self.is_built(): raise RuntimeError("Not fully built") m._load_hook.remove() m._init_hook.remove() delattr(m, "_init_hook") delattr(m, "_load_hook") def is_built(self): for v in itertools.chain(self._parameters.values(), self._buffers.values()): if is_lazy(v): return False return True def build(self): raise NotImplementedError() def _loader(self, state, pre): for k, v in itertools.chain(self._parameters.items(), self._buffers.items()): k = pre + k if k in state and v is not None: x = state[k] if is_lazy(v): if not is_lazy(x): with torch.no_grad(): v.materialize(x.shape) def _save_to_state_dict(self, dst, pre, keep): for n, p in self._parameters.items(): if p is not None: if not (is_lazy(p) or keep): p = p.detach() dst[pre + n] = p for n, b in self._buffers.items(): if b is not None and n not in self._non_persistent_buffers_set: if not (is_lazy(b) or keep): b = b.detach() dst[pre + n] = b def _replicate_for_data_parallel(): raise RuntimeError("Not fully built") class LazyLin(Lazy): hs = Hypers({"d_in", "d_out"}, {"bias": True}) def __init__(self, d_out=None, ps={}, hs=[], kw): if d_out is not None: kw.update(d_out=d_out) super().__init__(ps, [self.hs] + hs, kw) cfg = self.cfg kw = {"dtype": cfg.dtype, "device": cfg.device} self.weight = UninitializedParameter(kw) if cfg.bias: self.bias = UninitializedParameter(kw) else: self.register_parameter("bias", None) def build(self, x): cfg = self.cfg if not self.is_built(): with torch.no_grad(): cfg.d_in = x.shape[-1] self.weight.materialize((cfg.d_out, cfg.d_in)) if cfg.bias: self.bias.materialize((cfg.d_out,)) self.reset_params() def reset_params(self): cfg = self.cfg if self.is_built(): nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5)) b = 1 / math.sqrt(cfg.d_in) # nn.init.uniform_(self.weight, -b, b) if self.bias is not None: nn.init.uniform_(self.bias, -b, b) def forward(self, x): return F.linear(x, self.weight, self.bias) class Conv1D(Module): hs = Hypers() def __init__(self, n_y, n_x, ps={}, hs=[], kw): super().__init__(ps, [self.hs] + hs, kw) cfg = self.cfg cfg.n_y = n_y self.weight = Parameter(torch.empty(n_x, n_y)) self.bias = Parameter(torch.zeros(n_y)) nn.init.normal_(self.weight, std=0.02) def forward(self, x): s = x.size()[:-1] + (self.cfg.n_y,) y = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight) y = y.view(s) return y class SeqSummary(Module): hs = Hypers() def __init__(self, ps={}, hs=[], kw): super().__init__(ps, [self.hs] + hs, kw) cfg = self.cfg self.summy_type = getattr(cfg, "summy_type", "last") if self.summy_type == "attn": raise NotImplementedError self.summy = Identity() if hasattr(cfg, "sum_use_proj") and cfg.sum_use_proj: if hasattr(cfg, "sum_proj") and cfg.sum_proj and cfg.num_labels > 0: num_classes = cfg.num_labels else: num_classes = cfg.d_model self.summy = nn.Linear(cfg.d_model, num_classes) activation_string = getattr(cfg, "sum_act", None) self.act = qu.activation(activation_string, Identity()) self.drop_1 = Identity() if hasattr(cfg, "drop_sum_first") and cfg.drop_sum_first > 0: self.drop_1 = nn.Dropout(cfg.drop_sum_first) self.drop_2 = Identity() if hasattr(cfg, "summary_last_dropout") and cfg.summary_last_dropout > 0: self.drop_2 = nn.Dropout(cfg.summary_last_dropout) def forward(self, x, idx=None): if self.summy_type == "last": y = x[:, -1] elif self.summy_type == "first": y = x[:, 0] elif self.summy_type == "mean": y = x.mean(dim=1) elif self.summy_type == "cls_index": if idx is None: idx = torch.full_like( x[..., :1, :], x.shape[-2] - 1, dtype=torch.long, ) else: idx = idx.unsqueeze(-1).unsqueeze(-1) idx = idx.expand((-1,) * (idx.dim() - 1) + (x.size(-1),)) y = x.gather(-2, idx).squeeze(-2) elif self.summy_type == "attn": raise NotImplementedError y = self.drop_1(y) y = self.summy(y) y = self.act(y) y = self.drop_2(y) return y if __name__ == "__main__": h0 = Hypers() print(h0) h1 = Hypers({"a", "b"}, {"c": 3, "d": 4}) print(h1) c0 = Config() print(c0) c1 = Config({"b": 20}) print(c1) c2 = Config({"b": 20}, h1, d=40) print(c2) c3 = Config({"b": 20}, Hypers.merge([h1, Hypers(set(), {"a": 100})]), c=300, d=40) print(c3) c4 = Config(c3, Hypers.merge([h1, Hypers({"bbb"}, {"a": 0, "aaa": 0})]), c=0, ddd=0) print(c4)	{"/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": 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33,707	quantapix/qnarre	refs/heads/main	/qnarre/run/mlm.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 masked language modeling (BERT, ALBERT, RoBERTa...) import logging import math import random import torch from datasets import load_dataset from functools import partial from torch.utils.data import DataLoader from transformers import AutoModelForMaskedLM, DataCollatorForLanguageModeling from .params import TRAIN, EVAL, ALL, EACH from .runner import Runner as Base from .utils import group_texts log = logging.getLogger(__name__) class Runner(Base): AutoModel = AutoModelForMaskedLM @property def dataset(self): if self._dataset is None: ps = self.params if ps.dataset_name is not None: y = load_dataset(ps.dataset_name, ps.dataset_config) if EVAL not in y.keys(): y[EVAL] = load_dataset( ps.dataset_name, ps.dataset_config, split=f"train[:{ps.split_percent}%]" ) y[TRAIN] = load_dataset( ps.dataset_name, ps.dataset_config, split=f"train[{ps.split_percent}%:]" ) else: x, xs = None, {} if ps.eval_file is not None: xs[EVAL] = x = ps.eval_file if ps.train_file is not None: xs[TRAIN] = x = ps.train_file x = x.split(".")[-1] if x == "txt": x = "text" y = load_dataset(x, data_files=xs) if EVAL not in y.keys(): y[EVAL] = load_dataset(x, data_files=xs, split=f"train[:{ps.split_percent}%]") y[TRAIN] = load_dataset(x, data_files=xs, split=f"train[{ps.split_percent}%:]") self._dataset = y return self._dataset @property def cols(self): if self._cols is None: cs = self.dataset[TRAIN].column_names t = "text" if "text" in cs else cs[0] self._cols = {ALL: cs, EACH: [t]} return self._cols @property def tokenizer(self): if self._tokenizer is None: ps, t = self.params, super().tokenizer if ps.max_seq_length is None: b = t.model_max_length if b > 1024: log.warning(f"Using max_seq_length=1024") b = 1024 else: if ps.max_seq_length > t.model_max_length: log.warning(f"Using max_seq_length={t.model_max_length}") b = min(ps.max_seq_length, t.model_max_length) self.max_seq_length = b return self._tokenizer @property def train_ds(self): if self._train_ds is None: ps, mgr, ds = self.params, self.mgr, self.dataset if ps.line_by_line: with mgr.main_process_first(): self._dataset = y = ds.map( self.prep_for_train, batched=True, num_proc=ps.num_workers, remove_columns=[self.cols[EACH][0]], load_from_cache_file=not ps.overwrite_cache, desc="Running tokenizer line_by_line", ) else: with mgr.main_process_first(): y = ds.map( self.prep_for_train, batched=True, num_proc=ps.num_workers, remove_columns=self.cols[ALL], load_from_cache_file=not ps.overwrite_cache, desc="Running tokenizer on every text", ) with mgr.main_process_first(): self._dataset = y = y.map( partial(group_texts, self.max_seq_length), batched=True, num_proc=ps.num_workers, load_from_cache_file=not ps.overwrite_cache, desc=f"Grouping texts in blocks of {self.max_seq_length}", ) 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, c = self.params, self.cols[EACH][0] if ps.line_by_line: xs[c] = [x for x in xs[c] if len(x) > 0 and not x.isspace()] return self.tokenizer( xs[c], padding=self.padding, truncation=True, max_len=self.max_seq_length, return_special_tokens_mask=True, ) else: return self.tokenizer(xs[c], return_special_tokens_mask=True) @property def loaders(self): if self._loaders is None: ps = self.params c = DataCollatorForLanguageModeling(self.tokenizer, mlm_probability=ps.mlm_probability) 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 def eval_epoch(self, e): m, mgr = self.model, self.mgr m.eval() y = [] for xs in self.loaders[EVAL]: with torch.no_grad(): ys = m(**xs) y.append(mgr.gather(ys.loss.repeat(self.params.eval_batch_size))) y = torch.cat(y)[: len(self.eval_ds)] try: y = math.exp(torch.mean(y)) except OverflowError: y = float("inf") mgr.print(f"epoch {e}: perplexity: {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()	{"/qnarre/prep/convert/xlnet.py": 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33,708	quantapix/qnarre	refs/heads/main	/tools/triton/python/tutorials/02-fused-softmax.py	""" Fused Softmax ============= In this tutorial, you will write a fused softmax operation that is significantly faster than PyTorch's native op for a particular class of matrices: those whose rows can fit in the GPU's SRAM. In doing so, you will learn about: * The benefits of kernel fusion for bandwidth-bound operations. * Reduction operators in Triton. """ # %% # Motivations # ----------- # # Custom GPU kernels for elementwise additions are educationally valuable but won't get you very far in practice. # Let us consider instead the case of a simple (numerically stabilized) softmax operation: import torch import triton import triton.language as tl @torch.jit.script def naive_softmax(x): """Compute row-wise softmax of X using native pytorch We subtract the maximum element in order to avoid overflows. Softmax is invariant to this shift. """ # read MN elements ; write M elements x_max = x.max(dim=1)[0] # read MN + M elements ; write MN elements z = x - x_max[:, None] # read MN elements ; write MN elements numerator = torch.exp(z) # read MN elements ; write M elements denominator = numerator.sum(dim=1) # read MN + M elements ; write MN elements ret = numerator / denominator[:, None] # in total: read 5MN + 2M elements ; wrote 3MN + 2M elements return ret # %% # When implemented naively in PyTorch, computing :code:`y = naive_softmax(x)` for :math:`x \in R^{M \times N}` # requires reading :math:`5MN + 2M` elements from DRAM and writing back :math:`3MN + 2M` elements. # This is obviously wasteful; we'd prefer to have a custom "fused" kernel that only reads # X once and does all the necessary computations on-chip. # Doing so would require reading and writing back only :math:`MN` bytes, so we could # expect a theoretical speed-up of ~4x (i.e., :math:`(8MN + 4M) / 2MN`). # The `torch.jit.script` flags aims to perform this kind of "kernel fusion" automatically # but, as we will see later, it is still far from ideal. # %% # Compute Kernel # -------------- # # Our softmax kernel works as follows: each program loads a row of the input matrix X, # normalizes it and writes back the result to the output Y. # # Note that one important limitation of Triton is that each block must have a # power-of-two number of elements, so we need to internally "pad" each row and guard the # memory operations properly if we want to handle any possible input shapes: @triton.jit def softmax_kernel( output_ptr, input_ptr, input_row_stride, output_row_stride, n_cols, BLOCK_SIZE: tl.constexpr ): # The rows of the softmax are independent, so we parallelize across those row_idx = tl.program_id(0) # The stride represents how much we need to increase the pointer to advance 1 row row_start_ptr = input_ptr + row_idx * input_row_stride # The block size is the next power of two greater than n_cols, so we can fit each # row in a single block col_offsets = tl.arange(0, BLOCK_SIZE) input_ptrs = row_start_ptr + col_offsets # Load the row into SRAM, using a mask since BLOCK_SIZE may be > than n_cols row = tl.load(input_ptrs, mask=col_offsets < n_cols, other=-float('inf')) # Subtract maximum for numerical stability row_minus_max = row - tl.max(row, axis=0) # Note that exponentiation in Triton is fast but approximate (i.e., think __expf in CUDA) numerator = tl.exp(row_minus_max) denominator = tl.sum(numerator, axis=0) softmax_output = numerator / denominator # Write back output to DRAM output_row_start_ptr = output_ptr + row_idx * output_row_stride output_ptrs = output_row_start_ptr + col_offsets tl.store(output_ptrs, softmax_output, mask=col_offsets < n_cols) # %% # We can create a helper function that enqueues the kernel and its (meta-)arguments for any given input tensor. def softmax(x): n_rows, n_cols = x.shape # The block size is the smallest power of two greater than the number of columns in `x` BLOCK_SIZE = triton.next_power_of_2(n_cols) # Another trick we can use is to ask the compiler to use more threads per row by # increasing the number of warps (`num_warps`) over which each row is distributed. # You will see in the next tutorial how to auto-tune this value in a more natural # way so you don't have to come up with manual heuristics yourself. num_warps = 4 if BLOCK_SIZE >= 2048: num_warps = 8 if BLOCK_SIZE >= 4096: num_warps = 16 # Allocate output y = torch.empty_like(x) # Enqueue kernel. The 1D launch grid is simple: we have one kernel instance per row o # f the input matrix softmax_kernel[(n_rows,)]( y, x, x.stride(0), y.stride(0), n_cols, num_warps=num_warps, BLOCK_SIZE=BLOCK_SIZE, ) return y # %% # Unit Test # --------- # %% # We make sure that we test our kernel on a matrix with an irregular number of rows and columns. # This will allow us to verify that our padding mechanism works. torch.manual_seed(0) x = torch.randn(1823, 781, device='cuda') y_triton = softmax(x) y_torch = torch.softmax(x, axis=1) assert torch.allclose(y_triton, y_torch), (y_triton, y_torch) # %% # As expected, the results are identical. # %% # Benchmark # --------- # # Here we will benchmark our operation as a function of the number of columns in the input matrix -- assuming 4096 rows. # We will then compare its performance against (1) :code:`torch.softmax` and (2) the :code:`naive_softmax` defined above. @triton.testing.perf_report( triton.testing.Benchmark( x_names=['N'], # argument names to use as an x-axis for the plot x_vals=[ 128 * i for i in range(2, 100) ], # different possible values for `x_name` line_arg='provider', # argument name whose value corresponds to a different line in the plot line_vals=[ 'triton', 'torch-native', 'torch-jit', ], # possible values for `line_arg`` line_names=[ "Triton", "Torch (native)", "Torch (jit)", ], # label name for the lines styles=[('blue', '-'), ('green', '-'), ('green', '--')], # line styles ylabel="GB/s", # label name for the y-axis plot_name="softmax-performance", # name for the plot. Used also as a file name for saving the plot. args={'M': 4096}, # values for function arguments not in `x_names` and `y_name` ) ) def benchmark(M, N, provider): x = torch.randn(M, N, device='cuda', dtype=torch.float32) quantiles = [0.5, 0.2, 0.8] if provider == 'torch-native': ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.softmax(x, axis=-1), quantiles=quantiles) if provider == 'triton': ms, min_ms, max_ms = triton.testing.do_bench(lambda: softmax(x), quantiles=quantiles) if provider == 'torch-jit': ms, min_ms, max_ms = triton.testing.do_bench(lambda: naive_softmax(x), quantiles=quantiles) gbps = lambda ms: 2 * x.nelement() * x.element_size() * 1e-9 / (ms * 1e-3) return gbps(ms), gbps(max_ms), gbps(min_ms) benchmark.run(show_plots=True, print_data=True) # %% # In the above plot, we can see that: # - Triton is 4x faster than the Torch JIT. 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33,709	quantapix/qnarre	refs/heads/main	/qnarre/models/gpt_neox.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 torch.utils.checkpoint import checkpoint from .. import core as qc from ..core import attention as qa from ..core import forward as qf from ..core import output as qo from ..core import utils as qu from ..core.embed import Embed from ..core.mlp import Classifier, MLP, Predictor, Pool from ..prep.config.gpt_neox import PreTrained log = logging.get_logger(__name__) class ForCausal(PreTrained): def __init__(self, cfg): super().__init__(cfg) self.model = Model(cfg) self.proj = qc.Linear(cfg.d_model, cfg.s_vocab, bias=False) def forward(self, x=None, labels=None, kw): ys = self.model(x, kw) y = self.proj(ys[0]) loss = None if labels is not None: y2 = y[:, :-1, :].contiguous() l = labels.to(y.device)[:, 1:].contiguous() loss = nn.CrossEntropyLoss()(y2.view(-1, y2.size(-1)), l.view(-1)) ys = (y,) + ys[1:] + (loss,) return qo.LossCaches(ys) class ForSeqClass(PreTrained): def __init__(self, cfg): super().__init__(cfg) cfg.n_labels = cfg.n_labels self.model = Model(cfg) self.proj = qc.Linear(cfg.d_model, cfg.n_labels, bias=False) def forward(self, x=None, x_emb=None, labels=None, kw): cfg = self.cfg ys = self.model(x, kw) y = self.proj(ys[0]) if x is not None: b, _ = x.shape[:2] else: b, _ = x_emb.shape[:2] if cfg.PAD is None and b != 1: raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") if cfg.PAD is None: sequence_lengths = -1 else: if x is not None: sequence_lengths = (torch.ne(x, cfg.PAD).sum(-1) - 1).to(y.device) else: sequence_lengths = -1 log.warning( f"{self.__class__.__name__} will not detect padding tokens in `x_emb`. Results may be " "unexpected if using padding tokens in conjunction with `x_emb.`" ) y = y[torch.arange(b, device=y.device), sequence_lengths] loss = None if labels is not None: labels = labels.to(y.device) if cfg.problem is None: dt = labels.dtype if cfg.n_labels == 1: cfg.problem = "regression" elif cfg.n_labels > 1 and (dt == torch.long or dt == torch.int): cfg.problem = "single_label" else: cfg.problem = "multi_label" if cfg.problem == "regression": if cfg.n_labels == 1: loss = nn.MSELoss()(y.squeeze(), labels.squeeze()) else: loss = nn.MSELoss()(y, labels) elif cfg.problem == "single_label": loss = nn.CrossEntropyLoss()(y.view(-1, cfg.n_labels), labels.view(-1)) elif cfg.problem == "multi_label": loss = nn.BCEWithLogitsLoss()(y, labels) ys = (y,) + ys[2:] + (loss,) # ys[1:] return qo.LossCaches(ys) class Model(PreTrained): def __init__(self, cfg): super().__init__(cfg) self.cfg = cfg self.embed_in = nn.Embedding(cfg.s_vocab, cfg.d_model) self.layers = nn.ModuleList([Layer(cfg) for _ in range(cfg.n_lays)]) self.final_layer_norm = nn.LayerNorm(cfg.d_model, eps=cfg.layer_norm_eps) def forward( self, input_ids=None, mask=None, position_ids=None, head_m=None, x_emb=None, past_key_values=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 cfg.output_attentions ) output_hidden_states = ( output_hidden_states if output_hidden_states is not None else cfg.output_hidden_states ) return_dict = return_dict if return_dict is not None else cfg.use_return_dict y_cache = y_cache if y_cache is not None else cfg.y_cache if input_ids is not None and x_emb is not None: raise ValueError("You cannot specify both input_ids and x_emb at the same time") elif input_ids is not None: input_shape = input_ids.size() elif x_emb is not None: input_shape = x_emb.size()[:-1] else: raise ValueError("You have to specify either input_ids or x_emb") b, seq_length = input_shape if past_key_values is None: past_length = 0 past_key_values = tuple([None] * cfg.n_lays) else: past_length = past_key_values[0][0].size(-2) if position_ids is None: device = input_ids.device if input_ids is not None else x_emb.device position_ids = torch.arange( past_length, seq_length + past_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 mask is not None: assert b > 0, "b has to be defined and > 0" mask = mask.view(b, -1) mask = mask[:, None, None, :] mask = mask.to(dtype=self.dtype) # fp16 compatibility mask = (1.0 - mask) * torch.finfo(self.dtype).min head_m = self.get_head_m(head_m, cfg.n_lays) if x_emb is None: x_emb = self.embed_in(input_ids) hidden_states = x_emb 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 presents = () if y_cache else None all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for i, (layer, cache) in enumerate(zip(self.layers, past_key_values)): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(inputs): # None for cache return module(inputs, y_cache, None, output_attentions) return custom_forward outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer), hidden_states, mask, position_ids, head_m[i], ) else: outputs = layer( hidden_states, mask=mask, position_ids=position_ids, head_m=head_m[i], cache=cache, y_cache=y_cache, output_attentions=output_attentions, ) hidden_states = outputs[0] if y_cache is True: presents = presents + (outputs[1],) if output_attentions: all_attentions = all_attentions + (outputs[2 if y_cache else 1],) hidden_states = self.final_layer_norm(hidden_states) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_attentions, ) class Layer(nn.Module): def __init__(self, cfg): super().__init__() self.use_parallel_residual = cfg.use_parallel_residual self.input_layernorm = nn.LayerNorm(cfg.d_model, eps=cfg.layer_norm_eps) self.post_attention_layernorm = nn.LayerNorm(cfg.d_model, eps=cfg.layer_norm_eps) self.attention = Attention(cfg) self.mlp = MLP(cfg) def forward( self, hidden_states, mask=None, position_ids=None, head_m=None, y_cache=False, cache=None, output_attentions=False, ): attention_layer_outputs = self.attention( self.input_layernorm(hidden_states), mask=mask, position_ids=position_ids, cache=cache, head_m=head_m, y_cache=y_cache, output_attentions=output_attentions, ) attn_output = attention_layer_outputs[0] outputs = attention_layer_outputs[1:] if self.use_parallel_residual: mlp_output = self.mlp(self.post_attention_layernorm(hidden_states)) hidden_states = mlp_output + attn_output + hidden_states else: attn_output = attn_output + hidden_states mlp_output = self.mlp(self.post_attention_layernorm(attn_output)) hidden_states = mlp_output + attn_output if y_cache: outputs = (hidden_states,) + outputs # hidden_states, present, (attn_weights) else: outputs = (hidden_states,) + outputs[1:] # hidden_states, (attn_weights) return outputs class Attention(nn.Module): def __init__(self, cfg): super().__init__() self.n_heads = cfg.n_heads self.hidden_size = cfg.d_model self.head_size = self.hidden_size // self.n_heads self.rotary_ndims = int(self.head_size * cfg.rotary_pct) max_positions = cfg.n_pos self.register_buffer( "bias", torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view( 1, 1, max_positions, max_positions ), ) self.register_buffer("bias_m", torch.tensor(-1e9)) self.rotary_emb = RotaryEmbedding(self.rotary_ndims, cfg.n_pos, base=cfg.rotary_emb_base) self.norm_factor = torch.sqrt(torch.tensor(self.head_size, dtype=torch.float32)).to( torch.get_default_dtype() ) self.query_key_value = qc.Linear(cfg.d_model, 3 * cfg.d_model) self.dense = qc.Linear(cfg.d_model, cfg.d_model) def forward( self, hidden_states: torch.FloatTensor, mask: torch.FloatTensor, position_ids: torch.LongTensor, head_m=None, cache=None, y_cache=False, output_attentions=False, ): has_cache = cache is not None # Compute QKV # Attention heads [batch, seq_len, hidden_size] # --> [batch, seq_len, (np * 3 * head_size)] qkv = self.query_key_value(hidden_states) # [batch, seq_len, (num_heads * 3 * head_size)] # --> [batch, seq_len, num_heads, 3 * head_size] new_qkv_shape = qkv.size()[:-1] + (self.n_heads, 3 * self.head_size) qkv = qkv.view(new_qkv_shape) # [batch, seq_len, n_heads, 3 head_size] --> 3 [batch, n_heads, seq_len, head_size] query = qkv[..., : self.head_size].permute(0, 2, 1, 3) key = qkv[..., self.head_size : 2 * self.head_size].permute(0, 2, 1, 3) value = qkv[..., 2 * self.head_size :].permute(0, 2, 1, 3) # Compute rotary embeddings on rotary_ndims query_rot = query[..., : self.rotary_ndims] query_pass = query[..., self.rotary_ndims :] key_rot = key[..., : self.rotary_ndims] key_pass = key[..., self.rotary_ndims :] # Compute token offset for rotary embeddings (when decoding) seq_len = key.shape[-2] if has_cache: seq_len += cache[0].shape[-2] cos, sin = self.rotary_emb(value, seq_len=seq_len) query, key = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids) query = torch.cat((query, query_pass), dim=-1) key = torch.cat((key, key_pass), dim=-1) # Cache QKV values if has_cache: past_key = cache[0] past_value = cache[1] key = torch.cat((past_key, key), dim=-2) value = torch.cat((past_value, value), dim=-2) present = (key, value) if y_cache else None # Compute attention attn_output, attn_weights = self._attn(query, key, value, mask, head_m) # Reshape outputs attn_output = self._merge_heads(attn_output, self.n_heads, self.head_size) attn_output = self.dense(attn_output) outputs = (attn_output, present) if output_attentions: outputs += (attn_weights,) return outputs @classmethod def _split_heads(cls, tensor, n_heads, attn_head_size): """ Splits hidden dim into attn_head_size and n_heads """ # tensor: [bs, seq_len, hidden_size] new_shape = tensor.size()[:-1] + (n_heads, attn_head_size) # -> [bs, seq_len, n_heads, attn_head_size] tensor = tensor.view(new_shape) # -> [bs, n_heads, seq_len, attn_head_size] tensor = tensor.permute(0, 2, 1, 3) return tensor @classmethod def _merge_heads(cls, tensor, n_heads, attn_head_size): """ Merges attn_head_size dim and num_attn_heads dim into hidden dim """ # tensor [bs, n_heads, seq_len, attn_head_size] tensor = tensor.permute(0, 2, 1, 3).contiguous() # -> [bs, seq_len, n_heads, attn_head_size] tensor = tensor.view(tensor.size(0), tensor.size(1), n_heads * attn_head_size) # -> [bs, seq_len, hidden_size] return tensor def _attn(self, query, key, value, mask=None, head_m=None): # q, k, v: [bs, n_heads, seq_len, attn_head_size] # compute causal mask from causal mask buffer b, n_heads, query_length, attn_head_size = query.size() key_length = key.size(-2) causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length] query = query.view(b * n_heads, query_length, attn_head_size) key = key.view(b * n_heads, key_length, attn_head_size) attn_scores = torch.zeros( b * n_heads, query_length, key_length, dtype=query.dtype, device=key.device, ) attn_scores = torch.baddbmm( attn_scores, query, key.transpose(1, 2), beta=1.0, alpha=( torch.tensor(1.0, dtype=self.norm_factor.dtype, device=self.norm_factor.device) / self.norm_factor ), ) attn_scores = attn_scores.view(b, n_heads, query_length, key_length) mask_value = torch.finfo(attn_scores.dtype).min # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`. # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device` mask_value = torch.tensor(mask_value, dtype=attn_scores.dtype).to(attn_scores.device) attn_scores = torch.where(causal_mask, attn_scores, mask_value) if mask is not None: # Apply the attention mask attn_scores = attn_scores + mask attn_weights = nn.functional.softmax(attn_scores, dim=-1) attn_weights = attn_weights.to(value.dtype) # Mask heads if we want to if head_m is not None: attn_weights = attn_weights * head_m attn_output = torch.matmul(attn_weights, value) return attn_output, attn_weights def mask_func(attention_scores, ltor_mask): attention_scores.masked_fill_(~ltor_mask, torch.finfo(attention_scores.dtype).min) return attention_scores class RotaryEmbedding(torch.nn.Module): def __init__(self, dim, n_pos, base=10000, device=None): super().__init__() inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim)) self.register_buffer("inv_freq", inv_freq) # Build here to make `torch.jit.trace` work. self.max_seq_len_cached = n_pos t = torch.arange( self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype ) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.cos_cached = emb.cos()[None, None, :, :] self.sin_cached = emb.sin()[None, None, :, :] def forward(self, x, seq_len=None): # x: [bs, n_heads, seq_len, head_size] # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case. if seq_len > self.max_seq_len_cached: self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1).to(x.device) self.cos_cached = emb.cos()[None, None, :, :] self.sin_cached = emb.sin()[None, None, :, :] return self.cos_cached[:seq_len, ...].to(x.device), self.sin_cached[:seq_len, ...].to( x.device ) def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(q, k, cos, sin, position_ids): gather_indices = position_ids[:, None, :, None] # [bs, 1, seq_len, 1] gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3]) cos = 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33,710	quantapix/qnarre	refs/heads/main	/qnarre/base/claim.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 ..edit import fudge from .named import Tagged class Claim(Tagged): sign = '~' loss = 0 def __init__(self, , text, place, loss=None, _): super().__init__(tag=self.tag) self.text = fudge() if text == 'fudge' else text self.place = place if loss is not None: self.loss = loss def __str__(self): return '({}) {}'.format(self.sign, self.value) @property def factor(self): return self.place.doc.factor super().factor @property def bias(self): return self.place.doc.bias + super().bias @property def credibility(self): return self.weight @property def value(self): c = '#{}'.format(self.credibility) if self.credibility != 1 else '' a = ' ${}'.format(self.loss) if self.loss else '' return '{}"{}"{} {}'.format(c, self.text, a, self.place) @property def fields(self): fs = { 'Text': self.text, 'Credibility': self.credibility, 'Loss': self.loss } fs.update(self.place.fields) fs['Type'] = self.tag return fs class Place(Tagged): def __init__(self, doc, page, para): super().__init__(tag=self.tag) self.doc = doc self.page = page self.para = para def __str__(self): return '@ {}:{}:{}'.format(self.doc.name, self.page, self.para) @property def fields(self): fs = {'Page': self.page, 'Para': self.para} fs.update(self.doc.fields) fs['Type'] = self.tag return fs	{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], 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33,711	quantapix/qnarre	refs/heads/main	/qnarre/run/mae.py	from dataclasses import dataclass, field import torch from datasets import load_dataset from torchvision.transforms import ( Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor, ) from torchvision.transforms.functional import InterpolationMode from transformers import ( Trainer, TrainingArguments, ViTFeatureExtractor, ViTMAEConfig, ViTMAEForPreTraining, ) add_argument("--dataset_name", type=str, default="cifar10") @dataclass class DataTrainingArguments: image_column_name = field( default=None, metadata={"help": "The column name of the images in the files."} ) train_dir = field(default=None, metadata={"help": "A folder containing the training data."}) validation_dir = field( default=None, metadata={"help": "A folder containing the validation data."} ) train_val_split = field( default=0.15, metadata={"help": "Percent to split off of train for validation."} ) max_train_samples = field( default=None, metadata={ "help": "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." }, ) max_eval_samples = field( default=None, metadata={ "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." }, ) def __post_init__(self): data_files = dict() if self.train_dir is not None: data_files["train"] = self.train_dir if self.validation_dir is not None: data_files["val"] = self.validation_dir self.data_files = data_files if data_files else None @dataclass class ModelArguments: mask_ratio = field( default=0.75, metadata={"help": "The ratio of the number of masked tokens in the input sequence."}, ) norm_pix_loss = field( default=True, metadata={"help": "Whether or not to train with normalized pixel values as target."}, ) @dataclass class CustomTrainingArguments(TrainingArguments): base_lr = field( default=1e-3, metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}, ) def collate_fn(examples): pixel_values = torch.stack([example["pixel_values"] for example in examples]) return {"pixel_values": pixel_values} def main(): ds = load_dataset( data_args.dataset_name, data_args.dataset_config, data_files=data_args.data_files, cache_dir=model_args.cache_dir, ) # If we don't have a validation split, split off a percentage of train as validation. data_args.train_val_split = None if "validation" in ds.keys() else data_args.train_val_split if isinstance(data_args.train_val_split, float) and data_args.train_val_split > 0.0: split = ds["train"].train_test_split(data_args.train_val_split) ds["train"] = split["train"] ds["validation"] = split["test"] # Load pretrained model and feature extractor # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. config_kw = { "cache_dir": model_args.cache_dir, "revision": model_args.model_version, "use_auth_token": True if model_args.use_auth_token else None, } if model_args.config_name: config = ViTMAEConfig.from_pretrained(model_args.config_name, config_kw) elif model_args.model_name: config = ViTMAEConfig.from_pretrained(model_args.model_name, config_kw) else: config = ViTMAEConfig() logger.warning("You are instantiating a new config instance from scratch.") if model_args.config_overrides is not None: logger.info(f"Overriding config: {model_args.config_overrides}") config.update_from_string(model_args.config_overrides) logger.info(f"New config: {config}") # adapt config config.update( { "mask_ratio": model_args.mask_ratio, "norm_pix_loss": model_args.norm_pix_loss, } ) # create feature extractor if model_args.feature_extractor: feature_extractor = ViTFeatureExtractor.from_pretrained( model_args.feature_extractor, config_kw ) elif model_args.model_name: feature_extractor = ViTFeatureExtractor.from_pretrained(model_args.model_name, config_kw) else: feature_extractor = ViTFeatureExtractor() # create model if model_args.model_name: model = ViTMAEForPreTraining.from_pretrained( model_args.model_name, from_tf=bool(".ckpt" in model_args.model_name), config=config, cache_dir=model_args.cache_dir, revision=model_args.model_version, use_auth_token=True if model_args.use_auth_token else None, ) else: logger.info("Training new model") model = ViTMAEForPreTraining(config) if training_args.do_train: column_names = ds["train"].column_names else: column_names = ds["validation"].column_names if data_args.image_column_name is not None: image_column_name = data_args.image_column_name elif "image" in column_names: image_column_name = "image" elif "img" in column_names: image_column_name = "img" else: image_column_name = column_names[0] # transformations as done in original MAE paper # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py transforms = Compose( [ Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img), RandomResizedCrop( feature_extractor.size, scale=(0.2, 1.0), interpolation=InterpolationMode.BICUBIC ), RandomHorizontalFlip(), ToTensor(), Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std), ] ) def preprocess_images(examples): """Preprocess a batch of images by applying transforms.""" examples["pixel_values"] = [transforms(image) for image in examples[image_column_name]] return examples if training_args.do_train: if "train" not in ds: raise ValueError("--do_train requires a train dataset") if data_args.max_train_samples is not None: ds["train"] = ( ds["train"] .shuffle(seed=training_args.seed) .select(range(data_args.max_train_samples)) ) # Set the training transforms ds["train"].set_transform(preprocess_images) if training_args.do_eval: if "validation" not in ds: raise ValueError("--do_eval requires a validation dataset") if data_args.max_eval_samples is not None: ds["validation"] = ( ds["validation"] .shuffle(seed=training_args.seed) .select(range(data_args.max_eval_samples)) ) # Set the validation transforms ds["validation"].set_transform(preprocess_images) # Compute absolute learning rate total_train_batch_size = ( training_args.train_batch_size * training_args.grad_accumulation_steps * training_args.world_size ) if training_args.base_lr is not None: training_args.lr = training_args.base_lr * total_train_batch_size / 256 # Initialize our trainer trainer = Trainer( model=model, args=training_args, train_dataset=ds["train"] if training_args.do_train else None, eval_dataset=ds["validation"] if training_args.do_eval else None, tokenizer=feature_extractor, data_collator=collate_fn, ) # Training if training_args.do_train: checkpoint = None if training_args.resume_from_checkpoint is not None: checkpoint = training_args.resume_from_checkpoint elif last_checkpoint is not None: checkpoint = last_checkpoint train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() trainer.log_metrics("train", train_result.metrics) trainer.save_metrics("train", train_result.metrics) trainer.save_state() # Evaluation if training_args.do_eval: metrics = trainer.evaluate() trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) # Write model card and (optionally) push to hub kw = { "tasks": "masked-auto-encoding", "dataset": data_args.dataset_name, "tags": ["masked-auto-encoding"], } if training_args.push_to_hub: trainer.push_to_hub(kw) else: trainer.create_model_card(kw) if __name__ == "__main__": main()	{"/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": 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["/tools/triton/python/triton/language/__init__.py"]}
33,712	quantapix/qnarre	refs/heads/main	/qnarre/prep/dataset/cifar10.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 numpy as np import pickle from datasets.tasks import ImageClassification _URL = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz" _NAMES = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"] class Cifar10(ds.GeneratorBasedBuilder): BUILDER_CONFIGS = [ds.BuilderConfig(name="plain_text", version=ds.Version("1.0.0"))] def _info(self): return ds.DatasetInfo( description="", citation="", homepage="", license="", features=ds.Features( { "img": ds.Image(), "label": ds.features.ClassLabel(names=_NAMES), } ), supervised_keys=("img", "label"), task_templates=ImageClassification(image_column="img", label_column="label"), ) def _split_generators(self, mgr): fs = mgr.download(_URL) return [ ds.SplitGenerator( name=ds.Split.TRAIN, gen_kw={"files": mgr.iter_archive(fs), "split": "train"}, ), ds.SplitGenerator( name=ds.Split.TEST, gen_kw={"files": mgr.iter_archive(fs), "split": "test"}, ), ] def _generate_examples(self, fs, split): if split == "train": batches = [ "data_batch_1", "data_batch_2", "data_batch_3", "data_batch_4", "data_batch_5", ] if split == "test": batches = ["test_batch"] batches = [f"cifar-10-batches-py/{p}" for p in batches] for p, f in fs: if p in batches: d = pickle.load(f, encoding="bytes") xs = d[b"data"] ls = d[b"labels"] for i, _ in enumerate(xs): x = np.transpose(np.reshape(xs[i], (3, 32, 32)), (1, 2, 0)) yield f"{p}_{i}", {"img": x, "label": ls[i]}	{"/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", 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"/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"], 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33,713	quantapix/qnarre	refs/heads/main	/tools/triton/python/triton/runtime/driver.py	import abc import hashlib import os import tempfile from pathlib import Path from ..common.build import _build from .cache import get_cache_manager class DriverBase(metaclass=abc.ABCMeta): CUDA = 0 HIP = 1 @staticmethod def third_party_dir(): return os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "third_party") def __init__(self) -> None: pass # ----------------------------- # CUDA # ----------------------------- class CudaUtils(object): def __new__(cls): if not hasattr(cls, 'instance'): cls.instance = super(CudaUtils, cls).__new__(cls) return cls.instance def __init__(self): dirname = os.path.dirname(os.path.realpath(__file__)) src = Path(os.path.join(dirname, "backends", "cuda.c")).read_text() key = hashlib.md5(src.encode("utf-8")).hexdigest() cache = get_cache_manager(key) fname = "cuda_utils.so" cache_path = cache.get_file(fname) if cache_path is None: with tempfile.TemporaryDirectory() as tmpdir: src_path = os.path.join(tmpdir, "main.c") with open(src_path, "w") as f: f.write(src) so = _build("cuda_utils", src_path, tmpdir) with open(so, "rb") as f: cache_path = cache.put(f.read(), fname, binary=True) import importlib.util spec = importlib.util.spec_from_file_location("cuda_utils", cache_path) mod = importlib.util.module_from_spec(spec) spec.loader.exec_module(mod) self.load_binary = mod.load_binary self.get_device_properties = mod.get_device_properties class CudaDriver(DriverBase): def __new__(cls): if not hasattr(cls, 'instance'): cls.instance = super(CudaDriver, cls).__new__(cls) return cls.instance def __init__(self): self.utils = CudaUtils() self.backend = self.CUDA # ----------------------------- # HIP # ----------------------------- class HIPUtils(object): def __new__(cls): if not hasattr(cls, 'instance'): cls.instance = super(HIPUtils, cls).__new__(cls) return cls.instance def __init__(self): dirname = os.path.dirname(os.path.realpath(__file__)) src = Path(os.path.join(dirname, "backends", "hip.c")).read_text() key = hashlib.md5(src.encode("utf-8")).hexdigest() cache = get_cache_manager(key) fname = "hip_utils.so" cache_path = cache.get_file(fname) if cache_path is None: with tempfile.TemporaryDirectory() as tmpdir: src_path = os.path.join(tmpdir, "main.c") with open(src_path, "w") as f: f.write(src) so = _build("hip_utils", src_path, tmpdir) with open(so, "rb") as f: cache_path = cache.put(f.read(), fname, binary=True) import importlib.util spec = importlib.util.spec_from_file_location("hip_utils", cache_path) mod = importlib.util.module_from_spec(spec) spec.loader.exec_module(mod) self.load_binary = mod.load_binary self.get_device_properties = mod.get_device_properties class HIPDriver(DriverBase): def __new__(cls): if not hasattr(cls, 'instance'): cls.instance = super(HIPDriver, cls).__new__(cls) return cls.instance def __init__(self): self.utils = HIPUtils() self.backend = self.HIP class UnsupportedDriver(DriverBase): def __new__(cls): if not hasattr(cls, 'instance'): cls.instance = super(UnsupportedDriver, cls).__new__(cls) return cls.instance def __init__(self): self.utils = None self.backend = None # ----------------------------- # Driver # ----------------------------- class LazyProxy: def __init__(self, init_fn): self._init_fn = init_fn self._obj = None def _initialize_obj(self): if self._obj is None: self._obj = self._init_fn() def __getattr__(self, name): self._initialize_obj() return getattr(self._obj, name) def __setattr__(self, name, value): if name in ['_init_fn', '_obj']: super().__setattr__(name, value) else: self._initialize_obj() setattr(self._obj, name, value) def __delattr__(self, name): self._initialize_obj() delattr(self._obj, name) def __repr__(self): if self._obj is None: return f"<{self.__class__.__name__} for {self._init_fn} not yet initialized>" return repr(self._obj) def __str__(self): self._initialize_obj() return str(self._obj) def initialize_driver(): import torch if torch.version.hip is not None: return HIPDriver() elif torch.cuda.is_available(): return CudaDriver() else: return UnsupportedDriver() driver = LazyProxy(initialize_driver)	{"/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": 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["/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": 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33,714	quantapix/qnarre	refs/heads/main	/qnarre/models/deberta.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 collections.abc import Sequence 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 from ..prep.config.bert import PreTrained from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss log = logging.get_logger(__name__) 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 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(kw) self.proj = Classifier(kw) forward = qf.forward_tok class ForSeqClass(PreTrained): def __init__(self, config): super().__init__(config) n_labels = getattr(config, "n_labels", 2) self.n_labels = n_labels self.deberta = Model(config) self.pool = Pool(config) output_dim = self.pool.output_dim self.classifier = qc.Linear(output_dim, n_labels) drop_out = getattr(config, "cls_dropout", None) drop_out = self.config.drop if drop_out is None else drop_out self.drop = StableDropout(drop_out) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=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 outputs = self.deberta( input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, position_ids=position_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) encoder_layer = outputs[0] pooled_output = self.pool(encoder_layer) pooled_output = self.drop(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.n_labels == 1: # regression task loss_fn = nn.MSELoss() logits = logits.view(-1).to(labels.dtype) loss = loss_fn(logits, labels.view(-1)) elif labels.dim() == 1 or labels.size(-1) == 1: label_index = (labels >= 0).nonzero() labels = labels.long() if label_index.size(0) > 0: labeled_logits = torch.gather( logits, 0, label_index.expand(label_index.size(0), logits.size(1)) ) labels = torch.gather(labels, 0, label_index.view(-1)) loss_fct = CrossEntropyLoss() loss = loss_fct( labeled_logits.view(-1, self.n_labels).float(), labels.view(-1) ) else: loss = torch.tensor(0).to(logits) else: log_softmax = nn.LogSoftmax(-1) loss = -((log_softmax(logits) * labels).sum(-1)).mean() elif self.config.problem_type == "regression": loss_fct = MSELoss() if self.n_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.n_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hiddens=outputs.hiddens, attns=outputs.attns, ) class ForTokClass(PreTrained): _keys_to_ignore_on_load_unexpected = [r"pooler"] def __init__(self, config): super().__init__(config) self.n_labels = config.n_labels self.deberta = Model(config) self.drop = qc.Dropout(config.drop) self.classifier = qc.Linear(config.d_model, config.n_labels) # Initialize weights and apply final processing self.post_init() def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=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 outputs = self.deberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.drop(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.n_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hiddens=outputs.hiddens, attns=outputs.attns, ) 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 class Model(PreTrained): def __init__(self, config): super().__init__(config) self.embeddings = Embed(config) self.encoder = Encoder(config) self.z_steps = 0 self.config = config def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=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 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") device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(input_shape, device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) embedding_output = self.embeddings( input_ids=input_ids, token_type_ids=token_type_ids, position_ids=position_ids, mask=attention_mask, inputs_embeds=inputs_embeds, ) encoder_outputs = self.encoder( embedding_output, attention_mask, output_hidden_states=True, output_attentions=output_attentions, return_dict=return_dict, ) encoded_layers = encoder_outputs[1] if self.z_steps > 1: hiddens = encoded_layers[-2] layers = [self.encoder.layer[-1] for _ in range(self.z_steps)] query_states = encoded_layers[-1] rel_embeddings = self.encoder.get_rel_embedding() attention_mask = self.encoder.get_attention_mask(attention_mask) rel_pos = self.encoder.get_rel_pos(embedding_output) for layer in layers[1:]: query_states = layer( hiddens, attention_mask, output_attentions=False, query_states=query_states, relative_pos=rel_pos, rel_embeddings=rel_embeddings, ) encoded_layers.append(query_states) sequence_output = encoded_layers[-1] if not return_dict: return (sequence_output,) + encoder_outputs[(1 if output_hidden_states else 2) :] return qo.Base( y=sequence_output, hiddens=encoder_outputs.hiddens if output_hidden_states else None, attns=encoder_outputs.attns, ) class Encoder(qc.Module): def __init__(self, config): super().__init__() self.layer = nn.ModuleList([Layer(config) for _ in range(config.n_lays)]) self.relative_attention = getattr(config, "relative_attention", False) if self.relative_attention: self.max_relative_positions = getattr(config, "max_relative_positions", -1) if self.max_relative_positions < 1: self.max_relative_positions = config.n_pos self.rel_embeddings = qc.Embed(self.max_relative_positions * 2, config.d_model) self.gradient_checkpointing = False def get_rel_embedding(self): rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None return rel_embeddings def get_attention_mask(self, attention_mask): if attention_mask.dim() <= 2: extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) attention_mask = extended_attention_mask * extended_attention_mask.squeeze( -2 ).unsqueeze(-1) attention_mask = attention_mask.byte() elif attention_mask.dim() == 3: attention_mask = attention_mask.unsqueeze(1) return attention_mask def get_rel_pos(self, hiddens, query_states=None, relative_pos=None): if self.relative_attention and relative_pos is None: q = query_states.size(-2) if query_states is not None else hiddens.size(-2) relative_pos = build_relative_position(q, hiddens.size(-2), hiddens.device) return relative_pos def forward( self, hiddens, attention_mask, output_hidden_states=True, output_attentions=False, query_states=None, relative_pos=None, return_dict=True, ): attention_mask = self.get_attention_mask(attention_mask) relative_pos = self.get_rel_pos(hiddens, query_states, relative_pos) all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None if isinstance(hiddens, Sequence): next_kv = hiddens[0] else: next_kv = hiddens rel_embeddings = self.get_rel_embedding() 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, output_attentions) return custom_forward hiddens = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), next_kv, attention_mask, query_states, relative_pos, rel_embeddings, ) else: hiddens = layer_module( next_kv, attention_mask, query_states=query_states, relative_pos=relative_pos, rel_embeddings=rel_embeddings, output_attentions=output_attentions, ) if output_attentions: hiddens, att_m = hiddens if query_states is not None: query_states = hiddens if isinstance(hiddens, Sequence): next_kv = hiddens[i + 1] if i + 1 < len(self.layer) else None else: next_kv = hiddens if output_attentions: all_attentions = all_attentions + (att_m,) 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, all_attentions] if v is not None) return qo.Base( y=hiddens, hiddens=all_hidden_states, attns=all_attentions, ) class Layer(qc.Module): def __init__(self, cfg): super().__init__() self.attn = Attention(cfg) self.dense = qc.Linear(cfg.d_model, cfg.d_ff) self.act = qu.activation(cfg.act) self.proj = qc.Linear(cfg.d_ff, cfg.d_model) self.norm = LayerNorm(cfg.d_model, cfg.eps) self.drop = StableDropout(cfg.drop) def forward( self, x, attention_mask, query_states=None, relative_pos=None, rel_embeddings=None, output_attentions=False, ): y = self.attn( x, attention_mask, output_attentions=output_attentions, query_states=query_states, relative_pos=relative_pos, rel_embeddings=rel_embeddings, ) if output_attentions: y, att_matrix = y y = self.norm(y, self.drop(self.proj(self.act(self.dense(y))))) if output_attentions: return (y, att_matrix) else: return y class Attention(qc.Module): def __init__(self, config): super().__init__() self.self = SelfAttention(config) self.proj = qc.Linear(config.d_model, config.d_model) self.drop = StableDropout(config.drop) self.norm = LayerNorm(config.d_model, config.eps) def forward( self, x, attention_mask, output_attentions=False, query_states=None, relative_pos=None, rel_embeddings=None, ): y = self.self( x, attention_mask, output_attentions, query_states=query_states, relative_pos=relative_pos, rel_embeddings=rel_embeddings, ) if output_attentions: y, att_matrix = y if query_states is None: query_states = x y = (self.norm(query_states + self.drop(self.proj(y))),) if output_attentions: return (y, att_matrix) else: return y class SelfAttention(qc.Module): def __init__(self, config): super().__init__() if config.d_model % config.n_heads != 0: 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.in_proj = qc.Linear(config.d_model, self.all_head_size * 3, bias=False) self.q_bias = nn.Parameter(torch.zeros((self.all_head_size), dtype=torch.float)) self.v_bias = nn.Parameter(torch.zeros((self.all_head_size), dtype=torch.float)) self.pos_att_type = config.pos_att_type if config.pos_att_type is not None else [] self.relative_attention = getattr(config, "relative_attention", False) self.talking_head = getattr(config, "talking_head", False) if self.talking_head: self.head_logits_proj = qc.Linear(config.n_heads, config.n_heads, bias=False) self.head_weights_proj = qc.Linear(config.n_heads, config.n_heads, bias=False) if self.relative_attention: self.max_relative_positions = getattr(config, "max_relative_positions", -1) if self.max_relative_positions < 1: self.max_relative_positions = config.n_pos self.pos_dropout = StableDropout(config.drop) if "c2p" in self.pos_att_type: self.pos_proj = qc.Linear(config.d_model, self.all_head_size, bias=False) if "p2c" in self.pos_att_type: self.pos_q_proj = qc.Linear(config.d_model, self.all_head_size) self.drop = StableDropout(config.drop_attn) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.n_heads, -1) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hiddens, attention_mask, output_attentions=False, query_states=None, relative_pos=None, rel_embeddings=None, ): if query_states is None: qp = self.in_proj(hiddens) # .split(self.all_head_size, dim=-1) query_layer, key_layer, value_layer = self.transpose_for_scores(qp).chunk(3, dim=-1) else: def linear(w, b, x): if b is not None: return torch.matmul(x, w.t()) + b.t() else: return torch.matmul(x, w.t()) # + b.t() ws = self.in_proj.weight.chunk(self.n_heads 3, dim=0) qkvw = [ torch.cat([ws[i * 3 + k] for i in range(self.n_heads)], dim=0) for k in range(3) ] qkvb = [None] * 3 q = linear(qkvw[0], qkvb[0], query_states) k, v = [linear(qkvw[i], qkvb[i], hiddens) for i in range(1, 3)] query_layer, key_layer, value_layer = [self.transpose_for_scores(x) for x in [q, k, v]] query_layer = query_layer + self.transpose_for_scores(self.q_bias[None, None, :]) value_layer = value_layer + self.transpose_for_scores(self.v_bias[None, None, :]) rel_att = None # Take the dot product between "query" and "key" to get the raw attention scores. scale_factor = 1 + len(self.pos_att_type) scale = math.sqrt(query_layer.size(-1) * scale_factor) query_layer = query_layer / scale attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.relative_attention: rel_embeddings = self.pos_dropout(rel_embeddings) rel_att = self.disentangled_att_bias( query_layer, key_layer, relative_pos, rel_embeddings, scale_factor ) if rel_att is not None: attention_scores = attention_scores + rel_att # bxhxlxd if self.talking_head: attention_scores = self.head_logits_proj(attention_scores.permute(0, 2, 3, 1)).permute( 0, 3, 1, 2 ) attention_probs = XSoftmax.apply(attention_scores, attention_mask, -1) attention_probs = self.drop(attention_probs) if self.talking_head: attention_probs = self.head_weights_proj(attention_probs.permute(0, 2, 3, 1)).permute( 0, 3, 1, 2 ) 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] + (-1,) context_layer = context_layer.view(new_context_layer_shape) if output_attentions: return (context_layer, attention_probs) else: return context_layer def disentangled_att_bias( self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor ): if relative_pos is None: q = query_layer.size(-2) relative_pos = build_relative_position(q, key_layer.size(-2), query_layer.device) if relative_pos.dim() == 2: relative_pos = relative_pos.unsqueeze(0).unsqueeze(0) elif relative_pos.dim() == 3: relative_pos = relative_pos.unsqueeze(1) elif relative_pos.dim() != 4: raise ValueError( f"Relative position ids must be of dim 2 or 3 or 4. {relative_pos.dim()}" ) att_span = min(max(query_layer.size(-2), key_layer.size(-2)), self.max_relative_positions) relative_pos = relative_pos.long().to(query_layer.device) rel_embeddings = rel_embeddings[ self.max_relative_positions - att_span : self.max_relative_positions + att_span, : ].unsqueeze(0) score = 0 if "c2p" in self.pos_att_type: pos_key_layer = self.pos_proj(rel_embeddings) pos_key_layer = self.transpose_for_scores(pos_key_layer) c2p_att = torch.matmul(query_layer, pos_key_layer.transpose(-1, -2)) c2p_pos = torch.clamp(relative_pos + att_span, 0, att_span 2 - 1) c2p_att = torch.gather( c2p_att, dim=-1, index=c2p_dynamic_expand(c2p_pos, query_layer, relative_pos) ) score += c2p_att if "p2c" in self.pos_att_type: pos_query_layer = self.pos_q_proj(rel_embeddings) pos_query_layer = self.transpose_for_scores(pos_query_layer) pos_query_layer /= math.sqrt(pos_query_layer.size(-1) * scale_factor) if query_layer.size(-2) != key_layer.size(-2): r_pos = build_relative_position( key_layer.size(-2), key_layer.size(-2), query_layer.device ) else: r_pos = relative_pos p2c_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1) p2c_att = torch.matmul(key_layer, pos_query_layer.transpose(-1, -2)) p2c_att = torch.gather( p2c_att, dim=-1, index=p2c_dynamic_expand(p2c_pos, query_layer, key_layer) ).transpose(-1, -2) if query_layer.size(-2) != key_layer.size(-2): pos_index = relative_pos[:, :, :, 0].unsqueeze(-1) p2c_att = torch.gather( p2c_att, dim=-2, index=pos_dynamic_expand(pos_index, p2c_att, key_layer) ) score += p2c_att return score class Pool(qc.Module): def __init__(self, config): super().__init__() self.dense = qc.Linear(config.pooler_hidden_size, config.pooler_hidden_size) self.drop = StableDropout(config.pooler_dropout) self.config = config def forward(self, hiddens): context_token = hiddens[:, 0] context_token = self.drop(context_token) pooled_output = self.dense(context_token) pooled_output = qu.activation(self.config.pooler_hidden_act)(pooled_output) return pooled_output @property def output_dim(self): return self.config.d_model class XSoftmax(torch.autograd.Function): @staticmethod def forward(self, input, mask, dim): self.dim = dim rmask = ~(mask.bool()) output = input.masked_fill(rmask, float("-inf")) output = torch.softmax(output, self.dim) output.masked_fill_(rmask, 0) self.save_for_backward(output) return output @staticmethod def backward(self, grad_output): (output,) = self.saved_tensors inputGrad = softmax_backward_data(self, grad_output, output, self.dim, output) return inputGrad, None, None @staticmethod def symbolic(g, self, mask, dim): import torch.onnx.symbolic_helper as sym_help from torch.onnx.symbolic_opset9 import masked_fill, softmax mask_cast_value = g.op("Cast", mask, to_i=sym_help.cast_pytorch_to_onnx["Long"]) r_mask = g.op( "Cast", g.op( "Sub", g.op("Constant", value_t=torch.tensor(1, dtype=torch.int64)), mask_cast_value ), to_i=sym_help.cast_pytorch_to_onnx["Byte"], ) output = masked_fill(g, self, r_mask, g.op("Constant", value_t=torch.tensor(float("-inf")))) output = softmax(g, output, dim) return masked_fill( g, output, r_mask, g.op("Constant", value_t=torch.tensor(0, dtype=torch.uint8)) ) class DropoutContext(object): def __init__(self): self.drop = 0 self.mask = None self.scale = 1 self.reuse_mask = True def get_mask(input, local_context): if not isinstance(local_context, DropoutContext): drop = local_context mask = None else: drop = local_context.drop drop = local_context.scale mask = local_context.mask if local_context.reuse_mask else None if drop > 0 and mask is None: mask = (1 - torch.empty_like(input).bernoulli_(1 - drop)).bool() if isinstance(local_context, DropoutContext): if local_context.mask is None: local_context.mask = mask return mask, drop class XDropout(torch.autograd.Function): @staticmethod def forward(ctx, input, local_ctx): mask, drop = get_mask(input, local_ctx) ctx.scale = 1.0 / (1 - drop) if drop > 0: ctx.save_for_backward(mask) return input.masked_fill(mask, 0) ctx.scale else: return input @staticmethod def backward(ctx, grad_output): if ctx.scale > 1: (mask,) = ctx.saved_tensors return grad_output.masked_fill(mask, 0) * ctx.scale, None else: return grad_output, None class StableDropout(qc.Module): def __init__(self, drop_prob): super().__init__() self.drop_prob = drop_prob self.count = 0 self.context_stack = None def forward(self, x): if self.training and self.drop_prob > 0: return XDropout.apply(x, self.get_context()) return x def clear_context(self): self.count = 0 self.context_stack = None def init_context(self, reuse_mask=True, scale=1): if self.context_stack is None: self.context_stack = [] self.count = 0 for c in self.context_stack: c.reuse_mask = reuse_mask c.scale = scale def get_context(self): if self.context_stack is not None: if self.count >= len(self.context_stack): self.context_stack.append(DropoutContext()) ctx = self.context_stack[self.count] ctx.drop = self.drop_prob self.count += 1 return ctx else: return self.drop_prob class LayerNorm(qc.Module): def __init__(self, size, eps=1e-12): super().__init__() self.weight = nn.Parameter(torch.ones(size)) self.bias = nn.Parameter(torch.zeros(size)) self.variance_epsilon = eps def forward(self, hiddens): input_type = hiddens.dtype hiddens = hiddens.float() mean = hiddens.mean(-1, keepdim=True) variance = (hiddens - mean).pow(2).mean(-1, keepdim=True) hiddens = (hiddens - mean) / torch.sqrt(variance + self.variance_epsilon) hiddens = hiddens.to(input_type) y = self.weight * hiddens + self.bias return y class Embed(qc.Module): def __init__(self, config): super().__init__() PAD = getattr(config, "PAD", 0) self.d_embed = getattr(config, "d_embed", config.d_model) self.word_embeddings = qc.Embed(config.s_vocab, self.d_embed, padding_idx=PAD) self.position_biased_input = getattr(config, "position_biased_input", True) if not self.position_biased_input: self.position_embeddings = None else: self.position_embeddings = qc.Embed(config.n_pos, self.d_embed) if config.n_typ > 0: self.token_type_embeddings = qc.Embed(config.n_typ, self.d_embed) if self.d_embed != config.d_model: self.embed_proj = qc.Linear(self.d_embed, config.d_model, bias=False) self.norm = LayerNorm(config.d_model, config.eps) self.drop = StableDropout(config.drop) self.config = config # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer("position_ids", torch.arange(config.n_pos).expand((1, -1))) def forward( self, input_ids=None, token_type_ids=None, position_ids=None, mask=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: 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) if self.position_embeddings is not None: position_embeddings = self.position_embeddings(position_ids.long()) else: position_embeddings = torch.zeros_like(inputs_embeds) embeddings = inputs_embeds if self.position_biased_input: embeddings += position_embeddings if self.config.n_typ > 0: token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings += token_type_embeddings if self.d_embed != self.config.d_model: embeddings = self.embed_proj(embeddings) embeddings = self.norm(embeddings) if mask is not None: if mask.dim() != embeddings.dim(): if mask.dim() == 4: mask = mask.squeeze(1).squeeze(1) mask = mask.unsqueeze(2) mask = mask.to(embeddings.dtype) embeddings = embeddings * mask embeddings = self.drop(embeddings) return embeddings def build_relative_position(query_size, key_size, device): q_ids = torch.arange(query_size, dtype=torch.long, device=device) k_ids = torch.arange(key_size, dtype=torch.long, device=device) rel_pos_ids = q_ids[:, None] - k_ids.view(1, -1).repeat(query_size, 1) rel_pos_ids = rel_pos_ids[:query_size, :] rel_pos_ids = rel_pos_ids.unsqueeze(0) return rel_pos_ids @torch.jit.script def c2p_dynamic_expand(c2p_pos, query_layer, relative_pos): return c2p_pos.expand( [query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)] ) @torch.jit.script def p2c_dynamic_expand(c2p_pos, query_layer, key_layer): return c2p_pos.expand( [query_layer.size(0), query_layer.size(1), key_layer.size(-2), key_layer.size(-2)] ) @torch.jit.script def pos_dynamic_expand(pos_index, p2c_att, key_layer): return pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2))) LIST = [ "microsoft/deberta-base", "microsoft/deberta-large", 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33,715	quantapix/qnarre	refs/heads/main	/qnarre/models/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. # ============================================================================= # https://arxiv.org/abs/1911.02116 # https://arxiv.org/abs/2105.00572 import itertools import torch from dataclasses import dataclass from torch import nn from torch.nn import functional as F from transformers.activations import gelu 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 sin_embed from ..core.mlp import Classifier, MLP from ..prep.config.xlm import PreTrained log = logging.get_logger(__name__) class Model(PreTrained): def __init__(self, kw): super().__init__(kw) cfg = self.get_cfg(kw) assert not self.is_dec cfg.d_embed = cfg.d_emb # 512 by default cfg.d_model = cfg.d_embed * 4 # 2048 by default assert cfg.d_embed % cfg.n_heads == 0 self.pos_emb = qc.Embed(cfg.n_pos, cfg.d_embed, kw) if cfg.sin_embeds: sin_embed(cfg.n_pos, cfg.d_embed, out=self.pos_emb.weight) if cfg.n_langs > 1 and cfg.use_lang_emb: self.lang_emb = qc.Embed(self.n_langs, cfg.d_embed, kw) self.tok_emb = qc.Embed(self.s_vocab, cfg.d_embed, kw) self.norm_emb = qc.LayerNorm(cfg.d_embed, cfg.eps, kw) self.attns = qc.Stack() self.norm1 = qc.Stack() self.ffnet = qc.Stack() self.norm2 = qc.Stack() for _ in range(self.n_lays): self.attns.append(Attention(cfg.n_heads, cfg.d_embed, kw)) self.norm1.append(qc.LayerNorm(cfg.d_embed, cfg.eps, kw)) self.ffnet.append( MLP( gelu if cfg.gelu_activation else F.relu, drop=cfg.drop, d_model=cfg.d_embed, d_ff=cfg.d_model, kw, ) ) self.norm2.append(qc.LayerNorm(cfg.d_embed, cfg.eps, kw)) self.register_buffer("pos", torch.arange(cfg.n_pos).expand((1, -1))) def forward( self, x, cache=None, head_m=None, langs=None, lengths=[], mask=None, pos=None, typ=None, x_emb=None, *kw, ): cfg = self.cfg if x is None: b, n = x_emb.size()[:-1] else: b, n = x.size() d = x.device if x is not None else x_emb.device if lengths is None: if x is None: lengths = torch.tensor([n] b, device=d) else: lengths = (x != cfg.PAD).sum(dim=1).long() assert lengths.size(0) == b assert lengths.max().item() <= n mask, attn_mask = get_masks(n, lengths, cfg.causal, mask=mask) if pos is None: pos = self.pos[:, :n] else: assert pos.size() == (b, n) if langs is not None: assert langs.size() == (b, n) head_m = self.get_head_m(head_m, cfg.n_lays) if cache is not None and x is not None: _slen = n - cache["slen"] x = x[:, -_slen:] pos = pos[:, -_slen:] if langs is not None: langs = langs[:, -_slen:] mask = mask[:, -_slen:] attn_mask = attn_mask[:, -_slen:] if x_emb is None: x_emb = self.tok_emb(x) y = x_emb + self.pos_emb(pos).expand_as(x_emb) if langs is not None and self.use_lang_emb and cfg.n_langs > 1: y = y + self.lang_emb(langs) if typ is not None: y = y + self.tok_emb(typ) y = self.norm_emb(y) y = F.drop(y, p=self.drop, training=self.training) y = mask.unsqueeze(-1).to(y.dtype) attns = hiddens = () for i in range(cfg.n_lays): hiddens += (y,) ys = self.attns[i](y, attn_mask, cache=cache, head_m=head_m[i], kw) y = ys[0] attns += (ys[1],) y = F.drop(y, p=cfg.drop, training=self.training) y = y + y y = self.norm1[i](y) y = y + self.ffnet[i](y) y = self.norm2[i](y) y = mask.unsqueeze(-1).to(y.dtype) hiddens += (y,) if cache is not None: cache["slen"] += y.size(1) return qo.Base(y, attns, hiddens) class ForChoice(PreTrained): def __init__(self, kw): super().__init__(kw) cfg = self.get_cfg(kw) self.model = Model(kw) self.sum = qc.SeqSummary(kw) self.proj = qc.Linear(cfg.n_labels, 1, kw) def forward(self, x, mask=None, langs=None, typ=None, pos=None, x_emb=None, labels=None, kw): n = x.shape[1] if x is not None else x_emb.shape[1] x, mask, typ, pos, langs = qu.view_2D(x, mask, typ, pos, langs) x_emb = qu.view_3D(x_emb) ys = self.model(x=x, mask=mask, langs=langs, typ=typ, pos=pos, x_emb=x_emb, *kw) y = self.proj(self.sum(ys[0])).view(-1, n) loss = None if labels is not None: loss = nn.CrossEntropyLoss()(y, labels) ys = (y,) + ys[1:] + (loss,) return qo.WithLoss(ys) class ForQASimple(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 class ForQA(PreTrained): def __init__(self, kw): super().__init__(kw) self.get_cfg(kw) self.model = Model(kw) self.proj = qc.SQuADHead(kw) def forward( self, x, beg_pos=None, end_pos=None, is_impossible=None, cls_index=None, p_mask=None, kw ): ys = self.model(x, kw) y = self.proj( ys[0], beg_pos=beg_pos, end_pos=end_pos, cls_index=cls_index, is_impossible=is_impossible, p_mask=p_mask, *kw, ) ys = (y[0],) + ys[1:] + y[1:] return QATop(ys) @dataclass class QATop(qc.Output): logits: tuple = None attns: tuple = None hiddens: tuple = None loss: tuple = None top_beg = None top_beg_i = None top_end = None top_end_i = None class ForSeqClass(PreTrained): def __init__(self, kw): super().__init__(kw) self.get_cfg(kw) self.model = Model(kw) self.proj = qc.SeqSummary(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 LMHead(PreTrained): def __init__(self, kw): super().__init__(kw) self.get_cfg(kw) self.model = Model(kw) self.proj = Projector(kw) def forward(self, x, labels=None, kw): ys = self.model(x, kw) y = self.proj(ys[0], labels) y = y[0] if labels is None else y[1] loss = y[0] if labels is not None else None ys = (y,) + ys[1:] + (loss,) return qo.WithLoss(ys) class Projector(qc.Module): def __init__(self, kw): super().__init__(kw) cfg = self.get_cfg(kw) self.asm = cfg.asm if cfg.asm is False: self.proj = qc.Linear(cfg.d_embed, cfg.s_vocab, bias=True) else: self.proj = nn.AdaptiveLogSoftmaxWithLoss( in_features=cfg.d_embed, n_classes=cfg.s_vocab, cutoffs=cfg.asm_cutoffs, div_value=cfg.asm_div_value, head_bias=True, ) def forward(self, x, y=None): if self.asm is False: ys = (self.proj(x),) if y is not None: loss = F.cross_entropy(ys.view(-1, self.s_vocab), y.view(-1), reduction="mean") ys = (loss,) + ys else: ys = (self.proj.log_prob(x),) if y is not None: _, loss = self.proj(x, y) ys = (loss,) + ys return ys def get_masks(slen, lengths, causal, mask=None): alen = torch.arange(slen, dtype=torch.long, device=lengths.device) if mask is None: assert lengths.max().item() <= slen mask = alen < lengths[:, None] b = lengths.size(0) if causal: attn_mask = alen[None, None, :].repeat(b, slen, 1) <= alen[None, :, None] else: attn_mask = mask assert mask.size() == (b, slen) assert causal is False or attn_mask.size() == (b, slen, slen) return mask, attn_mask class Attention(qc.Module): hs = qc.Hypers({"d_model", "n_heads"}, {"drop_attn": 0.0}) NEW_ID = itertools.count() def __init__(self, ps={}, hs=[], kw): super().__init__(ps, [self.hs] + hs, kw) self.lay_id = next(Attention.NEW_ID) cfg = self.get_cfg(kw) n, d = cfg.n_heads, cfg.d_model cfg.s_head = int(d / n) cfg.scale = cfg.s_head-0.5 assert d % n == 0 self.query = qc.Linear(d, d, kw) self.key = qc.Linear(d, d, kw) self.value = qc.Linear(d, d, kw) self.drop = qc.Dropout(cfg.drop_attn, kw) self.proj = qc.Linear(d, d, kw) split_heads = qa.split_heads def forward(self, x, cache=None, enc=None, head_m=None, mask=None, kw): cfg = self.cfg q = self.split_heads(self.query(x) cfg.scale) if enc is None: k = self.split_heads(self.key(x)) v = self.split_heads(self.value(x)) if cache is not None: if self.lay_id in cache: k = torch.cat([cache[self.lay_id][0], k], dim=2) v = torch.cat([cache[self.lay_id][1], v], dim=2) else: if cache is None or self.lay_id not in cache: k = self.split_heads(self.key(enc)) v = self.split_heads(self.value(enc)) else: k, v = cache[self.lay_id] # ??? cache[self.lay_id] = (k, v) y = torch.matmul(q, k.transpose(2, 3)) b, qlen, _ = x.size() if enc is None: klen = qlen if cache is None else cache["slen"] + qlen else: klen = enc.size(1) s = (b, 1, qlen, klen) if mask.dim() == 3 else (b, 1, 1, klen) mask = (mask == 0).view(s).expand_as(y) y.masked_fill_(mask, -float("inf")) y = self.drop(F.softmax(y.float(), dim=-1).type_as(y)) if head_m is not None: y = y * head_m a = y y = torch.matmul(y, v) y = qa.join_heads(y) return self.proj(y), a, cache	{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], "/qnarre/prep/convert/bert.py": ["/qnarre/prep/config/bert.py", 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33,716	quantapix/qnarre	refs/heads/main	/tools/triton/python/triton/tools/aot.py	import argparse import sys import triton._C.libtriton.triton as libtriton import triton.compiler.compiler as tc if __name__ == '__main__': # valid source and target formats VALID_FORMATS = ['triton-ir', 'triton-gpu-ir', 'llvm-ir', 'ptx', 'amdgcn'] # set up the argument parser # TODO: conditional requirements parser = argparse.ArgumentParser() parser.add_argument('src', help="Source file to compile") parser.add_argument('--target', required=True, help="Target format, one of: " + ', '.join(VALID_FORMATS)) parser.add_argument('--sm', type=int, help="Compute capability to compile for") parser.add_argument('--ptx-version', type=int, help="PTX version to compile for") parser.add_argument('--gfx', type=str, help="AMDGPU target to compile for") parser.add_argument('--triple', type=str, help="target triple, for example: amdgcn-amd-amdhsa") parser.add_argument('--features', type=str, help="target features, for example: +sramecc,-xnack") parser.add_argument('--num_warps', type=int, help="number of warps to compile ttgir for") # parse the args args = parser.parse_args() # TODO: clean-up and re-use triton.compiler primitive functions # check for validity of format arguments if args.target not in VALID_FORMATS: print("Invalid target format: " + args.target) sys.exit(0) # parse source file to MLIR module context = libtriton.ir.context() module = libtriton.ir.parse_mlir_module(args.src, context) module.context = context # optimizer triton-ir module = tc.optimize_ttir(module, arch=args.sm) if args.target == 'triton-ir': print(module.str()) sys.exit(0) if not args.num_warps: args.num_warps = 4 # llvm-ir -> amdgcn if args.target == 'amdgcn': # auto detect available architecture and features # if nothing detected, set with default values arch_details = tc.get_amdgpu_arch_fulldetails() if not arch_details: arch_name = "" arch_triple = "amdgcn-amd-amdhsa" arch_features = "" else: arch_triple, arch_name, arch_features = arch_details # stop processing if architecture name is not automatically detected and is not set manually if not args.gfx and not arch_name: raise argparse.ArgumentError(None, "Must specify --gfx for AMDGCN compilation") # rewrite default and automatically detected values with manually provided data if args.gfx: arch_name = args.gfx if args.triple: arch_triple = args.triple if args.features: arch_features = args.features # triton-ir -> triton-gpu-ir # use compute_capability == 80 module = tc.ttir_to_ttgir(module, num_warps=args.num_warps) # num_stages=3, compute_capability=80) module = tc.optimize_ttgir(module, num_stages=3, arch=80) # triton-gpu-ir -> llvm-ir # use compute_capability == 80 module = tc.ttgir_to_llir(module, extern_libs=None, arch=80) # llvm-ir -> amdgcn asm, hsaco binary module, hsaco_path = tc.llir_to_amdgcn_and_hsaco(module, arch_name, arch_triple, arch_features) print(hsaco_path) print(module) sys.exit(0) if not args.sm: raise argparse.ArgumentError(None, "Must specify --sm for PTX compilation") # triton-ir -> triton-gpu-ir module = tc.ttir_to_ttgir(module, num_warps=args.num_warps) module = tc.optimize_ttgir(module, num_stages=3, arch=args.sm) if args.target == 'triton-gpu-ir': print(module.str()) sys.exit(0) # triton-gpu-ir -> llvm-ir module = tc.ttgir_to_llir(module, extern_libs=None, arch=args.sm) if args.target == 'llvm-ir': print(module) sys.exit(0) # llvm-ir -> ptx if args.target == 'ptx': if not args.ptx_version: raise argparse.ArgumentError(None, "Must specify --ptx-version for PTX compilation") module = tc.llir_to_ptx(module, arch=args.sm, ptx_version=args.ptx_version) # llvm-ir -> amdgcn if args.target == 'amdgcn': if not args.gfx: raise argparse.ArgumentError(None, "Must specify --gfx for AMDGCN compilation") module, hsaco_path = tc.llir_to_amdgcn_and_hsaco(module, args.gfx) print(module)	{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], 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33,717	quantapix/qnarre	refs/heads/main	/qnarre/base/conjecture.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 .claim import Claim from .proof import Proof from .narrative import Node class Conjecture(Node): claims = proofs = None def __init__(self, text=None, proofs=None, kw): super().__init__(kw) if self.claims is None: self.claims, self.proofs = [], [] if text: for k in ('factor', 'bias', 'weight'): kw.pop(k, None) self.claims.append(Claim(text=text, **kw)) if proofs: ps = (p.strip() for p in proofs.split('\|')) self.proofs.extend(Proof.create(name=p) for p in ps if p) @property def weight(self): cs = tuple(c.weight for c in self.claims) ps = tuple(p.weight for p in self.proofs) return self.partial(cs, ps) + self.bias class Reality(Conjecture): sign = '!r' @property def coherence(self): return self.weight @property def value(self): return '{} C{}'.format(super().value, self.coherence) @property def fields(self): fs = super().fields fs['Reality'] = self.name ls = [] for c in self.claims: fs2 = c.fields fs2.update(fs) fs2['Coherence'] = self.partial(c.weight) ls.append(fs2) for p in sorted(self.proofs, key=lambda p: p.sequence): fs2 = p.fields fs2['Topic'] = fs['Topic'] fs2['Narrative'] = fs['Narrative'] fs2['Reality'] = fs['Reality'] fs2['Coherence'] = self.partial(p.weight) ls.append(fs2) return ls class Dissent(Conjecture): agency = None @property def fragment(self): return self.weight @property def value(self): return '{} F{}'.format(super().value, self.fragment) @property def fields(self): fs = super().fields fs['Dissent'] = self.name ls = [] for c in self.claims: fs2 = c.fields fs2.update(fs) fs2['Fragment'] = self.partial(c.weight) ls.append(fs2) for p in sorted(self.proofs, key=lambda p: p.sequence): fs2 = p.fields fs2['Topic'] = fs['Topic'] fs2['Narrative'] = fs['Narrative'] fs2['Dissent'] = fs['Dissent'] fs2['Fragment'] = self.partial(p.weight) ls.append(fs2) return ls class Misreading(Dissent): sign = '?m' _factor = 0 class Fairness(Dissent): sign = '?a' _factor = 0.25 class Negligence(Dissent): sign = '?n' _factor = 0.25 class Proportionality(Dissent): sign = '?y' _factor = 0.5 class Contradiction(Dissent): sign = '?x' _factor = 0.5 class Isolation(Dissent): sign = '?i' _factor = 0.75 class Omission(Dissent): sign = '?o' _factor = 0.75 class Distortion(Dissent): sign = '?s' _factor = 1	{"/qnarre/prep/convert/xlnet.py": ["/qnarre/prep/config/xlnet.py", "/qnarre/models/xlnet.py"], 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33,718	quantapix/qnarre	refs/heads/main	/tools/triton/python/triton/testing.py	import functools import os import subprocess import sys from contextlib import contextmanager import triton._C.libtriton.triton as _triton def nvsmi(attrs): attrs = ','.join(attrs) cmd = ['nvidia-smi', '-i', '0', '--query-gpu=' + attrs, '--format=csv,noheader,nounits'] out = subprocess.check_output(cmd) ret = out.decode(sys.stdout.encoding).split(',') ret = [int(x) for x in ret] return ret def do_bench(fn, warmup=25, rep=100, grad_to_none=None, quantiles=None, fast_flush=True, return_mode="mean"): assert return_mode in ["min", "max", "mean", "median"] import torch """ Benchmark the runtime of the provided function. By default, return the median runtime of :code:`fn` along with the 20-th and 80-th performance percentile. :param fn: Function to benchmark :type fn: Callable :param warmup: Warmup time (in ms) :type warmup: int :param rep: Repetition time (in ms) :type rep: int :param grad_to_none: Reset the gradient of the provided tensor to None :type grad_to_none: torch.tensor, optional :param quantiles: Performance percentile to return in addition to the median. :type quantiles: list[float] :param fast_flush: Use faster kernel to flush L2 between measurements :type fast_flush: bool """ # Estimate the runtime of the function fn() torch.cuda.synchronize() start_event = torch.cuda.Event(enable_timing=True) end_event = torch.cuda.Event(enable_timing=True) start_event.record() for _ in range(5): fn() end_event.record() torch.cuda.synchronize() estimate_ms = start_event.elapsed_time(end_event) / 5 # compute number of warmup and repeat n_warmup = max(1, int(warmup / estimate_ms)) n_repeat = max(1, int(rep / estimate_ms)) # We maintain a buffer of 256 MB that we clear # before each kernel call to make sure that the L2 # doesn't contain any input data before the run start_event = [torch.cuda.Event(enable_timing=True) for i in range(n_repeat)] end_event = [torch.cuda.Event(enable_timing=True) for i in range(n_repeat)] if fast_flush: cache = torch.empty(int(256e6 // 4), dtype=torch.int, device='cuda') else: cache = torch.empty(int(256e6), dtype=torch.int8, device='cuda') # Warm-up for _ in range(n_warmup): fn() # Benchmark for i in range(n_repeat): # we don't want `fn` to accumulate gradient values # if it contains a backward pass. So we clear the # provided gradients if grad_to_none is not None: for x in grad_to_none: x.grad = None # we clear the L2 cache before each run cache.zero_() # record time of `fn` start_event[i].record() fn() end_event[i].record() # Record clocks torch.cuda.synchronize() times = torch.tensor([s.elapsed_time(e) for s, e in zip(start_event, end_event)]) if quantiles is not None: ret = torch.quantile(times, torch.tensor(quantiles)).tolist() if len(ret) == 1: ret = ret[0] return ret return getattr(torch, return_mode)(times).item() def assert_close(x, y, atol=None, rtol=None, err_msg=''): import numpy as np import torch # canonicalize arguments to be tensors if not isinstance(x, torch.Tensor): x = torch.tensor(x) if not isinstance(y, torch.Tensor): y = torch.tensor(y) # absolute tolerance if atol is None: atol = 1e-2 atol = atol(x.dtype) if callable(atol) else atol # relative tolerance hook if rtol is None: rtol = 0. rtol = rtol(x.dtype) if callable(rtol) else rtol # we use numpy instead of pytorch # as it seems more memory efficient # pytorch tends to oom on large tensors if isinstance(x, torch.Tensor): if x.dtype == torch.bfloat16: x = x.float() x = x.cpu().detach().numpy() if isinstance(y, torch.Tensor): if y.dtype == torch.bfloat16: y = y.float() y = y.cpu().detach().numpy() # we handle size==1 case separately as we can # provide better error message there if x.size > 1 or y.size > 1: np.testing.assert_allclose(x, y, atol=atol, rtol=rtol, equal_nan=True) return if not np.allclose(x, y, atol=atol, rtol=rtol): raise AssertionError(f'{err_msg} {x} is not close to {y} (atol={atol}, rtol={rtol})') class Benchmark: """ This class is used by the :code:`perf_report` function to generate line plots with a concise API. """ def __init__( self, x_names, x_vals, line_arg, line_vals, line_names, plot_name, args, xlabel='', ylabel='', x_log=False, y_log=False, color=None, styles=None, ): """ Constructor :param x_names: Name of the arguments that should appear on the x axis of the plot. If the list contains more than one element, all the arguments are assumed to have the same value. :type x_names: List[str] :param x_vals: List of values to use for the arguments in :code:`x_names`. :type x_vals: List[Any] :param line_arg: Argument name for which different values correspond to different lines in the plot. :type line_arg: str :param line_vals: List of values to use for the arguments in :code:`line_arg`. :type line_vals: List[str] :param line_names: Label names for the different lines. :type line_names: List[str] :param plot_name: Name of the plot. :type plot_name: str :param args: List of arguments to remain fixed throughout the benchmark. :type args: List[str] :param xlabel: Label for the x axis of the plot. :type xlabel: str, optional :param ylabel: Label for the y axis of the plot. :type ylabel: str, optional :param x_log: Whether the x axis should be log scale. :type x_log: bool, optional :param y_log: Whether the y axis should be log scale. :type y_log: bool, optional """ self.x_names = x_names self.x_vals = x_vals self.x_log = x_log self.line_arg = line_arg self.line_vals = line_vals self.line_names = line_names self.y_log = y_log self.styles = styles # plot info self.xlabel = xlabel self.ylabel = ylabel self.plot_name = plot_name self.args = args class Mark: def __init__(self, fn, benchmarks): self.fn = fn self.benchmarks = benchmarks def _run(self, bench, save_path, show_plots, print_data): import os import matplotlib.pyplot as plt import pandas as pd y_mean = bench.line_names y_min = [f'{x}-min' for x in bench.line_names] y_max = [f'{x}-max' for x in bench.line_names] df = pd.DataFrame(columns=[bench.x_names[0]] + y_mean + y_min + y_max) for x in bench.x_vals: x_args = {x_name: x for x_name in bench.x_names} row_mean, row_min, row_max = [], [], [] for y in bench.line_vals: ret = self.fn(x_args, {bench.line_arg: y}, *bench.args) try: y_mean, y_min, y_max = ret except TypeError: y_mean, y_min, y_max = ret, None, None row_mean += [y_mean] row_min += [y_min] row_max += [y_max] df.loc[len(df)] = [x] + row_mean + row_min + row_max if bench.plot_name: plt.figure() ax = plt.subplot() x = bench.x_names[0] for i, y in enumerate(bench.line_names): y_min, y_max = df[y + '-min'], df[y + '-max'] col = bench.styles[i][0] if bench.styles else None sty = bench.styles[i][1] if bench.styles else None ax.plot(df[x], df[y], label=y, color=col, ls=sty) if y_min is not None and y_max is not None: ax.fill_between(df[x], y_min, y_max, alpha=0.15, color=col) ax.legend() xlabel = bench.xlabel if bench.xlabel else " = ".join(bench.x_names) ax.set_xlabel(xlabel) ax.set_ylabel(bench.ylabel) # ax.set_title(bench.plot_name) ax.set_xscale("log" if bench.x_log else "linear") ax.set_yscale("log" if bench.y_log else "linear") if show_plots: plt.show() if save_path: plt.savefig(os.path.join(save_path, f"{bench.plot_name}.png")) df = df[[bench.x_names[0]] + bench.line_names] if print_data: print(bench.plot_name + ':') print(df) if save_path: df.to_csv(os.path.join(save_path, f"{bench.plot_name}.csv"), float_format='%.1f', index=False) def run(self, show_plots=False, print_data=False, save_path=''): has_single_bench = isinstance(self.benchmarks, Benchmark) benchmarks = [self.benchmarks] if has_single_bench else self.benchmarks if save_path: html = open(os.path.join(save_path, "results.html"), "w") html.write("<html><body>\n") for bench in benchmarks: self._run(bench, save_path, show_plots, print_data) if save_path: html.write(f"<image src=\"{bench.plot_name}.png\"/>\n") if save_path: html.write("</body></html>\n") def perf_report(benchmarks): """ Mark a function for benchmarking. The benchmark can then be executed by using the :code:`.run` method on the return value. :param benchmarks: Benchmarking configurations. :type benchmarks: List of :class:`Benchmark` """ wrapper = lambda fn: Mark(fn, benchmarks) return wrapper def get_dram_gbps(backend=None, device=None): ''' return DRAM bandwidth in GB/s ''' import torch from .runtime import driver if not backend: backend = _triton.runtime.backend.CUDA if not device: device = torch.cuda.current_device() mem_clock_khz = driver.utils.get_device_properties(device)["mem_clock_rate"] # in kHz bus_width = driver.utils.get_device_properties(device)["mem_bus_width"] bw_gbps = mem_clock_khz bus_width * 2 / 1e6 / 8 # In GB/s return bw_gbps def get_max_tensorcore_tflops(dtype, backend=None, device=None, clock_rate=None): import torch from .runtime import driver if not backend: backend = _triton.runtime.backend.CUDA if not device: device = torch.cuda.current_device() num_subcores = driver.utils.get_device_properties(device)["multiprocessor_count"] * 4 if not clock_rate: clock_rate = driver.utils.get_device_properties(device)["sm_clock_rate"] # in kHz capability = torch.cuda.get_device_capability(device) if capability[0] < 8: assert dtype == torch.float16 ops_per_sub_core = 256 # 2 4x4x4 Tensor Cores else: if dtype == torch.float32: ops_per_sub_core = 256 elif dtype in [torch.float16, torch.bfloat16]: ops_per_sub_core = 512 elif dtype == torch.int8: ops_per_sub_core = 1024 else: raise RuntimeError("dtype not supported") tflops = num_subcores * clock_rate * ops_per_sub_core * 1e-9 return tflops # create decorator that wraps test function into # a cuda-memcheck system call def cuda_memcheck(*target_kwargs): def decorator(test_fn): @functools.wraps(test_fn) def wrapper(args, *kwargs): import psutil ppid_name = psutil.Process(os.getppid()).name() run_cuda_memcheck = target_kwargs.items() <= kwargs.items() if run_cuda_memcheck and ppid_name != "cuda-memcheck": path = os.path.realpath(test_fn.__globals__["__file__"]) # get path of current file env = {"PATH": os.environ["PATH"], "PYTORCH_NO_CUDA_MEMORY_CACHING": "1"} assert 'request' in kwargs, "memcheck'ed test must have a (possibly unused) `request` fixture" test_id = kwargs['request'].node.callspec.id cmd = f"{path}::{test_fn.__name__}[{test_id}]" out = subprocess.run(["cuda-memcheck", "pytest", "-vs", cmd], capture_output=True, env=env) assert out.returncode == 0, "cuda-memcheck returned an error: bounds checking failed" assert "ERROR SUMMARY: 0 errors" in str(out.stdout) else: test_fn(args, *kwargs) return wrapper return decorator def nvsmi_attr(attrs): attrs = ",".join(attrs) cmd = [ "nvidia-smi", "-i", "0", "--query-gpu=" + attrs, "--format=csv,noheader,nounits", ] out = subprocess.check_output(cmd) ret = out.decode(sys.stdout.encoding).split(",") ret = [int(x) for x in ret] return ret @contextmanager def set_gpu_clock(ref_sm_clock=1350, ref_mem_clock=1215): try: subprocess.check_output(["nvidia-smi", "-i", "0", "-pm", "1"]) subprocess.check_output( [ "nvidia-smi", "-i", "0", f"--lock-gpu-clocks={ref_sm_clock},{ref_sm_clock}", ] ) subprocess.check_output( [ "nvidia-smi", "-i", "0", f"--lock-memory-clocks={ref_mem_clock},{ref_mem_clock}", ] ) cur_sm_clock = nvsmi_attr(["clocks.current.sm"])[0] cur_mem_clock = nvsmi_attr(["clocks.current.memory"])[0] assert abs(cur_sm_clock - ref_sm_clock) < 10, f"GPU SMs must run at {ref_sm_clock} MHz" assert abs(cur_mem_clock - ref_mem_clock) < 10, f"GPU SMs must run at {ref_mem_clock} MHz" tflops = 1e-6 2 * 108 * 4 * 256 * ref_sm_clock gbps = 640 * 2 * ref_mem_clock * 1e-3 yield tflops, gbps finally: subprocess.check_output(["nvidia-smi", "-i", "0", "-pm", "0"]) subprocess.check_output(["nvidia-smi", "-i", "0", "-rgc"]) subprocess.check_output(["nvidia-smi", "-i", "0", "-rmc"]) def get_max_simd_tflops(dtype, backend=None, device=None): import torch from .runtime import driver if not backend: backend = _triton.runtime.backend.CUDA if not device: device = torch.cuda.current_device() num_subcores = driver.utils.get_device_properties(device)["multiprocessor_count"] * 4 clock_rate = driver.utils.get_device_properties(device)["sm_clock_rate"] # in kHz capability = torch.cuda.get_device_capability() if capability[0] < 8: if dtype == torch.float32: ops_per_sub_core = 32 # 216 elif dtype == torch.float16: ops_per_sub_core = 64 else: raise RuntimeError("dtype not supported") else: if dtype == torch.float32: ops_per_sub_core = 32 elif dtype in [torch.float16, torch.bfloat16]: ops_per_sub_core = 64 else: raise RuntimeError("dtype not supported") tflops = num_subcores clock_rate * ops_per_sub_core * 1e-9 return tflops	{"/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", 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33,719	quantapix/qnarre	refs/heads/main	/qnarre/tokens/fast.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 from collections import defaultdict from tokenizers import Tokenizer as TokenizerFast from tokenizers.trainers import BpeTrainer, UnigramTrainer, WordLevelTrainer, WordPieceTrainer from .convert_slow_tokenizer import convert_slow_tokenizer from .file_utils import PaddingStrategy from .utils import PreTrainedTokenizer from .base import ( AddedToken, BatchEncoding, PreTrainedTokenizerBase, SpecialTokensMixin, TruncationStrategy, ) from transformers.utils import logging log = logging.get_logger(__name__) # Fast tokenizers (provided by HuggingFace tokenizer's library) can be saved in a single file TOKENIZER_FILE = "tokenizer.json" SPECIAL_TOKENS_MAP_FILE = "special_tokens_map.json" TOKENIZER_CONFIG_FILE = "tokenizer_config.json" # Slow tokenizers have an additional added tokens files ADDED_TOKENS_FILE = "added_tokens.json" MODEL_TO_TRAINER_MAPPING = { "BPE": BpeTrainer, "Unigram": UnigramTrainer, "WordLevel": WordLevelTrainer, "WordPiece": WordPieceTrainer, } VOCAB_FS = {"tokenizer_file": TOKENIZER_FILE} class PreTrainedTokenizerFast(PreTrainedTokenizerBase): vocab_fs = VOCAB_FS slow_tokenizer_class: PreTrainedTokenizer = None can_save_slow_tokenizer = True def __init__(self, args, kw): tokenizer_object = kw.pop("tokenizer_object", None) slow_tokenizer = kw.pop("__slow_tokenizer", None) fast_tokenizer_file = kw.pop("tokenizer_file", None) from_slow = kw.pop("from_slow", False) if from_slow and slow_tokenizer is None and self.slow_tokenizer_class is None: raise ValueError( "Cannot instantiate this tokenizer from a slow version. If it's based on sentencepiece, make sure you " "have sentencepiece installed." ) if tokenizer_object is not None: fast_tokenizer = tokenizer_object elif fast_tokenizer_file is not None and not from_slow: # We have a serialization from tokenizers which let us directly build the backend fast_tokenizer = TokenizerFast.from_file(fast_tokenizer_file) elif slow_tokenizer is not None: # We need to convert a slow tokenizer to build the backend fast_tokenizer = convert_slow_tokenizer(slow_tokenizer) elif self.slow_tokenizer_class is not None: # We need to create and convert a slow tokenizer to build the backend slow_tokenizer = self.slow_tokenizer_class(args, kw) fast_tokenizer = convert_slow_tokenizer(slow_tokenizer) else: raise ValueError( "Couldn't instantiate the backend tokenizer from one of: \n" "(1) a `tokenizers` library serialization file, \n" "(2) a slow tokenizer instance to convert or \n" "(3) an equivalent slow tokenizer class to instantiate and convert. \n" "You need to have sentencepiece installed to convert a slow tokenizer to a fast one." ) self._tokenizer = fast_tokenizer if slow_tokenizer is not None: kw.update(slow_tokenizer.init_kw) self._decode_use_source_tokenizer = False # We call this after having initialized the backend tokenizer because we update it. super().__init__(kw) @property def is_fast(self): return True @property def s_vocab(self): """ `int`: Size of the base vocabulary (without the added tokens). """ return self._tokenizer.get_vocab_size(with_added_tokens=False) def get_vocab(self): return self._tokenizer.get_vocab(with_added_tokens=True) @property def vocab(self): return self.get_vocab() def get_added_vocab(self): base_vocab = self._tokenizer.get_vocab(with_added_tokens=False) full_vocab = self._tokenizer.get_vocab(with_added_tokens=True) added_vocab = dict( (tok, index) for tok, index in full_vocab.items() if tok not in base_vocab ) return added_vocab def __len__(self): return self._tokenizer.get_vocab_size(with_added_tokens=True) @property def backend_tokenizer(self): return self._tokenizer @property def decoder(self): return self._tokenizer.decoder def _convert_encoding( self, encoding, 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, ): if return_token_type_ids is None: return_token_type_ids = "typ_ids" in self.model_input_names if return_attention_mask is None: return_attention_mask = "mask" in self.model_input_names if return_overflowing_tokens and encoding.overflowing is not None: encodings = [encoding] + encoding.overflowing else: encodings = [encoding] encoding_dict = defaultdict(list) for e in encodings: encoding_dict["input_ids"].append(e.ids) if return_token_type_ids: encoding_dict["typ_ids"].append(e.type_ids) if return_attention_mask: encoding_dict["mask"].append(e.mask) if return_special_tokens_mask: encoding_dict["special_tokens_mask"].append(e.special_tokens_mask) if return_offsets_mapping: encoding_dict["offset_mapping"].append(e.offsets) if return_length: encoding_dict["length"].append(len(e.ids)) return encoding_dict, encodings 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): index = self._tokenizer.token_to_id(token) if index is None: return self.unk_token_id return index def _convert_id_to_token(self, index): return self._tokenizer.id_to_token(int(index)) def _add_tokens(self, new_tokens, special_tokens=False): if special_tokens: return self._tokenizer.add_special_tokens(new_tokens) return self._tokenizer.add_tokens(new_tokens) def num_special_tokens_to_add(self, pair=False): return self._tokenizer.num_special_tokens_to_add(pair) def convert_ids_to_tokens(self, ids, skip_special_tokens=False): if isinstance(ids, int): return self._tokenizer.id_to_token(ids) tokens = [] for index in ids: index = int(index) if skip_special_tokens and index in self.all_special_ids: continue tokens.append(self._tokenizer.id_to_token(index)) return tokens def tokenize(self, text, pair=None, add_special_tokens=False, kw): return self.encode_plus( text=text, text_pair=pair, add_special_tokens=add_special_tokens, kw ).tokens() def set_truncation_and_padding( self, padding_strategy, truncation_strategy, max_len, stride, pad_to_multiple_of, ): _truncation = self._tokenizer.truncation _padding = self._tokenizer.padding # Set truncation and padding on the backend tokenizer if truncation_strategy == TruncationStrategy.DO_NOT_TRUNCATE: if _truncation is not None: self._tokenizer.no_truncation() else: target = { "max_len": max_len, "stride": stride, "strategy": truncation_strategy.value, "direction": self.truncation_side, } if _truncation is None: current = None else: current = {k: _truncation.get(k, None) for k in target} if current != target: self._tokenizer.enable_truncation(target) if padding_strategy == PaddingStrategy.DO_NOT_PAD: if _padding is not None: self._tokenizer.no_padding() else: length = max_len if padding_strategy == PaddingStrategy.MAX_LENGTH else None target = { "length": length, "direction": self.padding_side, "pad_id": self.PAD, "pad": self.pad, "pad_type_id": self.pad_token_type_id, "pad_to_multiple_of": pad_to_multiple_of, } if _padding != target: self._tokenizer.enable_padding(target) 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, ): if not isinstance(batch_text_or_text_pairs, list): raise TypeError( f"batch_text_or_text_pairs has to be a list (got {type(batch_text_or_text_pairs)})" ) # Set the truncation and padding strategy and restore the initial configuration self.set_truncation_and_padding( padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_len=max_len, stride=stride, pad_to_multiple_of=pad_to_multiple_of, ) encodings = self._tokenizer.encode_batch( batch_text_or_text_pairs, add_special_tokens=add_special_tokens, is_pretokenized=is_split_into_words, ) tokens_and_encodings = [ self._convert_encoding( encoding=encoding, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, ) for encoding in encodings ] sanitized_tokens = {} for key in tokens_and_encodings[0][0].keys(): stack = [e for item, _ in tokens_and_encodings for e in item[key]] sanitized_tokens[key] = stack sanitized_encodings = [e for _, item in tokens_and_encodings for e in item] if return_overflowing_tokens: overflow_to_sample_mapping = [] for i, (toks, _) in enumerate(tokens_and_encodings): overflow_to_sample_mapping += [i] * len(toks["input_ids"]) sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping for input_ids in sanitized_tokens["input_ids"]: self._eventual_warn_about_too_long_sequence(input_ids, max_len, verbose) return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors) 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, ): batched_input = [(text, text_pair)] if text_pair else [text] batched_output = self._batch_encode_plus( batched_input, is_split_into_words=is_split_into_words, 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_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, kw, ) # Return tensor is None, then we can remove the leading batch axis # Overflowing tokens are returned as a batch of output so we keep them in this case if return_tensors is None and not return_overflowing_tokens: batched_output = BatchEncoding( { key: value[0] if len(value) > 0 and isinstance(value[0], list) else value for key, value in batched_output.items() }, batched_output.encodings, ) self._eventual_warn_about_too_long_sequence(batched_output["input_ids"], max_len, verbose) return batched_output def convert_tokens_to_string(self, tokens): return self.backend_tokenizer.decoder.decode(tokens) def _decode( self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True, kw, ): self._decode_use_source_tokenizer = kw.pop("use_source_tokenizer", False) if isinstance(token_ids, int): token_ids = [token_ids] text = self._tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens) if clean_up_tokenization_spaces: clean_text = self.clean_up_tokenization(text) return clean_text else: return text def _save_pretrained( self, save_directory, file_names, legacy_format=None, filename_prefix=None, ): save_directory = str(save_directory) if self.slow_tokenizer_class is None and legacy_format is True: raise ValueError( "Your tokenizer does not have a legacy version defined and therefore cannot register this version. You " "might consider leaving the legacy_format at `None` or setting it to `False`." ) save_slow = ( (legacy_format is None or legacy_format is True) and self.slow_tokenizer_class is not None and self.can_save_slow_tokenizer ) save_fast = legacy_format is None or legacy_format is False if save_slow: added_tokens_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + ADDED_TOKENS_FILE, ) added_vocab = self.get_added_vocab() if added_vocab: with open(added_tokens_file, "w", encoding="utf-8") as f: out_str = json.dumps(added_vocab, ensure_ascii=False) f.write(out_str) vocab_files = self.save_vocabulary(save_directory, filename_prefix=filename_prefix) file_names = file_names + vocab_files + (added_tokens_file,) if save_fast: tokenizer_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + TOKENIZER_FILE ) self.backend_tokenizer.save(tokenizer_file) file_names = file_names + (tokenizer_file,) return file_names def train_new_from_iterator( self, text_iterator, s_vocab, new_special_tokens=None, special_tokens_map=None, kw ): tokenizer_json = json.loads(self._tokenizer.to_str()) # Remove added tokens for now (uses IDs of tokens) added_tokens = tokenizer_json.pop("added_tokens") # Remove post processor for now (uses IDs of tokens) post_processor = tokenizer_json.pop("post_processor") unk = None # Remove vocab if tokenizer_json["model"]["type"] == "BPE": tokenizer_json["model"]["vocab"] = {} tokenizer_json["model"]["merges"] = [] elif tokenizer_json["model"]["type"] == "Unigram": if tokenizer_json["model"]["unk_id"] is not None: unk_id = tokenizer_json["model"]["unk_id"] unk = tokenizer_json["model"]["vocab"][unk_id][0] if special_tokens_map is not None and unk in special_tokens_map: unk = special_tokens_map[unk] tokenizer_json["model"]["unk_id"] = 0 tokenizer_json["model"]["vocab"] = [[unk, 0.0]] elif tokenizer_json["model"]["type"] in ["WordLevel", "WordPiece"]: tokenizer_json["model"]["vocab"] = {} else: raise ValueError( f"This method does not support this type of tokenizer (found {tokenizer_json['model']['type']}) " "only BPE, Unigram, WordLevel and WordPiece." ) if ( special_tokens_map is not None and "unk" in tokenizer_json["model"] and tokenizer_json["model"]["unk"] in special_tokens_map ): tokenizer_json["model"]["unk"] = special_tokens_map[tokenizer_json["model"]["unk"]] tokenizer = TokenizerFast.from_str(json.dumps(tokenizer_json)) # Get the special tokens from the current tokenizer if none are specified. special_tokens = [] for added_token in added_tokens: special = added_token.pop("special", None) _ = added_token.pop("id", None) if tokenizer_json["model"]["type"] != "Unigram" and not special: continue if special_tokens_map is not None and added_token["content"] in special_tokens_map: added_token["content"] = special_tokens_map[added_token["content"]] special_tokens.append(AddedToken(added_token)) if new_special_tokens is not None: special_tokens.extend(new_special_tokens) # Trainer needs to know the end of word / continuing subword thingies in BPE if ( tokenizer_json["model"]["type"] == "BPE" and "continuing_subword_prefix" not in kw and tokenizer_json["model"]["continuing_subword_prefix"] is not None ): kw["continuing_subword_prefix"] = tokenizer_json["model"]["continuing_subword_prefix"] if ( tokenizer_json["model"]["type"] == "BPE" and "end_of_word_suffix" not in kw and tokenizer_json["model"]["end_of_word_suffix"] is not None ): kw["end_of_word_suffix"] = tokenizer_json["model"]["end_of_word_suffix"] if tokenizer_json["model"]["type"] == "Unigram" and unk is not None: kw["unk"] = unk trainer_class = MODEL_TO_TRAINER_MAPPING[tokenizer_json["model"]["type"]] trainer = trainer_class(s_vocab=s_vocab, special_tokens=special_tokens, kw) tokenizer.train_from_iterator(text_iterator, trainer=trainer) if post_processor is not None: trained_tokenizer_json = json.loads(tokenizer.to_str()) # Almost done, we just have to adjust the token IDs in the post processor if "special_tokens" in post_processor: for key in post_processor["special_tokens"]: tokens = post_processor["special_tokens"][key]["tokens"] if special_tokens_map is not None: tokens = [special_tokens_map.get(token, token) for token in tokens] post_processor["special_tokens"][key]["tokens"] = tokens post_processor["special_tokens"][key]["ids"] = [ tokenizer.token_to_id(token) for token in tokens ] for special_token in ["cls", "sep"]: if special_token in post_processor: token, _ = post_processor[special_token] if special_tokens_map is not None and token in special_tokens_map: token = special_tokens_map[token] token_id = tokenizer.token_to_id(token) post_processor[special_token] = [token, token_id] trained_tokenizer_json["post_processor"] = post_processor tokenizer = TokenizerFast.from_str(json.dumps(trained_tokenizer_json)) kw = self.init_kw.copy() # Map pad/cls/mask token at the Transformers level special_tokens_list = SpecialTokensMixin.SPECIAL_TOKENS_ATTRIBUTES.copy() special_tokens_list.remove("additional_special_tokens") for token in special_tokens_list: # Get the private one to avoid unnecessary warnings. if getattr(self, f"_{token}") is not None: special_token = getattr(self, token) if special_tokens_map is not None and special_token in special_tokens_map: special_token = special_tokens_map[special_token] special_token_full = getattr(self, f"_{token}") if isinstance(special_token_full, AddedToken): # Create an added token with the same parameters except the content kw[token] = AddedToken( special_token, single_word=special_token_full.single_word, lstrip=special_token_full.lstrip, rstrip=special_token_full.rstrip, normalized=special_token_full.normalized, ) else: kw[token] = special_token additional_special_tokens = self.additional_special_tokens if new_special_tokens is not None: additional_special_tokens.extend(new_special_tokens) if len(additional_special_tokens) > 0: 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"/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"]}
33,720	quantapix/qnarre	refs/heads/main	/qnarre/prep/dataset/swag.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 csv import datasets as ds _URLs = { "full": { "train": "https://raw.githubusercontent.com/rowanz/swagaf/master/data/train_full.csv", "val": "https://raw.githubusercontent.com/rowanz/swagaf/master/data/val_full.csv", }, "regular": { "train": "https://raw.githubusercontent.com/rowanz/swagaf/master/data/train.csv", "val": "https://raw.githubusercontent.com/rowanz/swagaf/master/data/val.csv", "test": "https://raw.githubusercontent.com/rowanz/swagaf/master/data/test.csv", }, } class Swag(ds.GeneratorBasedBuilder): BUILDER_CONFIGS = [ ds.BuilderConfig(name="regular", version=ds.Version("1.1.0")), ds.BuilderConfig(name="full", version=ds.Version("1.1.0")), ] DEFAULT_CONFIG_NAME = "regular" def _info(self): if self.config.name == "regular": features = ds.Features( { "video-id": ds.Value("string"), "fold-ind": ds.Value("string"), "startphrase": ds.Value("string"), "sent1": ds.Value("string"), "sent2": ds.Value("string"), "gold-source": ds.Value("string"), "ending0": ds.Value("string"), "ending1": ds.Value("string"), "ending2": ds.Value("string"), "ending3": ds.Value("string"), "label": ds.ClassLabel(names=["0", "1", "2", "3"]), } ) else: features = ds.Features( { "video-id": ds.Value("string"), "fold-ind": ds.Value("string"), "startphrase": ds.Value("string"), "gold-ending": ds.Value("string"), "distractor-0": ds.Value("string"), "distractor-1": ds.Value("string"), "distractor-2": ds.Value("string"), "distractor-3": ds.Value("string"), "gold-source": ds.Value("string"), "gold-type": ds.Value("string"), "distractor-0-type": ds.Value("string"), "distractor-1-type": ds.Value("string"), "distractor-2-type": ds.Value("string"), "distractor-3-type": ds.Value("string"), "sent1": ds.Value("string"), "sent2": ds.Value("string"), } ) return ds.DatasetInfo( description="", citation="", homepage="", license="", features=features, ) def _split_generators(self, mgr): fs = mgr.download_and_extract(_URLs[self.config.name]) splits = [ ds.SplitGenerator( name=ds.Split.TRAIN, gen_kw={"filepath": fs["train"], "split": "train"}, ), ds.SplitGenerator( name=ds.Split.VALIDATION, gen_kw={"filepath": fs["val"], "split": "val"}, ), ] if self.config.name == "regular": splits.append( ds.SplitGenerator( name=ds.Split.TEST, gen_kw={"filepath": fs["test"], "split": "test"}, ) ) return splits def _generate_examples(self, filepath, split): with open(filepath, "r", encoding="utf-8") as f: lines = list(csv.reader(f, delimiter=",")) for i, row in enumerate(lines[1:]): if self.config.name == "regular": yield i, { "video-id": row[1], "fold-ind": row[2], "startphrase": row[3], "sent1": row[4], "sent2": row[5], "gold-source": row[6], "ending0": row[7], "ending1": row[8], "ending2": row[9], "ending3": row[10], "label": -1 if split == "test" else row[11], } else: yield i, { "video-id": row[0], "fold-ind": row[1], "startphrase": row[2], "gold-ending": row[3], "distractor-0": row[4], "distractor-1": row[5], "distractor-2": row[6], "distractor-3": row[7], "gold-source": row[8], "gold-type": row[9], "distractor-0-type": row[10], "distractor-1-type": row[11], "distractor-2-type": row[12], "distractor-3-type": row[13], "sent1": row[14], "sent2": row[15], }	{"/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", 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33,721	quantapix/qnarre	refs/heads/main	/qnarre/prep/tokens/bart.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 regex as re from functools import lru_cache from ...tokens.utils import AddedToken, PreTrainedTokenizer VOCAB_FS = {"vocab_file": "vocab.json", "merges_file": "merges.txt"} VOCAB_MAP = { "vocab_file": { "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json", "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json", "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json", "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json", "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json", "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json", }, "merges_file": { "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt", "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt", "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt", "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt", "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt", "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt", }, } INPUT_CAPS = { "facebook/bart-base": 1024, "facebook/bart-large": 1024, "facebook/bart-large-mnli": 1024, "facebook/bart-large-cnn": 1024, "facebook/bart-large-xsum": 1024, "yjernite/bart_eli5": 1024, } @lru_cache() def bytes_to_unicode(): bs = ( list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) ) cs = bs[:] n = 0 for b in range(28): if b not in bs: bs.append(b) cs.append(28 + n) n += 1 cs = [chr(n) for n in cs] return dict(zip(bs, cs)) def get_pairs(word): pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs class Tokenizer(PreTrainedTokenizer): vocab_fs = VOCAB_FS vocab_map = VOCAB_MAP input_caps = INPUT_CAPS model_input_names = ["input_ids", "mask"] def __init__( self, vocab_file, merges_file, errors="replace", bos="<s>", eos="</s>", sep="</s>", cls="<s>", unk="<unk>", pad="<pad>", msk="<mask>", add_prefix_space=False, 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__( errors=errors, bos=bos, eos=eos, unk=unk, sep=sep, cls=cls, pad=pad, msk=msk, add_prefix_space=add_prefix_space, kw, ) with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} self.errors = errors # how to handle errors in decoding self.byte_encoder = bytes_to_unicode() self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} with open(merges_file, encoding="utf-8") as merges_handle: bpe_merges = merges_handle.read().split("\n")[1:-1] bpe_merges = [tuple(merge.split()) for merge in bpe_merges] self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) self.cache = {} self.add_prefix_space = add_prefix_space self.pat = re.compile( r"""'s\|'t\|'re\|'ve\|'m\|'ll\|'d\| ?\p{L}+\| ?\p{N}+\| ?[^\s\p{L}\p{N}]+\|\s+(?!\S)\|\s+""" ) @property def s_vocab(self): return len(self.encoder) def get_vocab(self): return dict(self.encoder, *self.added_tokens_encoder) def bpe(self, token): if token in self.cache: return self.cache[token] word = tuple(token) pairs = get_pairs(word) if not pairs: return token while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) except ValueError: new_word.extend(word[i:]) break else: new_word.extend(word[i:j]) i = j if word[i] == first and i < len(word) - 1 and word[i + 1] == second: new_word.append(first + second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = " ".join(word) self.cache[token] = word return word def _tokenize(self, text): bpe_tokens = [] for token in re.findall(self.pat, text): token = "".join(self.byte_encoder[b] for b in token.encode("utf-8")) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" ")) return bpe_tokens def _convert_token_to_id(self, token): return self.encoder.get(token, self.encoder.get(self.unk)) def _convert_id_to_token(self, index): return self.decoder.get(index) def convert_tokens_to_string(self, tokens): text = "".join(tokens) text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors) return text def save_vocabulary(self, dir, pre=None): vocab_file = os.path.join( dir, (pre + "-" if pre else "") + VOCAB_FS["vocab_file"], ) merge_file = os.path.join( dir, (pre + "-" if pre else "") + VOCAB_FS["merges_file"], ) with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, ensure_ascii=False)) index = 0 with open(merge_file, "w", encoding="utf-8") as writer: writer.write("#version: 0.2\n") for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning( f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive." " Please check that the tokenizer is not corrupted!" ) index = token_index writer.write(" ".join(bpe_tokens) + "\n") index += 1 return vocab_file, merge_file 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] cls = [self.cls_token_id] sep = [self.SEP] 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] 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 prepare_for_tokenization(self, text, is_split_into_words=False, **kw): add_prefix_space = kw.pop("add_prefix_space", self.add_prefix_space) if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()): text = " " + text return (text, 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", 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33,722	quantapix/qnarre	refs/heads/main	/qnarre/base/narrative.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 ..edit import fudge from .named import Named class Topic(Named): pass class Node(Named): _topic = _narrative = None def __init__(self, topic=None, narrative=None, kw): super().__init__(kw) topic = fudge(2) if topic == 'fudge' else topic if topic: self._topic = Topic.create(name=topic) narrative = fudge(2) if narrative == 'fudge' else narrative if narrative: self._narrative = Narrative.create(name=narrative) def __str__(self): t = '({} {}) {}' return t.format(self.sign, self.name, self.value) @property def topic(self): n = self.narrative return self._topic or (n.topic if n else n) @property def narrative(self): return self._narrative @property def value(self): t = self.topic.name if self.topic else '' n = self.narrative.name if self.narrative else '' return '[{}:{}]'.format(t, n) @property def fields(self): fs = super().fields fs['Topic'] = self.topic.name if self.topic else None fs['Narrative'] = self.narrative.name if self.narrative else None return fs class Narrative(Node): sign = ''	{"/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", 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33,723	quantapix/qnarre	refs/heads/main	/qnarre/prep/convert/transfo_xl.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 pickle import sys 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.models.transfo_xl import tokenization_transfo_xl as data_utils from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FS from transformers.utils import logging from ..config.transfo_xl import PreTrained from ...models.transfo_xl import LMHead logging.set_verbosity_info() log = logging.get_logger(__name__) data_utils.Vocab = data_utils.TransfoXLTokenizer data_utils.Corpus = data_utils.TransfoXLCorpus sys.modules["data_utils"] = data_utils sys.modules["vocabulary"] = data_utils def build_map(model, cfg): tf_to_pt_map = {} if hasattr(model, "transformer"): tf_to_pt_map.update( { "transformer/adaptive_softmax/cutoff_0/cluster_W": model.crit.cluster_weight, "transformer/adaptive_softmax/cutoff_0/cluster_b": model.crit.cluster_bias, } ) for i, (out_l, proj_l, tie_proj) in enumerate( zip(model.crit.out_layers, model.crit.out_projs, cfg.tie_projs) ): layer_str = f"transformer/adaptive_softmax/cutoff_{i}/" if cfg.tie_word_embeds: tf_to_pt_map.update({layer_str + "b": out_l.bias}) else: raise NotImplementedError # I don't think this is implemented in the TF code tf_to_pt_map.update( {layer_str + "lookup_table": out_l.weight, layer_str + "b": out_l.bias} ) if not tie_proj: tf_to_pt_map.update({layer_str + "proj": proj_l}) model = model.transformer for i, (embed_l, proj_l) in enumerate(zip(model.word_emb.emb_layers, model.word_emb.emb_projs)): layer_str = f"transformer/adaptive_embed/cutoff_{i}/" tf_to_pt_map.update( {layer_str + "lookup_table": embed_l.weight, layer_str + "proj_W": proj_l} ) for i, b in enumerate(model.layers): layer_str = f"transformer/layer_{i}/" tf_to_pt_map.update( { layer_str + "rel_attn/LayerNorm/gamma": b.dec_attn.layer_norm.weight, layer_str + "rel_attn/LayerNorm/beta": b.dec_attn.layer_norm.bias, layer_str + "rel_attn/o/kernel": b.dec_attn.o_net.weight, layer_str + "rel_attn/qkv/kernel": b.dec_attn.qkv_net.weight, layer_str + "rel_attn/r/kernel": b.dec_attn.r_net.weight, layer_str + "ff/LayerNorm/gamma": b.pos_ff.layer_norm.weight, layer_str + "ff/LayerNorm/beta": b.pos_ff.layer_norm.bias, layer_str + "ff/layer_1/kernel": b.pos_ff.CoreNet[0].weight, layer_str + "ff/layer_1/bias": b.pos_ff.CoreNet[0].bias, layer_str + "ff/layer_2/kernel": b.pos_ff.CoreNet[3].weight, layer_str + "ff/layer_2/bias": b.pos_ff.CoreNet[3].bias, } ) if cfg.untie_r: r_r_list = [] r_w_list = [] for b in model.layers: r_r_list.append(b.dec_attn.r_r_bias) r_w_list.append(b.dec_attn.r_w_bias) else: r_r_list = [model.r_r_bias] r_w_list = [model.r_w_bias] tf_to_pt_map.update({"transformer/r_r_bias": r_r_list, "transformer/r_w_bias": r_w_list}) return tf_to_pt_map def load_src_weights(model, cfg, src_path): tf_to_pt_map = build_map(model, cfg) 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 for name, p in tf_to_pt_map.items(): assert name in tf_weights array = tf_weights[name] if "kernel" in name or "proj" in name: array = np.transpose(array) if ("r_r_bias" in name or "r_w_bias" in name) and len(p) > 1: assert len(p) == array.shape[0] 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, cfg_path, save_path, ds_path): if ds_path: with open(ds_path, "rb") as fp: corpus = pickle.load(fp, encoding="latin1") v = save_path + "/" + VOCAB_FS["pretrained_vocab_file"] print(f"Saving vocab to: {v}") torch.save(corpus.vocab.__dict__, v) corpus_dict_no_vocab = corpus.__dict__ corpus_dict_no_vocab.pop("vocab", None) d = save_path + "/" + CORPUS_NAME print(f"Saving dataset to: {d}") torch.save(corpus_dict_no_vocab, d) if src_path: cfg_path = abspath(cfg_path) cfg = PreTrained() if cfg_path == "" else PreTrained.from_json_file(cfg_path) print(f"Building from config: {cfg}") m = LMHead(cfg) src_path = abspath(src_path) m = 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="", type=str) x.add_argument("--cfg_path", default="", type=str) x.add_argument("--save_path", default=None, type=str, required=True) x.add_argument("--ds_path", default="", type=str) y = x.parse_args() to_pytorch(y.src_path, y.cfg_path, y.save_path, y.ds_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", 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33,724	quantapix/qnarre	refs/heads/main	/qnarre/prep/metric/rouge.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 from rouge_score import rouge_scorer, scoring class Rouge(ds.Metric): def _info(self): return ds.MetricInfo( description="", citation="", inputs_description="", features=ds.Features( { "predictions": ds.Value("string", id="sequence"), "references": ds.Value("string", id="sequence"), } ), ) def _compute(self, preds, refs, rouge_types=None, use_agregator=True, use_stemmer=False): if rouge_types is None: rouge_types = ["rouge1", "rouge2", "rougeL", "rougeLsum"] scorer = rouge_scorer.RougeScorer(rouge_types=rouge_types, use_stemmer=use_stemmer) if use_agregator: aggregator = scoring.BootstrapAggregator() else: scores = [] for r, p in zip(refs, preds): score = scorer.score(r, p) if use_agregator: aggregator.add_scores(score) else: scores.append(score) if use_agregator: y = aggregator.aggregate() else: y = {} for k in scores[0]: y[k] = list(score[k] for score in scores) 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"], 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33,725	quantapix/qnarre	refs/heads/main	/qnarre/run/glue.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 sequence classification on GLUE import logging import random from datasets import load_dataset, load_metric from torch.utils.data import DataLoader from transformers import ( AutoConfig, AutoModelForSequenceClassification, DataCollatorWithPadding, PretrainedConfig, default_data_collator, ) from .params import TRAIN, EVAL, ALL, LABEL from .runner import Runner as Base log = logging.getLogger(__name__) task_to_keys = { "cola": ("sentence", None), "mnli": ("premise", "hypothesis"), "mrpc": ("sentence1", "sentence2"), "qnli": ("question", "sentence"), "qqp": ("question1", "question2"), "rte": ("sentence1", "sentence2"), "sst2": ("sentence", None), "stsb": ("sentence1", "sentence2"), "wnli": ("sentence1", "sentence2"), } class Runner(Base): AutoModel = AutoModelForSequenceClassification @property def dataset(self): if self._dataset is None: ps = self.params if ps.task_name is not None: y = load_dataset("glue", ps.task_name) else: y = super().dataset self._dataset = y return self._dataset @property def cols(self): if self._cols is None: ps, ds = self.params, self.dataset cs = self.dataset[TRAIN].column_names self.labels = ("",) if ps.task_name is not None: self.is_regression = ps.task_name == "stsb" if not self.is_regression: self.labels = ds[TRAIN].features[LABEL].names self.key1, self.key2 = task_to_keys[ps.task_name] else: self.is_regression = ds[TRAIN].features[LABEL].dtype in ["float32", "float64"] if not self.is_regression: self.labels = ds[TRAIN].unique(LABEL).sort() xs = [x for x in cs if x != LABEL] if "sentence1" in xs and "sentence2" in xs: self.key1, self.key2 = "sentence1", "sentence2" else: if len(xs) >= 2: self.key1, self.key2 = xs[:2] else: self.key1, self.key2 = xs[0], None self._cols = {ALL: cs} return self._cols @property def config(self): if self._config is None: ps = self.params self._config = AutoConfig.from_pretrained( ps.model_name, n_labels=len(self.labels), finetune=ps.task_name ) return self._config @property def model(self): if self._model is None: ps, ls = self.params, self.labels m = super().model self.ids = None if ( m.config.label2id != PretrainedConfig(n_labels=len(self.labels)).label2id and ps.task_name is not None and not self.is_regression ): ids = {l.lower(): i for l, i in m.config.label2id.items()} if list(sorted(ids.keys())) == list(sorted(ls)): log.info(f"Using config label map: {ids}") self.ids = {l(ids[l]) for l in ls} else: log.warning( f"Ignoring mismatched {list(sorted(ids.keys()))} vs {list(sorted(ls))}" ) elif ps.task_name is None: self.ids = {l: i for i, l in enumerate(ls)} if self.ids is not None: m.config.label2id = self.ids m.config.id2label = {i: l for l, i in self.config.label2id.items()} elif ps.task_name is not None and not self.is_regression: m.config.label2id = {l: i for i, l in enumerate(ls)} m.config.id2label = {i: l for l, i in self.config.label2id.items()} return self._model @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, k1, k2 = self.params, self.key1, self.key2 texts = (xs[k1],) if k2 is None else (xs[k1], xs[k2]) y = self.tokenizer(texts, padding=self.padding, max_len=ps.max_len, truncation=True) if LABEL in xs: if self.ids is None: y["labels"] = xs[LABEL] else: y["labels"] = [self.ids[x] for x in xs[LABEL]] return y @property def eval_ds(self): if self._eval_ds is None: ps, ds = self.params, self.dataset y = ds["validation_matched" if ps.task_name == "mnli" else EVAL] if ps.max_eval_samples is not None: y = y.select(range(ps.max_eval_samples)) self._eval_ds = y return self._eval_ds @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 = DataCollatorWithPadding( 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: ps = self.params if ps.task_name is not None: y = load_metric("glue", ps.task_name) else: y = load_metric("accuracy") self._metric = y return self._metric def eval_epoch(self, e): m, mgr = self.model, self.mgr m.eval() for xs in self.loaders[EVAL]: ys = m(xs) ys = ys.logits.argmax(dim=-1) if not self.is_regression else ys.logits.squeeze() self.metric.add_batch(predictions=mgr.gather(ys), references=mgr.gather(xs["labels"])) y = self.metric.compute() mgr.print(f"epoch {e}: {y}") def eval(self): ps, m, mgr = self.params, self.model, self.mgr if ps.task_name == "mnli": m.eval() for xs in self.loaders[EVAL]: 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"mnli-mm: {y}") def main(): ps = [("--task_name", {"type", "default": None, "choices": list(task_to_keys.keys())})] x = Runner(ps) x.dataset x.config x.tokenizer x.model x.loaders x.prepare() x.train() x.save() x.eval() if __name__ == "__main__": main() """ python glue.py \ --model_name bert-base-cased \ --task_name $TASK_NAME \ --max_len 128 \ --train_batch_size 32 \ --lr 2e-5 \ --out_dir /tmp/$TASK_NAME/ accelerate launch glue.py \ --model_name bert-base-cased \ --task_name $TASK_NAME \ --max_len 128 \ --train_batch_size 32 \ --lr 2e-5 \ --train_epochs 3 \ --out_dir /tmp/$TASK_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"], 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"/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"], 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"/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"]}
33,726	quantapix/qnarre	refs/heads/main	/qnarre/prep/metric/xnli.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 class Xnli(ds.Metric): def _info(self): return ds.MetricInfo( description="", citation="", inputs_description="", features=ds.Features( { "predictions": ds.Value("int64" if self.config_name != "sts-b" else "float32"), "references": ds.Value("int64" if self.config_name != "sts-b" else "float32"), } ), format="numpy", ) def _compute(self, preds, refs): return {"accuracy": _accuracy(preds, refs)} def _accuracy(preds, labels): return (preds == labels).mean()	{"/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"], 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33,727	quantapix/qnarre	refs/heads/main	/qnarre/base/doc/message.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 .edit import resolve from .nominals import para_split from .justifier import Justifier from .meta import converter, with_property from .realm import About, Sent, Sourced, Body from .part import Defaults, Titled, Dated, Tagged, Part class Defaults(Defaults): title = None summary = None subject = None subgroup = None body = None @converter('TxtRec') @converter('ScrRec') @with_property('body', Body.create) class Note(Dated, Sent, Tagged, Defaults): parent = None no_date = False @classmethod def convert(cls, rec, regy, ctxt, kw): kw.update(regy=regy) s = rec.slug try: n = regy[cls.slugify(s)] except KeyError: n = cls(label=s, kw) n.convert_from(rec, kw, ctxt=ctxt) return n def __init__(self, label, body=None, kw): super().__init__(label, kw) self.body = body def convert_from(self, rec, ctxt, kw): self.body = Body.create(self.body) self.body.texts = para_split(resolve(rec.text(ctxt), ctxt)) if rec.no_date: self.no_date = True super().convert_from(rec, kw, ctxt=ctxt) @property def date(self): return '' if self.no_date else super().date @property def html(self): b = self.body return b.html if b else super().html @property def justify(self): if self.parent: return self.parent.calc_just(f.justify for f in self.froms) return 'justify-content-start' @property def text_justify(self): return '' # return 'text-right' if self.justify == 'justify-content-end' else '' @property def background(self): for f in self.froms: if f.background: return 'background-color: #{};'.format(f.background) return 'background-color: #e8e8e8;' @converter('InlRec') @converter('FwdRec') @converter('EmlRec') @converter('MixRec') @with_property('replying', Note.create) class Message(About, Note): def __init__(self, label, replying=None, kw): super().__init__(label=label, kw) self.replying = replying def convert_from(self, rec, ctxt, kw): r = rec.hdr.replying if r: pass # self.replying = Message.convert(ctxt.recs[r], kw) super().convert_from(rec, kw, ctxt=ctxt) @converter('Chain') @with_property('notes', Note.creator) class Chain(Part, Titled, About, Sent, Tagged, Justifier, Defaults): @classmethod def convert(cls, chain, regy, ctxt, kw): kw.update(regy=regy) n = chain.name try: c = regy[cls.slugify(n)] except KeyError: c = cls(label=n, kw) c.convert_from(chain, kw, ctxt=ctxt) return c @classmethod def get_template(cls): return 'chain' def __init__(self, label, notes=(), kw): super().__init__(label, kw) self.notes = notes def convert_from(self, chain, ctxt, kw): kw.update(ctxt=ctxt) rs = ctxt.recs self.notes = (converter.convert(rs[n], kw) for n in chain.names) for n in self.notes: n.parent = self self.init_justs(f.justify for f in n.froms) super().convert_from(chain, kw) class Letter(Sourced, Message): pass class Post(Titled, About, Note): pass @converter('StoryRec') class StoryPost(Post): @classmethod def get_template(cls): return 'story' @converter('BlogRec') class BlogPost(Post): @classmethod def get_template(cls): return 'blog' @converter('PicRec') @converter('DocRec') class Doc(Titled, Sourced, Message): pass	{"/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", 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33,728	quantapix/qnarre	refs/heads/main	/qnarre/models/flash/gpt_neox.py	# Copyright (c) 2023, Tri Dao. import math import re from collections import OrderedDict import torch import torch.nn.functional as F from einops import rearrange from transformers import GPT2Config, GPTNeoXConfig def remap_state_dict_hf_gpt_neox(state_dict, config): def key_mapping_layers(key): return re.sub(r'^gpt_neox.', 'transformer.', 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.embed_in.', '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(config.vocab_size / 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('embed_out.weight') # 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.final_layer_norm.', r'transformer.ln_f.', key) key = re.sub(r'^transformer.layers.(\d+).input_layernorm.', r'transformer.layers.\1.norm1.', key) key = re.sub(r'^transformer.layers.(\d+).post_attention_layernorm.', r'transformer.layers.\1.norm2.', key) return key state_dict = OrderedDict((key_mapping_ln(k), v) for k, v in state_dict.items()) # MLP def key_mapping_mlp(key): key = re.sub(r'^transformer.layers.(\d+).mlp.dense_h_to_4h.', r'transformer.layers.\1.mlp.fc1.', key) key = re.sub(r'^transformer.layers.(\d+).mlp.dense_4h_to_h.', r'transformer.layers.\1.mlp.fc2.', key) return key state_dict = OrderedDict((key_mapping_mlp(k), v) for k, v in state_dict.items()) # Attention for l in range(config.n_layer): # We don't store these biases state_dict.pop(f'transformer.layers.{l}.attention.bias') state_dict.pop(f'transformer.layers.{l}.attention.masked_bias') # GPT-NeoX stores Wqkv as ((nheads 3 headdim), hidden_dim) # while we store Wqkv as ((3 nheads headdim), hidden_dim) headdim = config.hidden_size // config.num_attention_heads Wqkv = state_dict.pop(f'transformer.layers.{l}.attention.query_key_value.weight') state_dict[f'transformer.layers.{l}.mixer.Wqkv.weight'] = rearrange( Wqkv, '(nheads three headdim) ... -> (three nheads headdim) ...', three=3, headdim=headdim ) bqkv = state_dict.pop(f'transformer.layers.{l}.attention.query_key_value.bias') state_dict[f'transformer.layers.{l}.mixer.Wqkv.bias'] = rearrange( bqkv, '(nheads three headdim) -> (three nheads headdim)', three=3, headdim=headdim ) def key_mapping_attn(key): key = re.sub(r'^transformer.layers.(\d+).attention.dense.', r'transformer.layers.\1.mixer.out_proj.', key) key = re.sub(r'^transformer.layers.(\d+).attention.rotary_emb.', r'transformer.layers.\1.mixer.rotary_emb.', key) return key state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items()) return state_dict def gpt_neox_config_to_gpt2_config(gpt_neox_config: GPTNeoXConfig) -> GPT2Config: assert gpt_neox_config.rotary_emb_base == 10000 return GPT2Config( vocab_size=gpt_neox_config.vocab_size, n_positions=0, # No absolute position embedding n_embd=gpt_neox_config.hidden_size, n_layer=gpt_neox_config.num_hidden_layers, n_head=gpt_neox_config.num_attention_heads, n_inner=gpt_neox_config.intermediate_size, activation_function=gpt_neox_config.hidden_act, resid_pdrop=0.0, # No dropout embd_pdrop=0.0, attn_pdrop=0.0, layer_norm_epsilon=gpt_neox_config.layer_norm_eps, initializer_range=gpt_neox_config.initializer_range, bos_token_id=gpt_neox_config.bos_token_id, eos_token_id=gpt_neox_config.eos_token_id, # These are new arguments not in the original GPT2Config prenorm=True, parallel_block=gpt_neox_config.use_parallel_residual, parallel_block_tied_norm=False, rotary_emb_fraction=gpt_neox_config.rotary_pct, tie_word_embeddings=gpt_neox_config.tie_word_embeddings, )	{"/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"], 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33,729	quantapix/qnarre	refs/heads/main	/qnarre/models/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. # ============================================================================= # https://arxiv.org/abs/2001.08210 # https://github.com/pytorch/fairseq/tree/main/examples/mbart import random import torch 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 forward as qf from ..core import output as qo from ..core.embed import PosEmbed from ..core.mlp import Classifier from ..prep.config.mbart import PreTrained from . import bart log = logging.get_logger(__name__) class Model(PreTrained): def __init__(self, kw): super().__init__(kw) cfg = self.get_cfg(kw) self.emb = qc.Embed(cfg.s_vocab, cfg.d_model, kw) self.enc = Encoder(self.emb, kw) self.dec = Decoder(self.emb, kw) def forward( self, x, dec_head_m=None, dec_m=None, mask=None, x_dec_emb=None, x_dec=None, y_enc=None, kw, ): cfg = self.cfg if x_dec is None and x_dec_emb is None: x_dec = qu.shift_right2(x, cfg.PAD) if y_enc is None: y_enc = self.enc(x, kw, mask=mask) y = self.dec( x_dec, kw, enc_m=mask, enc=y_enc[0], head_m=dec_head_m, mask=dec_m, x_emb=x_dec_emb, ) ys = y + y_enc return qo.Seq2Seq(ys) class ForCausal(PreTrained): def __init__(self, kw): kw.update(is_dec=True, is_enc_dec=False) super().__init__(kw) cfg = self.get_cfg(kw) self.model = Decoder(kw) self.proj = qc.Linear(cfg.d_model, cfg.s_vocab, bias=False, kw) forward = bart.ForCausal.forward class ForCondGen(PreTrained): def __init__(self, kw): super().__init__(kw) cfg = self.get_cfg(kw) self.model = Model(kw) n = self.model.emb.cfg.n_embed self.proj = qc.Linear(cfg.d_model, n, bias=False, kw) self.register_buffer("final_logits_bias", torch.zeros((1, n))) def forward(self, x, labels=None, x_dec=None, kw): cfg = self.cfg if labels is not None: if x_dec is None: x_dec = qu.shift_right2(labels, cfg.PAD) ys = self.model(x, x_dec=x_dec, kw) y = self.proj(ys[0]) + self.final_logits_bias loss = None if labels is not None: loss = nn.CrossEntropyLoss()(y.view(-1, cfg.s_vocab), labels.view(-1)) ys = (y,) + ys[1:] + (loss,) return qo.LossSeq2Seq(ys) class ForQA(PreTrained): def __init__(self, kw): kw.update(n_labels=2) 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 class ForSeqClass(PreTrained): def __init__(self, kw): kw.update(n_labels=2) super().__init__(kw) cfg = self.get_cfg(kw) self.model = Model(kw) self.proj = Classifier(cfg.d_model, kw) forward = qf.forward_seq class Encoder(qc.Module): hs = qc.Hypers( {"d_model", "n_enc_lays", "n_pos", "s_vocab", "scale"}, {"drop": 0.0}, ) def __init__(self, tok_emb=None, ps={}, hs=[], kw): super().__init__(ps, [self.hs] + hs, kw) cfg = self.get_cfg(kw) m = cfg.d_model cfg.scale = m0.5 if cfg.scale else 1.0 self.tok_emb = qc.Embed(cfg.s_vocab, m, kw) if tok_emb is None else tok_emb self.pos_emb = PosEmbed(cfg.n_pos, m, kw) self.norm_emb = qc.LayerNorm(m, kw) self.lays = qc.Stack([EncLayer(kw) for _ in range(cfg.n_enc_lays)]) self.norm = qc.LayerNorm(m, kw) self.drop = qc.Dropout(cfg.drop, kw) self.grad_checkpoint = False def forward(self, x, head_m=None, mask=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) * cfg.scale y = x_emb + self.pos_emb(s) y = self.drop(self.norm_emb(y)) attns = hiddens = () if mask is not None: mask = qu.expand_mask(mask, x_emb.dtype) assert head_m is None or (head_m.size()[0] == (len(self.lays))) for i, lay in enumerate(self.lays): hiddens += (y,) if self.training and (random.uniform(0, 1) < cfg.drop_enc): continue h = head_m[i] if head_m is not None else None if self.grad_checkpoint and self.training: def create_forward(x): def forward(xs): return x(xs) return forward ys = checkpoint(create_forward(lay), y, head_m=h, mask=mask, kw) else: ys = lay(y, head_m=h, mask=mask, kw) y = ys[0] attns += (ys[1],) y = self.norm(y) hiddens += (y,) return qo.Base(y, attns, hiddens) class Decoder(qc.Module): hs = qc.Hypers( {"d_model", "n_dec_lays", "n_pos", "s_vocab", "scale"}, {"drop": 0.0}, ) def __init__(self, tok_emb=None, ps={}, hs=[], kw): super().__init__(ps, [self.hs] + hs, kw) cfg = self.get_cfg(kw) m = cfg.d_model cfg.scale = m0.5 if cfg.scale else 1.0 self.tok_emb = qc.Embed(cfg.s_vocab, m, kw) if tok_emb is None else tok_emb self.pos_emb = PosEmbed(cfg.n_pos, m, kw) self.norm_emb = qc.LayerNorm(m, kw) self.lays = qc.Stack([DecLayer(kw) for _ in range(cfg.n_dec_lays)]) self.norm = qc.LayerNorm(m, kw) self.drop = qc.Dropout(cfg.drop, kw) self.grad_checkpoint = False def prep_dec_m(self, mask, shape, x_emb, c_len): y = None if shape[-1] > 1: y = qu.causal_mask(shape, x_emb.dtype, c_len=c_len).to(self.device) if mask is not None: m = qu.expand_mask(mask, x_emb.dtype, len=shape[-1]) y = m if y is None else m + y return y def forward( self, x, cache=None, cross_m=None, enc_m=None, enc=None, head_m=None, mask=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) * cfg.scale c_len = cache[0][0].shape[2] if cache is not None else 0 y = x_emb + self.pos_emb(s, c_len) y = self.drop(self.norm_emb(y)) attns = caches = crosses = hiddens = () mask = self.prep_dec_m(mask, s, x_emb, c_len) if enc is not None and enc_m is not None: enc_m = qu.expand_mask(enc_m, x_emb.dtype, len=s[-1]) for m in [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,) 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(mask=mask, enc=enc, enc_m=enc_m, head_m=h, cross_m=c) 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=x) return forward ys = checkpoint(create_forward(lay), y, kw) else: ys = lay(y, cache=c, kw) y = ys[0] attns += (ys[1],) if enc is not None: crosses += (ys[2],) caches += (ys[-1],) y = self.norm(y) hiddens += (y,) return qo.CachesCrosses(y, attns, caches, crosses, hiddens) class EncLayer(qc.Module): hs = qc.Hypers( {"activation", "d_enc_ff", "d_model", "drop_act", "n_enc_heads"}, {"drop": 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 = bart.Attention(n_heads=cfg.n_enc_heads, kw) self.norm_refl = qc.LayerNorm(m, kw) self.act = qu.activation(cfg.activation) self.drop_act = qc.Dropout(cfg.drop_act, kw) self.ff = qc.Linear(m, cfg.d_enc_ff, kw) self.proj = qc.Linear(cfg.d_enc_ff, m, kw) self.norm = qc.LayerNorm(m, kw) self.drop = qc.Dropout(cfg.drop, kw) def forward(self, x, kw): y = self.norm_refl(x) y, a, _ = self.refl(y, kw) y = x + self.drop(y) x = y y = self.norm(y) y = self.drop_act(self.act(self.ff(y))) y = x + self.drop(self.proj(y)) if y.dtype == torch.float16 and (torch.isinf(y).any() or torch.isnan(y).any()): clamp = torch.finfo(y.dtype).max - 1000 y = torch.clamp(y, min=-clamp, max=clamp) return y, a class DecLayer(qc.Module): hs = qc.Hypers( {"activation", "d_dec_ff", "d_model", "drop_act", "n_dec_heads"}, {"drop": 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 = bart.Attention(n_heads=cfg.n_dec_heads, is_dec=True, kw) self.norm_refl = qc.LayerNorm(m, kw) self.act = qu.activation(cfg.activation) self.drop_act = qc.Dropout(cfg.drop_act, kw) self.attn = bart.Attention(n_heads=cfg.n_dec_heads, is_dec=True, kw) self.norm_attn = qc.LayerNorm(m, kw) self.ff = qc.Linear(m, cfg.d_dec_ff, kw) self.proj = qc.Linear(cfg.d_dec_ff, m, kw) self.norm = qc.LayerNorm(m, kw) self.drop = qc.Dropout(cfg.drop, kw) def forward(self, x, cache=None, cross_m=None, enc_m=None, enc=None, kw): y = self.norm_refl(x) c = cache[:2] if cache is not None else None y, a, kv = self.refl(y, cache=c, kw) y = x + self.drop(y) a2 = None if enc is not None: x = y y = self.norm_attn(y) c = cache[-2:] if cache is not None else None y, a2, kv2 = self.attn(y, cache=c, enc=enc, head_m=cross_m, mask=enc_m, **kw) y = x + self.drop(y) kv = kv + kv2 x = y y = self.norm(y) y = self.drop_act(self.act(self.ff(y))) y = x + self.drop(self.proj(y)) return y, a, a2, kv	{"/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": 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"/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"]}
33,730	quantapix/qnarre	refs/heads/main	/qnarre/base/doc/content.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 mimetypes import pprint as pp from .log import Logger from .base import config, digest from .nominals import para_join, nominal from .resource import Resource log = Logger(__name__) class Registry(Resource): @classmethod def globals(cls): return globals() def __init__(self, elems=None, kw): super().__init__(elems, kw) for k, v in tuple(self.items()): v = self.add_once(v) if v: self[k] = v else: del self[k] def __repr__(self): es = {k: v for k, v in self.items() if v and k is not v} es = pp.pformat(es, indent=4) return '{}({})'.format(type(self).__name__, es) @property def elems(self): return [v for k, v in self.items() if v and k is not v] rename_msg = Resource.rename def add_once(self, v): if isinstance(v, tuple): return tuple(self.add_once(i) for i in v) return super().add_once(v) def eml_content(self, part, name, _): return part.get_content() def eml_register(self, name, vs): vs = self.add_once(vs) try: os = self[name] assert os == vs or nominal(para_join(os)) == nominal(para_join(vs)) except KeyError: if vs: self[name] = vs return vs def extract(self, name, raw): vs = [] for i, p in enumerate(raw.walk()): if self.check_type(p): v = self.eml_content(p, name, i) if v: vs.append(v) return self.eml_register(name, tuple(vs)) class Texts(Registry): _res_path = config.qnar_dst + 'texts.qnr' def check_type(self, part): return part.get_content_type() == 'text/' + config.PLAIN def eml_register(self, name, vs): return vs def register(self, name, paras): super().eml_register(name, tuple(paras)) def expand(self, name, paras): if name in self: del self[name] self.register(name, paras) class Htmls(Registry): _res_path = config.qnar_dst + 'htmls.qnr' def check_type(self, part): return part.get_content_type() == 'text/' + config.HTML def eml_content(self, part, name, i): v = part.get_content() if v: d = digest(v.encode()) if d not in self: self[d] = 'aaa' # print('\nhtml', name, i, d) # return d class Attms(Registry): _res_path = config.qnar_dst + 'attms.qnr' def check_type(self, part): return part.get_content_maintype() not in ('multipart', 'text') def eml_content(self, part, name, i): try: v = part.get_content() if v: d = digest(v) if d not in self: self[d] = 'aaa' t = mimetypes.guess_extension(part.get_content_type()) print('\n', t, name, i, d, part.get_content_maintype(), part.get_filename()) except Exception as e: print(e) log.error('Error getting content {} {} {}', name, part.get_content_maintype(), part.get_filename())	{"/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", 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"/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"], 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["/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"], 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"/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"]}
33,731	quantapix/qnarre	refs/heads/main	/qnarre/prep/tokens/canine.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 INPUT_CAPS = { "nielsr/canine-s": 2048, } UNICODE_VOCAB_SIZE = 1114112 PAD = 0 CLS = 0xE000 SEP = 0xE001 BOS = 0xE002 MASK = 0xE003 RESERVED = 0xE004 SPECIAL_CODEPOINTS = { CLS: "[CLS]", SEP: "[SEP]", BOS: "[BOS]", MASK: "[MASK]", PAD: "[PAD]", RESERVED: "[RESERVED]", } SPECIAL_CODEPOINTS_BY_NAME = {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()} class Tokenizer(PreTrainedTokenizer): input_caps = INPUT_CAPS def __init__( self, bos=chr(CLS), eos=chr(SEP), sep=chr(SEP), cls=chr(CLS), pad=chr(PAD), msk=chr(MASK), add_prefix_space=False, model_max_length=2048, 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 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__( bos=bos, eos=eos, sep=sep, cls=cls, pad=pad, msk=msk, add_prefix_space=add_prefix_space, model_max_length=model_max_length, kw, ) self._special_codepoints = {} for codepoint, name in SPECIAL_CODEPOINTS.items(): self._special_codepoints[name] = codepoint self._special_codepoint_strings = { codepoint: name for name, codepoint in self._special_codepoints.items() } self._unicode_vocab_size = UNICODE_VOCAB_SIZE self._num_special_tokens = len(self._special_codepoints) @property def s_vocab(self): return self._unicode_vocab_size def _tokenize(self, text): return list(text) def _convert_token_to_id(self, token): try: return ord(token) except TypeError: raise ValueError(f"invalid token: '{token}'") def _convert_id_to_token(self, index): try: if index in SPECIAL_CODEPOINTS: return SPECIAL_CODEPOINTS[index] return chr(index) except TypeError: raise ValueError(f"invalid id: {index}") def convert_tokens_to_string(self, tokens): return "".join(tokens) def build_inputs_with_special_tokens(self, toks_0, toks_1=None): sep = [self.sep_token_id] cls = [self.cls_token_id] result = cls + toks_0 + sep if toks_1 is not None: result += toks_1 + sep return result 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) result = [1] + ([0] * len(toks_0)) + [1] if toks_1 is not None: result += ([0] * len(toks_1)) + [1] return result def create_token_type_ids_from_sequences(self, toks_0, toks_1=None): sep = [self.sep_token_id] cls = [self.cls_token_id] result = len(cls + toks_0 + sep) * [0] if toks_1 is not None: result += len(toks_1 + sep) * [1] return result def save_vocabulary(self, dir, pre=None): return ()	{"/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": 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"/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"]}
33,732	quantapix/qnarre	refs/heads/main	/notebooks/old/src/custom.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. # ============================================================================= # !pip install -U tf-nightly-2.0-preview # export TF_XLA_FLAGS=--tf_xla_cpu_global_jit import tensorflow as tf import dataset as qd import layers as ql ks = tf.keras kl = ks.layers def dump_dset(ps): ps.max_val = 10000 ps.num_samples = 1000 # 100000 ps.num_shards = 10 fs = [f for f in qd.dump(ps)] ps.dim_batch = 100 for i, _ in enumerate(qd.load(ps, fs).map(adapter)): pass print(f'dumped {i} batches of {ps.dim_batch} samples each') return fs @tf.function def formatter(d): ds = tf.RaggedTensor.from_sparse(d['defs']) n = ds.nrows() os = tf.RaggedTensor.from_sparse(d['op']) tf.debugging.assert_equal(n, os.nrows()) ss = tf.fill([n, 1], qd.SEP) enc = tf.concat([ds, ss, os, ss], axis=1) rs = tf.RaggedTensor.from_sparse(d['res']) tf.debugging.assert_equal(n, rs.nrows()) tgt = tf.concat([rs, tf.fill([n, 1], qd.STP)], axis=1) def rand_blank(x): y = x.flat_values mv = tf.shape(y)[0] s = mv // 2 i = tf.random.uniform([s], maxval=mv, dtype=tf.int32)[:, None] y = tf.tensor_scatter_nd_update(y, i, tf.zeros([s], dtype=tf.int32)) return x.with_flat_values(y) return {'enc': enc, 'dec': rand_blank(tgt), 'tgt': tgt} @tf.function def adapter(d): enc, dec, tgt = d['enc'], d['dec'], d['tgt'] return ( ( enc.flat_values, enc.row_splits, dec.flat_values, dec.row_splits, tgt.flat_values, tgt.row_splits, ), tgt.to_tensor(), ) def dset_for(ps, adapter=adapter): ds = tf.data.TFRecordDataset(list(qd.files(ps))) ds = ds.take(100).batch(ps.dim_batch) fs = { 'defs': tf.io.VarLenFeature(tf.int64), 'op': tf.io.VarLenFeature(tf.int64), 'res': tf.io.VarLenFeature(tf.int64), } ds = ds.map(lambda x: tf.io.parse_example(x, fs)).map(qd.caster) return ds.map(formatter).map(adapter) class ToRagged(kl.Layer): @tf.function def call(self, x): ys = [] for i in range(3): i = 2 fv, rs = x[i:i + 2] ys.append(tf.RaggedTensor.from_row_splits(fv, rs)) return ys class Frames(ql.Layer): def __init__(self, ps): super().__init__(ps, dtype=tf.int32) # , dynamic=True) s = (ps.dim_batch, ps.width_enc) kw = dict(initializer='zeros', trainable=False, use_resource=True) self.prev = self.add_weight('prev', shape=s, kw) @tf.function def call(self, x): xe, xd, xt = x ye = tf.concat([self.prev, xe], axis=1) el = tf.cast(xe.row_lengths(), dtype=tf.int32) ye = tf.gather_nd(ye, self.calc_idxs(el)) c = self.ps.width_dec - xd.bounding_shape(axis=1, out_type=tf.int32) yd = tf.pad(xd.to_tensor(), [[0, 0], [0, c]]) dl = tf.cast(xd.row_lengths(), dtype=tf.int32) p = tf.concat([ye, xt], axis=1) tl = tf.cast(xt.row_lengths(), dtype=tf.int32) p = tf.gather_nd(p, self.calc_idxs(tl)) self.prev.assign(p) return [ye, el, yd, dl] def calc_idxs(self, lens): b, w = self.ps.dim_batch, self.ps.width_enc y = tf.broadcast_to(tf.range(b)[:, None], [b, w]) i = tf.range(w)[None, ] + lens[:, None] y = tf.stack([y, i], axis=2) return y class Embed(ql.Layer): def __init__(self, ps): super().__init__(ps) s = (ps.dim_vocab, ps.dim_hidden) self.emb = self.add_weight('emb', shape=s) @tf.function def call(self, x): y, lens = x y = tf.nn.embedding_lookup(self.emb, y) y = y.shape[-1]*0.5 return [y, lens] class Encode(ql.Layer): def __init__(self, ps): super().__init__(ps) self.width = ps.width_enc self.encs = [Encoder(self, f'enc_{i}') for i in range(ps.dim_stacks)] @tf.function def call(self, x): y = x for e in self.encs: y = e(y) return y class Decode(ql.Layer): def __init__(self, ps): super().__init__(ps) self.width = ps.width_dec self.decs = [Decoder(self, f'dec_{i}') for i in range(ps.dim_stacks)] @tf.function def call(self, x): y, ye = x[:-1], x[-1] for d in self.decs: y = d(y + [ye]) return y class Debed(ql.Layer): def __init__(self, ps): super().__init__(ps) self.dbd = Dense(self, 'dbd', [ps.dim_hidden, ps.dim_vocab]) @tf.function def call(self, x): y, lens = x s = tf.shape(y) y = tf.reshape(y, [s[0] s[1], -1]) y = self.dbd(y) y = tf.reshape(y, [s[0], s[1], -1]) y = y[:, :tf.math.reduce_max(lens), :] return y class Encoder(tf.Module): def __init__(self, layer, name): super().__init__(name=name) with self.name_scope: self.reflect = Attention(layer, 'refl') self.conclude = Conclusion(layer, 'conc') @tf.function def __call__(self, x): y = x y = self.reflect(y + [y[0]]) y = self.conclude(y) return y class Decoder(tf.Module): def __init__(self, layer, name): super().__init__(name=name) with self.name_scope: self.reflect = Attention(layer, 'refl') self.consider = Attention(layer, 'cnsd') self.conclude = Conclusion(layer, 'conc') @tf.function def __call__(self, x): y, ye = x[:-1], x[-1] y = self.reflect(y + [y[0]]) y = self.consider(y + [ye]) y = self.conclude(y) return y class Attention(tf.Module): def __init__(self, layer, name): super().__init__(name=name) h = layer.ps.dim_hidden self.scale = 1 / (h*0.5) with self.name_scope: self.q = layer.add_weight('q', shape=(h, h)) self.k = layer.add_weight('k', shape=(h, h)) self.v = layer.add_weight('v', shape=(h, h)) @tf.function def __call__(self, x): x, lens, ctx = x off = tf.math.reduce_max(lens) q = tf.einsum('bni,ij->bnj', x[:, -off:, :], self.q) k = tf.einsum('bni,ij->bnj', ctx, self.k) y = tf.einsum('bni,bmi->bnm', q, k) # use lens y = tf.nn.softmax(y self.scale) v = tf.einsum('bni,ij->bnj', ctx, self.v) y = tf.einsum('bnm,bmi->bni', y, v) y = tf.concat([x[:, :-off, :], y], axis=1) return [y, lens] class Conclusion(tf.Module): def __init__(self, layer, name): super().__init__(name=name) self.layer = layer ps = layer.ps w = layer.width * ps.dim_hidden with self.name_scope: s = [w, ps.dim_dense] self.inflate = Dense(layer, 'infl', s, activation='relu') s = [ps.dim_dense, w] self.deflate = Dense(layer, 'defl', s, bias=False) @tf.function def __call__(self, x): y, lens = x w = self.layer.width d = self.layer.ps.dim_hidden y = tf.reshape(y, [-1, w * d]) y = self.inflate(y) y = self.deflate(y) y = tf.reshape(y, [-1, w, d]) return [y, lens] class Dense(ql.Dense): @tf.function def __call__(self, x): return super().__call__(x) def model_for(ps): x = [ks.Input(shape=(), dtype='int32'), ks.Input(shape=(), dtype='int64')] x += [ks.Input(shape=(), dtype='int32'), ks.Input(shape=(), dtype='int64')] x += [ks.Input(shape=(), dtype='int32'), ks.Input(shape=(), dtype='int64')] y = ToRagged()(x) y = Frames(ps)(y) embed = Embed(ps) ye = Encode(ps)(embed(y[:2])) yd = Decode(ps)(embed(y[2:]) + [ye[0]]) y = Debed(ps)(yd) m = ks.Model(inputs=x, outputs=y) m.compile(optimizer=ps.optimizer, loss=ps.loss, metrics=[ps.metric]) print(m.summary()) return m params = dict( dim_batch=5, dim_dense=150, dim_hidden=6, dim_stacks=2, dim_vocab=len(qd.vocab), loss=ks.losses.SparseCategoricalCrossentropy(from_logits=True), metric=ks.metrics.SparseCategoricalCrossentropy(from_logits=True), num_epochs=5, num_shards=2, optimizer=ks.optimizers.Adam(), width_dec=15, width_enc=25, ) if __name__ == '__main__': ps = qd.Params(**params) import advanced_tf.masking as qm qm.main_graph(ps, dset_for(ps), model_for(ps)) # import advanced_tf.ragged as qr # qr.main_eager(ps, dset_for(ps), model_for(ps))	{"/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": 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33,733	quantapix/qnarre	refs/heads/main	/qnarre/base/doc/util/rectifier.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 .sanitizer import QNERR class Rectify: end_re = re.compile(r'(:\d\d) ?:', re.ASCII) def __init__(self, path): self.path = path self.txt = pth.Path(path).read_text('ascii', QNERR) def fix_up(self): t = self.txt t = ' '.join(t.split()).strip() t = self.end_re.sub(r'\1:\n\n', t) # t = t.replace(' :', ' :\n\n') t = t.replace(' .', '.') p = pth.Path(self.path) p = p.with_name('new_' + p.stem).with_suffix('.txt') p.write_text(t, 'ascii', QNERR) if __name__ == '__main__': Rectify('test.txt').fix_up()	{"/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", 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33,734	quantapix/qnarre	refs/heads/main	/qnarre/models/nystromformer.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.bert import PreTrained from torch.nn import CrossEntropyLoss from ...pytorch_utils import ( apply_chunking_to_forward, ) log = logging.get_logger(__name__) LIST = [ "uw-madison/nystromformer-512", ] class NystromformerEmbeddings(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 + 2, 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.drop = qc.Dropout(config.drop) self.register_buffer("position_ids", torch.arange(config.n_pos).expand((1, -1)) + 2) self.pos_type = getattr(config, "pos_type", "absolute") self.register_buffer( "token_type_ids", torch.zeros( self.position_ids.size(), dtype=torch.long, device=self.position_ids.device ), 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 if self.pos_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.norm(embeddings) embeddings = self.drop(embeddings) return embeddings class NystromformerSelfAttention(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.num_landmarks = config.num_landmarks self.seq_len = config.segment_means_seq_len self.conv_kernel_size = config.conv_kernel_size if config.inv_coeff_init_option: self.init_option = config["inv_init_coeff_option"] else: self.init_option = "original" 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.conv_kernel_size is not None: self.conv = nn.Conv2d( in_channels=self.n_heads, out_channels=self.n_heads, kernel_size=(self.conv_kernel_size, 1), padding=(self.conv_kernel_size // 2, 0), bias=False, groups=self.n_heads, ) # Function to approximate Moore-Penrose inverse via the iterative method def iterative_inv(self, mat, n_iter=6): identity = torch.eye(mat.size(-1), device=mat.device) key = mat # The entries of key are positive and \|\|key\|\|_{\infty} = 1 due to softmax if self.init_option == "original": # This original implementation is more conservative to compute coefficient of Z_0. value = 1 / torch.max(torch.sum(key, dim=-2)) * key.transpose(-1, -2) else: # This is the exact coefficient computation, 1 / \|\|key\|\|_1, of initialization of Z_0, leading to faster convergence. value = ( 1 / torch.max(torch.sum(key, dim=-2), dim=-1).values[:, :, None, None] * key.transpose(-1, -2) ) for _ in range(n_iter): key_value = torch.matmul(key, value) value = torch.matmul( 0.25 * value, 13 * identity - torch.matmul( key_value, 15 * identity - torch.matmul(key_value, 7 * identity - key_value) ), ) return value def transpose_for_scores(self, layer): new_layer_shape = layer.size()[:-1] + (self.n_heads, self.attention_head_size) layer = layer.view(new_layer_shape) return layer.permute(0, 2, 1, 3) def forward(self, hiddens, attention_mask=None, output_attentions=False): mixed_query_layer = self.query(hiddens) 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) query_layer = query_layer / math.sqrt(math.sqrt(self.attention_head_size)) key_layer = key_layer / math.sqrt(math.sqrt(self.attention_head_size)) if self.num_landmarks == self.seq_len: attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in NystromformerModel forward() function) attention_scores = attention_scores + attention_mask attention_probs = F.softmax(attention_scores, dim=-1) context_layer = torch.matmul(attention_probs, value_layer) else: q_landmarks = query_layer.reshape( -1, self.n_heads, self.num_landmarks, self.seq_len // self.num_landmarks, self.attention_head_size, ).mean(dim=-2) k_landmarks = key_layer.reshape( -1, self.n_heads, self.num_landmarks, self.seq_len // self.num_landmarks, self.attention_head_size, ).mean(dim=-2) kernel_1 = torch.F.softmax( torch.matmul(query_layer, k_landmarks.transpose(-1, -2)), dim=-1 ) kernel_2 = torch.F.softmax( torch.matmul(q_landmarks, k_landmarks.transpose(-1, -2)), dim=-1 ) attention_scores = torch.matmul(q_landmarks, key_layer.transpose(-1, -2)) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in NystromformerModel forward() function) attention_scores = attention_scores + attention_mask kernel_3 = F.softmax(attention_scores, dim=-1) attention_probs = torch.matmul(kernel_1, self.iterative_inv(kernel_2)) new_value_layer = torch.matmul(kernel_3, value_layer) context_layer = torch.matmul(attention_probs, new_value_layer) if self.conv_kernel_size is not None: context_layer += self.conv(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,) return outputs # Copied from transformers.models.bert.modeling_bert.BertSelfOutput class NystromformerSelfOutput(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, pos_type=None): super().__init__() self.self = NystromformerSelfAttention(config, pos_type=pos_type) self.output = NystromformerSelfOutput(config) def forward(self, hiddens, attention_mask=None, output_attentions=False): self_outputs = self.self(hiddens, attention_mask, 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->Nystromformer class NystromformerIntermediate(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->Nystromformer class NystromformerOutput(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.add_cross_attention = config.add_cross_attention self.intermediate = NystromformerIntermediate(config) self.output = NystromformerOutput(config) def forward(self, hiddens, attention_mask=None, output_attentions=False): self_attention_outputs = self.attention( hiddens, attention_mask, output_attentions=output_attentions ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attns if we output attention weights 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 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.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, 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 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, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hiddens, attention_mask, ) else: layer_outputs = layer_module(hiddens, attention_mask, output_attentions) hiddens = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) 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, all_self_attentions] if v is not None ) return qo.CachesCrosses( y=hiddens, hiddens=all_hidden_states, attns=all_self_attentions, ) class Model(PreTrained): def __init__(self, config): super().__init__(config) self.config = config self.embeddings = NystromformerEmbeddings(config) self.encoder = Encoder(config) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=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 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 if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length)), device=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) extended_attention_mask = self.get_extended_attention_mask( attention_mask, input_shape, device ) 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, ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, 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, 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(kw) forward = qf.forward_masked class ForChoice(PreTrained): def __init__(self, config): super().__init__(config) self.nystromformer = Model(config) self.pre_classifier = qc.Linear(config.d_model, config.d_model) 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.nystromformer( 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, ) hidden_state = outputs[0] # (bs * num_choices, seq_len, dim) pooled_output = hidden_state[:, 0] # (bs * num_choices, dim) pooled_output = self.pre_classifier(pooled_output) # (bs * num_choices, dim) pooled_output = nn.ReLU()(pooled_output) # (bs * num_choices, dim) 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(cfg.d_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): kw.update(n_labels=2) super().__init__(kw) cfg = self.get_cfg(kw) 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33,735	quantapix/qnarre	refs/heads/main	/tools/triton/python/test/unit/operators/test_matmul.py	import itertools import pytest import torch import triton import triton.ops @pytest.mark.parametrize( "BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, NWARP, NSTAGE, M, N, K, AT, BT, DTYPE", itertools.chain( [ [ # 1 warp (16, 16, 16, 1, 1, 2, None, None, None, AT, BT, DTYPE), (32, 16, 16, 1, 1, 2, None, None, None, AT, BT, DTYPE), (16, 32, 16, 1, 1, 2, None, None, None, AT, BT, DTYPE), (16, 16, 32, 1, 1, 2, None, None, None, AT, BT, DTYPE), (32, 16, 32, 1, 1, 2, None, None, None, AT, BT, DTYPE), (16, 32, 32, 1, 1, 2, None, None, None, AT, BT, DTYPE), (16, 16, 64, 1, 1, 2, None, None, None, AT, BT, DTYPE), (64, 16, 64, 1, 1, 2, None, None, None, AT, BT, DTYPE), (16, 64, 64, 1, 1, 2, None, None, None, AT, BT, DTYPE), # 2 warp (64, 32, 64, 1, 2, 2, None, None, None, AT, BT, DTYPE), (32, 64, 64, 1, 2, 2, None, None, None, AT, BT, DTYPE), (64, 32, 16, 1, 2, 2, None, None, None, AT, BT, DTYPE), (32, 64, 16, 1, 2, 2, None, None, None, AT, BT, DTYPE), (128, 32, 32, 1, 2, 2, None, None, None, AT, BT, DTYPE), (32, 128, 32, 1, 2, 2, None, None, None, AT, BT, DTYPE), # 4 warp (128, 64, 16, 1, 4, 2, None, None, None, AT, BT, DTYPE), (64, 128, 16, 1, 4, 2, None, None, None, AT, BT, DTYPE), (128, 32, 32, 1, 4, 2, None, None, None, AT, BT, DTYPE), (32, 128, 32, 1, 4, 2, None, None, None, AT, BT, DTYPE), (128, 32, 64, 1, 4, 2, None, None, None, AT, BT, DTYPE), (32, 128, 64, 1, 4, 2, None, None, None, AT, BT, DTYPE), # 8 warp (128, 256, 16, 1, 8, 2, None, None, None, AT, BT, DTYPE), (256, 128, 16, 1, 8, 2, None, None, None, AT, BT, DTYPE), (256, 128, 32, 1, 8, 2, None, None, None, AT, BT, DTYPE), # split-k (64, 64, 16, 2, 4, 2, None, None, None, AT, BT, DTYPE), (64, 64, 16, 4, 4, 2, None, None, None, AT, BT, DTYPE), (64, 64, 16, 8, 4, 2, None, None, None, AT, BT, DTYPE), # variable input (128, 128, 32, 1, 4, 2, 1024, 1024, 1024, AT, BT, DTYPE), (128, 128, 32, 1, 4, 2, 384, 128, 640, AT, BT, DTYPE), (128, 128, 32, 1, 4, 2, 107, 233, 256, AT, BT, DTYPE), (128, 128, 32, 1, 4, 2, 107, 233, 311, AT, BT, DTYPE), ] for DTYPE in ["float16", "bfloat16", "float32"] for AT in [False, True] for BT in [False, True] ], # n-stage [ [ (16, 16, 16, 1, 1, STAGES, 1024, 1024, 1024, AT, BT, DTYPE), (64, 32, 64, 1, 2, STAGES, 1024, 1024, 1024, AT, BT, DTYPE), (128, 64, 16, 1, 4, STAGES, 1024, 1024, 1024, AT, BT, DTYPE), (256, 128, 32, 1, 8, STAGES, 1024, 1024, 1024, AT, BT, DTYPE), (128, 128, 32, 1, 4, STAGES, 384, 128, 640, AT, BT, DTYPE), # split-k (64, 64, 16, 8, 4, STAGES, 1024, 1024, 1024, AT, BT, DTYPE), (64, 64, 16, 8, 4, STAGES, 1024, 1024, 32, AT, BT, DTYPE), ] for DTYPE in ["float16", "bfloat16", "float32"] for AT in [False, True] for BT in [False, True] for STAGES in [2, 3, 4] ] ), ) def test_op(BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, NWARP, NSTAGE, M, N, K, AT, BT, DTYPE): capability = torch.cuda.get_device_capability() if capability[0] < 7: pytest.skip("Only test tl.dot() on devices with sm >= 70") if capability[0] < 8 and DTYPE == "bfloat16": pytest.skip("Only test bfloat16 on devices with sm >= 80") if DTYPE == "bfloat16" and SPLIT_K != 1: pytest.skip("bfloat16 matmuls don't allow split_k for now") torch.manual_seed(0) # nuke kernel decorators -- will set meta-parameters manually kwargs = {'BLOCK_M': BLOCK_M, 'BLOCK_N': BLOCK_N, 'BLOCK_K': BLOCK_K, 'SPLIT_K': SPLIT_K} pre_hook = None if SPLIT_K == 1 else lambda nargs: nargs['C'].zero_() configs = [triton.Config(kwargs=kwargs, num_warps=NWARP, num_stages=NSTAGE, pre_hook=pre_hook)] kernel = triton.ops._matmul.kernel kernel.configs = configs # kernel.run = kernel.run.run.run # get matrix shape M = BLOCK_M if M is None else M N = BLOCK_N if N is None else N K = BLOCK_K * SPLIT_K if K is None else K # allocate/transpose inputs DTYPE = {"float16": torch.float16, "bfloat16": torch.bfloat16, "float32": torch.float32}[DTYPE] a = .1 * torch.randn((K, M) if AT else (M, K), device="cuda", dtype=DTYPE) b = .1 * torch.randn((N, K) if BT else (K, N), device="cuda", dtype=DTYPE) a = a.t() if AT else a b = b.t() if BT else b # run test th_c = torch.matmul(a, b) try: tt_c = triton.ops.matmul(a, b) torch.testing.assert_allclose(th_c, tt_c, atol=1e-2, rtol=0) except triton.OutOfResources as e: pytest.skip(str(e))	{"/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": 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33,736	quantapix/qnarre	refs/heads/main	/qnarre/models/flash/gptj.py	# Copyright (c) 2023, Tri Dao. import math import re from collections import OrderedDict import torch import torch.nn.functional as F from transformers import GPT2Config, GPTJConfig def remap_state_dict_hf_gptj(state_dict, config): def key_mapping_layers(key): return re.sub(r'^transformer.h.', 'transformer.layers.', 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.wte.', '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(config.vocab_size / 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('lm_head.weight') # 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]) ) output_embeddings_bias = state_dict.pop('lm_head.bias') state_dict['lm_head.bias'] = F.pad( output_embeddings_bias, (0, vocab_size - output_embeddings_bias.shape[0]) ) # LayerNorm def key_mapping_ln(key): return re.sub(r'^transformer.layers.(\d+).ln_1.', r'transformer.layers.\1.norm1.', key) state_dict = OrderedDict((key_mapping_ln(k), v) for k, v in state_dict.items()) # MLP def key_mapping_mlp(key): key = re.sub(r'^transformer.layers.(\d+).mlp.fc_in.', r'transformer.layers.\1.mlp.fc1.', key) key = re.sub(r'^transformer.layers.(\d+).mlp.fc_out.', r'transformer.layers.\1.mlp.fc2.', key) return 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}.attn.q_proj.weight') Wk = state_dict.pop(f'transformer.layers.{l}.attn.k_proj.weight') Wv = state_dict.pop(f'transformer.layers.{l}.attn.v_proj.weight') state_dict[f'transformer.layers.{l}.mixer.Wqkv.weight'] = torch.cat([Wq, Wk, Wv], dim=0) # We don't store these biases state_dict.pop(f'transformer.layers.{l}.attn.bias') state_dict.pop(f'transformer.layers.{l}.attn.masked_bias') def key_mapping_attn(key): return re.sub(r'^transformer.layers.(\d+).attn.out_proj.', 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 gptj_config_to_gpt2_config(gptj_config: GPTJConfig) -> GPT2Config: headdim = gptj_config.n_embd // gptj_config.n_head return GPT2Config( vocab_size=gptj_config.vocab_size, n_positions=0, # No absolute position embedding n_embd=gptj_config.n_embd, n_layer=gptj_config.n_layer, n_head=gptj_config.n_head, n_inner=gptj_config.n_inner, activation_function=gptj_config.activation_function, resid_pdrop=gptj_config.resid_pdrop, embd_pdrop=gptj_config.embd_pdrop, attn_pdrop=gptj_config.attn_pdrop, layer_norm_epsilon=gptj_config.layer_norm_epsilon, initializer_range=gptj_config.initializer_range, bos_token_id=gptj_config.bos_token_id, eos_token_id=gptj_config.eos_token_id, # These are new arguments not in the original GPT2Config prenorm=True, parallel_block=True, parallel_block_tied_norm=True, rotary_emb_fraction=gptj_config.rotary_dim / headdim, rotary_emb_interleaved=True, tie_word_embeddings=False, qkv_proj_bias=False, out_proj_bias=False, lm_head_bias=True, )	{"/qnarre/prep/convert/xlnet.py": 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33,737	quantapix/qnarre	refs/heads/main	/qnarre/prep/tokens/pegasus.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 sentencepiece as spm from shutil import copyfile from ...tokens.utils import PreTrainedTokenizer SPIECE_UNDERLINE = "▁" VOCAB_FS = {"vocab_file": "spiece.model"} VOCAB_MAP = { "vocab_file": { "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model" } } INPUT_CAPS = { "google/pegasus-xsum": 512, } class Tokenizer(PreTrainedTokenizer): vocab_fs = VOCAB_FS vocab_map = VOCAB_MAP input_caps = INPUT_CAPS model_input_names = ["input_ids", "mask"] def __init__( self, vocab_file, pad="<pad>", eos="</s>", unk="<unk>", msk="<mask_2>", mask_token_sent="<mask_1>", additional_special_tokens=None, offset=103, sp_model_kw=None, kw, ): self.offset = offset if additional_special_tokens is not None: assert isinstance(additional_special_tokens, list) additional_special_tokens_extended = ( ([mask_token_sent] + additional_special_tokens) if mask_token_sent not in additional_special_tokens and mask_token_sent is not None else additional_special_tokens ) additional_special_tokens_extended += [ f"<unk_{i}>" for i in range(len(additional_special_tokens_extended), self.offset - 1) ] if len(set(additional_special_tokens_extended)) != len( additional_special_tokens_extended ): raise ValueError( f"Please make sure that the provided additional_special_tokens do not contain an incorrectly shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}." ) additional_special_tokens = additional_special_tokens_extended else: additional_special_tokens = [mask_token_sent] if mask_token_sent is not None else [] additional_special_tokens += [f"<unk_{i}>" for i in range(2, self.offset)] self.sp_model_kw = {} if sp_model_kw is None else sp_model_kw super().__init__( eos=eos, unk=unk, msk=msk, pad=pad, mask_token_sent=mask_token_sent, offset=offset, additional_special_tokens=additional_special_tokens, sp_model_kw=self.sp_model_kw, kw, ) self.mask_token_sent = mask_token_sent self.vocab_file = vocab_file self.sp_model = spm.SentencePieceProcessor(self.sp_model_kw) self.sp_model.Load(vocab_file) self.encoder[int, str] = { 0: self.pad, 1: self.eos, } if self.mask_token_sent is not None: self.encoder.update( { 2: self.mask_token_sent, 3: self.msk, } ) if self.offset > 0: self.encoder.update( {i + 3: additional_special_tokens[i] for i in range(1, self.offset - 1)} ) self.decoder[str, int] = {v: k for k, v in self.encoder.items()} @property def s_vocab(self): return len(self.sp_model) + self.offset 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 _tokenize(self, text): return self.sp_model.encode(text, out_type=str) def _convert_token_to_id(self, token): if token in self.decoder: return self.decoder[token] elif token in self.added_tokens_decoder: return self.added_tokens_decoder[token] sp_id = self.sp_model.piece_to_id(token) return sp_id + self.offset def _convert_id_to_token(self, index): if index in self.encoder: return self.encoder[index] elif index in self.added_tokens_encoder: return self.added_tokens_encoder[index] else: token = self.sp_model.IdToPiece(index - self.offset) return token def convert_tokens_to_string(self, tokens): out_string = self.sp_model.decode_pieces(tokens) return out_string def num_special_tokens_to_add(self, pair=False): return 1 def _special_token_mask(self, seq): all_special_ids = set(self.all_special_ids) all_special_ids.remove(self.unk_token_id) return [1 if x in all_special_ids else 0 for x in seq] def get_special_tokens_mask( self, toks_0, toks_1=None, has_specials=False, ): if has_specials: return self._special_token_mask(toks_0) elif toks_1 is None: return self._special_token_mask(toks_0) + [1] else: return self._special_token_mask(toks_0 + toks_1) + [1] def build_inputs_with_special_tokens(self, toks_0, toks_1=None): if toks_1 is None: return toks_0 + [self.EOS] return toks_0 + toks_1 + [self.EOS] 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"], 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33,738	quantapix/qnarre	refs/heads/main	/qnarre/base/doc/date.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 datetime as dt from qnarre.log import Logger from qnarre.base import config log = Logger(__name__) def slugify(name): return name.replace('\|', '_').replace(':', '-') class Date: date_re = re.compile(r'\d\d?:\d\d', re.ASCII) delta = dt.timedelta(hours=3, minutes=30) fudge = dt.timedelta(minutes=1) _name = None _raw = None @classmethod def has_date(cls, txt): return bool(cls.date_re.search(txt)) @classmethod def create_from(cls, txt, fmts, time=True, force=False): t = txt.strip() for f in fmts: try: d = dt.datetime.strptime(t, f).astimezone() if not d.second and (force or (time and not d.hour and not d.minute)): d = d.replace(second=1) return cls(d) except ValueError as e: err = e raise err @classmethod def from_txt(cls, txt): return cls.create_from( txt, ( '%b %d, %Y %I!%M!%S %p', '%b %d, %Y %I:%M:%S %p', '%m/%d/%y, %I:%M %p', '%m/%d/%y, %I:%M:%S %p', '%b %d, %Y %H:%M:%S', ), force=True) @classmethod def from_inl(cls, txt): txt = txt.replace('', '') return cls.create_from(txt, ( '%b %d, %Y %I:%M %p', '%m/%d/%Y %I:%M %p', '%b %d, %Y, at %I:%M %p', '%b %d, %Y, at %H:%M', '%a, %b %d, %Y at %I:%M %p', )) @classmethod def from_fwd(cls, txt): txt = txt.replace('', '') return cls.create_from(txt, ( '%B %d, %Y at %I:%M:%S %p %Z', '%A, %B %d, %Y %I:%M %p', '%A, %B %d, %Y, %I:%M %p', '%A, %B %d, %Y %I:%M:%S %p', '%a, %b %d, %Y at %I:%M %p', '%a %m/%d/%Y %I:%M %p', '%m/%d/%Y %I:%M %p', '%a, %b %d, %Y at %I:%M:%S %p', '%a, %b %d, %Y %I:%M %p', '%a %b %d %H:%M:%S %Y', '%a %b %d %H:%M:%S %Z %Y', '%a, %d %b %Y %H:%M:%S %z', '%a, %d %b %Y %H:%M:%S', '%B %d, %Y %I:%M:%S %p %Z', '%B %d, %Y %I:%M %p', '%d %b \'%y %I:%M', '%a, %B %d, %Y %I:%M %p', '%B %d, %Y, %I:%M:%S %p %Z', )) @classmethod def from_pth(cls, txt): return cls.create_from( txt, ( '%y-%m-%d', '%Y-%m-%d', ), time=False) @classmethod def from_file(cls, path): s = path.stat() try: t = s.st_birthtime except AttributeError: t = s.st_mtime return cls(dt.datetime.fromtimestamp(t).astimezone()) @classmethod def scanner(cls, path, names=((), ()), suffs=(), date=None, npath=None): i = 0 incs, excs = names with os.scandir(path) as es: for e in sorted(es, key=lambda e: e.name): p = pth.Path(e.path) old = p.stem try: d = cls.from_pth(old) except ValueError: d = date new = None else: assert date is None new = d.short new = None if new == old else new s = p.suffix if npath is not None or new is not None: new = (npath or path) / (new or old) new = new.with_suffix(s) if p.is_file(): if not suffs or s in suffs: if d is date: i += 1 yield d, p, new, i else: yield d, p, new, 0 elif p.is_dir(): if old in incs or (not incs and old not in excs): yield from cls.scanner(p, names, suffs, d, new) else: log.warning('Skipping dir {}', p.name) def __init__(self, spec): if isinstance(spec, str): self._name = spec else: assert isinstance(spec, dt.datetime) self._raw = spec def __repr__(self): return '{}({!r})'.format(type(self).__name__, self.name) @property def name(self): if self._name is None: r = self._raw ms = r.microsecond if r.hour == r.minute == r.second == 0: self._name = '{0:%y-%m-%d}\|{1:0>3d}'.format(r, ms) else: ms = ':{}'.format(ms) if ms else '' self._name = '{0:%y-%m-%d}\|{0:%H:%M:%S}'.format(r) + ms return self._name @property def raw(self): if self._raw is None: n = self._name c = len(n.split(':')) if c == 4: f = '%y-%m-%d\|%H:%M:%S:%f' elif c == 3: f = '%y-%m-%d\|%H:%M:%S' else: assert c == 1 f = '%y-%m-%d\|%f' self._raw = dt.datetime.strptime(n, f).astimezone() return self._raw @property def short(self): return '{0:%y-%m-%d}'.format(self.raw) @property def proximity(self): return '{0:%y-%m-%d}'.format(self.raw - self.delta) @property def slug(self): return slugify(self.name) @property def micro(self): return self.raw.microsecond @micro.setter def micro(self, value): self._raw = self.raw.replace(microsecond=value) if self._name is not None: del self._name @property def zero_secs(self): r = self.raw f = '{0:%y-%m-%d}\|{0:%H:%M:00}' n = f.format(r) if r.second: return n, f.format(r + self.fudge) return n, @property def to_inl(self): return '{0:%b %d, %Y, at %I:%M %p}'.format(self.raw) @property def to_rst(self): return '{0:%Y-%m-%d %H:%M:%S}'.format(self.raw) def compare(self, other): if not isinstance(other, type(self)): return NotImplemented s = self.raw.replace(microsecond=0) o = other.raw.replace(microsecond=0) if s == o: return config.EQ if s == o.replace(second=0): return config.LT if o == s.replace(second=0): return config.GT if s == (o + self.fudge).replace(second=0): return config.LT if o == (s + self.fudge).replace(second=0): return config.GT def after(self, others): m = max(d.micro for d in others if self.compare(d) is config.EQ) self.micro = m + 1 def next_hour(self, delta=1): return type(self)(self.raw + dt.timedelta(hours=delta)) def next_sec(self, delta=1): return type(self)(self.raw + dt.timedelta(seconds=delta))	{"/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"], 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"/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"], 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"/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"]}
33,739	quantapix/qnarre	refs/heads/main	/qnarre/try/mul.py	# %% import torch import triton import triton.language as tl @triton.autotune( configs=[ triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 64, 'GROUP': 8}, num_stages=3, num_warps=8), triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 32, 'GROUP': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32, 'GROUP': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 32, 'GROUP': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 32, 'GROUP': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 32, 'GROUP': 8}, num_stages=4, num_warps=4), triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 32, 'GROUP': 8}, num_stages=5, num_warps=2), triton.Config({'BLOCK_M': 32, 'BLOCK_N': 64, 'BLOCK_K': 32, 'GROUP': 8}, num_stages=5, num_warps=2), ], key=['M', 'N', 'K'], ) @triton.jit def matmul_kernel( x1_ptr, x2_ptr, y_ptr, M, N, K, stride_m, stride_k1, stride_k2, stride_n, stride_ym, stride_yn, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, GROUP: tl.constexpr, ACT: tl.constexpr, ): pid = tl.program_id(axis=0) m = tl.cdiv(M, BLOCK_M) n = tl.cdiv(N, BLOCK_N) g = GROUP * n first = (pid // g) * GROUP size = min(m - first, GROUP) pid_m = first + (pid % size) pid_n = (pid % g) // size offs_m = (pid_m * BLOCK_M + tl.arange(0, BLOCK_M)) % M offs_n = (pid_n * BLOCK_N + tl.arange(0, BLOCK_N)) % N offs_k = tl.arange(0, BLOCK_K) x1s = x1_ptr + (offs_m[:, None] * stride_m + offs_k[None, :] * stride_k1) x2s = x2_ptr + (offs_k[:, None] * stride_k2 + offs_n[None, :] * stride_n) y = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32) for k in range(0, tl.cdiv(K, BLOCK_K)): x1 = tl.load(x1s, mask=offs_k[None, :] < K - k * BLOCK_K, other=0.0) x2 = tl.load(x2s, mask=offs_k[:, None] < K - k * BLOCK_K, other=0.0) y += tl.dot(x1, x2) x1s += BLOCK_K * stride_k1 x2s += BLOCK_K * stride_k2 if ACT == "leaky_relu": y = leaky_relu(y) y = y.to(tl.float16) offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) ys = y_ptr + stride_ym * offs_m[:, None] + stride_yn * offs_n[None, :] tl.store(ys, y, mask=(offs_m[:, None] < M) & (offs_n[None, :] < N)) @triton.jit def leaky_relu(x): y = x + 1 return tl.where(y >= 0, y, 0.01 * y) # %% def matmul(x1, x2, act=""): assert x1.shape[1] == x2.shape[0] assert x1.is_contiguous() assert x2.is_contiguous() M, K = x1.shape K, N = x2.shape y = torch.empty((M, N), device=x1.device, dtype=x1.dtype) grid = lambda x: (triton.cdiv(M, x['BLOCK_M']) * triton.cdiv(N, x['BLOCK_N']),) matmul_kernel[grid]( x1, x2, y, M, N, K, x1.stride(0), x1.stride(1), x2.stride(0), x2.stride(1), y.stride(0), y.stride(1), ACT=act ) return y # %% torch.manual_seed(0) x1 = torch.randn((512, 512), device='cuda', dtype=torch.float16) x2 = torch.randn((512, 512), device='cuda', dtype=torch.float16) y_torch = torch.matmul(x1, x2) y_triton = matmul(x1, x2) print(f"torch={y_torch}") print(f"triton={y_triton}") if torch.allclose(y_triton, y_torch, atol=1e-2, rtol=0): print("✅ Triton and Torch match") else: print("❌ Triton and Torch differ") # %% @triton.testing.perf_report( triton.testing.Benchmark( x_names=['M', 'N', 'K'], x_vals=[128 * i for i in range(2, 33)], line_arg='provider', line_vals=['cublas', 'triton'], line_names=["cuBLAS", "Triton"], styles=[('green', '-'), ('blue', '-')], ylabel="TFLOPS", plot_name="matmul-performance", args={}, ) ) def benchmark(M, N, K, provider): x1 = torch.randn((M, K), device='cuda', dtype=torch.float16) x2 = torch.randn((K, N), device='cuda', dtype=torch.float16) qs = [0.5, 0.2, 0.8] if provider == 'cublas': ms, min, max = triton.testing.do_bench(lambda: torch.matmul(x1, x2), quantiles=qs) if provider == 'triton': ms, min, max = triton.testing.do_bench(lambda: matmul(x1, x2), quantiles=qs) y = lambda x: 2 * M * N * K * 1e-12 / (x * 1e-3) return y(ms), y(max), y(min) # %% benchmark.run(show_plots=True, print_data=True) # %%	{"/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"], 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33,740	quantapix/qnarre	refs/heads/main	/tools/triton/python/triton/language/extra/cuda.py	import os from .. import core __path__ = os.path.dirname(os.path.abspath(__file__)) @core.extern def globaltimer(_builder=None): return core.extern_elementwise("cuda", os.path.join(__path__, "cuda.bc"), [], {tuple(): ("globaltimer", core.dtype("int64")), }, is_pure=False, _builder=_builder) @core.extern def smid(_builder=None): return core.extern_elementwise("cuda", os.path.join(__path__, "cuda.bc"), [], {tuple(): ("smid", core.dtype("int32")), }, is_pure=True, _builder=_builder)	{"/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", 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33,741	quantapix/qnarre	refs/heads/main	/tools/triton/python/triton/__init__.py	"""isort:skip_file""" __version__ = '2.1.0' # --------------------------------------- # Note: import order is significant here. # submodules from .runtime import ( autotune, Config, heuristics, JITFunction, KernelInterface, reinterpret, TensorWrapper, OutOfResources, MockTensor, ) from .runtime.jit import jit from .compiler import compile, CompilationError from .debugger.debugger import program_ids_from_grid from . import language from . import testing __all__ = [ "autotune", "cdiv", "CompilationError", "compile", "Config", "heuristics", "impl", "jit", "JITFunction", "KernelInterface", "language", "MockTensor", "next_power_of_2", "ops", "OutOfResources", "reinterpret", "runtime", "TensorWrapper", "testing", "program_ids_from_grid", ] # ------------------------------------- # misc. utilities that don't fit well # into any specific module # ------------------------------------- def cdiv(x, y): return (x + y - 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33,742	quantapix/qnarre	refs/heads/main	/tools/triton/python/test/unit/language/test_core.py	# flake8: noqa: F821,F841 import itertools import os import re from typing import Optional, Union import numpy as np import pytest import torch from numpy.random import RandomState import triton import triton._C.libtriton.triton as _triton import triton.language as tl from triton.runtime.jit import JITFunction, TensorWrapper, reinterpret int_dtypes = ['int8', 'int16', 'int32', 'int64'] uint_dtypes = ['uint8', 'uint16', 'uint32', 'uint64'] float_dtypes = ['float16', 'float32', 'float64'] dtypes = int_dtypes + uint_dtypes + float_dtypes dtypes_with_bfloat16 = dtypes + ['bfloat16'] torch_dtypes = ['bool'] + int_dtypes + ['uint8'] + float_dtypes + ['bfloat16'] def _bitwidth(dtype: str) -> int: # ex.: "int64" -> 64 return int(re.search(r'(\d+)$', dtype).group(1)) def numpy_random(shape, dtype_str, rs: Optional[RandomState] = None, low=None, high=None): """ Override `rs` if you're calling this function twice and don't want the same result for both calls. """ if isinstance(shape, int): shape = (shape, ) if rs is None: rs = RandomState(seed=17) if dtype_str in int_dtypes + uint_dtypes: iinfo = np.iinfo(getattr(np, dtype_str)) low = iinfo.min if low is None else max(low, iinfo.min) high = iinfo.max if high is None else min(high, iinfo.max) dtype = getattr(np, dtype_str) x = rs.randint(low, high, shape, dtype=dtype) x[x == 0] = 1 # Hack. Never return zero so tests of division don't error out. return x elif dtype_str in float_dtypes: return rs.normal(0, 1, shape).astype(dtype_str) elif dtype_str == 'bfloat16': return (rs.normal(0, 1, shape).astype('float32').view('uint32') & np.uint32(0xffff0000)).view('float32') elif dtype_str in ['bool', 'int1', 'bool_']: return rs.normal(0, 1, shape) > 0.0 else: raise RuntimeError(f'Unknown dtype {dtype_str}') def to_triton(x: np.ndarray, device='cuda', dst_type=None) -> Union[TensorWrapper, torch.Tensor]: ''' Note: We need dst_type because the type of x can be different from dst_type. For example: x is of type `float32`, dst_type is `bfloat16`. If dst_type is None, we infer dst_type from x. ''' t = x.dtype.name if t in uint_dtypes: signed_type_name = t.lstrip('u') # e.g. "uint16" -> "int16" x_signed = x.astype(getattr(np, signed_type_name)) return reinterpret(torch.tensor(x_signed, device=device), getattr(tl, t)) else: if t == 'float32' and dst_type == 'bfloat16': return torch.tensor(x, device=device).bfloat16() return torch.tensor(x, device=device) def torch_dtype_name(dtype) -> str: if isinstance(dtype, triton.language.dtype): return dtype.name elif isinstance(dtype, torch.dtype): # 'torch.int64' -> 'int64' m = re.match(r'^torch\.(\w+)$', str(dtype)) return m.group(1) else: raise TypeError(f'not a triton or torch dtype: {type(dtype)}') def to_numpy(x): if isinstance(x, TensorWrapper): return x.base.cpu().numpy().astype(getattr(np, torch_dtype_name(x.dtype))) elif isinstance(x, torch.Tensor): if x.dtype is torch.bfloat16: return x.cpu().float().numpy() return x.cpu().numpy() else: raise ValueError(f"Not a triton-compatible tensor: {x}") def patch_kernel(template, to_replace): kernel = triton.JITFunction(template.fn) for key, value in to_replace.items(): kernel.src = kernel.src.replace(key, value) return kernel def check_type_supported(dtype): ''' skip test if dtype is not supported on the current device ''' cc = torch.cuda.get_device_capability() if cc[0] < 8 and (dtype is tl.bfloat16 or dtype == "bfloat16" or dtype is torch.bfloat16): pytest.skip("bfloat16 is only supported on NVGPU with cc >= 80") class MmaLayout: def __init__(self, version, warps_per_cta): self.version = version self.warps_per_cta = str(warps_per_cta) def __str__(self): return f"#triton_gpu.mma<{{versionMajor={self.version[0]}, versionMinor={self.version[1]}, warpsPerCTA={self.warps_per_cta}}}>" class BlockedLayout: def __init__(self, size_per_thread, threads_per_warp, warps_per_cta, order): self.sz_per_thread = str(size_per_thread) self.threads_per_warp = str(threads_per_warp) self.warps_per_cta = str(warps_per_cta) self.order = str(order) def __str__(self): return f"#triton_gpu.blocked<{{sizePerThread={self.sz_per_thread}, threadsPerWarp={self.threads_per_warp}, warpsPerCTA={self.warps_per_cta}, order={self.order}}}>" class SharedLayout: def __init__(self, vec, per_phase, max_phase, order): self.vec = str(vec) self.per_phase = str(per_phase) self.max_phase = str(max_phase) self.order = str(order) def __str__(self): return f"#triton_gpu.shared<{{vec={self.vec}, perPhase={self.per_phase}, maxPhase={self.max_phase}, order={self.order}}}>" @pytest.mark.parametrize("dtype_x", list(dtypes) + ["bfloat16"]) def test_empty_kernel(dtype_x, device='cuda'): SIZE = 128 @triton.jit def kernel(X, SIZE: tl.constexpr): pass check_type_supported(dtype_x) x = to_triton(numpy_random(SIZE, dtype_str=dtype_x), device=device, dst_type=dtype_x) kernel[(1, )](x, SIZE=SIZE, num_warps=4) # generic test functions def _test_unary(dtype_x, expr, numpy_expr=None, device='cuda'): check_type_supported(dtype_x) # early return if dtype_x is not supported SIZE = 128 # define the kernel / launch-grid @triton.jit def kernel(Z, X, SIZE: tl.constexpr): off = tl.arange(0, SIZE) x = tl.load(X + off) z = GENERATE_TEST_HERE tl.store(Z + off, z) kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': expr}) # inputs x = numpy_random(SIZE, dtype_str=dtype_x) if 'log' in expr: x = np.abs(x) + 0.01 # reference result z_ref = eval(expr if numpy_expr is None else numpy_expr) # triton result x_tri = to_triton(x, device=device, dst_type=dtype_x) z_tri = to_triton(np.empty_like(z_ref), device=device, dst_type=dtype_x) kernel[(1, )](z_tri, x_tri, SIZE=SIZE, num_warps=4) # compare np.testing.assert_allclose(z_ref, to_numpy(z_tri), rtol=0.01) def _binary_op_dtype_override(a: str, b: str) -> Optional[np.dtype]: """ Given two dtype strings, returns the numpy dtype Triton thinks binary operations on the two types should return. Returns None if the return value matches numpy. This is generally needed because Triton and pytorch return narrower floating point types than numpy in mixed operations, and because Triton follows C/C++ semantics around mixed signed/unsigned operations, and numpy/pytorch do not. """ overrides = { ('float16', 'int16'): np.float16, ('float16', 'int32'): np.float16, ('float16', 'int64'): np.float16, ('float16', 'uint16'): np.float16, ('float16', 'uint32'): np.float16, ('float16', 'uint64'): np.float16, ('int8', 'uint8'): np.uint8, ('int8', 'uint16'): np.uint16, ('int8', 'uint32'): np.uint32, ('int8', 'uint64'): np.uint64, ('int16', 'uint16'): np.uint16, ('int16', 'uint32'): np.uint32, ('int16', 'uint64'): np.uint64, ('int32', 'uint32'): np.uint32, ('int32', 'uint64'): np.uint64, ('int64', 'uint64'): np.uint64, } key = (a, b) if a < b else (b, a) return overrides.get(key) def _test_binary(dtype_x, dtype_y, expr, numpy_expr=None, mode_x='real', mode_y='real', device='cuda', y_low=None, y_high=None): check_type_supported(dtype_x) # early return if dtype_x is not supported check_type_supported(dtype_y) SIZE = 128 # define the kernel / launch-grid @triton.jit def kernel(Z, X, Y, SIZE: tl.constexpr): off = tl.arange(0, SIZE) x = tl.load(X + off) y = tl.load(Y + off) z = GENERATE_TEST_HERE tl.store(Z + off, z) kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': expr}) # inputs rs = RandomState(17) x = numpy_random(SIZE, dtype_str=dtype_x, rs=rs) y = numpy_random(SIZE, dtype_str=dtype_y, rs=rs, low=y_low, high=y_high) if mode_x == 'nan': x[:] = float('nan') if mode_y == 'nan': y[:] = float('nan') # reference result z_ref = eval(expr if numpy_expr is None else numpy_expr) dtype_z = _binary_op_dtype_override(dtype_x, dtype_y) if dtype_z is not None: z_ref = z_ref.astype(dtype_z) # triton result x_tri = to_triton(x, device=device, dst_type=dtype_x) y_tri = to_triton(y, device=device, dst_type=dtype_y) z_tri = to_triton(np.empty(SIZE, dtype=z_ref.dtype), device=device) kernel[(1, )](z_tri, x_tri, y_tri, SIZE=SIZE, num_warps=4) np.testing.assert_allclose(z_ref, to_numpy(z_tri), err_msg=expr, rtol=0.01) def _mod_operation_ill_conditioned(dtype_x, dtype_y) -> bool: # The result of x % y is ill-conditioned if x % y is much smaller than x. # pytorch/CUDA has slightly different (probably better) rounding on # remainders than stock LLVM. We currently don't expect to match it # bit-for-bit. return (dtype_x, dtype_y) in [ ('int32', 'bfloat16'), ('int32', 'float16'), ('int32', 'float32'), ('int64', 'bfloat16'), ('int64', 'float16'), ('int64', 'float32'), ('int64', 'float64'), ('uint16', 'bfloat16'), ('uint16', 'float16'), ('uint16', 'float32'), ('uint32', 'bfloat16'), ('uint32', 'float16'), ('uint32', 'float32'), ('uint64', 'bfloat16'), ('uint64', 'float16'), ('uint64', 'float32'), ('uint64', 'float64'), ] # --------------- # test binary ops # --------------- @pytest.mark.parametrize("dtype_x, dtype_y, op", [ (dtype_x, dtype_y, op) for op in ['+', '-', '', '/', '%'] for dtype_x in dtypes_with_bfloat16 for dtype_y in dtypes_with_bfloat16 ]) def test_bin_op(dtype_x, dtype_y, op, device='cuda'): expr = f' x {op} y' if op == '%' and dtype_x in int_dtypes + uint_dtypes and dtype_y in int_dtypes + uint_dtypes: # LLVM has 'numpy.fmod', not 'numpy.remainder', semantics on integer remainders. numpy_expr = 'np.fmod(x, y)' elif op in ('/', '%') and dtype_x in ('int16', 'float16', 'bfloat16') and dtype_y in ('int16', 'float16', 'bfloat16'): # Triton promotes 16-bit floating-point / and % to 32-bit because there # are no native div or FRem operations on float16. Since we have to # convert anyway, we may as well take the accuracy bump. numpy_expr = f'x.astype(np.float32) {op} y.astype(np.float32)' elif (dtype_x in uint_dtypes and dtype_y in int_dtypes and _bitwidth(dtype_x) >= _bitwidth(dtype_y)): numpy_expr = f'x.astype(np.{dtype_x}) {op} y.astype(np.{dtype_x})' elif (dtype_y in uint_dtypes and dtype_x in int_dtypes and _bitwidth(dtype_y) >= _bitwidth(dtype_x)): numpy_expr = f'x.astype(np.{dtype_y}) {op} y.astype(np.{dtype_y})' else: numpy_expr = None if op == '%' and _mod_operation_ill_conditioned(dtype_x, dtype_y): with pytest.raises(AssertionError, match='Not equal to tolerance'): _test_binary(dtype_x, dtype_y, expr, numpy_expr, device=device) elif (op in ('%', '/') and ((dtype_x in int_dtypes and dtype_y in uint_dtypes) or (dtype_x in uint_dtypes and dtype_y in int_dtypes))): with pytest.raises(triton.CompilationError) as exc_info: _test_binary(dtype_x, dtype_y, expr, numpy_expr, device=device) assert re.match('Cannot use . because they have different signedness', str(exc_info.value.__cause__)) else: _test_binary(dtype_x, dtype_y, expr, numpy_expr, device=device) @pytest.mark.parametrize("dtype_x, dtype_y", [(dtype_x, dtype_y) for dtype_x in int_dtypes for dtype_y in int_dtypes] + [(dtype_x, dtype_y) for dtype_x in uint_dtypes for dtype_y in uint_dtypes] ) def test_floordiv(dtype_x, dtype_y, device='cuda'): # Triton has IEEE, not numpy/torch, semantics for %, and those carry # through to //, so we have to use a nonstandard expression to get a # reference result for //. expr = 'x // y' numpy_expr = '((x - np.fmod(x, y)) / y)' _test_binary(dtype_x, dtype_y, expr, numpy_expr, device=device) def test_unsigned_name_mangling(device='cuda'): # Test that uint32 and int32 are mangled differently by the compiler SIZE = 128 # define the kernel / launch-grid @triton.jit def kernel(O1, O2, X, Y, SIZE: tl.constexpr): off = tl.arange(0, SIZE) x = tl.load(X + off) y = tl.load(Y + off) out1 = tl.abs(x) # uint32 -> nop out2 = tl.abs(-y) # int32 -> should have an effect tl.store(O1 + off, out1) tl.store(O2 + off, out2) dtype_x = 'uint32' dtype_y = 'int32' # inputs rs = RandomState(17) x = numpy_random(SIZE, dtype_str=dtype_x, rs=rs) y = numpy_random(SIZE, dtype_str=dtype_y, rs=rs) # reference result expect = (np.abs(x), np.abs(-y)) # triton result x_tri = to_triton(x, device=device, dst_type=dtype_x) y_tri = to_triton(y, device=device, dst_type=dtype_y) actual = tuple( to_triton(np.empty_like(e), device=device) for e in expect ) kernel[(1, )](actual[0], actual[1], x_tri, y_tri, SIZE=SIZE, num_warps=4) # Bitwise op, so expect exact equality assert (expect[0] == to_numpy(actual[0])).all() assert (expect[1] == to_numpy(actual[1])).all() # --------------- # test bitwise ops # --------------- @pytest.mark.parametrize("dtype_x, dtype_y, op", [ (dtype_x, dtype_y, op) for op in ['&', '\|', '^'] for dtype_x in dtypes + dtypes_with_bfloat16 for dtype_y in dtypes + dtypes_with_bfloat16 ]) def test_bitwise_op(dtype_x, dtype_y, op, device='cuda'): expr = f'x {op} y' if (dtype_x in uint_dtypes and dtype_y in int_dtypes and _bitwidth(dtype_x) >= _bitwidth(dtype_y)): numpy_expr = f'x.astype(np.{dtype_x}) {op} y.astype(np.{dtype_x})' elif (dtype_y in uint_dtypes and dtype_x in int_dtypes and _bitwidth(dtype_y) >= _bitwidth(dtype_x)): numpy_expr = f'x.astype(np.{dtype_y}) {op} y.astype(np.{dtype_y})' else: numpy_expr = None if 'float' in dtype_x + dtype_y: with pytest.raises(triton.CompilationError) as exc_info: _test_binary(dtype_x, dtype_y, expr, numpy_expr='np.array([])', device=device) # The CompilationError must have been caused by a C++ exception with this text. assert re.match('invalid operands of type', str(exc_info.value.__cause__)) else: _test_binary(dtype_x, dtype_y, expr, numpy_expr, device=device) @pytest.mark.parametrize("dtype_x, dtype_y, op", [ (dtype_x, dtype_y, op) for op in ['<<', '>>'] for dtype_x in int_dtypes + uint_dtypes for dtype_y in int_dtypes + uint_dtypes ]) def test_shift_op(dtype_x, dtype_y, op, device='cuda'): expr = f'x {op} y' bw = max(_bitwidth(dtype_x), _bitwidth(dtype_y)) if dtype_x.startswith('int'): dtype_z = f'int{bw}' else: dtype_z = f'uint{bw}' numpy_expr = f'x.astype(np.{dtype_z}) {op} y.astype(np.{dtype_z})' _test_binary(dtype_x, dtype_y, expr, numpy_expr, device=device, y_low=0, y_high=65) # --------------- # test compare ops # --------------- ops = ['==', '!=', '>', '<', '>=', '<='] @pytest.mark.parametrize("dtype_x, dtype_y, op, mode_x, mode_y", # real [ (dtype_x, dtype_y, op, 'real', 'real') for op in ops for dtype_x in dtypes for dtype_y in dtypes ] + # NaNs [('float32', 'float32', op, mode_x, mode_y) for op in ops for mode_x, mode_y in [('nan', 'real'), ('real', 'nan'), ('nan', 'nan')] ]) def test_compare_op(dtype_x, dtype_y, op, mode_x, mode_y, device='cuda'): expr = f'x {op} y' if (dtype_x in uint_dtypes and dtype_y in int_dtypes and _bitwidth(dtype_x) >= _bitwidth(dtype_y)): numpy_expr = f'x.astype(np.{dtype_x}) {op} y.astype(np.{dtype_x})' elif (dtype_y in uint_dtypes and dtype_x in int_dtypes and _bitwidth(dtype_y) >= _bitwidth(dtype_x)): numpy_expr = f'x.astype(np.{dtype_y}) {op} y.astype(np.{dtype_y})' else: numpy_expr = None _test_binary(dtype_x, dtype_y, expr, numpy_expr, mode_x=mode_x, mode_y=mode_y, device=device) # --------------- # test broadcast # --------------- @pytest.mark.parametrize("dtype", dtypes_with_bfloat16) def test_broadcast(dtype): @triton.jit def broadcast_kernel(x_ptr, y_ptr, y_broadcasted_ptr, M: tl.constexpr, N: tl.constexpr): offset1 = tl.arange(0, M) offset2 = tl.arange(0, N) x = tl.load(x_ptr + N * offset1[:, None] + offset2[None, :]) y = tl.load(y_ptr + offset2) _, y_broadcasted = tl.broadcast(x, y) tl.store(y_broadcasted_ptr + N * offset1[:, None] + offset2[None, :], y_broadcasted) M = 32 N = 64 rs = RandomState(17) x = numpy_random((M, N), dtype_str=dtype, rs=rs) y = numpy_random(N, dtype_str=dtype, rs=rs) _, y_broadcasted_np = np.broadcast_arrays(x, y) x_tri = to_triton(x, device='cuda', dst_type=dtype) y_tri = to_triton(y, device='cuda', dst_type=dtype) y_broadcasted_tri = to_triton(np.empty((M, N), dtype=y_broadcasted_np.dtype), device='cuda', dst_type=dtype) broadcast_kernel[(1,)](x_tri, y_tri, y_broadcasted_tri, M=M, N=N) assert (y_broadcasted_np == to_numpy(y_broadcasted_tri)).all() # ---------------- # test expand_dims # ---------------- def test_expand_dims(): @triton.jit def expand_dims_kernel(dummy, N: tl.constexpr): offset1 = tl.arange(0, N) t = tl.expand_dims(offset1, 0) tl.static_assert(t.shape == [1, N]) t = tl.expand_dims(offset1, 1) tl.static_assert(t.shape == [N, 1]) t = tl.expand_dims(offset1, -1) tl.static_assert(t.shape == [N, 1]) t = tl.expand_dims(offset1, -2) tl.static_assert(t.shape == [1, N]) t = tl.expand_dims(offset1, (0, -1)) tl.static_assert(t.shape == [1, N, 1]) t = tl.expand_dims(offset1, (0, 1, 3)) tl.static_assert(t.shape == [1, 1, N, 1]) t = tl.expand_dims(offset1, (-4, 2, -1)) tl.static_assert(t.shape == [1, N, 1, 1]) t = tl.expand_dims(offset1, (3, 1, 2)) tl.static_assert(t.shape == [N, 1, 1, 1]) N = 32 dummy_tensor = torch.empty((), device="cuda") expand_dims_kernel[(1,)](dummy_tensor, N) def test_expand_dims_error_cases(): @triton.jit def dim_out_of_range1(dummy, N: tl.constexpr): offset1 = tl.arange(0, N) t = tl.expand_dims(offset1, -2) t = tl.expand_dims(offset1, -3) @triton.jit def dim_out_of_range2(dummy, N: tl.constexpr): offset1 = tl.arange(0, N) t = tl.expand_dims(offset1, 1) t = tl.expand_dims(offset1, 2) @triton.jit def duplicate_dim1(dummy, N: tl.constexpr): offset1 = tl.arange(0, N) t = tl.expand_dims(offset1, (0, 0)) @triton.jit def duplicate_dim2(dummy, N: tl.constexpr): offset1 = tl.arange(0, N) t = tl.expand_dims(offset1, (0, -3)) N = 32 dummy_tensor = torch.empty((), device="cuda") with pytest.raises(triton.CompilationError, match="invalid axis -3"): dim_out_of_range1[(1,)](dummy_tensor, N) with pytest.raises(triton.CompilationError, match="invalid axis 2"): dim_out_of_range2[(1,)](dummy_tensor, N) with pytest.raises(triton.CompilationError, match=r"duplicate axes, normalized axes = \[0, 0\]"): duplicate_dim1[(1,)](dummy_tensor, N) with pytest.raises(triton.CompilationError, match=r"duplicate axes, normalized axes = \[0, 0\]"): duplicate_dim2[(1,)](dummy_tensor, N) # --------------- # test where # --------------- @pytest.mark.parametrize("dtype", dtypes_with_bfloat16 + ["int32"]) def test_where(dtype): select_ptrs = False if dtype == "int32": dtype = "int64" select_ptrs = True check_type_supported(dtype) @triton.jit def where_kernel(cond_ptr, a_ptr, b_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr, TEST_POINTERS: tl.constexpr, TEST_SCALAR_POINTERS: tl.constexpr): offsets = tl.program_id(axis=0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) mask = offsets < n_elements decide = tl.load(cond_ptr + offsets, mask=mask) if TEST_SCALAR_POINTERS: ptr = tl.where(tl.load(cond_ptr), a_ptr, b_ptr) output = tl.load(ptr + offsets, mask=mask) else: if TEST_POINTERS: a = tl.load(a_ptr + offsets, mask=mask).to(tl.pi32_t) b = tl.load(b_ptr + offsets, mask=mask).to(tl.pi32_t) else: a = tl.load(a_ptr + offsets, mask=mask) b = tl.load(b_ptr + offsets, mask=mask) output = tl.where(decide, a, b) tl.store(output_ptr + offsets, output, mask=mask) SIZE = 1_000 rs = RandomState(17) cond = numpy_random(SIZE, 'bool', rs) x = numpy_random(SIZE, dtype_str=dtype, rs=rs) y = numpy_random(SIZE, dtype_str=dtype, rs=rs) z = np.where(cond, x, y) cond_tri = to_triton(cond, device='cuda') x_tri = to_triton(x, device='cuda', dst_type=dtype) y_tri = to_triton(y, device='cuda', dst_type=dtype) z_tri = to_triton(np.empty(SIZE, dtype=z.dtype), device='cuda', dst_type=dtype) grid = lambda meta: (triton.cdiv(SIZE, meta['BLOCK_SIZE']),) where_kernel[grid](cond_tri, x_tri, y_tri, z_tri, SIZE, BLOCK_SIZE=1024, TEST_POINTERS=select_ptrs, TEST_SCALAR_POINTERS=False) assert (z == to_numpy(z_tri)).all() if select_ptrs: where_kernel[grid](cond_tri, x_tri, y_tri, z_tri, SIZE, BLOCK_SIZE=1024, TEST_POINTERS=select_ptrs, TEST_SCALAR_POINTERS=True) z = np.where(cond[0], x, y) assert (z == to_numpy(z_tri)).all() def test_where_broadcast(): @triton.jit def where_kernel(cond_ptr, a_ptr, out_ptr, BLOCK_SIZE: tl.constexpr): xoffsets = tl.arange(0, BLOCK_SIZE)[:, None] yoffsets = tl.arange(0, BLOCK_SIZE)[None, :] mask = tl.load(cond_ptr + yoffsets) vals = tl.load(a_ptr + yoffsets + BLOCK_SIZE * xoffsets) res = tl.where(mask, vals, 0.) tl.store(out_ptr + yoffsets + BLOCK_SIZE * xoffsets, res) @triton.jit def where_scalar_condition(a_ptr, out_ptr, BLOCK_SIZE: tl.constexpr): xoffsets = tl.arange(0, BLOCK_SIZE)[:, None] yoffsets = tl.arange(0, BLOCK_SIZE)[None, :] mask = 0 vals = tl.load(a_ptr + yoffsets + BLOCK_SIZE * xoffsets) res = tl.where(mask, vals, 0.) tl.store(out_ptr + yoffsets + BLOCK_SIZE * xoffsets, res) SIZE = 32 dtype = 'float32' rs = RandomState(17) x = numpy_random((SIZE, SIZE), dtype_str=dtype, rs=rs) mask = numpy_random(SIZE, 'bool', rs=rs) z = np.where(mask, x, 0) cond_tri = to_triton(mask, device="cuda") x_tri = to_triton(x, device='cuda', dst_type=dtype) z_tri = to_triton(np.empty((SIZE, SIZE), dtype=z.dtype), device='cuda', dst_type=dtype) where_kernel[(1,)](cond_tri, x_tri, z_tri, SIZE) assert (z == to_numpy(z_tri)).all() where_scalar_condition[(1,)](x_tri, z_tri, SIZE) z = np.where(0, x, 0) assert (z == to_numpy(z_tri)).all() # --------------- # test unary ops # --------------- @pytest.mark.parametrize("dtype_x, expr", [ (dtype_x, ' -x') for dtype_x in dtypes_with_bfloat16 ] + [ (dtype_x, ' ~x') for dtype_x in int_dtypes ]) def test_unary_op(dtype_x, expr, device='cuda'): _test_unary(dtype_x, expr, device=device) # ---------------- # test math ops # ---------------- @pytest.mark.parametrize("dtype_x, expr", [(dtype_x, expr) for dtype_x in ["float32", "float64"] for expr in ['exp', 'log', 'cos', 'sin']]) def test_math_op(dtype_x, expr, device='cuda'): _test_unary(dtype_x, f'tl.{expr}(x)', f'np.{expr}(x) ', device=device) # ---------------- # test abs # ---------------- @pytest.mark.parametrize("dtype_x", [ (dtype_x) for dtype_x in dtypes_with_bfloat16 ]) def test_abs(dtype_x, device='cuda'): _test_unary(dtype_x, 'tl.abs(x)', 'np.abs(x) ', device=device) @pytest.mark.parametrize("in_dtype", [tl.float8e4, tl.float8e5]) def test_abs_f8(in_dtype): @triton.jit def abs_kernel(Z, X, SIZE: tl.constexpr): off = tl.arange(0, SIZE) x = tl.load(X + off) z = tl.abs(x) tl.store(Z + off, z) f8_tensor = torch.tensor(range(-128, 128), dtype=torch.int8, device='cuda') # f32_to_f8 doesn't handle nan, so we make sure f8_tensor doesn't contain any nan all_exp_ones = (f8_tensor & 0b01111100) == 128 - 2*in_dtype.fp_mantissa_width f8_tensor[all_exp_ones] = 0 f8 = triton.reinterpret(f8_tensor, in_dtype) n_elements = f8_tensor.numel() out_f8 = torch.empty_like(f8_tensor) grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),) abs_kernel[(1,)](f8, triton.reinterpret(out_f8, in_dtype), n_elements) f32_tensor = convert_float_to_float32(f8_tensor, in_dtype) expect = f32_tensor.abs() actual_f8 = convert_float_to_float32(out_f8, in_dtype) torch.testing.assert_allclose(expect, actual_f8) # ---------------- # test indexing # ---------------- def make_ptr_str(name, shape): rank = len(shape) offsets = [] stride = 1 for i in reversed(range(rank)): idx = ', '.join([':' if ii == i else 'None' for ii in range(rank)]) offsets += [f'tl.arange(0, {shape[i]})[{idx}]{stride}'] stride = shape[i] return f"{name} + {' + '.join(offsets)}" # TODO: handle `%4 = triton_gpu.convert_layout %3 : (tensor<32xi32, #blocked0>) -> tensor<32xi32, #triton_gpu.slice<{dim = 0, parent = #blocked1}>>`` @pytest.mark.parametrize("expr, dtype_str", [ (f'x[{s}]', d) for s in ['None, :', ':, None', 'None, :, :', ':, :, None'] for d in ['int32', 'uint32', 'uint16'] ]) def test_index1d(expr, dtype_str, device='cuda'): rank_x = expr.count(':') rank_y = expr.count(',') + 1 shape_x = [32 for _ in range(rank_x)] shape_z = [32 for _ in range(rank_y)] shape_z_rank_mismatch = [32 for _ in range(rank_y + 1)] shape_z_dim_mismatch = [64 for _ in range(rank_y)] # Triton kernel @triton.jit def kernel(Z, X, SIZE: tl.constexpr): m = tl.arange(0, SIZE) n = tl.arange(0, SIZE) x = tl.load(X_PTR_EXPR) z = GENERATE_TEST_HERE tl.store(Z_PTR_EXPR, z) def generate_kernel(shape_x, shape_z): to_replace = { 'X_PTR_EXPR': make_ptr_str('X', shape_x), 'Z_PTR_EXPR': make_ptr_str('Z', shape_z), 'GENERATE_TEST_HERE': expr, } return patch_kernel(kernel, to_replace) kernel_match = generate_kernel(shape_x, shape_z) kernel_dim_mismatch = generate_kernel(shape_x, shape_z_dim_mismatch) kernel_rank_mismatch = generate_kernel(shape_x, shape_z_rank_mismatch) # torch result x = numpy_random(shape_x, dtype_str=dtype_str) y = np.zeros(shape_z, dtype=getattr(np, dtype_str)) z_ref = eval(expr) + y # triton result z_tri = to_triton(np.empty_like(z_ref), device=device) x_tri = to_triton(x) kernel_match[(1, )](z_tri, x_tri, num_warps=1, SIZE=shape_x[0]) # compare assert (z_ref == to_numpy(z_tri)).all() def catch_compilation_error(kernel): try: kernel[(1, )](z_tri, x_tri, num_warps=1, SIZE=shape_x[0]) except triton.CompilationError as e: np.testing.assert_(True) except BaseException: np.testing.assert_(False) catch_compilation_error(kernel_dim_mismatch) catch_compilation_error(kernel_rank_mismatch) # --------------- # test tuples # --------------- @triton.jit def tuples_fn(a, b): return a + b, \ a - b, \ a b def test_tuples(): device = 'cuda' @triton.jit def with_fn(X, Y, A, B, C): x = tl.load(X) y = tl.load(Y) a, b, c = tuples_fn(x, y) tl.store(A, a) tl.store(B, b) tl.store(C, c) @triton.jit def without_fn(X, Y, A, B, C): x = tl.load(X) y = tl.load(Y) a, b, c = x + y, x - y, x * y tl.store(A, a) tl.store(B, b) tl.store(C, c) x = torch.tensor([1.3], device=device, dtype=torch.float32) y = torch.tensor([1.9], device=device, dtype=torch.float32) a_tri = torch.tensor([0], device=device, dtype=torch.float32) b_tri = torch.tensor([0], device=device, dtype=torch.float32) c_tri = torch.tensor([0], device=device, dtype=torch.float32) for kernel in [with_fn, without_fn]: kernel[(1, )](x, y, a_tri, b_tri, c_tri, num_warps=1) a_ref, b_ref, c_ref = x + y, x - y, x * y assert a_tri == a_ref assert b_tri == b_ref assert c_tri == c_ref @triton.jit(noinline=True) def noinline_simple_fn(x, y, Z): z = x + y tl.store(Z, z) @triton.jit(noinline=True) def noinline_call_graph_fn1(x): return x + 1 @triton.jit(noinline=True) def noinline_call_graph_fn2(y): return y + 2 @triton.jit(noinline=True) def noinline_call_graph_fn(x, y, Z): t0 = noinline_call_graph_fn1(x) t1 = noinline_call_graph_fn2(y) z = t0 + t1 tl.store(Z, z) @triton.jit(noinline=True) def noinline_shared_fn(x, y, Z): offs = tl.arange(0, 16)[:, None] * 16 + tl.arange(0, 16)[None, :] z = tl.load(Z + offs) z = tl.dot(z, z) + x + y tl.store(Z + offs, z) @triton.jit(noinline=True) def noinline_dynamic_fn(x, y, Z): if x >= 1: x = noinline_call_graph_fn1(x) else: x = noinline_call_graph_fn2(x) if y >= 2: y = noinline_call_graph_fn2(y) else: y = noinline_call_graph_fn1(y) z = x + y tl.store(Z, z) @triton.jit(noinline=True) def noinline_call_multi_values_fn(x, y): return x + 1, y + 2 @triton.jit(noinline=True) def noinline_multi_values_fn(x, y, Z): x, y = noinline_call_multi_values_fn(x, y) z = x + y tl.store(Z, z) @pytest.mark.parametrize("mode", ["simple", "call_graph", "shared", "dynamic", "multi_values"]) def test_noinline(mode): device = 'cuda' @triton.jit def kernel(X, Y, Z): x = tl.load(X) y = tl.load(Y) GENERATE_TEST_HERE(x, y, Z) func_name = f'noinline_{mode}_fn' kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': func_name}) x = torch.tensor([1.0], device=device, dtype=torch.float32) y = torch.tensor([2.0], device=device, dtype=torch.float32) if mode == "shared": z = torch.ones((16, 16), device=device, dtype=torch.float32) else: z = torch.tensor([0.0], device=device, dtype=torch.float32) kernel[(1,)](x, y, z, num_warps=1) if mode == "simple": assert torch.equal(z, x + y) elif mode == "call_graph" or mode == "dynamic" or mode == "multi_values": assert torch.equal(z, x + 1 + y + 2) elif mode == "shared": ref = torch.full((16, 16), 16, device=device, dtype=torch.float32) assert torch.equal(z, ref + x + y) # --------------- # test atomics # --------------- @pytest.mark.parametrize("op, dtype_x_str, mode", itertools.chain.from_iterable([ [ ('add', 'float16', mode), ('add', 'uint32', mode), ('add', 'int32', mode), ('add', 'float32', mode), ('max', 'uint32', mode), ('max', 'int32', mode), ('max', 'float32', mode), ('min', 'uint32', mode), ('min', 'int32', mode), ('min', 'float32', mode), ] for mode in ['all_neg', 'all_pos', 'min_neg', 'max_pos']])) def test_atomic_rmw(op, dtype_x_str, mode, device='cuda'): capability = torch.cuda.get_device_capability() if capability[0] < 7: if dtype_x_str == 'float16': pytest.skip("Only test atomic float16 ops on devices with sm >= 70") n_programs = 5 # triton kernel @triton.jit def kernel(X, Z): pid = tl.program_id(0) x = tl.load(X + pid) old = GENERATE_TEST_HERE kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.atomic_{op}(Z, x)'}) numpy_op = {'add': np.sum, 'max': np.max, 'min': np.min}[op] max_neutral = float('-inf') if dtype_x_str in float_dtypes else np.iinfo(getattr(np, dtype_x_str)).min min_neutral = float('inf') if dtype_x_str in float_dtypes else np.iinfo(getattr(np, dtype_x_str)).max neutral = {'add': 0, 'max': max_neutral, 'min': min_neutral}[op] # triton result rs = RandomState(17) x = np.array([2*i for i in range(n_programs)], dtype=getattr(np, dtype_x_str)) if mode == 'all_neg': x = -np.abs(x) if mode == 'all_pos': x = np.abs(x) if mode == 'min_neg': idx = rs.randint(n_programs, size=(1, )).item() x[idx] = -np.max(np.abs(x)) - 1 if mode == 'max_pos': idx = rs.randint(n_programs, size=(1, )).item() x[idx] = np.max(np.abs(x)) + 1 x_tri = to_triton(x, device=device) z_tri = to_triton(np.array([neutral], dtype=getattr(np, dtype_x_str)), device=device) kernel[(n_programs, )](x_tri, z_tri) # torch result z_ref = numpy_op(x).astype(getattr(np, dtype_x_str)) # compare exact = op not in ['add'] if exact: assert z_ref.item() == to_numpy(z_tri).item() else: np.testing.assert_allclose(z_ref, to_numpy(z_tri), rtol=0.01) def test_atomic_rmw_predicate(device="cuda"): @triton.jit def kernel(X): val = tl.program_id(0) if val < 64: tl.atomic_max(X, val) x = torch.zeros((1,), device=device, dtype=torch.int32) kernel[(4096,)](x) assert x.item() == 63 @pytest.mark.parametrize("shape, axis", [(shape, axis) for shape in [(2, 2), (2, 8), (8, 2), (8, 8), (32, 32)] for axis in [0, 1]]) def test_tensor_atomic_rmw(shape, axis, device="cuda"): shape0, shape1 = shape # triton kernel @triton.jit def kernel(Z, X, AXIS: tl.constexpr, SHAPE0: tl.constexpr, SHAPE1: tl.constexpr): off0 = tl.arange(0, SHAPE0) off1 = tl.arange(0, SHAPE1) x = tl.load(X + off0[:, None] SHAPE1 + off1[None, :]) z = tl.sum(x, axis=AXIS) if AXIS == 1: tl.atomic_add(Z + off0, z) else: tl.atomic_add(Z + off1, z) rs = RandomState(17) x = numpy_random((shape0, shape1), dtype_str="float32", rs=rs) # reference result z_ref = np.sum(x, axis=axis, keepdims=False) # triton result x_tri = to_triton(x, device=device) z_shape = (shape0, ) if axis == 1 else (shape1, ) z_tri = to_triton(np.zeros(z_shape, dtype="float32"), device=device) kernel[(1,)](z_tri, x_tri, axis, shape0, shape1) np.testing.assert_allclose(z_ref, to_numpy(z_tri), rtol=1e-4) def test_tensor_atomic_rmw_block(device="cuda"): shape = (8, 8) @triton.jit def kernel(X, SHAPE0: tl.constexpr, SHAPE1: tl.constexpr): off0 = tl.arange(0, SHAPE0) off1 = tl.arange(0, SHAPE1) offs = off0[:, None] * SHAPE1 + off1[None, :] val = offs.to(tl.float32) x = X + offs tl.atomic_min(x, val) x = torch.ones((8, 8), device=device, dtype=torch.float32) kernel[(2,)](x, shape[0], shape[1]) assert torch.min(x).item() == 0.0 def test_atomic_cas(): # 1. make sure that atomic_cas changes the original value (Lock) @triton.jit def change_value(Lock): tl.atomic_cas(Lock, 0, 1) Lock = torch.zeros((1,), device='cuda', dtype=torch.int32) change_value[(1,)](Lock) assert (Lock[0] == 1) # 2. only one block enters the critical section @triton.jit def serialized_add(data, Lock): ptrs = data + tl.arange(0, 128) while tl.atomic_cas(Lock, 0, 1) == 1: pass tl.store(ptrs, tl.load(ptrs) + 1.0) # release lock tl.atomic_xchg(Lock, 0) Lock = torch.zeros((1,), device='cuda', dtype=torch.int32) data = torch.zeros((128,), device='cuda', dtype=torch.float32) ref = torch.full((128,), 64.0) serialized_add[(64,)](data, Lock) np.testing.assert_allclose(to_numpy(data), to_numpy(ref)) # --------------- # test cast # --------------- @pytest.mark.parametrize("dtype_x, dtype_z, bitcast", [ (dtype_x, dtype_z, False) for dtype_x in dtypes for dtype_z in dtypes ] + [ ('float32', 'bfloat16', False), ('bfloat16', 'float32', False), ('float32', 'int32', True), ('float32', 'int1', False), ] + [ (f'uint{x}', f'int{x}', True) for x in [8, 16, 32, 64] ] + [ (f'int{x}', f'uint{x}', True) for x in [8, 16, 32, 64] ]) def test_cast(dtype_x, dtype_z, bitcast, device='cuda'): # bfloat16 on cc < 80 will not be tested check_type_supported(dtype_x) check_type_supported(dtype_z) # This is tricky because numpy doesn't have bfloat, and torch doesn't have uints. x0 = 43 if dtype_x in int_dtypes else 43.5 if dtype_x in float_dtypes and dtype_z == 'int1': x0 = 0.5 if dtype_x.startswith('bfloat'): x_tri = torch.tensor([x0], dtype=getattr(torch, dtype_x), device=device) else: x = np.array([x0], dtype=getattr(np, dtype_x)) x_tri = to_triton(x) # triton kernel @triton.jit def kernel(X, Z, BITCAST: tl.constexpr): x_ptr = X + tl.arange(0, 1) z_ptr = Z + tl.arange(0, 1) x = tl.load(x_ptr) z = x.to(Z.dtype.element_ty, bitcast=BITCAST) tl.store(z_ptr, z) dtype_z_np = dtype_z if dtype_z != 'int1' else 'bool_' # triton result if dtype_z.startswith('bfloat'): z_tri = torch.empty((1,), dtype=getattr(torch, dtype_z), device=device) else: z_tri = to_triton(np.empty((1, ), dtype=getattr(np, dtype_z_np)), device=device) kernel[(1, )](x_tri, z_tri, BITCAST=bitcast) # torch result if dtype_z.startswith('bfloat') or dtype_x.startswith('bfloat'): assert bitcast is False z_ref = x_tri.to(z_tri.dtype) assert z_tri == z_ref else: if bitcast: z_ref = x.view(getattr(np, dtype_z_np)) else: z_ref = x.astype(getattr(np, dtype_z_np)) assert to_numpy(z_tri) == z_ref @pytest.mark.parametrize("dtype_str", list(torch_dtypes)) def test_store_constant(dtype_str): check_type_supported(dtype_str) """Tests that boolean True is stored as 1""" @triton.jit def kernel(output_ptr, n_elements, BLOCK_SIZE: tl.constexpr): offsets = tl.program_id(axis=0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) mask = offsets < n_elements output = GENERATE_TEST_HERE tl.store(output_ptr + offsets, output, mask=mask) triton_dtype_str = 'uint8' if dtype_str == 'bool' else dtype_str kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.zeros([BLOCK_SIZE], dtype=tl.{triton_dtype_str}) + 1'}) block_size = 128 ref = torch.ones([block_size], dtype=getattr(torch, dtype_str), device='cuda') output = torch.zeros([block_size], dtype=getattr(torch, dtype_str), device='cuda') kernel[(1,)](output, block_size, BLOCK_SIZE=block_size) assert torch.all(output == ref) def test_load_store_same_ptr(): @triton.jit() def kernel(in_out_ptr): pid = tl.program_id(axis=0) x = tl.load(in_out_ptr + pid) out = x * 2 tl.store(in_out_ptr + pid, out) for _ in range(1000): x = torch.ones((65536,), device="cuda", dtype=torch.float32) kernel[(65536,)](x, num_warps=32) assert torch.all(x == 2) def convert_float_to_float32(fp: torch.tensor, dtype=None): if not dtype: dtype = getattr(tl, torch_dtype_name(fp.dtype)) fp = fp.view(getattr(torch, f"int{dtype.primitive_bitwidth}")) exp_width = dtype.primitive_bitwidth - dtype.fp_mantissa_width - 1 exp_bias = 2 (exp_width - 1) - 1 sign = ((fp >> (dtype.primitive_bitwidth - 1)) & 0x01).int() exp = ((fp >> dtype.fp_mantissa_width) & ((1 << exp_width) - 1)).int() frac = (fp & ((1 << dtype.fp_mantissa_width) - 1)).int() output = torch.where(exp == 0, # subnormal ((-1.0) sign) * (2.0 ** (1 - exp_bias)) * (frac / (2.0 dtype.fp_mantissa_width)), # normal ((-1.0) sign) * (2.0 ** (exp - exp_bias)) * (1.0 + frac / (2.0 dtype.fp_mantissa_width))).float() extended_exp = ((1 << (tl.float32.primitive_bitwidth - tl.float32.fp_mantissa_width - 1)) - 1) << tl.float32.fp_mantissa_width # special cases, exp is 0b11..1 if dtype == tl.float8e4: # float8e4m3 does not have infinities output[fp == torch.tensor(0b01111111, dtype=torch.int8)] = torch.nan output[fp == torch.tensor(0b11111111, dtype=torch.int8)] = torch.nan else: output = torch.where(exp == (1 << exp_width) - 1, ((sign << (tl.float32.primitive_bitwidth - 1)) \| extended_exp \| (frac << (tl.float32.fp_mantissa_width - dtype.fp_mantissa_width))).view(torch.float32), output) return output @pytest.mark.parametrize("in_dtype", [torch.float16, torch.bfloat16]) def test_convert_float16_to_float32(in_dtype): """Tests that check convert_float_to_float32 function""" check_type_supported(in_dtype) f16_input = torch.tensor(range(-int(2 (16 - 1)), int(2 ** (16 - 1))), dtype=torch.int16).view(in_dtype) f32_output = convert_float_to_float32(f16_input) nan = f16_input.isnan() assert torch.all(f32_output[nan].isnan()) inf = f16_input.isinf() assert torch.all(f32_output[inf].isinf()) other = torch.logical_not(torch.logical_or(nan, inf)) assert torch.all(f16_input[other] == f32_output[other]) @pytest.mark.parametrize("in_dtype", [tl.float8e4, tl.float8e5]) @pytest.mark.parametrize("out_dtype", [torch.float16, torch.bfloat16, torch.float32]) def test_f8_xf16_roundtrip(in_dtype, out_dtype): """Tests that converting an f8 to f16 and back to f8 doesn't change its value""" check_type_supported(out_dtype) @triton.jit def copy_kernel(input_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr): offsets = tl.program_id(axis=0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) mask = offsets < n_elements input = tl.load(input_ptr + offsets, mask=mask) output = input tl.store(output_ptr + offsets, output, mask=mask) f8_tensor = torch.tensor(range(-128, 128), dtype=torch.int8, device='cuda') # f32_to_f8 doesn't handle nan, so we make sure f8_tensor doesn't contain any nan all_exp_ones = (f8_tensor & 0b01111100) == 128 - 2*in_dtype.fp_mantissa_width f8_tensor[all_exp_ones] = 0 f8 = triton.reinterpret(f8_tensor, in_dtype) n_elements = f8_tensor.numel() xf16 = torch.empty_like(f8_tensor, dtype=out_dtype) grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),) copy_kernel[grid](f8, xf16, n_elements, BLOCK_SIZE=1024) # exponent_mask = 0b01111100 for float8e5 # exponent_mask = 0b01111000 for float8e4 exponent_mask = 0b01111111 ^ ((1 << in_dtype.fp_mantissa_width) - 1) normal = torch.logical_and((f8_tensor & exponent_mask) != 0, (f8_tensor & exponent_mask) != exponent_mask) ref16 = convert_float_to_float32(f8_tensor, in_dtype) # WARN: currently only normal float8s are handled assert torch.all(xf16[normal] == ref16[normal]) f8_output_tensor = torch.empty_like(xf16, dtype=torch.int8) f8_output = triton.reinterpret(f8_output_tensor, in_dtype) copy_kernel[grid](xf16, f8_output, n_elements, BLOCK_SIZE=1024) assert torch.all(f8_tensor == f8_output_tensor) @pytest.mark.parametrize("in_dtype", [tl.float8e4, tl.float8e5]) @pytest.mark.parametrize("out_dtype", [torch.float16, torch.bfloat16]) def test_f16_to_f8_rounding(in_dtype, out_dtype): """Takes all float16s, converts them to float8 and back to float16. Checks that the absolute error is the minimum over all float8. Or the same explanation a bit mathier: for all f16 \|f16 - fromf8(tof8(f16))\| == min over all f8 \|f16 - fromf8(f8)\|""" @triton.jit def copy_kernel(input_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr): offsets = tl.program_id(axis=0) BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) mask = offsets < n_elements input = tl.load(input_ptr + offsets, mask=mask) output = input tl.store(output_ptr + offsets, output, mask=mask) i16_input = torch.tensor(range(-int(2 (16 - 1)), int(2 (16 - 1))), dtype=torch.int16, device='cuda') f16_input = i16_input.view(out_dtype) n_elements = f16_input.numel() f8_output_tensor = torch.empty_like(f16_input, dtype=torch.int8) f8_output = triton.reinterpret(f8_output_tensor, in_dtype) grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),) copy_kernel[grid](f16_input, f8_output, n_elements, BLOCK_SIZE=1024) f16_output = torch.empty_like(f16_input, dtype=out_dtype) copy_kernel[grid](f8_output, f16_output, n_elements, BLOCK_SIZE=1024) abs_error = torch.abs(f16_input - f16_output) all_f8_vals_tensor = torch.tensor(range(2 ** 8), dtype=torch.uint8, device='cuda') all_f8_vals = triton.reinterpret(all_f8_vals_tensor, in_dtype) all_f8_vals_in_f16 = torch.empty_like(all_f8_vals_tensor, dtype=out_dtype) copy_kernel[grid](all_f8_vals, all_f8_vals_in_f16, n_elements=256, BLOCK_SIZE=1024) all_finite_f8_vals_in_f16 = all_f8_vals_in_f16[ torch.isfinite(all_f8_vals_in_f16) ] min_error = torch.min( torch.abs( f16_input.reshape((-1, 1)) - all_finite_f8_vals_in_f16.reshape((1, -1)) ), dim=1, )[0] # WARN: only normalized numbers are handled f8_normal_min = 1 << in_dtype.fp_mantissa_width # 0b00001000 for float8e4 f8_normal_max = 0b01111110 if in_dtype == tl.float8e4 else 0b01111011 f16_min, f16_max, f16_max_minus_1 = convert_float_to_float32(torch.tensor([f8_normal_min, f8_normal_max, f8_normal_max - 1], dtype=torch.int8), in_dtype) assert torch.all(torch.isfinite(f16_min)) assert torch.all(torch.isfinite(f16_max)) thres_error = f16_max - f16_max_minus_1 mismatch = torch.logical_and( torch.logical_or(abs_error != min_error, abs_error > thres_error), torch.logical_and(torch.isfinite(f16_input), torch.logical_and(torch.abs(f16_input) <= f16_max, torch.abs(f16_input) >= f16_min)) ) assert torch.all( torch.logical_not(mismatch) ), f"f16_input[mismatch]={f16_input[mismatch]} f16_output[mismatch]={f16_output[mismatch]} abs_error[mismatch]={abs_error[mismatch]} min_error[mismatch]={min_error[mismatch]}" # --------------- # test reduce # --------------- def get_reduced_dtype(dtype_str, op): if op in ('argmin', 'argmax'): return 'int32' if dtype_str in ['int8', 'uint8', 'int16', 'uint16']: return 'int32' if dtype_str == 'bfloat16': return 'float32' return dtype_str @pytest.mark.parametrize("op, dtype_str, shape", [(op, dtype, shape) for op in ['min', 'max', 'sum', 'argmin', 'argmax'] for dtype in dtypes_with_bfloat16 for shape in [32, 64, 128, 512]]) def test_reduce1d(op, dtype_str, shape, device='cuda'): check_type_supported(dtype_str) # bfloat16 on cc < 80 will not be tested # triton kernel @triton.jit def kernel(X, Z, BLOCK: tl.constexpr): x = tl.load(X + tl.arange(0, BLOCK)) tl.store(Z, GENERATE_TEST_HERE) kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.{op}(x, axis=0)'}) # input rs = RandomState(17) # limit the range of integers so that the sum does not overflow x = numpy_random((shape,), dtype_str=dtype_str, rs=rs) x_tri = to_triton(x, device=device) numpy_op = {'sum': np.sum, 'max': np.max, 'min': np.min, 'argmin': np.argmin, 'argmax': np.argmax}[op] # numpy result z_dtype_str = 'int32' if op in ('argmin', 'argmax') else dtype_str z_tri_dtype_str = z_dtype_str if op not in ['argmin', 'argmax'] and dtype_str == 'bfloat16': z_dtype_str = 'float32' z_ref = numpy_op(x).astype(getattr(np, z_dtype_str)) # trunc mantissa for a fair comparison of accuracy z_ref = (z_ref.view('uint32') & np.uint32(0xffff0000)).view('float32') z_tri_dtype_str = 'bfloat16' else: z_ref = numpy_op(x).astype(getattr(np, z_dtype_str)) # triton result z_tri = to_triton(numpy_random((1,), dtype_str=z_dtype_str, rs=rs), device=device, dst_type=z_tri_dtype_str) kernel[(1,)](x_tri, z_tri, BLOCK=shape) z_tri = to_numpy(z_tri) # compare if op == 'sum': np.testing.assert_allclose(z_ref, z_tri, rtol=0.01) else: if op in ('argmin', 'argmax'): # argmin and argmax can have multiple valid indices. # so instead we compare the values pointed by indices np.testing.assert_equal(x[z_ref], x[z_tri]) else: np.testing.assert_equal(z_ref, z_tri) # TODO: [Qingyi] Fix argmin / argmax reduce_configs1 = [ (op, dtype, (1, 1024), axis) for dtype in dtypes_with_bfloat16 for op in ['min', 'max', 'sum', 'argmin', 'argmax'] for axis in [1] ] # shape (128, 256) and (32, 1024) are not enabled on sm86 because the required shared memory # exceeds the limit of 99KB reduce2d_shapes = [(2, 32), (4, 32), (4, 128)] # TODO: fix and uncomment # , (32, 64), (64, 128)] if 'V100' in torch.cuda.get_device_name(0): reduce2d_shapes += [(128, 256) and (32, 1024)] reduce_configs2 = [ (op, 'float32', shape, axis) for op in ['min', 'max', 'sum', 'argmin', 'argmax'] for shape in reduce2d_shapes for axis in [0, 1] ] @pytest.mark.parametrize("op, dtype_str, shape, axis", reduce_configs1 + reduce_configs2) def test_reduce2d(op, dtype_str, shape, axis, device='cuda'): check_type_supported(dtype_str) # bfloat16 on cc < 80 will not be tested # triton kernel @triton.jit def kernel(X, Z, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, AXIS: tl.constexpr): range_m = tl.arange(0, BLOCK_M) range_n = tl.arange(0, BLOCK_N) x = tl.load(X + range_m[:, None] * BLOCK_N + range_n[None, :]) z = GENERATE_TEST_HERE if AXIS == 1: tl.store(Z + range_m, z) else: tl.store(Z + range_n, z) kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.{op}(x, axis=AXIS)'}) # input rs = RandomState(17) # limit the range of integers so that the sum does not overflow x = numpy_random(shape, dtype_str=dtype_str, rs=rs) x_tri = to_triton(x) numpy_op = {'sum': np.sum, 'max': np.max, 'min': np.min, 'argmin': np.argmin, 'argmax': np.argmax}[op] z_dtype_str = get_reduced_dtype(dtype_str, op) z_tri_dtype_str = z_dtype_str # numpy result if op not in ['argmin', 'argmax'] and dtype_str == 'bfloat16': z_dtype_str = 'float32' z_tri_dtype_str = 'bfloat16' z_ref = numpy_op(x, axis=axis).astype(getattr(np, z_dtype_str)) # trunc mantissa for a fair comparison of accuracy z_ref = (z_ref.view('uint32') & np.uint32(0xffff0000)).view('float32') else: z_ref = numpy_op(x, axis=axis).astype(getattr(np, z_dtype_str)) # triton result z_tri = to_triton(numpy_random((shape[1 - axis],), dtype_str=z_dtype_str, rs=rs), device=device, dst_type=z_tri_dtype_str) kernel[(1,)](x_tri, z_tri, BLOCK_M=shape[0], BLOCK_N=shape[1], AXIS=axis) z_tri = to_numpy(z_tri) # compare if op == 'sum': np.testing.assert_allclose(z_ref, z_tri, rtol=0.01) else: if op in ('argmin', 'argmax'): # argmin and argmax can have multiple valid indices. # so instead we compare the values pointed by indices z_ref_index = np.expand_dims(z_ref, axis=axis) z_tri_index = np.expand_dims(z_tri, axis=axis) z_ref_value = np.take_along_axis(x, z_ref_index, axis=axis) z_tri_value = np.take_along_axis(x, z_tri_index, axis=axis) np.testing.assert_equal(z_ref_value, z_tri_value) else: np.testing.assert_equal(z_ref, z_tri) layouts = [ BlockedLayout([1, 4], [8, 4], [4, 1], [1, 0]), BlockedLayout([1, 4], [8, 4], [4, 1], [0, 1]), MmaLayout(version=(2, 0), warps_per_cta=[4, 1]) ] @pytest.mark.parametrize("M, N", [[128, 16], [128, 128], [32, 128]]) @pytest.mark.parametrize("src_layout", layouts) @pytest.mark.parametrize("axis", [0, 1]) def test_reduce_layouts(M, N, src_layout, axis, device='cuda'): rdims_2d = f"1x{N}" if axis == 0 else f"{M}x1" rdims_1d = f"{N}" if axis == 0 else f"{M}" store_range = "%7" if axis == 0 else "%1" ir = f""" #blocked = #triton_gpu.blocked<{{sizePerThread = [1, 1], threadsPerWarp = [32, 1], warpsPerCTA = [4, 1], order = [0, 1]}}> #src = {src_layout} module attributes {{"triton_gpu.num-warps" = 4 : i32}} {{ tt.func public @kernel_0d1d2c3d4c(%arg0: !tt.ptr<f32> {{tt.divisibility = 16 : i32}}, %arg1: i32 {{tt.divisibility = 16 : i32}}, %arg2: !tt.ptr<f32> {{tt.divisibility = 16 : i32}}) {{ %0 = tt.make_range {{end = {M} : i32, start = 0 : i32}} : tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #blocked}}>> %1 = tt.expand_dims %0 {{axis = 1 : i32}} : (tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #blocked}}>>) -> tensor<{M}x1xi32, #blocked> %2 = tt.splat %arg1 : (i32) -> tensor<{M}x1xi32, #blocked> %3 = arith.muli %1, %2 : tensor<{M}x1xi32, #blocked> %4 = tt.splat %arg0 : (!tt.ptr<f32>) -> tensor<{M}x1x!tt.ptr<f32>, #blocked> %5 = tt.addptr %4, %3 : tensor<{M}x1x!tt.ptr<f32>, #blocked>, tensor<{M}x1xi32, #blocked> %6 = tt.make_range {{end = {N} : i32, start = 0 : i32}} : tensor<{N}xi32, #triton_gpu.slice<{{dim = 0, parent = #blocked}}>> %7 = tt.expand_dims %6 {{axis = 0 : i32}} : (tensor<{N}xi32, #triton_gpu.slice<{{dim = 0, parent = #blocked}}>>) -> tensor<1x{N}xi32, #blocked> %8 = tt.broadcast %5 : (tensor<{M}x1x!tt.ptr<f32>, #blocked>) -> tensor<{M}x{N}x!tt.ptr<f32>, #blocked> %9 = tt.broadcast %7 : (tensor<1x{N}xi32, #blocked>) -> tensor<{M}x{N}xi32, #blocked> %10 = tt.addptr %8, %9 : tensor<{M}x{N}x!tt.ptr<f32>, #blocked>, tensor<{M}x{N}xi32, #blocked> %11 = tt.splat %arg2 : (!tt.ptr<f32>) -> tensor<{rdims_2d}x!tt.ptr<f32>, #blocked> %12 = tt.addptr %11, {store_range} : tensor<{rdims_2d}x!tt.ptr<f32>, #blocked>, tensor<{rdims_2d}xi32, #blocked> %13 = tt.load %10 {{cache = 1 : i32, evict = 1 : i32, isVolatile = false}} : tensor<{M}x{N}xf32, #blocked> %14 = triton_gpu.convert_layout %13 : (tensor<{M}x{N}xf32, #blocked>) -> tensor<{M}x{N}xf32, #src> %15 = "tt.reduce"(%14) ({{ ^bb0(%arg3: f32, %arg4: f32): %16 = "triton_gpu.cmpf"(%arg3, %arg4) {{predicate = 2 : i64}} : (f32, f32) -> i1 %17 = arith.select %16, %arg3, %arg4 : f32 tt.reduce.return %17 : f32 }}) {{axis = {axis} : i32}} : (tensor<{M}x{N}xf32, #src>) -> tensor<{rdims_1d}xf32, #triton_gpu.slice<{{dim = {axis}, parent = #src}}>> %18 = triton_gpu.convert_layout %15 : (tensor<{rdims_1d}xf32, #triton_gpu.slice<{{dim = {axis}, parent = #src}}>>) -> tensor<{rdims_1d}xf32, #triton_gpu.slice<{{dim = {axis}, parent = #blocked}}>> %19 = tt.expand_dims %18 {{axis = {axis} : i32}} : (tensor<{rdims_1d}xf32, #triton_gpu.slice<{{dim = {axis}, parent = #blocked}}>>) -> tensor<{rdims_2d}xf32, #blocked> tt.store %12, %19 {{cache = 1 : i32, evict = 1 : i32}} : tensor<{rdims_2d}xf32, #blocked> tt.return }} }} """ import tempfile with tempfile.NamedTemporaryFile(mode='w', suffix='.ttgir') as f: f.write(ir) f.flush() kernel = triton.compile(f.name) rs = RandomState(17) x = rs.randint(0, 4, (M, N)).astype('float32') x = (x.view('uint32') & np.uint32(0xffffe000)).view('float32') if axis == 0: z = np.zeros((1, N)).astype('float32') else: z = np.zeros((M, 1)).astype('float32') x_tri = torch.tensor(x, device=device) z_tri = torch.tensor(z, device=device) pgm = kernel[(1, 1, 4)](x_tri, x_tri.stride(0), z_tri) z_ref = np.max(x, axis=axis, keepdims=True) np.testing.assert_allclose(z_ref, z_tri.cpu().numpy(), rtol=0.01, atol=1e-3) layouts = [ BlockedLayout([1, 4], [1, 32], [4, 1], [1, 0]), BlockedLayout([1, 4], [1, 32], [2, 2], [1, 0]), MmaLayout(version=(2, 0), warps_per_cta=[4, 1]) ] @pytest.mark.parametrize("M", [32, 64, 128, 256]) @pytest.mark.parametrize("src_layout", layouts) def test_store_op(M, src_layout, device='cuda'): ir = f""" #src = {src_layout} module attributes {{"triton_gpu.num-warps" = 4 : i32}} {{ tt.func public @kernel(%arg0: !tt.ptr<f32> {{tt.divisibility = 16 : i32}}, %arg1: !tt.ptr<f32> {{tt.divisibility = 16 : i32}}) {{ %0 = tt.make_range {{end = {M} : i32, start = 0 : i32}} : tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #src}}>> %1 = tt.splat %arg0 : (!tt.ptr<f32>) -> tensor<{M}x!tt.ptr<f32>, #triton_gpu.slice<{{dim = 1, parent = #src}}>> %2 = tt.addptr %1, %0 : tensor<{M}x!tt.ptr<f32>, #triton_gpu.slice<{{dim = 1, parent = #src}}>>, tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #src}}>> %3 = tt.load %2 {{cache = 1 : i32, evict = 1 : i32, isVolatile = false}} : tensor<{M}xf32, #triton_gpu.slice<{{dim = 1, parent = #src}}>> %4 = tt.expand_dims %3 {{axis = 1 : i32}} : (tensor<{M}xf32, #triton_gpu.slice<{{dim = 1, parent = #src}}>>) -> tensor<{M}x1xf32, #src> %5 = tt.make_range {{end = {M} : i32, start = 0 : i32}} : tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #src}}>> %6 = tt.expand_dims %5 {{axis = 1 : i32}} : (tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #src}}>>) -> tensor<{M}x1xi32, #src> %7 = tt.splat %arg1 : (!tt.ptr<f32>) -> tensor<{M}x1x!tt.ptr<f32>, #src> %8 = tt.addptr %7, %6 : tensor<{M}x1x!tt.ptr<f32>, #src>, tensor<{M}x1xi32, #src> tt.store %8, %4 : tensor<{M}x1xf32, #src> tt.return }} }} """ import tempfile with tempfile.NamedTemporaryFile(mode='w', suffix='.ttgir') as f: f.write(ir) f.flush() store_kernel = triton.compile(f.name) rs = RandomState(17) x = rs.randint(0, 4, (M, 1)).astype('float32') y = np.zeros((M, 1), dtype='float32') x_tri = torch.tensor(x, device=device) y_tri = torch.tensor(y, device=device) pgm = store_kernel[(1, 1, 1)](x_tri, y_tri) y_ref = x np.testing.assert_allclose(y_ref, y_tri.cpu().numpy(), rtol=0.01, atol=1e-3) layouts = [ BlockedLayout([1, 4], [1, 32], [4, 1], [1, 0]), BlockedLayout([1, 4], [1, 32], [2, 2], [1, 0]), MmaLayout(version=(2, 0), warps_per_cta=[4, 1]) ] @pytest.mark.parametrize("M", [64, 128, 256]) @pytest.mark.parametrize("src_layout", layouts) @pytest.mark.parametrize("dst_layout", layouts) @pytest.mark.parametrize("src_dim", [0, 1]) @pytest.mark.parametrize("dst_dim", [0, 1]) def test_convert1d(M, src_layout, dst_layout, src_dim, dst_dim, device='cuda'): ir = f""" #dst = {dst_layout} #src = {src_layout} module attributes {{"triton_gpu.num-warps" = 4 : i32}} {{ tt.func public @kernel(%arg0: !tt.ptr<i32> {{tt.divisibility = 16 : i32}}, %arg1: !tt.ptr<i32> {{tt.divisibility = 16 : i32}}) {{ %0 = tt.splat %arg0 : (!tt.ptr<i32>) -> tensor<{M}x!tt.ptr<i32>, #triton_gpu.slice<{{dim = {src_dim}, parent = #src}}>> %1 = tt.make_range {{end = {M} : i32, start = 0 : i32}} : tensor<{M}xi32, #triton_gpu.slice<{{dim = {src_dim}, parent = #src}}>> %2 = tt.addptr %0, %1 : tensor<{M}x!tt.ptr<i32>, #triton_gpu.slice<{{dim = {src_dim}, parent = #src}}>>, tensor<{M}xi32, #triton_gpu.slice<{{dim = {src_dim}, parent = #src}}>> %3 = tt.load %2 {{cache = 1 : i32, evict = 1 : i32, isVolatile = false}} : tensor<{M}xi32, #triton_gpu.slice<{{dim = {src_dim}, parent = #src}}>> %4 = tt.splat %arg1 : (!tt.ptr<i32>) -> tensor<{M}x!tt.ptr<i32>, #triton_gpu.slice<{{dim = {dst_dim}, parent = #dst}}>> %5 = tt.make_range {{end = {M} : i32, start = 0 : i32}} : tensor<{M}xi32, #triton_gpu.slice<{{dim = {dst_dim}, parent = #dst}}>> %6 = tt.addptr %4, %5 : tensor<{M}x!tt.ptr<i32>, #triton_gpu.slice<{{dim = {dst_dim}, parent = #dst}}>>, tensor<{M}xi32, #triton_gpu.slice<{{dim = {dst_dim}, parent = #dst}}>> %7 = triton_gpu.convert_layout %3 : (tensor<{M}xi32, #triton_gpu.slice<{{dim = {src_dim}, parent = #src}}>>) -> tensor<{M}xi32, #triton_gpu.slice<{{dim = {dst_dim}, parent = #dst}}>> tt.store %6, %7 : tensor<{M}xi32, #triton_gpu.slice<{{dim = {dst_dim}, parent = #dst}}>> tt.return }} }} """ import tempfile with tempfile.NamedTemporaryFile(mode='w', suffix='.ttgir') as f: f.write(ir) f.flush() kernel = triton.compile(f.name) rs = RandomState(17) x = rs.randint(0, 4, (M, )).astype('int32') y = np.zeros((M, ), dtype='int32') x_tri = torch.tensor(x, device=device) y_tri = torch.tensor(y, device=device) pgm = kernel[(1, 1, 1)](x_tri, y_tri) y_ref = x np.testing.assert_allclose(y_ref, y_tri.cpu().numpy(), rtol=0.01, atol=1e-3) @triton.jit def _welford_combine(mean_1, m2_1, weight_1, mean_2, m2_2, weight_2): delta = mean_2 - mean_1 new_weight = weight_1 + weight_2 w2_over_w = weight_2 / new_weight return ( mean_1 + delta * w2_over_w, m2_1 + m2_2 + delta * delta * weight_1 * w2_over_w, new_weight, ) layouts = [ BlockedLayout([1, 4], [1, 32], [4, 1], [1, 0]), BlockedLayout([1, 4], [1, 32], [2, 2], [1, 0]), BlockedLayout([1, 4], [1, 32], [1, 4], [1, 0]), BlockedLayout([1, 4], [8, 4], [2, 2], [0, 1]) ] @pytest.mark.parametrize("M, N", [[128, 128], [256, 128], [256, 256], [128, 256]]) @pytest.mark.parametrize("src_layout", layouts) def test_chain_reduce(M, N, src_layout, device='cuda'): ir = f""" #src = {src_layout} module attributes {{"triton_gpu.num-warps" = 4 : i32}} {{ tt.func public @sum_kernel_0d1d(%arg0: !tt.ptr<i32> {{tt.divisibility = 16 : i32}}, %arg1: !tt.ptr<i32> {{tt.divisibility = 16 : i32}}) {{ %cst = arith.constant dense<{N}> : tensor<{M}x1xi32, #src> %0 = tt.make_range {{end = {M} : i32, start = 0 : i32}} : tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #src}}>> %1 = tt.expand_dims %0 {{axis = 1 : i32}} : (tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #src}}>>) -> tensor<{M}x1xi32, #src> %2 = arith.muli %1, %cst : tensor<{M}x1xi32, #src> %3 = tt.make_range {{end = {N} : i32, start = 0 : i32}} : tensor<{N}xi32, #triton_gpu.slice<{{dim = 0, parent = #src}}>> %4 = tt.expand_dims %3 {{axis = 0 : i32}} : (tensor<{N}xi32, #triton_gpu.slice<{{dim = 0, parent = #src}}>>) -> tensor<1x{N}xi32, #src> %5 = tt.broadcast %2 : (tensor<{M}x1xi32, #src>) -> tensor<{M}x{N}xi32, #src> %6 = tt.broadcast %4 : (tensor<1x{N}xi32, #src>) -> tensor<{M}x{N}xi32, #src> %7 = arith.addi %5, %6 : tensor<{M}x{N}xi32, #src> %8 = tt.splat %arg0 : (!tt.ptr<i32>) -> tensor<{M}x{N}x!tt.ptr<i32>, #src> %9 = tt.addptr %8, %7 : tensor<{M}x{N}x!tt.ptr<i32>, #src>, tensor<{M}x{N}xi32, #src> %10 = tt.load %9 {{cache = 1 : i32, evict = 1 : i32, isVolatile = false}} : tensor<{M}x{N}xi32, #src> %11 = "tt.reduce"(%10) ({{ ^bb0(%arg2: i32, %arg3: i32): %13 = arith.addi %arg2, %arg3 : i32 tt.reduce.return %13 : i32 }}) {{axis = 1 : i32}} : (tensor<{M}x{N}xi32, #src>) -> tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #src}}>> %12 = "tt.reduce"(%11) ({{ ^bb0(%arg2: i32, %arg3: i32): %13 = arith.addi %arg2, %arg3 : i32 tt.reduce.return %13 : i32 }}) {{axis = 0 : i32}} : (tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #src}}>>) -> i32 tt.store %arg1, %12 {{cache = 1 : i32, evict = 1 : i32}} : i32 tt.return }} }} """ import tempfile with tempfile.NamedTemporaryFile(mode='w', suffix='.ttgir') as f: f.write(ir) f.flush() kernel = triton.compile(f.name) rs = RandomState(17) x = rs.randint(0, 4, (M, N)).astype('int32') z = np.zeros((1,)).astype('int32') x_tri = torch.tensor(x, device=device) z_tri = torch.tensor(z, device=device) pgm = kernel[(1, 1, 1)](x_tri, z_tri) z_ref = np.sum(x) np.testing.assert_allclose(z_ref, z_tri.cpu().numpy(), rtol=0.01, atol=1e-3) def test_generic_reduction(device='cuda'): @triton.jit def var_mean_kernel(X, out_mean, out_var, BLOCK: tl.constexpr): xindex = tl.arange(0, BLOCK) x = tl.load(X + xindex) mean = x m2 = tl.zeros_like(x) weight = tl.full(x.shape, 1, x.dtype) (mean, m2, weight) = tl.reduce((mean, m2, weight), 0, _welford_combine) tl.store(out_mean, mean) tl.store(out_var, m2 / weight) SIZE = 512 x = torch.rand(SIZE, device=device) out_mean = torch.empty((), device=device) out_var = torch.empty((), device=device) var_mean_kernel[(1,)](x, out_mean, out_var, BLOCK=SIZE) expect_var, expect_mean = torch.var_mean(x, dim=0, correction=0) torch.testing.assert_close(out_mean, expect_mean) torch.testing.assert_close(out_var, expect_var) # --------------- # test permute # --------------- @pytest.mark.parametrize("dtype_str, shape, perm", [(dtype, shape, perm) # TODO: bfloat16 for dtype in ['float16', 'float32'] for shape in [(64, 64), (128, 128)] for perm in [(1, 0)]]) def test_permute(dtype_str, shape, perm, device='cuda'): check_type_supported(dtype_str) # bfloat16 on cc < 80 will not be tested # triton kernel @triton.jit def kernel(X, stride_xm, stride_xn, Z, stride_zm, stride_zn, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr): off_m = tl.arange(0, BLOCK_M) off_n = tl.arange(0, BLOCK_N) Xs = X + off_m[:, None] * stride_xm + off_n[None, :] * stride_xn Zs = Z + off_m[:, None] * stride_zm + off_n[None, :] * stride_zn tl.store(Zs, tl.load(Xs)) # input x = numpy_random(shape, dtype_str=dtype_str) # triton result z_tri = to_triton(np.empty_like(x), device=device, dst_type=dtype_str) z_tri_contiguous = to_triton(np.empty_like(x), device=device, dst_type=dtype_str) x_tri = to_triton(x, device=device, dst_type=dtype_str) pgm = kernel[(1, 1)](x_tri, x_tri.stride(0), x_tri.stride(1), z_tri, z_tri.stride(1), z_tri.stride(0), BLOCK_M=shape[0], BLOCK_N=shape[1]) pgm_contiguous = kernel[(1, 1)](x_tri, x_tri.stride(1), x_tri.stride(0), z_tri_contiguous, z_tri_contiguous.stride(0), z_tri_contiguous.stride(1), BLOCK_M=shape[0], BLOCK_N=shape[1]) # numpy result z_ref = x.transpose(perm) # compare np.testing.assert_allclose(to_numpy(z_tri), z_ref) np.testing.assert_allclose(to_numpy(z_tri_contiguous), z_ref) # parse ptx to make sure ld/st are vectorized ptx = pgm.asm['ptx'] assert 'ld.global.v4' in ptx assert 'st.global.v4' in ptx ptx = pgm_contiguous.asm['ptx'] assert 'ld.global.v4' in ptx assert 'st.global.v4' in ptx # --------------- # test dot # --------------- @pytest.mark.parametrize("M, N, K, num_warps, col_a, col_b, epilogue, allow_tf32, in_dtype, out_dtype", [(shape, 4, False, False, epilogue, allow_tf32, in_dtype, out_dtype) for shape in [(64, 64, 64), (16, 16, 16)] for epilogue in ['none', 'trans', 'add-matrix', 'add-rows', 'add-cols', 'softmax', 'chain-dot'] for allow_tf32 in [True, False] for in_dtype, out_dtype in [('float16', 'float16'), ('float16', 'float32'), ('float32', 'float32')] if not (allow_tf32 and (in_dtype in ['float16']))] + [(shape_nw, col_a, col_b, 'none', allow_tf32, in_dtype, out_dtype) for shape_nw in [[128, 256, 32, 8], [128, 16, 32, 4], [32, 128, 64, 4], [128, 128, 64, 4], [64, 128, 128, 4], [32, 128, 64, 2], [64, 64, 32, 4], [32, 32, 128, 16], [128, 128, 64, 2], [64, 128, 128, 2]] for allow_tf32 in [True] for col_a in [True, False] for col_b in [True, False] for in_dtype, out_dtype in [('int8', 'int8'), ('float16', 'float16'), ('float16', 'float32'), ('float32', 'float32')]]) def test_dot(M, N, K, num_warps, col_a, col_b, epilogue, allow_tf32, in_dtype, out_dtype, device='cuda'): capability = torch.cuda.get_device_capability() if capability[0] < 7: pytest.skip("Only test tl.dot() on devices with sm >= 70") if capability[0] < 8: if in_dtype == 'int8': pytest.skip("Only test int8 on devices with sm >= 80") elif in_dtype == 'float32' and allow_tf32: pytest.skip("Only test tf32 on devices with sm >= 80") if capability[0] == 7: if (M, N, K, num_warps) == (128, 256, 32, 8): pytest.skip("shared memory out of resource") if out_dtype == 'float16': # TODO: support out_dtype=float16 for tl.dot on V100 pytest.skip("Only test out_dtype=float16 on devices with sm >=80") torch.backends.cuda.matmul.allow_tf32 = allow_tf32 # triton kernel @triton.jit def kernel(X, stride_xm, stride_xk, Y, stride_yk, stride_yn, W, stride_wn, stride_wl, Z, stride_zm, stride_zn, out_dtype: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, ADD_MATRIX: tl.constexpr, ADD_ROWS: tl.constexpr, ADD_COLS: tl.constexpr, ALLOW_TF32: tl.constexpr, DO_SOFTMAX: tl.constexpr, CHAIN_DOT: tl.constexpr, COL_A: tl.constexpr, COL_B: tl.constexpr): off_m = tl.arange(0, BLOCK_M) off_n = tl.arange(0, BLOCK_N) off_l = tl.arange(0, BLOCK_N) off_k = tl.arange(0, BLOCK_K) Xs = X + off_m[:, None] stride_xm + off_k[None, :] * stride_xk Ys = Y + off_k[:, None] * stride_yk + off_n[None, :] * stride_yn Ws = W + off_n[:, None] * stride_wn + off_l[None, :] * stride_wl Zs = Z + off_m[:, None] * stride_zm + off_n[None, :] * stride_zn x = tl.load(Xs) y = tl.load(Ys) z = tl.dot(x, y, allow_tf32=ALLOW_TF32, out_dtype=out_dtype) if ADD_MATRIX: z += tl.load(Zs) if ADD_ROWS: ZRs = Z + off_m * stride_zm z += tl.load(ZRs)[:, None] if ADD_COLS: ZCs = Z + off_n * stride_zn z += tl.load(ZCs)[None, :] if DO_SOFTMAX: max = tl.max(z, 1) z = z - max[:, None] num = tl.exp(z.to(tl.float32)).to(max.dtype) den = tl.sum(num, 1) z = num / den[:, None] if CHAIN_DOT: w = tl.load(Ws) z = tl.dot(z.to(w.dtype), w, out_dtype=out_dtype) tl.store(Zs, z) # input rs = RandomState(17) if col_a: x = numpy_random((K, M), dtype_str=in_dtype, rs=rs).T else: x = numpy_random((M, K), dtype_str=in_dtype, rs=rs) if col_b: y = numpy_random((N, K), dtype_str=in_dtype, rs=rs).T else: y = numpy_random((K, N), dtype_str=in_dtype, rs=rs) w = numpy_random((N, N), dtype_str=in_dtype, rs=rs) if 'int' not in in_dtype: x = .1 y = .1 if in_dtype == 'float32' and allow_tf32: x = (x.view('uint32') & np.uint32(0xffffe000)).view('float32') y = (y.view('uint32') & np.uint32(0xffffe000)).view('float32') w = (w.view('uint32') & np.uint32(0xffffe000)).view('float32') x_tri = to_triton(x, device=device) y_tri = to_triton(y, device=device) w_tri = to_triton(w, device=device) # triton result if out_dtype == 'int8': z = 1 + numpy_random((M, N), dtype_str='int32', rs=rs) else: z = 1 + numpy_random((M, N), dtype_str=in_dtype, rs=rs) * .1 z_tri = to_triton(z, device=device) if epilogue == 'trans': z_tri = torch.as_strided(z_tri, (M, N), z_tri.stride()[::-1]) if out_dtype == 'int8': out_dtype = tl.int8 elif out_dtype == 'float16' and epilogue != 'softmax': # TODO: for out_dtype == 'float16' and epilogue == 'softmax', it will # fail with the following error: 'llvm.fmul' op requires the same type # for all operands and results out_dtype = tl.float16 else: out_dtype = tl.float32 pgm = kernel[(1, 1)](x_tri, x_tri.stride(0), x_tri.stride(1), y_tri, y_tri.stride(0), y_tri.stride(1), w_tri, w_tri.stride(0), w_tri.stride(1), z_tri, z_tri.stride(0), z_tri.stride(1), out_dtype, COL_A=col_a, COL_B=col_b, BLOCK_M=M, BLOCK_K=K, BLOCK_N=N, ADD_MATRIX=epilogue == 'add-matrix', ADD_ROWS=epilogue == 'add-rows', ADD_COLS=epilogue == 'add-cols', DO_SOFTMAX=epilogue == 'softmax', CHAIN_DOT=epilogue == 'chain-dot', ALLOW_TF32=allow_tf32, num_warps=num_warps) # torch result if in_dtype == 'int8': z_ref = np.matmul(x.astype(np.float32), y.astype(np.float32())).astype(np.int32) else: z_ref = np.matmul(x, y) if epilogue == 'add-matrix': z_ref += z if epilogue == 'add-rows': z_ref += z[:, 0][:, None] if epilogue == 'add-cols': z_ref += z[0, :][None, :] if epilogue == 'softmax': num = np.exp(z_ref - np.max(z_ref, axis=-1, keepdims=True)) denom = np.sum(num, axis=-1, keepdims=True) z_ref = num / denom if epilogue == 'chain-dot': z_ref = np.matmul(z_ref, w) # compare # print(z_ref[:,0], z_tri[:,0]) if in_dtype == 'float32': # XXX: Somehow there's a larger difference when we use float32 np.testing.assert_allclose(z_ref, to_numpy(z_tri), rtol=0.01, atol=1e-3) elif out_dtype == tl.float16: np.testing.assert_allclose(z_ref, to_numpy(z_tri), rtol=0.01, atol=1e-3) else: np.testing.assert_allclose(z_ref, to_numpy(z_tri), rtol=0.01) # make sure ld/st are vectorized ptx = pgm.asm['ptx'] if (K > 16 or N > 16 or M > 16) and (M * N // (num_warps * 32) >= 4): # XXX: skip small sizes because they are not vectorized assert 'ld.global.v4' in ptx assert 'st.global.v4' in ptx if in_dtype == 'float32' and allow_tf32: assert 'mma.sync.aligned.m16n8k8.row.col.f32.tf32.tf32.f32' in ptx elif in_dtype == 'float32' and allow_tf32: assert 'mma.sync.aligned.m16n8k8.row.col.f32.tf32.tf32.f32' not in ptx elif in_dtype == 'int8': assert 'mma.sync.aligned.m16n8k32.row.col.satfinite.s32.s8.s8.s32' in ptx elif out_dtype == tl.float16: assert 'mma.sync.aligned.m16n8k16.row.col.f16.f16.f16.f16' in ptx @pytest.mark.parametrize("dtype_str", int_dtypes + float_dtypes + ['bfloat16']) def test_full(dtype_str): dtype = getattr(torch, dtype_str) check_type_supported(dtype) # bfloat16 on cc < 80 will not be tested @triton.jit def kernel_static(out): a = GENERATE_TEST_HERE out_ptr = out + tl.arange(0, 128)[:] tl.store(out_ptr, a) @triton.jit def kernel_dynamic(out, val, dtype: tl.constexpr): a = tl.full((128,), val, dtype) out_ptr = out + tl.arange(0, 128)[:] tl.store(out_ptr, a) kernel_static_patched = patch_kernel(kernel_static, {'GENERATE_TEST_HERE': f"tl.full((128,), 2, tl.{dtype_str})"}) out_static = torch.zeros((128), dtype=dtype, device="cuda") kernel_static_patched[(1,)](out_static) out_dynamic = torch.zeros((128), dtype=dtype, device="cuda") kernel_dynamic[(1,)](out_dynamic, 2, getattr(triton.language, dtype_str)) assert torch.all(out_static == 2) assert torch.all(out_dynamic == 2) @pytest.mark.parametrize("literal, dtype_str", [(1e+50, "f64"), (1e+10, "f32"), (1.0, "f32"), ('float("inf")', "f32"), ('float("-inf")', "f32"), ('float("nan")', "f32"), ('float("-nan")', "f32"), (0., "f32"), (5, "i32"), (2*40, "i64"),]) def test_constexpr(literal, dtype_str): @triton.jit def kernel(out_ptr): val = GENERATE_TEST_HERE tl.store(out_ptr.to(tl.pointer_type(val.dtype)), val) kernel_patched = patch_kernel(kernel, {'GENERATE_TEST_HERE': f"{literal}"}) out = torch.zeros((1,), dtype=torch.float32, device="cuda") h = kernel_patched[(1,)](out) assert re.search(r"arith.constant . : " + dtype_str, h.asm["ttir"]) is not None # TODO: uncomment once DotOperandEncoding::getElemsPerThread is implemented # @pytest.mark.parametrize("dtype_str", ['float32', 'float16']) # def test_dot_without_load(dtype_str): # @triton.jit # def _kernel(out): # a = GENERATE_TEST_HERE # b = GENERATE_TEST_HERE # c = tl.dot(a, b) # out_ptr = out + tl.arange(0, 32)[:, None] * 32 + tl.arange(0, 32)[None, :] # tl.store(out_ptr, c) # kernel = patch_kernel(_kernel, {'GENERATE_TEST_HERE': f"tl.full((32, 32), 1.0, tl.{dtype_str})"}) # a = torch.ones((32, 32), dtype=getattr(torch, dtype_str), device="cuda") # b = torch.ones((32, 32), dtype=getattr(torch, dtype_str), device="cuda") # out_ref = torch.matmul(a, b) # out = torch.zeros((32, 32), dtype=getattr(torch, dtype_str), device="cuda") # kernel[(1,)](out) # assert torch.all(out == out_ref) # --------------- # test arange # --------------- @pytest.mark.parametrize("start", [0, 1, 7, 16]) def test_arange(start, device='cuda'): BLOCK = 128 z_tri = torch.empty(BLOCK, dtype=torch.int32, device=device) @triton.jit def _kernel(z, BLOCK: tl.constexpr, START: tl.constexpr, END: tl.constexpr): off = tl.arange(0, BLOCK) val = tl.arange(START, END) tl.store(z + off, val) _kernel[(1,)](z_tri, START=start, END=start + BLOCK, BLOCK=BLOCK) z_ref = torch.arange(start, BLOCK + start, dtype=torch.int32, device=device) np.testing.assert_allclose(to_numpy(z_tri), to_numpy(z_ref)) # --------------- # test load # --------------- @pytest.mark.parametrize("dtype_str, size, size_diff", [(dtype_str, size, size_diff) for dtype_str in torch_dtypes for size in [128, 512] for size_diff in [0, 1, 2, 3, 4]]) def test_masked_load(dtype_str, size, size_diff, device='cuda'): dtype = getattr(torch, dtype_str) check_type_supported(dtype) # bfloat16 on cc < 80 will not be tested input_size = size - size_diff output_size = size if dtype_str == 'bool': input = torch.randint(0, 2, (input_size,), dtype=dtype, device=device) elif dtype_str in int_dtypes or dtype_str in uint_dtypes: input = torch.randint(0, 127, (input_size,), dtype=dtype, device=device) else: input = torch.rand(input_size, dtype=dtype, device=device) output = torch.zeros((output_size,), dtype=dtype, device=device) @triton.jit def _kernel(in_ptr, out_ptr, in_size: tl.constexpr, out_size: tl.constexpr): in_offsets = tl.arange(0, out_size) # Load inputs. x = GENERATE_TEST_HERE # Store output output_offsets = tl.arange(0, out_size) tl.store(out_ptr + output_offsets, x) mask_str = "mask=in_offsets < in_size, other=1" if size_diff > 0 else "None" kernel = patch_kernel(_kernel, {'GENERATE_TEST_HERE': f"tl.load(in_ptr + in_offsets, {mask_str})"}) kernel[(1,)](input, output, input_size, output_size) reference_out = torch.cat((input, torch.ones((size_diff,), dtype=dtype, device=device))) # print((output - reference_out).nonzero()) torch.testing.assert_allclose(output, reference_out) # Testing masked loads with an intermate copy to shared memory run. @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16, torch.float32]) def test_masked_load_shared_memory(dtype, device='cuda'): check_type_supported(dtype) # bfloat16 on cc < 80 will not be tested M = 32 N = 32 K = 16 in1 = torch.rand((M, K), dtype=dtype, device=device) in2 = torch.rand((K, N), dtype=dtype, device=device) out = torch.zeros((M, N), dtype=dtype, device=device) @triton.jit def _kernel(in1_ptr, in2_ptr, output_ptr, in_stride, in2_stride, out_stride, in_numel, in2_numel, out_numel, M: tl.constexpr, N: tl.constexpr, K: tl.constexpr): M_offsets = tl.arange(0, M) N_offsets = tl.arange(0, N) K_offsets = tl.arange(0, K) in_offsets = M_offsets[:, None] * in_stride + K_offsets[None, :] in2_offsets = K_offsets[:, None] * in2_stride + N_offsets[None, :] # Load inputs. x = tl.load(in1_ptr + in_offsets, mask=in_offsets < M * K) w = tl.load(in2_ptr + in2_offsets, mask=in2_offsets < K * N) # Without a dot product the memory doesn't get promoted to shared. o = tl.dot(x, w, out_dtype=tl.float32) # Store output output_offsets = M_offsets[:, None] * out_stride + N_offsets[None, :] tl.store(output_ptr + output_offsets, o, mask=output_offsets < M * N) pgm = _kernel[(1,)](in1, in2, out, in1.stride()[0], in2.stride()[0], out.stride()[0], in1.numel(), in2.numel(), out.numel(), M=M, N=N, K=K) reference_out = torch.matmul(in1, in2) torch.testing.assert_allclose(out, reference_out, atol=1e-2, rtol=0) @pytest.mark.parametrize("cache", ["", ".ca", ".cg"]) def test_load_cache_modifier(cache): src = torch.empty(128, device='cuda') dst = torch.empty(128, device='cuda') @triton.jit def _kernel(dst, src, CACHE: tl.constexpr): offsets = tl.arange(0, 128) x = tl.load(src + offsets, cache_modifier=CACHE) tl.store(dst + offsets, x) pgm = _kernel[(1,)](dst, src, CACHE=cache) ptx = pgm.asm['ptx'] if cache == '': assert 'ld.global.ca' not in ptx assert 'ld.global.cg' not in ptx if cache == '.cg': assert 'ld.global.cg' in ptx assert 'ld.global.ca' not in ptx if cache == '.ca': assert 'ld.global.ca' in ptx assert 'ld.global.cg' not in ptx @pytest.mark.parametrize("N", [16, 10, 11, 1024]) def test_vectorization(N): src = torch.empty(1024, device='cuda') dst = torch.empty(1024, device='cuda') @triton.jit def _kernel(dst, src, N, BLOCK_SIZE: tl.constexpr): offsets = tl.program_id(0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) x = tl.load(src + offsets, mask=offsets < N) tl.store(dst + offsets, x, mask=offsets < N) pgm = _kernel[(1,)](dst, src, N=N, BLOCK_SIZE=src.shape[0]) ptx = pgm.asm["ptx"] if N % 16 == 0: assert "ld.global.v4.b32" in ptx else: assert "ld.global.b32" in ptx # np.testing.assert_allclose(dst, src[:N]) @pytest.mark.parametrize("has_hints", [False, True]) def test_vectorization_hints(has_hints): src = torch.empty(1024, device='cuda') dst = torch.empty(1024, device='cuda') off = torch.zeros(1, device='cuda', dtype=torch.int32) @triton.jit def _kernel(dst, src, off, N, BLOCK_SIZE: tl.constexpr, HINT: tl.constexpr): offsets = tl.program_id(0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) offsets = offsets + tl.load(off) if HINT: tl.max_contiguous(tl.multiple_of(offsets, 1024), 1024) x = tl.load(src + offsets, mask=offsets < N) tl.store(dst + offsets, x, mask=offsets < N) pgm = _kernel[(1,)](dst, src, off, N=1024, BLOCK_SIZE=src.shape[0], HINT=has_hints) ptx = pgm.asm["ptx"] if has_hints: assert "ld.global.v4.b32" in ptx else: assert "ld.global.v4.b32" not in ptx # --------------- # test store # --------------- # --------------- # test if # --------------- # --------------- # test for # --------------- # --------------- # test while # --------------- # --------------- # test default # --------------- # TODO: can't be local to test_default @triton.jit def _impl(value=10): return value def test_default(): value = 5 ret0 = torch.zeros(1, dtype=torch.int32, device='cuda') ret1 = torch.zeros(1, dtype=torch.int32, device='cuda') @triton.jit def _kernel(ret0, ret1, value): tl.store(ret0, _impl()) tl.store(ret1, _impl(value)) _kernel[(1,)](ret0, ret1, value) assert ret0.item() == 10 assert ret1.item() == value # --------------- # test noop # ---------------- def test_noop(device='cuda'): @triton.jit def kernel(x): pass x = to_triton(numpy_random((1,), dtype_str='int32'), device=device) kernel[(1, )](x) @pytest.mark.parametrize("device", ['cuda', 'cpu', 'cpu_pinned']) def test_pointer_arguments(device): @triton.jit def kernel(x): pass pin_memory = 'pinned' in device x = torch.empty(1024, device=device.split('_')[0], pin_memory=pin_memory) if device == "cpu": with pytest.raises(ValueError): kernel[(1,)](x) else: kernel[(1, )](x) @pytest.mark.parametrize("value, value_type", [ (-1, 'i32'), (0, 'i32'), (-231, 'i32'), (231 - 1, 'i32'), (231, 'i64'), (232 - 1, 'i64'), (232, 'i64'), (263 - 1, 'i64'), (-263, 'i64'), (263, 'u64'), (2*64 - 1, 'u64') ]) def test_value_specialization(value: int, value_type: str, device='cuda') -> None: spec_type = None def cache_hook(args, kwargs): nonlocal spec_type spec_type = kwargs["compile"]["signature"][0] JITFunction.cache_hook = cache_hook @triton.jit def kernel(VALUE, X): pass x = torch.tensor([3.14159], device='cuda') pgm = kernel[(1, )](value, x) JITFunction.cache_hook = None assert spec_type == value_type # -------------------- # value specialization # -------------------- @pytest.mark.parametrize( "value, overflow", [(264 - 1, False), (264, True), (-263, False), (-2*63 - 1, True)] ) def test_value_specialization_overflow(value: int, overflow: bool, device='cuda') -> None: @triton.jit def kernel(VALUE, X): pass x = torch.tensor([3.14159], device='cuda') if overflow: with pytest.raises(OverflowError): kernel[(1, )](value, x) else: kernel[(1, )](value, x) # ---------------- # test constexpr # ---------------- @pytest.mark.parametrize("op", ['+', '-', '', '/', '%', '<', '>', '<<', '>>', '&', '^', '\|']) @pytest.mark.parametrize("is_lhs_constexpr", [False, True]) @pytest.mark.parametrize("is_rhs_constexpr", [True, False]) def test_bin_op_constexpr(op, is_lhs_constexpr, is_rhs_constexpr): @triton.jit def kernel(Z, X, Y): x = tl.load(X) y = tl.load(Y) z = GENERATE_TEST_HERE tl.store(Z, z) if op in ['<<', '>>', '&', '^', '\|']: # int op x_str = "3" if is_lhs_constexpr else "x" y_str = "4" if is_rhs_constexpr else "y" x = numpy_random((1,), dtype_str="int32") y = numpy_random((1,), dtype_str="int32") else: x_str = "3.14" if is_lhs_constexpr else "x" y_str = "4.13" if is_rhs_constexpr else "y" x = numpy_random((1,), dtype_str="float32") y = numpy_random((1,), dtype_str="float32") kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f"{x_str} {op} {y_str}"}) z = np.array(eval(f"{x_str} {op} {y_str}")) x_tri = to_triton(x) y_tri = to_triton(y) z_tri = to_triton(np.empty((1,), dtype=z.dtype)) kernel[(1,)](z_tri, x_tri, y_tri) np.testing.assert_allclose(z, to_numpy(z_tri)) def test_constexpr_shape(): @triton.jit def kernel(X): off = tl.arange(0, 128 + 128) tl.store(X + off, off) x_tri = to_triton(np.empty((256, ), dtype=np.int32)) kernel[(1,)](x_tri) np.testing.assert_equal(to_numpy(x_tri), np.arange(0, 256)) def test_constexpr_scalar_shape(): @triton.jit def kernel(X, s): off = tl.arange(0, 256) val = off % (256 // s) tl.store(X + off, val) x_tri = to_triton(np.empty((256, ), dtype=np.int32)) kernel[(1,)](x_tri, 32) np.testing.assert_equal(to_numpy(x_tri), np.arange(0, 256) % 8) # ------------- # test call # ------------- @triton.jit def val_multiplier(val, i): return val * i @triton.jit(noinline=True) def val_multiplier_noinline(val, i): return val * i @triton.jit def vecmul_kernel(ptr, n_elements, rep, type: tl.constexpr): pid = tl.program_id(axis=0) offsets = pid * 128 + tl.arange(0, 128) mask = offsets < n_elements vec = tl.load(ptr + offsets, mask=mask) for i in range(1, rep): if type == "inline": vec = val_multiplier(vec, i) else: vec = val_multiplier_noinline(vec, i) tl.store(ptr + offsets, vec, mask=mask) @pytest.mark.parametrize("type", ["inline", "noinline"]) def test_call(type): @triton.jit def kernel(ptr, n_elements, num1, num2, type: tl.constexpr): vecmul_kernel(ptr, n_elements, num1, type) vecmul_kernel(ptr, n_elements, num2, type) size = 1024 rand_val = numpy_random((size,), dtype_str="float32") rand_val_tri = to_triton(rand_val, device='cuda') err_msg = "" try: kernel[(size // 128,)](rand_val_tri, size, 3, 5, type) except Exception as e: err_msg = str(e) if type == "noinline": assert err_msg is not "" else: ans = rand_val * 1 * 2 * 1 * 2 * 3 * 4 np.testing.assert_equal(to_numpy(rand_val_tri), ans) # ------------- # test if # ------------- @pytest.mark.parametrize("if_type", ["if", "if_exp", "if_and"]) def test_if(if_type): @triton.jit def kernel(Cond, XTrue, XFalse, Ret, IfType: tl.constexpr, BoolVar: tl.constexpr): pid = tl.program_id(0) cond = tl.load(Cond) if IfType == "if": if pid % 2 == 0: tl.store(Ret, tl.load(XTrue)) else: tl.store(Ret, tl.load(XFalse)) elif IfType == "if_exp": tl.store(Ret, tl.load(XTrue)) if pid % 2 else tl.store(Ret, tl.load(XFalse)) elif IfType == "if_and": if BoolVar and pid % 2 == 0: tl.store(Ret, tl.load(XTrue)) else: tl.store(Ret, tl.load(XFalse)) cond = torch.ones(1, dtype=torch.int32, device='cuda') x_true = torch.tensor([3.14], dtype=torch.float32, device='cuda') x_false = torch.tensor([1.51], dtype=torch.float32, device='cuda') ret = torch.empty(1, dtype=torch.float32, device='cuda') kernel[(1,)](cond, x_true, x_false, ret, if_type, True) assert torch.equal(ret, x_true) def test_num_warps_pow2(): dst = torch.empty(128, device='cuda') @triton.jit def _kernel(dst): pass with pytest.raises(AssertionError, match='must be a power of 2'): _kernel[(1,)](dst=dst, num_warps=3) _kernel[(1,)](dst=dst, num_warps=1) _kernel[(1,)](dst=dst, num_warps=2) _kernel[(1,)](dst=dst, num_warps=4) # ------------- # test extern # ------------- @pytest.mark.parametrize("dtype_str, expr, lib_path", [('int32', 'math.ffs', ''), ('float32', 'math.log2', ''), ('float32', 'math.scalbn', ''), ('float32', 'math.pow', tl.math.libdevice_path()), ('float64', 'math.pow_dtype', tl.math.libdevice_path()), ('float64', 'math.norm4d', '')]) def test_math_tensor(dtype_str, expr, lib_path): @triton.jit def kernel(X, Y, BLOCK: tl.constexpr): x = tl.load(X + tl.arange(0, BLOCK)) y = GENERATE_TEST_HERE tl.store(Y + tl.arange(0, BLOCK), y) shape = (128, ) rs = RandomState(17) # limit the range of integers so that the sum does not overflow x = numpy_random(shape, dtype_str=dtype_str, rs=rs) if expr == 'math.log2': kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.broadcast_to(tl.{expr}(5.0), x.shape)'}) y_ref = np.log2(5.0) elif expr == 'math.ffs': kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.{expr}(x)'}) y_ref = np.zeros(shape, dtype=x.dtype) for i in range(shape[0]): y_ref[i] = (int(x[i]) & int(-x[i])).bit_length() elif expr == 'math.scalbn': kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.{expr}(x, 2)'}) y_ref = x * pow(2, 2) elif expr == 'math.pow_dtype': x = np.abs(x) kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.math.pow(x, 0.5)'}) y_ref = np.power(x, 0.5) elif expr == 'math.pow': # numpy does not allow negative factors in power, so we use abs() x = np.abs(x) kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.{expr}(x, x)'}) y_ref = np.power(x, x) elif expr == 'math.pow_dtype': x = np.abs(x) kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': 'tl.math.pow(x, 0.5)'}) y_ref = np.power(x, 0.5) elif expr == 'math.norm4d': kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.{expr}(x, x, x, x)'}) y_ref = np.sqrt(4 * np.power(x, 2)) x_tri = to_triton(x) # triton result y_tri = to_triton(numpy_random((shape[0],), dtype_str=dtype_str, rs=rs), device='cuda') kernel[(1,)](x_tri, y_tri, BLOCK=shape[0], extern_libs={'libdevice': lib_path}) # compare if expr == 'math.ffs': np.testing.assert_equal(y_ref, to_numpy(y_tri)) else: np.testing.assert_allclose(y_ref, to_numpy(y_tri), rtol=0.01) @pytest.mark.parametrize("dtype_str, expr, lib_path", [('float32', 'math.pow', ''), ('float64', 'math.pow_dtype', ''), ('float64', 'math.pow', tl.math.libdevice_path())]) def test_math_scalar(dtype_str, expr, lib_path): @triton.jit def kernel(X, Y, BLOCK: tl.constexpr): x = X y = GENERATE_TEST_HERE tl.store(Y + tl.arange(0, BLOCK), y) shape = (128, ) rs = RandomState(17) # limit the range of integers so that the sum does not overflow x = numpy_random((1,), dtype_str=dtype_str, rs=rs) y_ref = np.zeros(shape, dtype=x.dtype) # numpy does not allow negative factors in power, so we use abs() if expr == 'math.pow': x = np.abs(x) kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': 'tl.math.pow(x, x)'}) y_ref[:] = np.power(x, x) elif expr == 'math.pow_dtype': x = np.abs(x) kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': 'tl.math.pow(x, 0.5)'}) y_ref[:] = np.power(x, 0.5) # triton result x_tri = to_triton(x)[0].item() y_tri = to_triton(numpy_random((shape[0],), dtype_str=dtype_str, rs=rs), device='cuda') kernel[(1,)](x_tri, y_tri, BLOCK=shape[0], extern_libs={'libdevice': lib_path}) # compare np.testing.assert_allclose(y_ref, to_numpy(y_tri), rtol=0.01) # ----------------------- # test control flow # ----------------------- @pytest.mark.parametrize("lo, hi, iv", [(235, 235 + 20, 1), (235, 235 + 20, 2), (235, 235 + 20, 3), (15, -16, -1), (15, -16, -2), (15, -16, -3), (-18, -22, -1), (22, 18, -1)]) def test_for_iv(lo, hi, iv): @triton.jit def kernel(Out, lo, hi, iv: tl.constexpr): acc = 0 acc = acc.to(tl.int64) for i in range(lo, hi, iv): acc += i tl.store(Out, acc) lo = 235 hi = 235 + 20 out = to_triton(np.zeros((1,), dtype=np.int64), device='cuda') kernel[(1,)](out, lo, hi, iv) assert out[0] == sum(range(lo, hi, iv)) def test_if_else(): @triton.jit def kernel(Cond, TrueVal, FalseVal, Out): if tl.load(Cond): val = tl.load(TrueVal) else: val = tl.load(FalseVal) tl.store(Out, val) out = to_triton(np.zeros((1,), dtype=np.int32), device='cuda') true_val = to_triton(np.full((1,), 1, dtype=np.int32), device='cuda') false_val = to_triton(np.full((1,), 2, dtype=np.int32), device='cuda') cond = to_triton(np.zeros((1,), dtype=np.int32), device='cuda') # True cond[0] = True kernel[(1,)](cond, true_val, false_val, out) assert to_numpy(out)[0] == true_val[0] # False cond[0] = False kernel[(1,)](cond, true_val, false_val, out) assert to_numpy(out)[0] == false_val[0] @pytest.mark.parametrize("mode", ["dynamic", "static"]) def test_if_return(mode): @triton.jit def kernel(ExitEarly, Out, cond: tl.constexpr, mode: tl.constexpr): if mode == "dynamic": if tl.load(ExitEarly): tl.store(Out, 0) return else: if cond: tl.store(Out, 0) return tl.store(Out, 1) out = to_triton(np.zeros((1,), dtype=np.int32), device='cuda') exit_early = to_triton(np.zeros((1,), dtype=np.int32), device='cuda') # exit early path taken exit_early[0] = 1 kernel[(1,)](exit_early, out, True, mode) assert to_numpy(out)[0] == 0 # exit early path not taken exit_early[0] = 0 kernel[(1,)](exit_early, out, False, mode) assert to_numpy(out)[0] == 1 @triton.jit def add_fn(x): return x + 1 @triton.jit(noinline=True) def add_fn_noinline(x): return x + 1 @triton.jit def add_fn_return(x, pid): if pid == 0: return x + 1 else: return x + 2 @triton.jit def add_fn_expr(Out, x): tl.store(Out, x) @triton.jit def add_fn_static_cond(x, cond: tl.constexpr): if cond == "": return x else: return x + 1 @pytest.mark.parametrize("call_type", ["attribute", "attribute_jit", "jit", "jit_if", "jit_ifexp", "jit_expr", "jit_static_cond", "jit_noinline", "jit_extern"]) def test_if_call(call_type): @triton.jit def kernel(Out, call_type: tl.constexpr): pid = tl.program_id(0) o = tl.load(Out) if call_type == "attribute": # call attribute if pid == 0: a = o a = a.to(tl.int32).to(tl.int32) + 1 o = a elif call_type == "attribute_jit": # call attribute and jit function if pid == 0: a = o a = tl.load(Out + add_fn(a) - 1).to(tl.int32) + 1 o = a elif call_type == "jit": if pid == 0: # regular function call a = o a = add_fn(a) o = a elif call_type == "jit_if": # function without end_if block if pid == 0: a = o a = add_fn_return(a, pid) o = a elif call_type == "jit_ifexp": # ifexp expression if pid == 0: a = o a = add_fn(a) if pid == 0 else add_fn_return(a, pid) o = a elif call_type == "jit_expr": # call without return if pid == 0: a = o + 1 add_fn_expr(Out, a) o = a elif call_type == "jit_static_cond": if pid == 0: a = o + 1 add_fn_static_cond(o, call_type) o = a elif call_type == "jit_noinline": if pid == 0: a = o + 1 add_fn_noinline(a) o = a elif call_type == "jit_extern": if pid == 0: a = o + 1 tl.cdiv(a, a) o = a tl.store(Out, o) out = to_triton(np.zeros((1,), dtype=np.int32), device='cuda') kernel[(1,)](out, call_type) assert to_numpy(out)[0] == 1 @pytest.mark.parametrize("_cond1", [True, False]) @pytest.mark.parametrize("_cond2", [True, False]) @pytest.mark.parametrize("_cond3", [True, False]) def test_nested_if_else_return(_cond1, _cond2, _cond3): @triton.jit def kernel(Cond1, Cond2, Cond3, Val1, Val2, Val3, Out): val = 0 if tl.load(Cond1): if tl.load(Cond2): val = tl.load(Val1) else: return else: if tl.load(Cond3): val = tl.load(Val2) else: val = tl.load(Val3) tl.store(Out, val) out = to_triton(np.full((1,), -1, dtype=np.int32), device='cuda') cond1 = to_triton(np.full((1,), _cond1, dtype=np.int32), device='cuda') cond2 = to_triton(np.full((1,), _cond2, dtype=np.int32), device='cuda') cond3 = to_triton(np.full((1,), _cond3, dtype=np.int32), device='cuda') val1 = to_triton(np.full((1,), 1, dtype=np.int32), device='cuda') val2 = to_triton(np.full((1,), 2, dtype=np.int32), device='cuda') val3 = to_triton(np.full((1,), 3, dtype=np.int32), device='cuda') kernel[(1,)](cond1, cond2, cond3, val1, val2, val3, out) targets = { (True, True, True): val1[0], (True, True, False): val1[0], (True, False, True): out[0], (True, False, False): out[0], (False, True, True): val2[0], (False, True, False): val3[0], (False, False, True): val2[0], (False, False, False): val3[0], } assert out[0] == targets[(_cond1, _cond2, _cond3)] def test_while(): @triton.jit def kernel(InitI, Bound, CutOff, OutI, OutJ): init_i = tl.load(InitI) curr_i = init_i j = 0 while curr_i == init_i and j < tl.load(Bound): curr_i = curr_i + (j == tl.load(CutOff)) j += 1 tl.store(OutI, curr_i) tl.store(OutJ, j) out_i = to_triton(np.zeros((1,), dtype=np.int32), device='cuda') out_j = to_triton(np.zeros((1,), dtype=np.int32), device='cuda') init_i = to_triton(np.full((1,), 1, dtype=np.int32), device='cuda') bound = to_triton(np.full((1,), 10, dtype=np.int32), device='cuda') cut_off = to_triton(np.full((1,), 5, dtype=np.int32), device='cuda') kernel[(1,)](init_i, bound, cut_off, out_i, out_j) assert out_i[0] == init_i[0] + 1 assert out_j[0] == cut_off[0] + 1 # def test_for_if(): # @triton.jit # def kernel(bound, cutoff, M, N): # m = 0 # n = 0 # for i in range(bound): # if i > cutoff: # m = m + 1 # else: # n = n + 1 # tl.store(M, m) # tl.store(N, n) # m = to_triton(np.zeros((1,), dtype=np.int32), device='cuda') # n = to_triton(np.zeros((1,), dtype=np.int32), device='cuda') # kernel[(1,)](10, 7, m, n) # print(m[0]) # print(n[0]) # ----------------------- # test extra # ----------------------- def test_globaltimer(): @triton.jit def kernel(Out1, Out2): start = tl.extra.cuda.globaltimer() off = tl.arange(0, 128) for i in range(100): tl.store(Out1 + off, tl.load(Out1 + off) + 1) end = tl.extra.cuda.globaltimer() tl.store(Out2, end - start) out1 = to_triton(np.zeros((128,), dtype=np.int64), device='cuda') out2 = to_triton(np.zeros((1,), dtype=np.int64), device='cuda') h = kernel[(1,)](out1, out2) assert out2[0] > 0 # 2 inlined globaltimers + one extra in the wrapper extern function assert h.asm["ptx"].count("%globaltimer") == 3 def test_smid(): @triton.jit def kernel(Out): tl.store(Out + tl.program_id(0), tl.extra.cuda.smid()) out = to_triton(np.zeros((1024,), dtype=np.int32), device='cuda') h = kernel[(out.shape[0],)](out) assert out.sort()[0].unique().shape[0] > 0 assert h.asm["ptx"].count("%smid") == 2 # ----------------------- # test layout conversions # ----------------------- # TODO: backend should be tested separately layouts = [ # MmaLayout(version=1, warps_per_cta=[1, 4]), MmaLayout(version=(2, 0), warps_per_cta=[1, 4]), # MmaLayout(version=1, warps_per_cta=[4, 1]), MmaLayout(version=(2, 0), warps_per_cta=[4, 1]), BlockedLayout([1, 8], [2, 16], [4, 1], [1, 0]), BlockedLayout([1, 4], [4, 8], [2, 2], [1, 0]), BlockedLayout([1, 1], [1, 32], [2, 2], [1, 0]), BlockedLayout([8, 1], [16, 2], [1, 4], [0, 1]), BlockedLayout([4, 1], [8, 4], [2, 2], [0, 1]), BlockedLayout([1, 1], [32, 1], [2, 2], [0, 1]), BlockedLayout([4, 4], [1, 32], [4, 1], [1, 0]) ] intermediate_layouts = [ None, SharedLayout(1, 1, 1, [1, 0]), SharedLayout(4, 2, 4, [1, 0]), SharedLayout(2, 2, 4, [1, 0]), ] @pytest.mark.parametrize("shape", [(128, 128)]) @pytest.mark.parametrize("dtype", ['float16']) @pytest.mark.parametrize("src_layout", layouts) @pytest.mark.parametrize("interm_layout", intermediate_layouts) @pytest.mark.parametrize("dst_layout", layouts) def test_convert2d(dtype, shape, src_layout, interm_layout, dst_layout, device='cuda'): if str(src_layout) == str(dst_layout): pytest.skip() if 'mma' in str(src_layout) and 'mma' in str(dst_layout): pytest.skip() layouts = f""" #src = {src_layout} #dst = {dst_layout} """ if interm_layout is None else f""" #src = {src_layout} #interm = {interm_layout} #dst = {dst_layout} """ conversion = f""" %12 = triton_gpu.convert_layout %9 : (tensor<128x128xi32, #src>) -> tensor<128x128xi32, #dst> %13 = triton_gpu.convert_layout %11 : (tensor<128x128xf16, #src>) -> tensor<128x128xf16, #dst> """ if interm_layout is None else f""" %15 = triton_gpu.convert_layout %9 : (tensor<128x128xi32, #src>) -> tensor<128x128xi32, #interm> %16 = triton_gpu.convert_layout %15 : (tensor<128x128xi32, #interm>) -> tensor<128x128xi32, #src> %17 = triton_gpu.convert_layout %11 : (tensor<128x128xf16, #src>) -> tensor<128x128xf16, #interm> %18 = triton_gpu.convert_layout %17 : (tensor<128x128xf16, #interm>) -> tensor<128x128xf16, #src> %12 = triton_gpu.convert_layout %16 : (tensor<128x128xi32, #src>) -> tensor<128x128xi32, #dst> %13 = triton_gpu.convert_layout %18 : (tensor<128x128xf16, #src>) -> tensor<128x128xf16, #dst> """ ir = layouts + """ module attributes {"triton_gpu.num-warps" = 4 : i32} { tt.func public @kernel_0d1d(%arg0: !tt.ptr<f16> {tt.divisibility = 16 : i32}, %arg1: !tt.ptr<f16> {tt.divisibility = 16 : i32}) { %cst = arith.constant dense<128> : tensor<128x1xi32, #src> %0 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #src}>> %1 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #src}>> %2 = tt.splat %arg0 : (!tt.ptr<f16>) -> tensor<128x128x!tt.ptr<f16>, #src> %4 = tt.expand_dims %0 {axis = 1 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #src}>>) -> tensor<128x1xi32, #src> %5 = arith.muli %4, %cst : tensor<128x1xi32, #src> %6 = tt.expand_dims %1 {axis = 0 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #src}>>) -> tensor<1x128xi32, #src> %7 = tt.broadcast %6 : (tensor<1x128xi32, #src>) -> tensor<128x128xi32, #src> %8 = tt.broadcast %5 : (tensor<128x1xi32, #src>) -> tensor<128x128xi32, #src> %9 = arith.addi %8, %7 : tensor<128x128xi32, #src> %10 = tt.addptr %2, %9 : tensor<128x128x!tt.ptr<f16>, #src>, tensor<128x128xi32, #src> %11 = tt.load %10 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x128xf16, #src> %3 = tt.splat %arg1 : (!tt.ptr<f16>) -> tensor<128x128x!tt.ptr<f16>, #dst> """ + conversion + """ %14 = tt.addptr %3, %12 : tensor<128x128x!tt.ptr<f16>, #dst>, tensor<128x128xi32, #dst> tt.store %14, %13 : tensor<128x128xf16, #dst> tt.return } } """ x = to_triton(numpy_random(shape, dtype_str=dtype)) z = torch.empty_like(x) # write the IR to a temporary file using mkstemp import tempfile with tempfile.NamedTemporaryFile(mode='w', suffix='.ttgir') as f: f.write(ir) f.flush() kernel = triton.compile(f.name) kernel[(1, 1, 1)](x.data_ptr(), z.data_ptr()) assert torch.equal(z, x) def test_load_scalar_with_mask(): @triton.jit def kernel(Input, Index, Out, N: int): index = tl.load(Index) scalar = tl.load(Input + index, mask=index < N, other=0) tl.store(Out, scalar, mask=index < N) Index = torch.tensor([0], dtype=torch.int32, device='cuda') Input = torch.tensor([0], dtype=torch.int32, device='cuda') Out = torch.empty_like(Index, device='cuda') kernel[(1,)](Input, Index, Out, Index.numel()) assert Out.data[0] == 0 # This test is used to test our own PTX codegen for float16 and int16 conversions # maybe delete it later after ptxas has been fixed @pytest.mark.parametrize("dtype_str", ['float16', 'int16']) def test_ptx_cast(dtype_str): @triton.jit def kernel(in_ptr0, out_ptr2, xnumel, rnumel, dtype: tl.constexpr, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rbase = tl.arange(0, RBLOCK)[None, :] x0 = xindex _tmp4 = (tl.zeros([XBLOCK, RBLOCK], dtype) - 10000).to(dtype) for roffset in range(0, rnumel, RBLOCK): rindex = roffset + rbase rmask = rindex < rnumel r1 = rindex tmp0 = tl.load(in_ptr0 + (r1 + (197 * x0)), rmask & xmask).to(dtype) tmp1 = 2 tmp2 = tmp0 * tmp1 tmp3 = tmp2.to(dtype) tmp5 = _tmp4 < tmp3 _tmp4 = tl.where(rmask & xmask & tmp5, tmp3, _tmp4) tl.store(out_ptr2 + (r1 + (197 * x0) + tl.zeros([XBLOCK, RBLOCK], tl.int32)), _tmp4, rmask & xmask) torch.manual_seed(123) if dtype_str == 'int16': torch_dtype = torch.int16 triton_dtype = tl.int32 else: torch_dtype = torch.float16 triton_dtype = tl.float32 s0 = 4 buf11 = -torch.ones((6 * s0, 197, 197), device='cuda', dtype=torch_dtype) buf14 = -torch.ones((s0, 6, 197, 197), device='cuda', dtype=torch_dtype) kernel[(4728,)](buf11, buf14, 1182 * s0, 197, triton_dtype, 1, 256, num_warps=2) 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33,743	quantapix/qnarre	refs/heads/main	/qnarre/prep/tokens/transfo_xl.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 glob import os import pickle import re import numpy as np import sacremoses as sm from collections import Counter, OrderedDict from ...file_utils import cached_path, is_torch_available, torch_only_method from ...tokens.utils import PreTrainedTokenizer VOCAB_FS = { "pretrained_vocab_file": "vocab.pkl", "pretrained_vocab_file_torch": "vocab.bin", "vocab_file": "vocab.txt", } VOCAB_MAP = { "pretrained_vocab_file": { "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/vocab.pkl", } } INPUT_CAPS = { "transfo-xl-wt103": None, } PRETRAINED_CORPUS_ARCHIVE_MAP = { "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/corpus.bin", } CORPUS_NAME = "corpus.bin" MATCH_NUMBERS = r"(?<=\d)[,.](?=\d)", r" @\g<0>@ " DETOKENIZE_NUMBERS = [(r" @\,@ ", r","), (r" @\.@ ", r".")] def tokenize_numbers(text_array): tokenized = [] for i in range(len(text_array)): reg, sub = MATCH_NUMBERS replaced = re.sub(reg, sub, text_array[i]).split() tokenized.extend(replaced) return tokenized def detokenize_numbers(text): for reg, sub in DETOKENIZE_NUMBERS: text = re.sub(reg, sub, text) return text class Tokenizer(PreTrainedTokenizer): vocab_fs = VOCAB_FS vocab_map = VOCAB_MAP input_caps = INPUT_CAPS model_input_names = ["input_ids"] def __init__( self, special=None, min_freq=0, max_size=None, lower_case=False, delimiter=None, vocab_file=None, pretrained_vocab_file=None, never_split=None, unk="<unk>", eos="<eos>", additional_special_tokens=["<formula>"], language="en", kw, ): super().__init__( special=special, min_freq=min_freq, max_size=max_size, lower_case=lower_case, delimiter=delimiter, vocab_file=vocab_file, pretrained_vocab_file=pretrained_vocab_file, never_split=never_split, unk=unk, eos=eos, additional_special_tokens=additional_special_tokens, language=language, kw, ) if never_split is None: never_split = self.all_special_tokens if special is None: special = [] self.counter = Counter() self.special = special self.min_freq = min_freq self.max_size = max_size self.lower_case = lower_case self.delimiter = delimiter self.vocab_file = vocab_file self.never_split = never_split self.punctuation_symbols = '!"#$%&()+,-./\\:;<=>?@[\\]^_`{\|}~' self.punction_without_space_before_pattern = re.compile( rf"[^\s][{self.punctuation_symbols}]" ) self.punctuation_with_space_around_pattern = ( self._compile_space_around_punctuation_pattern() ) self.language = language self.moses_punct_normalizer = sm.MosesPunctNormalizer(language) self.moses_tokenizer = sm.MosesTokenizer(language) self.moses_detokenizer = sm.MosesDetokenizer(language) try: vocab_dict = None if pretrained_vocab_file is not None: with open(pretrained_vocab_file, "rb") as f: vocab_dict = pickle.load(f) if type(vocab_dict) == int: if not is_torch_available(): raise ImportError( "Not trying to load dict with PyTorch as you need to install pytorch to load " "from a PyTorch pretrained vocabulary, " "or activate it with environment variables USE_TORCH=1 and USE_TF=0." ) vocab_dict = torch.load(pretrained_vocab_file) if vocab_dict is not None: for key, value in vocab_dict.items(): if key not in self.__dict__: self.__dict__[key] = value elif vocab_file is not None: self.build_vocab() except Exception as e: raise ValueError( f"Unable to parse file {pretrained_vocab_file}. Unknown format. " "If you tried to load a model saved through TokenizerFast, " "please note they are not compatible." ) from e if vocab_file is not None: self.build_vocab() @property def do_lower_case(self): return self.lower_case def _compile_space_around_punctuation_pattern(self): look_ahead_for_special_token = f"(?=[{self.punctuation_symbols}])" look_ahead_to_match_all_except_space = r"(?=[^\s])" return re.compile(r"" + look_ahead_for_special_token + look_ahead_to_match_all_except_space) def count_file(self, path, verbose=False, add_eos=False): if verbose: logger.info(f"counting file {path} ...") assert os.path.exists(path), f"Input file {path} not found" sents = [] with open(path, "r", encoding="utf-8") as f: for idx, line in enumerate(f): if verbose and idx > 0 and idx % 500000 == 0: logger.info(f" line {idx}") symbols = self.tokenize(line, add_eos=add_eos) self.counter.update(symbols) sents.append(symbols) return sents def count_sents(self, sents, verbose=False): if verbose: logger.info(f"counting {len(sents)} sents ...") for idx, symbols in enumerate(sents): if verbose and idx > 0 and idx % 500000 == 0: logger.info(f" line {idx}") self.counter.update(symbols) def _build_from_file(self, vocab_file): self.idx2sym = [] self.sym2idx = OrderedDict() with open(vocab_file, "r", encoding="utf-8") as f: for line in f: symb = line.strip().split()[0] self.add_symbol(symb) if "<UNK>" in self.sym2idx: self.unk_idx = self.sym2idx["<UNK>"] elif "<unk>" in self.sym2idx: self.unk_idx = self.sym2idx["<unk>"] else: raise ValueError("No <unknown> token in vocabulary") def save_vocabulary(self, dir, pre=None): if os.path.isdir(dir): vocab_file = os.path.join( dir, (pre + "-" if pre else "") + VOCAB_FS["pretrained_vocab_file"], ) else: vocab_file = (pre + "-" if pre else "") + dir with open(vocab_file, "wb") as f: pickle.dump(self.__dict__, f) return (vocab_file,) def build_vocab(self): if self.vocab_file: logger.info(f"building vocab from {self.vocab_file}") self._build_from_file(self.vocab_file) logger.info(f"final vocab size {len(self)}") else: logger.info(f"building vocab with min_freq={self.min_freq}, max_size={self.max_size}") self.idx2sym = [] self.sym2idx = OrderedDict() for sym in self.special: self.add_special(sym) for sym, cnt in self.counter.most_common(self.max_size): if cnt < self.min_freq: break self.add_symbol(sym) logger.info(f"final vocab size {len(self)} from {len(self.counter)} unique tokens") @torch_only_method def encode_file(self, path, ordered=False, verbose=False, add_eos=True, add_double_eos=False): if verbose: logger.info(f"encoding file {path} ...") assert os.path.exists(path), f"Output file {path} not found" encoded = [] with open(path, "r", encoding="utf-8") as f: for idx, line in enumerate(f): if verbose and idx > 0 and idx % 500000 == 0: logger.info(f" line {idx}") symbols = self.tokenize(line, add_eos=add_eos, add_double_eos=add_double_eos) encoded.append(self.convert_to_tensor(symbols)) if ordered: encoded = torch.cat(encoded) return encoded @torch_only_method def encode_sents(self, sents, ordered=False, verbose=False): if verbose: logger.info(f"encoding {len(sents)} sents ...") encoded = [] for idx, symbols in enumerate(sents): if verbose and idx > 0 and idx % 500000 == 0: logger.info(f" line {idx}") encoded.append(self.convert_to_tensor(symbols)) if ordered: encoded = torch.cat(encoded) return encoded def add_special(self, sym): if sym not in self.sym2idx: self.idx2sym.append(sym) self.sym2idx[sym] = len(self.idx2sym) - 1 setattr(self, f"{sym.strip('<>')}_idx", self.sym2idx[sym]) def add_symbol(self, sym): if sym not in self.sym2idx: self.idx2sym.append(sym) self.sym2idx[sym] = len(self.idx2sym) - 1 def move_added_token(self, token, target_idx): assert token in self.added_tokens_encoder assert token not in self.idx2sym self.idx2sym.insert(target_idx, token) self.sym2idx[token] = target_idx for idx in range(target_idx + 1, len(self.idx2sym)): current_sym = self.idx2sym[idx] self.sym2idx[current_sym] = idx old_index = self.added_tokens_encoder[token] del self.added_tokens_decoder[old_index] del self.added_tokens_encoder[token] def moses_punct_norm(self, text): return self.moses_punct_normalizer.normalize(text) def moses_tokenize(self, text): return self.moses_tokenizer.tokenize( text, aggressive_dash_splits=True, return_str=False, escape=False, protected_patterns=self.never_split, ) def moses_pipeline(self, text): text = self.moses_punct_norm(text) text = self.moses_tokenize(text) text = tokenize_numbers(text) return text def _convert_id_to_token(self, idx): assert 0 <= idx < len(self) return self.idx2sym[idx] def _convert_token_to_id(self, sym): if sym in self.sym2idx: return self.sym2idx[sym] else: if hasattr(self, "unk_idx"): return self.sym2idx.get(sym, self.unk_idx) elif "<unk>" in self.sym2idx: return self.sym2idx["<unk>"] elif "<UNK>" in self.sym2idx: return self.sym2idx["<UNK>"] else: raise ValueError( "Token not in vocabulary and no <unk> token in vocabulary for replacement" ) def convert_tokens_to_string(self, tokens): out_string = self.moses_detokenizer.detokenize(tokens) return detokenize_numbers(out_string).strip() @torch_only_method def convert_to_tensor(self, symbols): return torch.LongTensor(self.convert_tokens_to_ids(symbols)) @property def s_vocab(self): return len(self.idx2sym) def get_vocab(self): return dict(self.sym2idx, self.added_tokens_encoder) def _tokenize(self, line, add_eos=False, add_double_eos=False): line = line.strip() if self.lower_case: line = line.lower() if self.delimiter == "": symbols = line else: symbols = self.moses_pipeline(line) if add_double_eos: return ["<S>"] + symbols + ["<S>"] elif add_eos: return symbols + ["<eos>"] else: return symbols class LMOrderedIterator(object): def __init__(self, data, bsz, bptt, device="cpu", ext_len=None): self.bsz = bsz self.bptt = bptt self.ext_len = ext_len if ext_len is not None else 0 self.device = device self.n_step = data.size(0) // bsz data = data.narrow(0, 0, self.n_step bsz) self.data = data.view(bsz, -1).t().contiguous().to(device) self.n_batch = (self.n_step + self.bptt - 1) // self.bptt def get_batch(self, i, bptt=None): if bptt is None: bptt = self.bptt seq_len = min(bptt, self.data.size(0) - 1 - i) end_idx = i + seq_len beg_idx = max(0, i - self.ext_len) data = self.data[beg_idx:end_idx] target = self.data[i + 1 : i + 1 + seq_len] data_out = data.transpose(0, 1).contiguous().to(self.device) target_out = target.transpose(0, 1).contiguous().to(self.device) return data_out, target_out, seq_len def get_fixlen_iter(self, start=0): for i in range(start, self.data.size(0) - 1, self.bptt): yield self.get_batch(i) def get_varlen_iter(self, start=0, std=5, min_len=5, max_deviation=3): max_len = self.bptt + max_deviation * std i = start while True: bptt = self.bptt if np.random.random() < 0.95 else self.bptt / 2.0 bptt = min(max_len, max(min_len, int(np.random.normal(bptt, std)))) data, target, seq_len = self.get_batch(i, bptt) i += seq_len yield data, target, seq_len if i >= self.data.size(0) - 2: break def __iter__(self): return self.get_fixlen_iter() class LMShuffledIterator(object): def __init__(self, data, bsz, bptt, device="cpu", ext_len=None, shuffle=False): self.data = data self.bsz = bsz self.bptt = bptt self.ext_len = ext_len if ext_len is not None else 0 self.device = device self.shuffle = shuffle def get_sent_stream(self): epoch_indices = ( np.random.permutation(len(self.data)) if self.shuffle else np.array(range(len(self.data))) ) for idx in epoch_indices: yield self.data[idx] @torch_only_method def stream_iterator(self, sent_stream): streams = [None] * self.bsz data = torch.LongTensor(self.bptt, self.bsz) target = torch.LongTensor(self.bptt, self.bsz) n_retain = 0 while True: # data : [n_retain+bptt x bsz] # target : [bptt x bsz] data[n_retain:].fill_(-1) target.fill_(-1) valid_batch = True for i in range(self.bsz): n_filled = 0 try: while n_filled < self.bptt: if streams[i] is None or len(streams[i]) <= 1: streams[i] = next(sent_stream) # number of new tokens to fill in n_new = min(len(streams[i]) - 1, self.bptt - n_filled) # first n_retain tokens are retained from last batch data[n_retain + n_filled : n_retain + n_filled + n_new, i] = streams[i][ :n_new ] target[n_filled : n_filled + n_new, i] = streams[i][1 : n_new + 1] streams[i] = streams[i][n_new:] n_filled += n_new except StopIteration: valid_batch = False break if not valid_batch: return data_out = data.transpose(0, 1).contiguous().to(self.device) target_out = target.transpose(0, 1).contiguous().to(self.device) yield data_out, target_out, self.bptt n_retain = min(data.size(0), self.ext_len) if n_retain > 0: data[:n_retain] = data[-n_retain:] data.resize_(n_retain + self.bptt, data.size(1)) def __iter__(self): # sent_stream is an iterator sent_stream = self.get_sent_stream() for batch in self.stream_iterator(sent_stream): yield batch class LMMultiFileIterator(LMShuffledIterator): def __init__(self, paths, vocab, bsz, bptt, device="cpu", ext_len=None, shuffle=False): self.paths = paths self.vocab = vocab self.bsz = bsz self.bptt = bptt self.ext_len = ext_len if ext_len is not None else 0 self.device = device self.shuffle = shuffle def get_sent_stream(self, path): sents = self.vocab.encode_file(path, add_double_eos=True) if self.shuffle: np.random.shuffle(sents) sent_stream = iter(sents) return sent_stream def __iter__(self): if self.shuffle: np.random.shuffle(self.paths) for path in self.paths: # sent_stream is an iterator sent_stream = self.get_sent_stream(path) for batch in self.stream_iterator(sent_stream): yield batch class TransfoXLCorpus(object): @classmethod @torch_only_method def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, inputs, kw): vocab = Tokenizer.from_pretrained(pretrained_model_name_or_path, inputs, *kw) if pretrained_model_name_or_path in PRETRAINED_CORPUS_ARCHIVE_MAP: corpus_file = PRETRAINED_CORPUS_ARCHIVE_MAP[pretrained_model_name_or_path] else: corpus_file = os.path.join(pretrained_model_name_or_path, CORPUS_NAME) try: resolved_corpus_file = cached_path(corpus_file, cache_dir=cache_dir) except EnvironmentError: logger.error( f"Corpus '{pretrained_model_name_or_path}' was not found in corpus list " f"({', '.join(PRETRAINED_CORPUS_ARCHIVE_MAP.keys())}. " f"We assumed '{pretrained_model_name_or_path}' was a path or url but couldn't find files {corpus_file} " "at this path or url." ) return None if resolved_corpus_file == corpus_file: logger.info(f"loading corpus file {corpus_file}") else: logger.info(f"loading corpus file {corpus_file} from cache at {resolved_corpus_file}") # Instantiate tokenizer. corpus = cls(inputs, *kw) corpus_dict = torch.load(resolved_corpus_file) for key, value in corpus_dict.items(): corpus.__dict__[key] = value corpus.vocab = vocab if corpus.train is not None: corpus.train = torch.tensor(corpus.train, dtype=torch.long) if corpus.valid is not None: corpus.valid = torch.tensor(corpus.valid, dtype=torch.long) if corpus.test is not None: corpus.test = torch.tensor(corpus.test, dtype=torch.long) return corpus def __init__(self, args, *kw): self.vocab = Tokenizer(args, *kw) self.dataset = None self.train = None self.valid = None self.test = None def build_corpus(self, path, dataset): self.dataset = dataset if self.dataset in ["ptb", "wt2", "enwik8", "text8"]: self.vocab.count_file(os.path.join(path, "train.txt")) self.vocab.count_file(os.path.join(path, "valid.txt")) self.vocab.count_file(os.path.join(path, "test.txt")) elif self.dataset == "wt103": self.vocab.count_file(os.path.join(path, "train.txt")) elif self.dataset == "lm1b": train_path_pattern = os.path.join( path, "1-billion-word-language-modeling-benchmark-r13output", "training-monolingual.tokenized.shuffled", "news.en-", ) train_paths = glob.glob(train_path_pattern) # the vocab will load from file when build_vocab() is called self.vocab.build_vocab() if self.dataset in ["ptb", "wt2", "wt103"]: self.train = self.vocab.encode_file(os.path.join(path, "train.txt"), ordered=True) self.valid = self.vocab.encode_file(os.path.join(path, "valid.txt"), ordered=True) self.test = self.vocab.encode_file(os.path.join(path, "test.txt"), ordered=True) elif self.dataset in ["enwik8", "text8"]: self.train = self.vocab.encode_file( os.path.join(path, "train.txt"), ordered=True, add_eos=False ) self.valid = self.vocab.encode_file( os.path.join(path, "valid.txt"), ordered=True, add_eos=False ) self.test = self.vocab.encode_file( os.path.join(path, "test.txt"), ordered=True, add_eos=False ) elif self.dataset == "lm1b": self.train = train_paths self.valid = self.vocab.encode_file( os.path.join(path, "valid.txt"), ordered=False, add_double_eos=True ) self.test = self.vocab.encode_file( os.path.join(path, "test.txt"), ordered=False, add_double_eos=True ) def get_iterator(self, split, args, kw): if split == "train": if self.dataset in ["ptb", "wt2", "wt103", "enwik8", "text8"]: data_iter = LMOrderedIterator(self.train, args, *kw) elif self.dataset == "lm1b": kw["shuffle"] = True data_iter = LMMultiFileIterator(self.train, self.vocab, args, *kw) elif split in ["valid", "test"]: data = self.valid if split == "valid" else self.test if self.dataset in ["ptb", "wt2", "wt103", "enwik8", "text8"]: data_iter = LMOrderedIterator(data, args, *kw) elif self.dataset == "lm1b": data_iter = LMShuffledIterator(data, args, kw) else: data_iter = None raise ValueError(f"Split not recognized: {split}") return data_iter @torch_only_method def get_lm_corpus(datadir, dataset): fn = os.path.join(datadir, "cache.pt") fn_pickle = os.path.join(datadir, "cache.pkl") if os.path.exists(fn): logger.info("Loading cached dataset...") corpus = torch.load(fn_pickle) elif os.path.exists(fn): logger.info("Loading cached dataset from pickle...") with open(fn, "rb") as fp: corpus = pickle.load(fp) else: logger.info(f"Producing dataset {dataset}...") kw = {} if dataset in ["wt103", "wt2"]: kw["special"] = ["<eos>"] kw["lower_case"] = False elif dataset == "ptb": kw["special"] = ["<eos>"] kw["lower_case"] = True elif dataset == "lm1b": kw["special"] = [] kw["lower_case"] = False kw["vocab_file"] = os.path.join(datadir, "1b_word_vocab.txt") elif dataset in ["enwik8", "text8"]: pass corpus = TransfoXLCorpus(datadir, dataset, 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"/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"]}
33,744	quantapix/qnarre	refs/heads/main	/tools/triton/python/triton/compiler/make_launcher.py	import hashlib import os import tempfile from ..common import _build from ..runtime.cache import get_cache_manager from ..runtime.jit import version_key def is_hip(): import torch return torch.version.hip is not None # ----- stub -------- def make_so_cache_key(version_hash, signature, constants): # Get unique key for the compiled code signature = {k: 'ptr' if v[0] == '' else v for k, v in signature.items()} key = f"{version_hash}-{''.join(signature.values())}{constants}" key = hashlib.md5(key.encode("utf-8")).hexdigest() return key def make_stub(name, signature, constants): # name of files that are cached so_cache_key = make_so_cache_key(version_key(), signature, constants) so_cache_manager = get_cache_manager(so_cache_key) so_name = f"{name}.so" # retrieve stub from cache if it exists cache_path = so_cache_manager.get_file(so_name) if cache_path is None: with tempfile.TemporaryDirectory() as tmpdir: src = generate_launcher(constants, signature) src_path = os.path.join(tmpdir, "main.c") with open(src_path, "w") as f: f.write(src) so = _build(name, src_path, tmpdir) with open(so, "rb") as f: return so_cache_manager.put(f.read(), so_name, binary=True) else: return cache_path # ----- source code generation -------- def ty_to_cpp(ty): if ty[0] == '': return "hipDeviceptr_t" if is_hip() else "CUdeviceptr" return { "i1": "int32_t", "i8": "int8_t", "i16": "int16_t", "i32": "int32_t", "i64": "int64_t", "u32": "uint32_t", "u64": "uint64_t", "fp16": "float", "bf16": "float", "fp32": "float", "f32": "float", "fp64": "double", }[ty] def generate_launcher(constants, signature): arg_decls = ', '.join(f"{ty_to_cpp(ty)} arg{i}" for i, ty in signature.items()) def _extracted_type(ty): if ty[0] == '': return "PyObject" return { 'i1': 'int32_t', 'i32': 'int32_t', 'i64': 'int64_t', 'u32': 'uint32_t', 'u64': 'uint64_t', 'fp16': 'float', 'bf16': 'float', 'fp32': 'float', 'f32': 'float', 'fp64': 'double', }[ty] def format_of(ty): return { "PyObject": "O", "float": "f", "double": "d", "long": "l", "uint32_t": "I", "int32_t": "i", "uint64_t": "K", "int64_t": "L", }[ty] format = "iiiiiKKOOO" + ''.join([format_of(_extracted_type(ty)) for ty in signature.values()]) # generate glue code if is_hip(): src = f""" #define __HIP_PLATFORM_AMD__ #include <hip/hip_runtime.h> #include <Python.h> #include <stdio.h> static inline void gpuAssert(hipError_t code, const char file, int line) {{ if (code != HIP_SUCCESS) {{ const char* prefix = "Triton Error [HIP]: "; const char* str = hipGetErrorString(code); char err[1024] = {{0}}; snprintf(err, 1024, "%s Code: %d, Messsage: %s", prefix, code, str ); PyErr_SetString(PyExc_RuntimeError, err); }} }} #define HIP_CHECK(ans) {{ gpuAssert((ans), __FILE__, __LINE__); }} static void _launch(int gridX, int gridY, int gridZ, int num_warps, int shared_memory, hipStream_t stream, hipFunction_t function, {arg_decls}) {{ void params[] = {{ {', '.join(f"&arg{i}" for i in signature.keys() if i not in constants)} }}; if (gridXgridYgridZ > 0) {{ HIP_CHECK(hipModuleLaunchKernel(function, gridX, gridY, gridZ, 64num_warps, 1, 1, shared_memory, stream, params, 0)); }} }} typedef struct _DevicePtrInfo {{ hipDeviceptr_t dev_ptr; bool valid; }} DevicePtrInfo; static inline DevicePtrInfo getPointer(PyObject obj, int idx) {{ DevicePtrInfo ptr_info; ptr_info.dev_ptr = 0; ptr_info.valid = true; if (PyLong_Check(obj)) {{ ptr_info.dev_ptr = (hipDeviceptr_t)PyLong_AsUnsignedLongLong(obj); return ptr_info; }} if (obj == Py_None) {{ // valid nullptr return ptr_info; }} PyObject ptr = PyObject_GetAttrString(obj, "data_ptr"); if (ptr) {{ PyObject empty_tuple = PyTuple_New(0); PyObject ret = PyObject_Call(ptr, empty_tuple, NULL); Py_DECREF(empty_tuple); Py_DECREF(ptr); if (!PyLong_Check(ret)) {{ PyErr_SetString(PyExc_TypeError, "data_ptr method of Pointer object must return 64-bit int"); ptr_info.valid = false; return ptr_info; }} ptr_info.dev_ptr = (hipDeviceptr_t)PyLong_AsUnsignedLongLong(ret); if (!ptr_info.dev_ptr) return ptr_info; uint64_t dev_ptr; hipError_t status = hipPointerGetAttribute(&dev_ptr, HIP_POINTER_ATTRIBUTE_DEVICE_POINTER, ptr_info.dev_ptr); if (status == hipErrorInvalidValue) {{ PyErr_Format(PyExc_ValueError, "Pointer argument (at %d) cannot be accessed from Triton (cpu tensor?)", idx); ptr_info.valid = false; }} ptr_info.dev_ptr = (hipDeviceptr_t)dev_ptr; return ptr_info; }} PyErr_SetString(PyExc_TypeError, "Pointer argument must be either uint64 or have data_ptr method"); return ptr_info; }} static PyObject* launch(PyObject* self, PyObject* args) {{ int gridX, gridY, gridZ; uint64_t _stream; uint64_t _function; int num_warps; int shared_memory; PyObject launch_enter_hook = NULL; PyObject launch_exit_hook = NULL; PyObject compiled_kernel = NULL; {' '.join([f"{_extracted_type(ty)} _arg{i}; " for i, ty in signature.items()])} if (!PyArg_ParseTuple(args, \"{format}\", &gridX, &gridY, &gridZ, &num_warps, &shared_memory, &_stream, &_function, &launch_enter_hook, &launch_exit_hook, &compiled_kernel, {', '.join(f"&_arg{i}" for i, ty in signature.items())})) {{ return NULL; }} if (launch_enter_hook != Py_None) {{ PyObject_CallObject(launch_enter_hook, args); }} // raise exception asap {"; ".join([f"DevicePtrInfo ptr_info{i} = getPointer(_arg{i}, {i}); if (!ptr_info{i}.valid) return NULL;" if ty[0] == "" else "" for i, ty in signature.items()])}; _launch(gridX, gridY, gridZ, num_warps, shared_memory, (hipStream_t)_stream, (hipFunction_t)_function, {', '.join(f"ptr_info{i}.dev_ptr" if ty[0]=="" else f"_arg{i}" for i, ty in signature.items())}); if (launch_exit_hook != Py_None) {{ PyObject_CallObject(launch_exit_hook, args); }} if (PyErr_Occurred()) {{ return NULL; }} // return None Py_INCREF(Py_None); return Py_None; }} static PyMethodDef ModuleMethods[] = {{ {{"launch", launch, METH_VARARGS, "Entry point for all kernels with this signature"}}, {{NULL, NULL, 0, NULL}} // sentinel }}; static struct PyModuleDef ModuleDef = {{ PyModuleDef_HEAD_INIT, \"__triton_launcher\", NULL, //documentation -1, //size ModuleMethods }}; PyMODINIT_FUNC PyInit___triton_launcher(void) {{ PyObject m = PyModule_Create(&ModuleDef); if(m == NULL) {{ return NULL; }} PyModule_AddFunctions(m, ModuleMethods); return m; }} """ else: src = f""" #include \"cuda.h\" #include <stdbool.h> #include <Python.h> static inline void gpuAssert(CUresult code, const char file, int line) {{ if (code != CUDA_SUCCESS) {{ const char prefix = "Triton Error [CUDA]: "; const char* str; cuGetErrorString(code, &str); char err[1024] = {{0}}; strcat(err, prefix); strcat(err, str); PyErr_SetString(PyExc_RuntimeError, err); }} }} #define CUDA_CHECK(ans) {{ gpuAssert((ans), __FILE__, __LINE__); }} static void _launch(int gridX, int gridY, int gridZ, int num_warps, int shared_memory, CUstream stream, CUfunction function, {arg_decls}) {{ void params[] = {{ {', '.join(f"&arg{i}" for i in signature.keys() if i not in constants)} }}; if(gridXgridYgridZ > 0){{ CUDA_CHECK(cuLaunchKernel(function, gridX, gridY, gridZ, 32num_warps, 1, 1, shared_memory, stream, params, 0)); }} }} typedef struct _DevicePtrInfo {{ CUdeviceptr dev_ptr; bool valid; }} DevicePtrInfo; static inline DevicePtrInfo getPointer(PyObject obj, int idx) {{ DevicePtrInfo ptr_info; ptr_info.dev_ptr = 0; ptr_info.valid = true; if (PyLong_Check(obj)) {{ ptr_info.dev_ptr = PyLong_AsUnsignedLongLong(obj); return ptr_info; }} if (obj == Py_None) {{ // valid nullptr return ptr_info; }} PyObject ptr = PyObject_GetAttrString(obj, "data_ptr"); if(ptr){{ PyObject empty_tuple = PyTuple_New(0); PyObject ret = PyObject_Call(ptr, empty_tuple, NULL); Py_DECREF(empty_tuple); Py_DECREF(ptr); if (!PyLong_Check(ret)) {{ PyErr_SetString(PyExc_TypeError, "data_ptr method of Pointer object must return 64-bit int"); ptr_info.valid = false; return ptr_info; }} ptr_info.dev_ptr = PyLong_AsUnsignedLongLong(ret); if(!ptr_info.dev_ptr) return ptr_info; uint64_t dev_ptr; int status = cuPointerGetAttribute(&dev_ptr, CU_POINTER_ATTRIBUTE_DEVICE_POINTER, ptr_info.dev_ptr); if (status == CUDA_ERROR_INVALID_VALUE) {{ PyErr_Format(PyExc_ValueError, "Pointer argument (at %d) cannot be accessed from Triton (cpu tensor?)", idx); ptr_info.valid = false; }} ptr_info.dev_ptr = dev_ptr; Py_DECREF(ret); // Thanks ChatGPT! return ptr_info; }} PyErr_SetString(PyExc_TypeError, "Pointer argument must be either uint64 or have data_ptr method"); return ptr_info; }} static PyObject* launch(PyObject* self, PyObject* args) {{ int gridX, gridY, gridZ; uint64_t _stream; uint64_t _function; int num_warps; int shared_memory; PyObject launch_enter_hook = NULL; PyObject launch_exit_hook = NULL; PyObject compiled_kernel = NULL; {' '.join([f"{_extracted_type(ty)} _arg{i}; " for i, ty in signature.items()])} if(!PyArg_ParseTuple(args, \"{format}\", &gridX, &gridY, &gridZ, &num_warps, &shared_memory, &_stream, &_function, &launch_enter_hook, &launch_exit_hook, &compiled_kernel, {', '.join(f"&_arg{i}" for i, ty in signature.items())})) {{ return NULL; }} if (launch_enter_hook != Py_None) {{ PyObject_CallObject(launch_enter_hook, args); }} // raise exception asap {"; ".join([f"DevicePtrInfo ptr_info{i} = getPointer(_arg{i}, {i}); if (!ptr_info{i}.valid) return NULL;" if ty[0] == "" else "" for i, ty in signature.items()])}; _launch(gridX, gridY, gridZ, num_warps, shared_memory, (CUstream)_stream, (CUfunction)_function, {', '.join(f"ptr_info{i}.dev_ptr" if ty[0]=="" else f"_arg{i}"for i, ty in signature.items())}); if (launch_exit_hook != Py_None) {{ PyObject_CallObject(launch_exit_hook, args); }} if(PyErr_Occurred()) {{ return NULL; }} // return None Py_INCREF(Py_None); return Py_None; }} static PyMethodDef ModuleMethods[] = {{ {{"launch", launch, METH_VARARGS, "Entry point for all kernels with this signature"}}, {{NULL, NULL, 0, NULL}} // sentinel }}; static struct PyModuleDef ModuleDef = {{ PyModuleDef_HEAD_INIT, \"__triton_launcher\", NULL, //documentation -1, //size ModuleMethods }}; PyMODINIT_FUNC PyInit___triton_launcher(void) {{ PyObject m = PyModule_Create(&ModuleDef); if(m == NULL) {{ return NULL; }} PyModule_AddFunctions(m, ModuleMethods); return m; }} """ return src	{"/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"]}
33,745	quantapix/qnarre	refs/heads/main	/tools/triton/python/triton/compiler/errors.py	import ast from typing import Optional, Union class CompilationError(Exception): source_line_count_max_in_message = 12 def _format_message(self) -> str: node = self.node if self.src is None: source_excerpt = " <source unavailable>" else: source_excerpt = self.src.split('\n')[:node.lineno][-self.source_line_count_max_in_message:] if source_excerpt: source_excerpt.append(' ' * node.col_offset + '^') source_excerpt = '\n'.join(source_excerpt) else: source_excerpt = " <source empty>" message = "at {}:{}:{}".format(node.lineno, node.col_offset, source_excerpt) if self.error_message: message += '\n' + self.error_message return message def __init__(self, src: Optional[str], node: ast.AST, error_message: Union[str, None]): self.src = src self.node = node self.error_message = error_message self.message = self._format_message() def set_source_code(self, src: Optional[str]): self.src = src self.message = self._format_message() def __str__(self): return self.message def __repr__(self): return "{}({!r})".format(type(self).__name__, self.message) def __reduce__(self): # this is necessary to make CompilationError picklable return type(self), (self.src, self.node, self.error_message) class CompileTimeAssertionFailure(CompilationError): """Specific exception for failed tests in `static_assert` invocations""" pass class UnsupportedLanguageConstruct(CompilationError): pass	{"/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"], 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33,746	quantapix/qnarre	refs/heads/main	/qnarre/prep/tokens/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 collections import os from ...tokens.utils import PreTrainedTokenizer from .bert import BasicTokenizer, WordpieceTokenizer VOCAB_FS = {"vocab_file": "prophetnet.tokenizer"} VOCAB_MAP = { "vocab_file": { "microsoft/prophetnet-large-uncased": "https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/prophetnet.tokenizer", } } PRETRAINED_INIT_CONFIGURATION = { "microsoft/prophetnet-large-uncased": {"do_lower_case": True}, } INPUT_CAPS = { "microsoft/prophetnet-large-uncased": 512, } 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 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, unk="[UNK]", sep="[SEP]", x_sep_token="[X_SEP]", pad="[PAD]", msk="[MASK]", tokenize_chinese_chars=True, strip_accents=None, kw, ): super().__init__( do_lower_case=do_lower_case, do_basic_tokenize=do_basic_tokenize, never_split=never_split, unk=unk, sep=sep, x_sep_token=x_sep_token, pad=pad, msk=msk, tokenize_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, kw, ) self.unique_no_split_tokens.append(x_sep_token) 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 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 the token is part of the never_split set 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 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 ([0] len(toks_0)) + [1] return ([0] * len(toks_0)) + [1] + ([0] * len(toks_1)) + [1] def create_token_type_ids_from_sequences(self, toks_0, toks_1=None): sep = [self.sep_token_id] if toks_1 is None: return len(toks_0 + sep) * [0] return len(toks_0 + sep) * [0] + len(toks_1 + sep) * [1] 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,) def build_inputs_with_special_tokens(self, toks_0, toks_1=None): if toks_1 is None: return toks_0 + [self.sep_token_id] sep = [self.sep_token_id] return toks_0 + sep + toks_1 + sep	{"/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"], 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33,747	quantapix/qnarre	refs/heads/main	/qnarre/prep/config/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. # ============================================================================= from collections import OrderedDict from ... import core as qc from .bert import PreTrained class Roberta(PreTrained): hs = qc.Hypers( kw=dict( act="gelu", BOS=0, d_ff=3072, d_model=768, drop_attn=0.1, drop=0.1, EOS=2, eps=1e-05, grad_checkpoint=True, init_range=0.02, model_type="roberta", n_heads=12, n_lays=12, n_pos=514, n_typ=1, PAD=1, s_vocab=50265, ) ) def __init__(self, PAD=1, BOS=0, EOS=2, kw): super().__init__(PAD=PAD, BOS=BOS, EOS=EOS, kw) def _init_weights(self, module): if isinstance(module, qc.Linear): module.weight.data.normal_(mean=0.0, std=self.cfg.init_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, qc.Embedding): module.weight.data.normal_(mean=0.0, std=self.cfg.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_grad_checkpoint(self, module, value=False): if isinstance(module, Encoder): module.grad_checkpoint = value MAP = { "roberta-base": dict( archs=["ForMasked"], ), "roberta-large": dict( archs=["ForMasked"], d_ff=4096, d_model=1024, n_heads=16, n_lays=24, ), "roberta-large-mnli": dict( _num_labels=3, archs=["ForSeqClass"], d_ff=4096, d_model=1024, id2label={"0": "CONTRADICTION", "1": "NEUTRAL", "2": "ENTAILMENT"}, label2id={"CONTRADICTION": 0, "NEUTRAL": 1, "ENTAILMENT": 2}, n_heads=16, n_lays=24, ), "distilroberta-base": dict( archs=["ForMasked"], n_lays=6, ), "roberta-base-openai-detector": dict( archs=["ForSeqClass"], y_prev=True, ), "roberta-large-openai-detector": dict( archs=["ForSeqClass"], d_ff=4096, d_model=1024, n_heads=16, n_lays=24, y_prev=True, ), } class Onnx: @property def inputs(self): return OrderedDict( [ ("input_ids", {0: "batch", 1: "sequence"}), ("mask", {0: "batch", 1: "sequence"}), ] )	{"/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", 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33,748	quantapix/qnarre	refs/heads/main	/qnarre/base/doc/category.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 .junk import Junk from .log import Logger from .resource import Resource from .base import config, LnkSubject, LnkTopic, LnkSource from .part import slugify, Alias # needed dynamically log = Logger(__name__) class Category(Resource): @classmethod def globals(cls): return globals() def __setitem__(self, k, name): ns = set(self.get(k, ())) ns.add(name) super().__setitem__(k, tuple(ns)) def grapher(self, links=(), *kw): lk = self.link if links is not None and (not links or lk in links): for k, ns in self.items(): if isinstance(ns, tuple): for n in ns: yield n, k, lk def rename_msg(self, old, new): def renamer(): for k, v in self._elems.items(): if isinstance(v, tuple): yield k, tuple(n if n != old else new for n in v) else: yield k, v self._elems = {k: v for k, v in renamer()} class Subjects(Category): _res_path = config.qnar_dst + 'subjs.qnr' link = LnkSubject junk = Junk(('Re:', 'RE:', 'SAFE:', 'Fwd:', 'FW:', 'Fw:', '')) @classmethod def dejunk(cls, txt, ctxt=None, _): t = txt if txt else '' if t: t = cls.junk.dejunk_line(t) if ctxt: t = ctxt.normalize_line(t) return t def __init__(self, elems=None, kw): super().__init__(elems, *kw) if not elems: for a in config.subject_aliases: self._elems.setdefault(slugify(a[1]), ()) self.add_alias(a) class Topics(Category): _res_path = config.qnar_dst + 'topics.qnr' link = LnkTopic def __init__(self, elems=None, kw): super().__init__(elems, kw) if not elems: for a in config.topic_aliases: self._elems.setdefault(slugify(a[1]), ()) self.add_alias(*a) class Sources(Category): _res_path = config.qnar_dst + 'sources.qnr' link = LnkSource def res_ref(self, ref): print('res_ref', ref) ns = self.get(ref, ()) if ns: return ns[0] log.warning('Failed to resolve source ref {}', ref) return ''	{"/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": 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33,749	quantapix/qnarre	refs/heads/main	/tools/triton/python/triton/runtime/__init__.py	from .autotuner import (Autotuner, Config, Heuristics, OutOfResources, autotune, heuristics) from .driver import driver from .jit import (JITFunction, KernelInterface, MockTensor, TensorWrapper, reinterpret, version_key) __all__ = [ "driver", "Config", "Heuristics", "autotune", "heuristics", "JITFunction", "KernelInterface", "version_key", "reinterpret", "TensorWrapper", "OutOfResources", "MockTensor", "Autotuner", ]	{"/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": 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33,750	quantapix/qnarre	refs/heads/main	/qnarre/base/doc/__init__.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 pathlib as pth import importlib as imp __version__ = '0.2.0' def to_tag(suff): return { '.boot': 'boot', '.org': 'org', '.preset': 'preset', '.py': 'net', '.txt': 'doc', }[suff] def to_class(tag): m = imp.import_module('qnarre.core') return getattr(m, tag.capitalize()) def create_from(, tag, name=None, kw): kw.update(tag=tag) # c = globals()[tag.capitalize()] c = to_class(tag) if name is None: return c(kw) return c.create(name=name, kw) def load_from(path, kw): kw.update(path=path) t = to_tag(path.suffix) n = path.stem if t == 'doc': n = '{}s/{}/{}'.format(kw['genre'], kw['author'], n) print('{}:{}'.format(t, n)) return create_from(tag=t, name=n, kw) def load_docs(, root, genres=None, authors=None, preset=None, kw): kw.update(root=root) if genres is None: from qnarre.core import all_genres genres = all_genres for g in genres: kw.update(genre=g) def scan_dir(path, kw): if path.exists(): with os.scandir(path) as es: for e in es: p = pth.Path(e.path) if p.name.startswith('.') or p.name.startswith('_'): continue if p.is_dir(): a = p.stem if authors is None or a in authors: yield from scan_dir(p, author=a, kw) elif p.is_file(): if p.suffix == '.txt': yield load_from(p.relative_to(root), kw) elif preset is not None and p.suffix == '.preset': ps = load_from(p.relative_to(root), kw) preset.update(ps.props) yield from scan_dir(root / (g + 's'), 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", 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33,751	quantapix/qnarre	refs/heads/main	/qnarre/base/doc/util/mirror.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 shutil as sh import filecmp as fl import pathlib as pth from hashlib import blake2b from .log import Logger from .date import Date from .base import config, num_to_name from .resource import Resource log = Logger(__name__) PDF = '.pdf' DIG = '.dig' RST = '.rst' def digest(path): d, s = blake2b(digest_size=20), 0 with open(path, 'rb') as f: for b in iter(lambda: f.read(65536), b''): s += len(b) d.update(b) assert s == path.stat().st_size return d.hexdigest() def digester(path, names=((), ())): incs, excs = names with os.scandir(path) as es: for e in es: p = pth.Path(e.path) if p.is_file(): yield p, digest(p) elif p.is_dir(): n = p.name if n in incs or (not incs and n not in excs): yield from digester(p, names) def copy(src, dst): dst.parent.mkdir(parents=True, exist_ok=True) if isinstance(src, pth.Path): if dst.exists(): assert fl.cmp(str(src), str(dst), False) else: sh.copy2(str(src), str(dst)) else: assert isinstance(src, str) if dst.exists(): assert src == dst.read_text() else: dst.write_text(src) class Entry: def __init__(self, base, args): _, p, np, self.seqn = args self.path = p.relative_to(base) self.npath = None if np is None else np.relative_to(base) self.dpaths = None def digest(self, base): return digest(base / self.path) def reflect_arch(self, oarch, narch, dig, suffs): np = self.npath np = narch / (self.path if np is None else np) s = np.suffix if self.seqn and (not suffs or s in suffs): np = np.with_name(num_to_name(self.seqn)).with_suffix(s) copy(oarch / self.path, np) if not suffs or s in suffs: assert dig == digest(np) def reflect_repo(self, nrepo, dig, suffs): np = self.npath np = nrepo / (self.path if np is None else np) s = np.suffix if self.seqn and (not suffs or s in suffs): np = np.with_name(num_to_name(self.seqn)) op, ss = self.dpaths for e, s in ss: sp = op.with_suffix(s) dp = np.with_suffix(s) if e: sp = op.with_name(op.stem + e).with_suffix(s) if s == PDF: dp = np.with_name(np.stem + e).with_suffix(s) if RST not in ss: pass copy(sp, dp) if DIG not in ss: copy(dig, np.with_suffix(DIG)) def project(self, narch, nrepo, dig, suffs): p = self.npath p = self.path if p is None else p s = p.suffix if self.seqn and (not suffs or s in suffs): p = p.with_name(num_to_name(self.seqn)).with_suffix(s) copy(narch / p, nrepo / p) def reflect(self, oarch, narch, nrepo, dig, suffs): self.reflect_arch(oarch, narch, dig, suffs) if self.dpaths: self.reflect_repo(nrepo, dig, suffs) else: self.project(narch, nrepo, dig, suffs) class Mirror(Resource): _res_path = 'mirror.qnr' @classmethod def globals(cls): return globals() def __init__(self, elems=None, arch='arch', repo='repo', names=((), ()), ends=(config.ENH, ), suffs=(), kw): super().__init__(elems, kw) self.arch = arch self.repo = repo self.names = names self.ends = ends self.suffs = suffs def scan_arch(self): b = self.base / self.arch ss = self.suffs for r in Date.scanner(b, self.names, ss): e = Entry(b, r) if not ss or e.path.suffix in ss: d = e.digest(b) if d in self: oe = self[d] if e.path != oe.path: log.warning('Duplicates {} and {}', e.path, oe.path) else: self[d] = e else: self[e] = e def scanner(self, path): nss = {} incs, excs = self.names with os.scandir(path) as es: for e in es: p = pth.Path(e.path) if p.is_file(): n = p.stem for d in self.ends: if n.endswith(d): s = d, p.suffix nss.setdefault(n[:-len(d)], []).append(s) break else: s = None, p.suffix nss.setdefault(n, []).append(s) elif p.is_dir(): n = p.name if n in incs or (not incs and n not in excs): yield from self.scanner(p) else: log.warning('Skipping dir {}', n) for n, ss in nss.items(): yield path / n, tuple(ss) def scan_repo(self): b = self.base / self.repo for p, ss in self.scanner(b): d = None for e, s in ss: nd = None if s == DIG: nd = p.with_suffix(s).read_text() elif e is None and s == PDF: nd = digest(p.with_suffix(s)) if nd is not None: if d is None: d = nd else: assert d == nd if d is None: log.warning('No digest anchor for {}', p) continue if d not in self: if not self.names[0]: log.warning('No archive for {}', p) else: e = self[d] if e.dpaths: op, oss = e.dpaths log.warning('Duplicates {} and {}', str(op), str(p)) else: self[d].dpaths = p, ss def load(self): self.scan_arch() self.scan_repo() return self def reflect(self, narch='new-arch', nrepo='new-repo'): oarch = self.base / self.arch narch = self.base / narch nrepo = self.base / nrepo for d, e in self.items(): e.reflect(oarch, narch, nrepo, d, self.suffs) if not self.names[0]: old = sorted(d for _, d in digester(oarch, self.names)) new = sorted(d for _, d in digester(narch, self.names)) assert old == new orepo = self.base / self.repo old = {d for _, d in digester(orepo, self.names)} new = {d for _, d in digester(nrepo, self.names)} assert old <= new print('... completed, checked') return self if __name__ == '__main__': from .args import BArgs a = BArgs() a = a.parse_args() ns = ((), ()) Mirror.create(a.base, names=ns).load().reflect()	{"/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": 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33,752	quantapix/qnarre	refs/heads/main	/qnarre/models/bert.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 torch.utils.checkpoint import checkpoint from .. import core as qc from ..core import attention as qa from ..core import forward as qf from ..core import output as qo from ..core import utils as qu from ..core.embed import Embed from ..core.mlp import Classifier, MLP, Predictor, Pool from ..prep.config.bert import PreTrained log = logging.get_logger(__name__) class ForChoice(PreTrained): def __init__(self, kw): super().__init__(kw) self.get_cfg(kw) self.model = Model(kw) self.proj = Classifier(n_labels=1, kw) def forward(self, x, x_emb=None, mask=None, typ=None, pos=None, labels=None, kw): n = x.shape[1] if x is not None else x_emb.shape[1] x, mask, typ, pos = qu.view_2D(x, mask, typ, pos) x_emb = qu.view_3D(x_emb) ys = self.model(x, x_emb=x_emb, mask=mask, typ=typ, pos=pos, kw) y = self.proj(ys[1]).view(-1, n) loss = None if labels is not None: loss = nn.CrossEntropyLoss()(y, labels) ys = (y,) + ys[1:] + (loss,) return qo.WithLoss(ys) 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 ForNext(PreTrained): def __init__(self, kw): super().__init__(kw) self.get_cfg(kw) self.model = Model(kw) self.proj = Classifier(n_labels=2, kw) def forward(self, x, labels=None, kw): ys = self.model(x, kw) y = self.proj(ys[1]) loss = None if labels is not None: loss = nn.CrossEntropyLoss()(y.view(-1, 2), labels.view(-1)) ys = (y,) + ys[1:] + (loss,) return qo.WithLoss(ys) class ForPreTraining(PreTrained): def __init__(self, kw): super().__init__(kw) self.get_cfg(kw) self.model = Model(kw) self.proj = Predictor(kw) self.next = Classifier(n_labels=2, kw) def forward(self, x, labels=None, next=None, kw): cfg = self.cfg ys = self.model(x, *kw) y = self.proj(ys[0]) n = self.next(ys[1]) loss = None if labels is not None and next is not None: f = nn.CrossEntropyLoss() loss = f(y.view(-1, cfg.s_vocab), labels.view(-1)) + f(n.view(-1, 2), next.view(-1)) ys = (y, n) + ys[2:] + (loss,) return qo.LossSeq(ys) 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 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 LMHead(PreTrained): def __init__(self, kw): super().__init__(kw) self.get_cfg(kw) self.model = Model(add_pool=False, kw) self.proj = Predictor(kw) def forward(self, labels=None, kw): ys = self.model(kw) y = self.proj(ys[0]) loss = None if labels is not None: y2 = y[:, :-1, :].contiguous() l = labels[:, 1:].contiguous() loss = nn.CrossEntropyLoss()(y2.view(-1, self.cfg.s_vocab), l.view(-1)) ys = (y,) + ys[1:] + (loss,) return qo.LossCrosses(ys) class Masked(PreTrained): def __init__(self, kw): super().__init__(kw) self.get_cfg(kw) self.model = Model(add_pool=False, kw) self.proj = Predictor(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: y2 = y[:, :-1, :].contiguous() l = labels[:, 1:].contiguous() loss = nn.CrossEntropyLoss()(y2.view(-1, self.cfg.s_vocab), l.view(-1)) ys = (y,) + ys[2:] + (loss,) return qo.LossCrosses(ys) 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 def forward( self, x, cache=None, enc_m=None, enc=None, head_m=None, mask=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 n_kv = cache[0][0].shape[2] if cache is not None else 0 if mask is None: b, n = s mask = torch.ones(((b, n + n_kv)), device=d) 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, kw, n_kv=n_kv, x_emb=x_emb) ys = self.enc(ys, kw, cache=cache, enc_m=enc_m, enc=enc, head_m=head_m, mask=mask) if self.pool is not None: ys += (self.pool(ys[0]),) return qo.PoolsCrosses(ys) class Encoder(qc.Module): hs = qc.Hypers({"add_cross", "n_lays"}) def __init__(self, n_lays=None, ps={}, hs=[], kw): if n_lays is not None: kw.update(n_lays=n_lays) super().__init__(ps, [self.hs] + hs, kw) cfg = self.get_cfg(kw) self.lays = qc.Stack([Layer(kw) for _ in range(cfg.n_lays)]) self.grad_checkpoint = False 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, mask=h) else: ys = lay(y, kw, cache=c, mask=h) y = ys[0] attns += (ys[1],) if cfg.add_cross: crosses += (ys[2],) caches += (ys[-1],) hiddens += (y,) return qo.CachesCrosses(y, attns, cache, crosses, hiddens) class Layer(qc.Module): hs = qc.Hypers({}, {"is_dec": False}) def __init__(self, add_cross=None, ps={}, hs=[], kw): super().__init__(ps, [self.hs] + hs, kw) cfg = self.get_cfg(kw) self.attn = Attention(kw) if add_cross: assert cfg.is_dec self.cross = Attention(pos_type="absolute", kw) self.proj = MLP(kw) def forward(self, x, enc=None, cache=None, kw): c = cache[:2] if cache is not None else None y, a, kv = self.attn(x, cache=c, kw) a2 = None if cfg.is_dec and enc is not None: c = cache[-2:] if cache is not None else None y, a2, kv2 = self.cross(y, cache=c, enc=enc, kw) kv = kv + kv2 return self.proj(y), a, a2, kv class Attention(qc.Module): hs = qc.Hypers( {"d_model", "drop", "n_heads", "n_pos"}, {"drop_attn": 0.0, "pos_type": "absolute"}, ) def __init__(self, pos_type=None, ps={}, hs=[], kw): if pos_type is not None: kw.update(pos_type=pos_type) super().__init__(ps, [self.hs] + hs, kw) cfg = self.get_cfg(kw) d, n = cfg.d_model, cfg.n_heads assert d % n == 0 # or cfg.d_embed is not None cfg.s_head = s = d // n cfg.scale = 1 / (s0.5) self.query = qc.Linear(d, d, kw) self.key = qc.Linear(d, d, kw) self.value = qc.Linear(d, d, kw) self.drop_attn = qc.Dropout(cfg.drop_attn, kw) if cfg.pos_type == "relative_key" or cfg.pos_type == "relative_key_query": self.emb = qc.Embed(2 cfg.n_pos - 1, s, kw) self.proj = qc.Linear(d, d, kw) self.drop = qc.Dropout(cfg.drop, kw) self.norm = qc.LayerNorm(d, 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, v = cache a = torch.matmul(q, k.transpose(-1, -2)) t = cfg.pos_type if t == "relative_key" or t == "relative_key_query": n_q, n_k = q.shape[2], k.shape[2] kw = dict(device=x.device, dtype=torch.long) left = torch.tensor(n_k - 1 if self.id_dec else n_q, kw).view(-1, 1) right = torch.arange(n_k, *kw).view(1, -1) p = self.emb(left - right + cfg.n_pos - 1).to(dtype=q.dtype) if t == "relative_key": a += torch.einsum("bhld,lrd->bhlr", q, p) elif t == "relative_key_query": a += torch.einsum("bhld,lrd->bhlr", q, p) + torch.einsum("bhrd,lrd->bhlr", k, p) a = cfg.scale if mask is not None: a += mask a = self.drop_attn(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.d_model,)) return self.norm(x + 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33,753	quantapix/qnarre	refs/heads/main	/qnarre/base/doc/realm.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 contextlib as cl import collections.abc as abc from itertools import chain from .exporter import Exporter from .nominals import para_join from .base import config, num_to_name, lister from .meta import converter, with_class_init, with_property from .part import Defaults, Textual, Titled, Part, Contact, Role, Topic realms = config.OPEN, config.PUBL, config.PROT, config.PRIV, config.ROOT @with_class_init() class Realm(Part): fixers = () @classmethod def init(cls): cls.realms = {} for l, r in enumerate(realms): cls.realms[r] = cls(r, level=l) def __init__(self, label, level, kw): super().__init__(label, kw) self._level = level @property def level(self): return self._level @cl.contextmanager def realm_as(current): Realm.current = current yield del Realm.current class Realmed(abc.MutableSequence): def __init__(self, kw): super().__init__(kw) self._elems = [None] * len(Realm.realms) def __len__(self): return len(self._elems) def __iter__(self): return iter(self._elems) def __getitem__(self, i): return self._elems[i] def __setitem__(self, i, e): self._elems[i] = e def __delitem__(self, i): raise NotImplementedError def insert(self, i, e): raise NotImplementedError @property def realm(self): try: return Realm.current except AttributeError: return @property def current(self): c = None if self.realm: for i in range(self.realm.level + 1): e = self._elems[i] c = c if e is None else e return c @current.setter def current(self, e): self[self.realm.level] = e class with_realm: default = None def __init__(self, name, creator, default=None): self.name = name self.multi = name.endswith('s') if default is not None: self.default = default elif self.multi: self.default = () self.creator = creator def __call__(self, cls): d = self.default def getter(self): return self.current or d c = self.creator if self.multi: def setter(self, vs): self[self.realm.level] = None if vs is None else tuple(c(vs)) else: def setter(self, v): self[self.realm.level] = None if v is None else c(v) setattr(cls, self.name, property(getter, setter)) return cls class Patched(Realmed): fixers = Realm.fixers patchers = () @property def current(self): r = None if self.realm: for i in range(self.realm.level + 1): e = self._elems[i] if e is None: if r is not None: try: r = self.patchers[i].patch(r) except IndexError: pass else: r = e if r is not None: try: r = self.fixers[i].fix(r) except IndexError: pass return r def parse_range(rng): def parse_one(r): ps = r.split('-') assert 0 < len(ps) < 3 ps = [int(i) for i in ps] s = ps[0] e = s if len(ps) == 1 else ps[1] if s > e: e, s = s, e return range(s, e + 1) return set(chain([parse_one(r) for r in rng.split(',')])) def get_list(realm, source, rng, ctxt, _): if source: b = ctxt.base / ctxt.imgs_src d = (b / realm / source).with_suffix('') if d.exists(): return sorted(str(p.relative_to(b)) for p in lister(d, rng)) return () @with_realm('sources', Textual.creator) class Sourced(Patched): def __init__(self, sources=(), kw): super().__init__(kw) self.sources = sources def convert_from(self, rec, kw): s = rec.hdr.source or rec.source ps = s.split('[') if len(ps) == 2: s, r = ps r = set(num_to_name(i) for i in parse_range(r[:-1])) else: r = () self.sources = get_list(str(self.realm), s, r, kw) super().convert_from(rec, kw) @with_property('topic', Topic.create) @with_realm('text', Textual.create) class Subject(Part, Patched, Defaults): def __init__(self, label, topic=None, text=None, kw): super().__init__(label, kw) self.topic = topic self.text = text def __str__(self): return str(self.text or self.name) @property def group(self): return self.topic @with_property('subject', Subject.create) class About: def __init__(self, subject=None, kw): super().__init__(kw) self.subject = subject @property def subgroup(self): return self.subject def convert_from(self, rec, ctxt, kw): n = rec.subject(ctxt) if n: s = self.subject if s: s.text = n else: t = rec.topic(ctxt) if t: s = '{}_{}'.format(t, n) t = Topic.create(t, kw) self.subject = Subject.create(s or n, topic=t, text=n, kw) super().convert_from(rec, kw, ctxt=ctxt) @with_realm('texts', Textual.creator) class Body(Patched): @classmethod def create(cls, i, kw): return i if isinstance(i, cls) else cls(kw) def __init__(self, texts=(), kw): super().__init__(kw) self.texts = tuple(texts) def __str__(self): return para_join(t.text for t in self.texts) @property def html(self): return Exporter.markdown.reset().convert(str(self)) @converter('Contact') @with_realm('contact', Contact.create) @with_property('body', Body.create) class Agent(Part, Realmed, Titled, Defaults): role = None background = None justify = 0 @classmethod def convert(cls, contact, ctxt=None, kw): c = Contact.create(contact, kw) r, b, j, s = config.all_traits.get(c.slug, (None, None, 0, c.slug)) a = cls.create('a' + (s or c.slug), kw) if not a[a.realm.level]: a.contact = c if r: a.role = Role.create(r, kw) if b: a.background = b if j: a.justify = j return a def __init__(self, label, contact=None, body=None, kw): super().__init__(label, kw) self.contact = contact from django.utils.lorem_ipsum import sentence, paragraphs self.body = body or paragraphs(3) if not self.title: self.title = self.name.title() if not self.summary: self.summary = sentence().capitalize() @property def name(self): c = self.contact return c.name if c else super().name @property def subgroup(self): return self.role @with_property('froms', Agent.creator) @with_property('tos', Agent.creator) @with_property('ccs', Agent.creator) @with_property('bccs', Agent.creator) class Sent: def __init__(self, froms=(), tos=(), ccs=(), bccs=(), kw): super().__init__(kw) self.froms = froms self.tos = tos self.ccs = ccs self.bccs = bccs def convert_from(self, rec, kw): self.froms = (Agent.convert(c, kw) for c in (rec.hdr.from_ or ())) self.tos = (Agent.convert(c, kw) for c in (rec.hdr.to or ())) self.ccs = (Agent.convert(c, kw) for c in (rec.hdr.cc or ())) self.bccs = (Agent.convert(c, kw) for c in (rec.hdr.bcc or ())) super().convert_from(rec, *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", 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"/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"]}
33,754	quantapix/qnarre	refs/heads/main	/qnarre/base/net.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 pathlib as pth from .. import create_from, load_from from .author import Author from .claim import Claim, Place from .named import Named, Saved, Preset class Boot(Preset): _nodes = None @property def nodes(self): if self._nodes is None: self._nodes = ns = [] for t, ps in self.props.items(): for kw in ps: ns.append(create_from(tag=t, kw)) # print(ns[-1].name) return self._nodes def from_text(self, txt, kw): super().from_text(txt, *kw) ts = list(self.props.pop('topic', ())) ns = list(self.props.pop('narrative', ())) for d in self.props.pop('narratives', ()): t, n = d.split(':') ts.append({'name': t}) ns.append({'topic': t, 'name': n}) self.props['topic'] = tuple(ts) self.props['narrative'] = tuple(ns) class Net(Saved, Named): suff = '.py' docs = None org = None def __init__(self, , root, docs=None, org=None, kw): super().__init__(root=root, kw) p = pth.Path('{}.boot'.format(self.name)) self.boot = load_from(root=root, path=p) if docs: self.docs = docs if org: self.org = org def from_text(self, txt, preset=None, kw): self.docs = preset or {} self.docs.update(eval(txt or '{}')) kw.update(tag='org', name=self.name, net=self) self.org = create_from(kw) def to_text(self, _): return repr(self.docs) def nodes(self): self.boot.nodes for d, gs in self.docs.items(): d = create_from(tag='doc', name=d) an = d.author at = Author.create(name=an).tag for gi, rs in enumerate(gs, start=1): for ri, ns in enumerate(rs, start=1): p = Place(d, gi, ri) for kw in ns: kw = kw.copy() kw.setdefault(at, an) kw.setdefault('tag', Claim.to_tag()) yield create_from(place=p, 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"], 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33,755	quantapix/qnarre	refs/heads/main	/tools/triton/python/triton/language/random.py	import triton from . import core as tl PHILOX_KEY_A: tl.constexpr = 0x9E3779B9 PHILOX_KEY_B: tl.constexpr = 0xBB67AE85 PHILOX_ROUND_A: tl.constexpr = 0xD2511F53 PHILOX_ROUND_B: tl.constexpr = 0xCD9E8D57 N_ROUNDS_DEFAULT = 10 # Default number of rounds for philox # ------------------- # randint # ------------------- @triton.jit def philox_impl(c0, c1, c2, c3, k0, k1, n_rounds: tl.constexpr = N_ROUNDS_DEFAULT): """ Run `n_rounds` rounds of Philox for state (c0, c1, c2, c3) and key (k0, k1). """ for _ in tl.static_range(n_rounds): # for _ in range(n_rounds): # update random state A = PHILOX_ROUND_A B = PHILOX_ROUND_B _c0, _c2 = c0, c2 c0 = tl.umulhi(B, _c2) ^ c1 ^ k0 c2 = tl.umulhi(A, _c0) ^ c3 ^ k1 c1 = B * _c2 c3 = A * _c0 # raise key k0 = k0 + PHILOX_KEY_A k1 = k1 + PHILOX_KEY_B return c0, c1, c2, c3 @triton.jit def philox(seed, c0, c1, c2, c3, n_rounds: tl.constexpr = N_ROUNDS_DEFAULT): seed = seed.to(tl.uint64) seed_hi = ((seed >> 32) & 0xffffffff).to(tl.uint32) seed_lo = (seed & 0xffffffff).to(tl.uint32) c0 = c0.to(tl.uint32, bitcast=True) c1 = c1.to(tl.uint32, bitcast=True) c2 = c2.to(tl.uint32, bitcast=True) c3 = c3.to(tl.uint32, bitcast=True) return philox_impl(c0, c1, c2, c3, seed_lo, seed_hi, n_rounds) @triton.jit def randint(seed, offset, n_rounds: tl.constexpr = N_ROUNDS_DEFAULT): """ Given a :code:`seed` scalar and an :code:`offset` block, returns a single block of random :code:`int32`. If you need multiple streams of random numbers, using `randint4x` is likely to be faster than calling `randint` 4 times. :param seed: The seed for generating random numbers. :param offsets: The offsets to generate random numbers for. """ ret, _, _, _ = randint4x(seed, offset, n_rounds) return ret @triton.jit def randint4x(seed, offset, n_rounds: tl.constexpr = N_ROUNDS_DEFAULT): """ Given a :code:`seed` scalar and an :code:`offset` block, returns four blocks of random :code:`int32`. This is the maximally efficient entry point to Triton's Philox pseudo-random number generator. :param seed: The seed for generating random numbers. :param offsets: The offsets to generate random numbers for. """ # _0 = tl.zeros(offset.shape, offset.dtype) _0 = offset * 0 return philox(seed, offset, _0, _0, _0, n_rounds) # ------------------- # rand # ------------------- # @triton.jit # def uint32_to_uniform_float(x): # """ # Numerically stable function to convert a random uint32 into a random float uniformly sampled in [0, 1). # """ # two_to_the_minus_32: tl.constexpr = 2.328306e-10 # return x * two_to_the_minus_32 @triton.jit def uint32_to_uniform_float(x): """ Numerically stable function to convert a random uint32 into a random float uniformly sampled in [0, 1). """ x = x.to(tl.int32, bitcast=True) # maximum value such that `MAX_INT * scale < 1.0` (with float rounding) scale = 4.6566127342e-10 x = tl.where(x < 0, -x - 1, x) return x * scale @triton.jit def rand(seed, offset, n_rounds: tl.constexpr = N_ROUNDS_DEFAULT): """ Given a :code:`seed` scalar and an :code:`offset` block, returns a block of random :code:`float32` in :math:`U(0, 1)`. :param seed: The seed for generating random numbers. :param offsets: The offsets to generate random numbers for. """ offset = offset.to(tl.uint32, bitcast=True) source = randint(seed, offset, n_rounds) return uint32_to_uniform_float(source) @triton.jit def rand4x(seed, offsets, n_rounds: tl.constexpr = N_ROUNDS_DEFAULT): """ Given a :code:`seed` scalar and an :code:`offsets` block, returns a 4 blocks of random :code:`float32` in :math:`U(0, 1)`. :param seed: The seed for generating random numbers. :param offsets: The offsets to generate random numbers for. """ offsets = offsets.to(tl.uint32, bitcast=True) i1, i2, i3, i4 = randint4x(seed, offsets, n_rounds) u1 = uint32_to_uniform_float(i1) u2 = uint32_to_uniform_float(i2) u3 = uint32_to_uniform_float(i3) u4 = uint32_to_uniform_float(i4) return u1, u2, u3, u4 # ------------------- # randn # ------------------- @triton.jit def pair_uniform_to_normal(u1, u2): """Box-Muller transform""" u1 = tl.maximum(1.0e-7, u1) th = 6.283185307179586 * u2 r = tl.sqrt(-2.0 * tl.log(u1)) return r * tl.cos(th), r * tl.sin(th) @triton.jit def randn(seed, offset, n_rounds: tl.constexpr = N_ROUNDS_DEFAULT): """ Given a :code:`seed` scalar and an :code:`offset` block, returns a block of random :code:`float32` in :math:`\\mathcal{N}(0, 1)`. :param seed: The seed for generating random numbers. :param offsets: The offsets to generate random numbers for. """ i1, i2, _, _ = randint4x(seed, offset, n_rounds) u1 = uint32_to_uniform_float(i1) u2 = uint32_to_uniform_float(i2) n1, _ = pair_uniform_to_normal(u1, u2) return n1 @triton.jit def randn4x(seed, offset, n_rounds: tl.constexpr = N_ROUNDS_DEFAULT): """ Given a :code:`seed` scalar and an :code:`offset` block, returns a 4 blocks of random :code:`float32` in :math:`\\mathcal{N}(0, 1)`. :param seed: The seed for generating random numbers. :param offsets: The offsets to generate random numbers for. 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"/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"]}
33,756	Jasperabez/PPTSynchro	refs/heads/master	/hook-win10toast.py	from PyInstaller.utils.hooks import copy_metadata datas = copy_metadata('win10toast')	{"/Threads/comms_thread.py": ["/Handlers/SocketHandler.py", "/config.py"], "/ControllerServer.py": ["/config.py", "/Threads/toggle_thread.py", "/Threads/controller_thread.py", "/Threads/comms_thread.py", "/Threads/bt_comms_thread.py"], "/Threads/toggle_thread.py": ["/config.py"], "/main.py": ["/config.py", "/ControllerServer.py", "/ControllerClient.py", "/SniffKey.py"], "/ControllerClient.py": ["/config.py", "/Handlers/SocketHandler.py", "/Handlers/BtCommsHandler.py"], "/Threads/controller_thread.py": ["/Handlers/PptHandler.py", "/config.py"], "/Threads/bt_comms_thread.py": ["/Handlers/BtCommsHandler.py", "/config.py"], "/SniffKey.py": ["/config.py"]}
33,757	Jasperabez/PPTSynchro	refs/heads/master	/Threads/comms_thread.py	from Handlers.SocketHandler import SocketHandler from config import * def CommsThread(program_state, thread_lock, comms_msg_queue): comms_socket = SocketHandler(SERVER_HOST, SERVER_PORT) comms_socket.Open() while True: data, addr = comms_socket.RecvMsg() if program_state.isSet(): thread_lock.acquire() comms_msg_queue.append(data) thread_lock.release()	{"/Threads/comms_thread.py": ["/Handlers/SocketHandler.py", "/config.py"], "/ControllerServer.py": ["/config.py", "/Threads/toggle_thread.py", "/Threads/controller_thread.py", "/Threads/comms_thread.py", "/Threads/bt_comms_thread.py"], "/Threads/toggle_thread.py": ["/config.py"], "/main.py": ["/config.py", "/ControllerServer.py", "/ControllerClient.py", "/SniffKey.py"], "/ControllerClient.py": ["/config.py", "/Handlers/SocketHandler.py", "/Handlers/BtCommsHandler.py"], "/Threads/controller_thread.py": ["/Handlers/PptHandler.py", "/config.py"], "/Threads/bt_comms_thread.py": ["/Handlers/BtCommsHandler.py", "/config.py"], "/SniffKey.py": ["/config.py"]}
33,758	Jasperabez/PPTSynchro	refs/heads/master	/ControllerServer.py	import threading import logging from config import * from Threads.toggle_thread import ToggleThread from Threads.controller_thread import ControllerThread if COMMS_TYPE == "Wifi": from Threads.comms_thread import CommsThread else: from Threads.bt_comms_thread import CommsThread command_bind_funcs = {} comms_msg_queue = [] logging.basicConfig(handlers=[logging.FileHandler(filename=LOG_FILE, encoding='utf-8')], level=LOGGING_LEVEL) def AddCommandBindFuncs(command, func): command_bind_funcs[command] = func def NextPptSlide(slide_handler): print("pptnext") slide_handler.NextSlide() def PrevPptSlide(slide_handler): print("pptprev") slide_handler.PrevSlide() def main(): AddCommandBindFuncs("next", NextPptSlide) AddCommandBindFuncs("prev", PrevPptSlide) program_state = threading.Event() thread_lock = threading.Lock() t1 = threading.Thread(name='toggle', target=ToggleThread, args=(program_state, thread_lock)) t2 = threading.Thread(name='controller', target=ControllerThread, args=(program_state, thread_lock, command_bind_funcs, comms_msg_queue)) t3 = threading.Thread(name='comms', target=CommsThread, args=(program_state, thread_lock, comms_msg_queue)) t1.start() t2.start() t3.start() program_state.set()	{"/Threads/comms_thread.py": ["/Handlers/SocketHandler.py", "/config.py"], "/ControllerServer.py": ["/config.py", "/Threads/toggle_thread.py", "/Threads/controller_thread.py", "/Threads/comms_thread.py", "/Threads/bt_comms_thread.py"], "/Threads/toggle_thread.py": ["/config.py"], "/main.py": ["/config.py", "/ControllerServer.py", "/ControllerClient.py", "/SniffKey.py"], "/ControllerClient.py": ["/config.py", "/Handlers/SocketHandler.py", "/Handlers/BtCommsHandler.py"], "/Threads/controller_thread.py": ["/Handlers/PptHandler.py", "/config.py"], "/Threads/bt_comms_thread.py": ["/Handlers/BtCommsHandler.py", "/config.py"], "/SniffKey.py": ["/config.py"]}

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