Add reaserch sile resault.

qwen/research_silu.py

@@ -0,0 +1,123 @@
+import torch
+import sys
+import math
+from modelscope import snapshot_download
+from transformers import AutoTokenizer
+from transformers import AutoConfig
+
+from modeling_qwen import QWenLMHeadModel
+from modeling_qwen import QwenRunner
+import numpy as np
+
+import torch.nn.functional as F
+
+from qwen_generation_utils import (
+    make_context,
+    decode_tokens,
+)
+
+sys.path.append("..")
+from tools import show
+
+seed = 4321
+torch.manual_seed(seed)
+torch.cuda.manual_seed_all(seed)
+
+model_dir = snapshot_download("qwen/Qwen-1_8B-Chat")
+# model_dir = "/home/colin/.cache/modelscope/hub/qwen/Qwen-1_8B-Chat"
+
+config, kwargs = AutoConfig.from_pretrained(
+    "./",
+    return_unused_kwargs=True,
+    trust_remote_code=True,
+    code_revision=None,
+    _commit_hash=None,
+)
+model = QWenLMHeadModel(config)
+
+print(model)
+
+tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
+
+model = model.from_pretrained(model_dir)
+if torch.cuda.device_count() > 0:
+    model = model.cuda()
+model = model.eval()
+
+index = 0
+
+
+class ResearchRunner(QwenRunner):
+    def __init__(self, model):
+        super().__init__(model)
+
+    def prepareInput(self, tokenizer, query, query_assistant, history, system):
+        start_to = [151644]
+        n_to = [198]
+        end_to = [151645]
+        system_str = "system\nYou are a helpful assistant."
+        user_str = "user\n" + query
+        aassistant_str = "assistant\n" + query_assistant
+
+        system_token = start_to + tokenizer.encode(system_str, allowed_special=set()) + end_to + n_to
+        user_token = start_to + tokenizer.encode(user_str, allowed_special=set()) + end_to + n_to
+        aassistant_token = start_to + tokenizer.encode(aassistant_str, allowed_special=set())
+
+        tokens = system_token + user_token + aassistant_token
+        tokens = user_token + aassistant_token
+        tokens = start_to + tokenizer.encode("user\nHi你好\nassistant\n我是", allowed_special=set())
+
+        return "", tokens
+
+    def forwardQWenBlock(
+        self,
+        block,
+        hidden_states,
+        rotary_pos_emb_list=None,
+    ):
+        layernorm_output = block.ln_1(hidden_states)
+
+        attn_outputs = self.forwardAttention(block.attn, layernorm_output, rotary_pos_emb_list)
+        attn_output = attn_outputs[0]
+        layernorm_input = attn_output + hidden_states
+
+        layernorm_output = block.ln_2(layernorm_input)
+        a1 = block.mlp.w1(layernorm_output)
+        a2 = block.mlp.w2(layernorm_output)
+
+        activation = (F.relu(a2) > 0).to(float)
+        act_mean = torch.mean(activation, 2)
+        print("Layer:" + str(block.index))
+        print(act_mean.cpu())
+        global index
+        if index == 0:
+            activation = activation.reshape(activation.shape[1], 64, -1)
+            show.DumpTensorToImage(activation, "./temp/activation_layer_" + str(block.index) + ".png")
+
+        intermediate_parallel = a1 * F.silu(a2)
+        mlp_output = block.mlp.c_proj(intermediate_parallel)
+
+        hidden_states = layernorm_input + mlp_output
+        return hidden_states
+
+    def isFinish(self, next_tokens):
+        global index
+        index = index + 1
+        finish, next = super().isFinish(next_tokens)
+        return finish, next
+
+
+para = list(model.parameters())
+runner = ResearchRunner(model)
+
+output_ids, history, decoded = runner.Chat(tokenizer, "你好!!", "")
+print(decoded)
+
+
+tokens = []
+for i, token in enumerate(output_ids):
+    de = tokenizer.decode([token])
+    de = str(i + 1).zfill(3) + " : " + repr(de)
+    tokens.append(de)
+
+show.DumpListToFile(tokens, "./temp/token_decode_list.txt")