Add model config json files.
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Colin
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Readme.md
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Readme.md
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@ -5,19 +5,23 @@
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input_ids = tokenizer.build_chat_input(query, history=history, role=role)
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input_ids -> [1, 6] 1:batch_num 6:sequence_length
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inputs_embeds -> [6, 1, 4096] 4096:hidden_size
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rotary_pos_emb -> [6, 1, 32, 2] 32:pos的编码维度 2:cos+sin
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for
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input_ids -> [1, 6] 1:batch_num 6:sequence_length
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inputs_embeds -> [6, 1, 4096] 4096:hidden_size
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rotary_pos_emb -> [6, 1, 32, 2] 32:pos的编码维度 2:cos+sin
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hidden_states = inputs_embeds
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for layers : GLMBlock(hidden_states, rotary_pos_emb)
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hidden_states = self.final_layernorm(hidden_states)
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hidden_states = hidden_states[-1:]
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lm_logits = self.output_layer(hidden_states)
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lm_logits = lm_logits.transpose(0, 1).contiguous() -> [1, 1, 65024]
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hidden_states = inputs_embeds
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for layers : GLMBlock(hidden_states, rotary_pos_emb)
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hidden_states = self.final_layernorm(hidden_states)
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hidden_states = hidden_states[-1:]
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lm_logits = self.output_layer(hidden_states)
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lm_logits = lm_logits.transpose(0, 1).contiguous() -> [1, 1, 65024]
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probs = softmax(lm_logits) -> [1, 65024]
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next_tokens = torch.multinomial(probs, num_samples=1) 采样 -> [1] 1:batch_num
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input_ids = torch.cat([input_ids, next_tokens) -> [1, 7] 1:batch_num
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probs = softmax(lm_logits) -> [1, 65024]
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next_tokens = torch.multinomial(probs, num_samples=1) 采样 -> [1] 1:batch_num
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if next_tokens == eos_token_id 推理结束退出循环
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input_ids = torch.cat([input_ids, next_tokens) -> [1, 7] 1:batch_num
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response = tokenizer.decode(outputs)
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@ -0,0 +1,42 @@
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{
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"_name_or_path": "THUDM/chatglm3-6b",
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"model_type": "chatglm",
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"architectures": [
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"ChatGLMModel"
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],
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"auto_map": {
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"AutoConfig": "configuration_chatglm.ChatGLMConfig",
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"AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration",
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"AutoModelForCausalLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
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"AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
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"AutoModelForSequenceClassification": "modeling_chatglm.ChatGLMForSequenceClassification"
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},
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"add_bias_linear": false,
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"add_qkv_bias": true,
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"apply_query_key_layer_scaling": true,
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"apply_residual_connection_post_layernorm": false,
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"attention_dropout": 0.0,
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"attention_softmax_in_fp32": true,
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"bias_dropout_fusion": true,
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"ffn_hidden_size": 13696,
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"fp32_residual_connection": false,
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"hidden_dropout": 0.0,
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"hidden_size": 4096,
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"kv_channels": 128,
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"layernorm_epsilon": 1e-05,
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"multi_query_attention": true,
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"multi_query_group_num": 2,
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"num_attention_heads": 32,
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"num_layers": 28,
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"original_rope": true,
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"padded_vocab_size": 65024,
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"post_layer_norm": true,
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"rmsnorm": true,
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"seq_length": 8192,
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"use_cache": true,
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"torch_dtype": "float16",
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"transformers_version": "4.30.2",
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"tie_word_embeddings": false,
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"eos_token_id": 2,
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"pad_token_id": 0
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}
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@ -0,0 +1 @@
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{"framework":"Pytorch","task":"chatbot"}
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@ -700,10 +700,10 @@ class ChatGLMForConditionalGeneration(nn.Module):
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# finished sentences should add a padding token to next
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pad_token = pad_token_id * isFinished
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next_tokens = next_tokens * (1 - isFinished) + pad_token
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input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
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isFinished = isFinished | next_tokens.eq(eos_token_id_tensor)
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if isFinished.min() == 1: # all batch is finish
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break
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input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
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return input_ids
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@ -0,0 +1,207 @@
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{
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"metadata": {
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"total_size": 12487168064
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},
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"weight_map": {
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|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue