Upload pytorch_model.bin

README.md

@@ -9,7 +9,7 @@ tags:
 ---
 # ChatGLM-6B-INT4
 <p align="center">
-    👋 Join our <a href="https://join.slack.com/t/chatglm/shared_invite/zt-1th2q5u69-7tURzFuOPanmuHy9hsZnKA" target="_blank">Slack</a> and <a href="https://github.com/THUDM/ChatGLM-6B/blob/main/resources/WECHAT.md" target="_blank">WeChat</a>
+    👋 Join our <a href="https://join.slack.com/t/chatglm/shared_invite/zt-1udqapmrr-ocT1DS_mxWe6dDY8ahRWzg" target="_blank">Slack</a> and <a href="https://github.com/THUDM/ChatGLM-6B/blob/main/resources/WECHAT.md" target="_blank">WeChat</a>
 </p>
 
 ## 介绍

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:35828b49cf23cbae4c27788d4b04fc68c79a276300e09f14d72a49b0b738b4a9
+oid sha256:245786435bde9f4593c105ea846fa461fe42bc63c12b738d0272fcaed6276645
 size 3893083075

quantization.py

@@ -7,6 +7,7 @@ import bz2
 import torch
 import base64
 import ctypes
+import sys
 from transformers.utils import logging
 
 from typing import List
@@ -142,8 +143,12 @@ class CPUKernel:
                     kernel_file = source_code[:-2] + ".so"
 
                     if compile_parallel_kernel:
-                        compile_command = "gcc -O3 -fPIC -pthread -fopenmp -std=c99 {} -shared -o {}".format(
-                            source_code, kernel_file)
+                        if sys.platform != 'darwin':
+                            compile_command = "gcc -O3 -fPIC -pthread -fopenmp -std=c99 {} -shared -o {}".format(
+                                source_code, kernel_file)
+                        else:
+                            compile_command = "clang -O3 -fPIC -pthread -Xclang -fopenmp -lomp -std=c99 {} -shared -o {}".format(
+                                source_code, kernel_file)
                         print("Compiling", compile_command)
                         exit_state = os.system(compile_command)
                         if not exit_state:
@@ -442,7 +447,6 @@ class QuantizedEmbedding(Embedding):  # TODO: backward, check empty_init
 def load_cpu_kernel(**kwargs):
     global cpu_kernels
     cpu_kernels = CPUKernel(**kwargs)
-    assert cpu_kernels.load
 
 
 def quantize(model, weight_bit_width, use_quantization_cache=False, empty_init=False, **kwargs):
@@ -453,9 +457,8 @@ def quantize(model, weight_bit_width, use_quantization_cache=False, empty_init=F
     dense_h_to_4h_quantization_cache = None
     dense_4h_to_h_quantization_cache = None
 
-    try:
-        load_cpu_kernel(**kwargs)
-    except:
+    load_cpu_kernel(**kwargs)
+    if not cpu_kernels.load:
         if kernels is None:  # CUDA kernels failed
             print("Cannot load cpu or cuda kernel, quantization failed:")
             assert kernels is not None

tokenization_chatglm.py

@@ -31,6 +31,9 @@ class TextTokenizer:
     def tokenize(self, text):
         return self.sp.EncodeAsPieces(text)
 
+    def convert_tokens_to_string(self, tokens):
+        return self.sp.DecodePieces(tokens)
+
     def convert_tokens_to_ids(self, tokens):
         return [self.sp.PieceToId(token) for token in tokens]
 
@@ -111,16 +114,25 @@ class SPTokenizer:
         tokens = [x + self.num_image_tokens for x in tmp]
         return tokens if add_dummy_prefix else tokens[2:]
 
-    def decode(self, text_ids: List[int]) -> str:
-        ids = [int(_id) - self.num_image_tokens for _id in text_ids]
-        ids = [_id for _id in ids if _id >= 0]
-        text = self._get_text_tokenizer().decode(ids)
+    def postprocess(self, text):
         text = text.replace("<n>", "\n")
         text = text.replace(SPTokenizer.get_tab_token(), "\t")
         for i in range(2, self.max_blank_length + 1):
             text = text.replace(self.get_blank_token(i), " " * i)
         return text
 
+    def decode(self, text_ids: List[int]) -> str:
+        ids = [int(_id) - self.num_image_tokens for _id in text_ids]
+        ids = [_id for _id in ids if _id >= 0]
+        text = self._get_text_tokenizer().decode(ids)
+        text = self.postprocess(text)
+        return text
+
+    def decode_tokens(self, tokens: List[str]) -> str:
+        text = self._get_text_tokenizer().convert_tokens_to_string(tokens)
+        text = self.postprocess(text)
+        return text
+
     def tokenize(
             self, text: str, linebreak=True, whitespaces=True, add_dummy_prefix=True
     ) -> List[str]:
@@ -256,11 +268,12 @@ class ChatGLMTokenizer(PreTrainedTokenizer):
 
         return seq
 
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        return self.sp_tokenizer.decode_tokens(tokens)
+
     def _decode(
             self,
             token_ids: Union[int, List[int]],
-            skip_special_tokens: bool = False,
-            clean_up_tokenization_spaces: bool = True,
             **kwargs
     ) -> str:
         if isinstance(token_ids, int):
@@ -269,7 +282,7 @@ class ChatGLMTokenizer(PreTrainedTokenizer):
             return ""
         if self.pad_token_id in token_ids:  # remove pad
             token_ids = list(filter((self.pad_token_id).__ne__, token_ids))
-        return self.sp_tokenizer.decode(token_ids)
+        return super()._decode(token_ids, **kwargs)
 
     def _convert_token_to_id(self, token):
         """ Converts a token (str) in an id using the vocab. """