Add model config json files.

This commit is contained in:
Colin 2023-12-22 19:14:22 +08:00
parent 72787b9268
commit 9c19c9f285
5 changed files with 267 additions and 13 deletions

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@ -5,19 +5,23 @@
input_ids = tokenizer.build_chat_input(query, history=history, role=role) input_ids = tokenizer.build_chat_input(query, history=history, role=role)
input_ids -> [1, 6] 1:batch_num 6:sequence_length for
inputs_embeds -> [6, 1, 4096] 4096:hidden_size input_ids -> [1, 6] 1:batch_num 6:sequence_length
rotary_pos_emb -> [6, 1, 32, 2] 32:pos的编码维度 2:cos+sin inputs_embeds -> [6, 1, 4096] 4096:hidden_size
rotary_pos_emb -> [6, 1, 32, 2] 32:pos的编码维度 2:cos+sin
hidden_states = inputs_embeds hidden_states = inputs_embeds
for layers : GLMBlock(hidden_states, rotary_pos_emb) for layers : GLMBlock(hidden_states, rotary_pos_emb)
hidden_states = self.final_layernorm(hidden_states) hidden_states = self.final_layernorm(hidden_states)
hidden_states = hidden_states[-1:] hidden_states = hidden_states[-1:]
lm_logits = self.output_layer(hidden_states) lm_logits = self.output_layer(hidden_states)
lm_logits = lm_logits.transpose(0, 1).contiguous() -> [1, 1, 65024] lm_logits = lm_logits.transpose(0, 1).contiguous() -> [1, 1, 65024]
probs = softmax(lm_logits) -> [1, 65024] probs = softmax(lm_logits) -> [1, 65024]
next_tokens = torch.multinomial(probs, num_samples=1) 采样 -> [1] 1:batch_num next_tokens = torch.multinomial(probs, num_samples=1) 采样 -> [1] 1:batch_num
input_ids = torch.cat([input_ids, next_tokens) -> [1, 7] 1:batch_num
if next_tokens == eos_token_id 推理结束退出循环
input_ids = torch.cat([input_ids, next_tokens) -> [1, 7] 1:batch_num
response = tokenizer.decode(outputs) response = tokenizer.decode(outputs)

42
chatglm/config.json Normal file
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@ -0,0 +1,42 @@
{
"_name_or_path": "THUDM/chatglm3-6b",
"model_type": "chatglm",
"architectures": [
"ChatGLMModel"
],
"auto_map": {
"AutoConfig": "configuration_chatglm.ChatGLMConfig",
"AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration",
"AutoModelForCausalLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
"AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
"AutoModelForSequenceClassification": "modeling_chatglm.ChatGLMForSequenceClassification"
},
"add_bias_linear": false,
"add_qkv_bias": true,
"apply_query_key_layer_scaling": true,
"apply_residual_connection_post_layernorm": false,
"attention_dropout": 0.0,
"attention_softmax_in_fp32": true,
"bias_dropout_fusion": true,
"ffn_hidden_size": 13696,
"fp32_residual_connection": false,
"hidden_dropout": 0.0,
"hidden_size": 4096,
"kv_channels": 128,
"layernorm_epsilon": 1e-05,
"multi_query_attention": true,
"multi_query_group_num": 2,
"num_attention_heads": 32,
"num_layers": 28,
"original_rope": true,
"padded_vocab_size": 65024,
"post_layer_norm": true,
"rmsnorm": true,
"seq_length": 8192,
"use_cache": true,
"torch_dtype": "float16",
"transformers_version": "4.30.2",
"tie_word_embeddings": false,
"eos_token_id": 2,
"pad_token_id": 0
}

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@ -0,0 +1 @@
{"framework":"Pytorch","task":"chatbot"}

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@ -700,10 +700,10 @@ class ChatGLMForConditionalGeneration(nn.Module):
# finished sentences should add a padding token to next # finished sentences should add a padding token to next
pad_token = pad_token_id * isFinished pad_token = pad_token_id * isFinished
next_tokens = next_tokens * (1 - isFinished) + pad_token next_tokens = next_tokens * (1 - isFinished) + pad_token
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
isFinished = isFinished | next_tokens.eq(eos_token_id_tensor) isFinished = isFinished | next_tokens.eq(eos_token_id_tensor)
if isFinished.min() == 1: # all batch is finish if isFinished.min() == 1: # all batch is finish
break break
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
return input_ids return input_ids

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@ -0,0 +1,207 @@
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