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610 lines
22 KiB
Plaintext
610 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"source": [
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"# pip install transformers"
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],
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"outputs": [],
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"execution_count": 1,
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"metadata": {
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"gather": {
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"logged": 1724129002162
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}
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# pip install torch torchvision torchaudio -U"
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],
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"outputs": [],
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"execution_count": 2,
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"metadata": {
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"jupyter": {
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"source_hidden": false,
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"outputs_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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},
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"gather": {
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"logged": 1724129002227
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}
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# pip install flash-attn --no-build-isolation"
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],
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"outputs": [],
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"execution_count": 3,
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"metadata": {
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"jupyter": {
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"source_hidden": false,
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"outputs_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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},
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"gather": {
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"logged": 1724129002308
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}
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# ! pip install flash_attn -U"
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],
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"outputs": [],
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"execution_count": 4,
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"metadata": {
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"jupyter": {
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"source_hidden": false,
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"outputs_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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},
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"gather": {
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"logged": 1724129002392
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}
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}
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},
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{
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"cell_type": "code",
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"source": [
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"from torch import bfloat16\n",
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"import transformers"
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],
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"outputs": [],
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"execution_count": 5,
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"metadata": {
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"jupyter": {
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"source_hidden": false,
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"outputs_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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},
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"gather": {
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"logged": 1724129010695
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}
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}
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},
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{
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"cell_type": "code",
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"source": [
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"model_id = \"../Phi3MOE\""
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],
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"outputs": [],
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"execution_count": 6,
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"metadata": {
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"jupyter": {
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"source_hidden": false,
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"outputs_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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},
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"gather": {
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"logged": 1724129010747
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}
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}
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},
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{
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"cell_type": "code",
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"source": [
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"model = transformers.AutoModelForCausalLM.from_pretrained(\n",
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" model_id,\n",
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" trust_remote_code=True,\n",
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" torch_dtype=bfloat16,\n",
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" device_map='auto'\n",
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")\n"
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],
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "Loading checkpoint shards: 0%| | 0/17 [00:00<?, ?it/s]",
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"application/vnd.jupyter.widget-view+json": {
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"version_major": 2,
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"version_minor": 0,
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"model_id": "5cd80a487bad430ba0455753bdeff5fa"
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}
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},
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"metadata": {}
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},
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{
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"output_type": "stream",
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"name": "stderr",
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"text": "WARNING:root:Some parameters are on the meta device device because they were offloaded to the cpu.\n"
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}
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],
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"execution_count": 7,
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"metadata": {
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"jupyter": {
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"source_hidden": false,
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"outputs_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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},
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"gather": {
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"logged": 1724129511030
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}
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}
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},
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{
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"cell_type": "code",
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"source": [
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"model.eval()"
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],
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"outputs": [
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{
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"output_type": "execute_result",
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"execution_count": 8,
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"data": {
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"text/plain": "PhiMoEForCausalLM(\n (model): PhiMoEModel(\n (embed_tokens): Embedding(32064, 4096)\n (layers): ModuleList(\n (0-31): 32 x PhiMoEDecoderLayer(\n (self_attn): PhiMoESdpaAttention(\n (q_proj): Linear(in_features=4096, out_features=4096, bias=True)\n (k_proj): Linear(in_features=4096, out_features=1024, bias=True)\n (v_proj): Linear(in_features=4096, out_features=1024, bias=True)\n (o_proj): Linear(in_features=4096, out_features=4096, bias=True)\n (rotary_emb): Phi3LongRoPEScaledRotaryEmbedding()\n )\n (block_sparse_moe): PhiMoESparseMoeBlock(\n (gate): Linear(in_features=4096, out_features=16, bias=False)\n (experts): ModuleList(\n (0-15): 16 x PhiMoEBlockSparseTop2MLP(\n (w1): Linear(in_features=4096, out_features=6400, bias=False)\n (w2): Linear(in_features=6400, out_features=4096, bias=False)\n (w3): Linear(in_features=4096, out_features=6400, bias=False)\n (act_fn): SiLU()\n )\n )\n )\n (input_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n (post_attention_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n )\n )\n (norm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n )\n (lm_head): Linear(in_features=4096, out_features=32064, bias=True)\n)"
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},
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"metadata": {}
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}
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],
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"execution_count": 8,
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"metadata": {
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"jupyter": {
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"source_hidden": false,
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"outputs_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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},
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"gather": {
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"logged": 1724129511125
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}
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}
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},
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{
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"cell_type": "code",
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"source": [
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"tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)"
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],
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"outputs": [],
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"execution_count": 9,
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"metadata": {
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"jupyter": {
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"source_hidden": false,
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"outputs_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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},
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"gather": {
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"logged": 1724129511238
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}
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# generate_text = transformers.pipeline(\n",
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"# model=model, tokenizer=tokenizer,\n",
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"# return_full_text=False, # if using langchain set True\n",
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"# task=\"text-generation\",\n",
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"# # we pass model parameters here too\n",
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"# temperature=0.1, # 'randomness' of outputs, 0.0 is the min and 1.0 the max\n",
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"# top_p=0.15, # select from top tokens whose probability add up to 15%\n",
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"# top_k=0, # select from top 0 tokens (because zero, relies on top_p)\n",
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"# max_new_tokens=2048, # max number of tokens to generate in the output\n",
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"# repetition_penalty=1.1 # if output begins repeating increase\n",
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"# )\n",
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"\n",
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"pipe = transformers.pipeline(\n",
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"\n",
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" \"text-generation\",\n",
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"\n",
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" model=model,\n",
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"\n",
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" tokenizer=tokenizer,\n",
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"\n",
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")"
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],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stderr",
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"text": "2024-08-20 04:51:54.431510: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n2024-08-20 04:51:54.431543: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\nThe model 'PhiMoEForCausalLM' is not supported for text-generation. Supported models are ['BartForCausalLM', 'BertLMHeadModel', 'BertGenerationDecoder', 'BigBirdForCausalLM', 'BigBirdPegasusForCausalLM', 'BioGptForCausalLM', 'BlenderbotForCausalLM', 'BlenderbotSmallForCausalLM', 'BloomForCausalLM', 'CamembertForCausalLM', 'LlamaForCausalLM', 'CodeGenForCausalLM', 'CohereForCausalLM', 'CpmAntForCausalLM', 'CTRLLMHeadModel', 'Data2VecTextForCausalLM', 'DbrxForCausalLM', 'ElectraForCausalLM', 'ErnieForCausalLM', 'FalconForCausalLM', 'FuyuForCausalLM', 'GemmaForCausalLM', 'Gemma2ForCausalLM', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'JambaForCausalLM', 'JetMoeForCausalLM', 'LlamaForCausalLM', 'MambaForCausalLM', 'Mamba2ForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', 'MistralForCausalLM', 'MixtralForCausalLM', 'MptForCausalLM', 'MusicgenForCausalLM', 'MusicgenMelodyForCausalLM', 'MvpForCausalLM', 'NemotronForCausalLM', 'OlmoForCausalLM', 'OpenLlamaForCausalLM', 'OpenAIGPTLMHeadModel', 'OPTForCausalLM', 'PegasusForCausalLM', 'PersimmonForCausalLM', 'PhiForCausalLM', 'Phi3ForCausalLM', 'PLBartForCausalLM', 'ProphetNetForCausalLM', 'QDQBertLMHeadModel', 'Qwen2ForCausalLM', 'Qwen2MoeForCausalLM', 'RecurrentGemmaForCausalLM', 'ReformerModelWithLMHead', 'RemBertForCausalLM', 'RobertaForCausalLM', 'RobertaPreLayerNormForCausalLM', 'RoCBertForCausalLM', 'RoFormerForCausalLM', 'RwkvForCausalLM', 'Speech2Text2ForCausalLM', 'StableLmForCausalLM', 'Starcoder2ForCausalLM', 'TransfoXLLMHeadModel', 'TrOCRForCausalLM', 'WhisperForCausalLM', 'XGLMForCausalLM', 'XLMWithLMHeadModel', 'XLMProphetNetForCausalLM', 'XLMRobertaForCausalLM', 'XLMRobertaXLForCausalLM', 'XLNetLMHeadModel', 'XmodForCausalLM'].\n"
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}
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],
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"execution_count": 10,
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"metadata": {
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"jupyter": {
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"source_hidden": false,
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"outputs_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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},
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"gather": {
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"logged": 1724129523437
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}
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}
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},
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{
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"cell_type": "code",
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"source": [
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"generation_args = {\n",
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"\n",
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" \"max_new_tokens\": 512,\n",
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"\n",
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" \"return_full_text\": False,\n",
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"\n",
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" \"temperature\": 0.3,\n",
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"\n",
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" \"do_sample\": False,\n",
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"\n",
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"}"
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],
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"outputs": [],
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"execution_count": 11,
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"metadata": {
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"jupyter": {
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"source_hidden": false,
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"outputs_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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},
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"gather": {
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"logged": 1724129523505
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}
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}
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},
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{
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"cell_type": "code",
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"source": [
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"sys_msg = \"\"\"You are a helpful AI assistant, you are an agent capable of using a variety of tools to answer a question. Here are a few of the tools available to you:\n",
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"\n",
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"- Blog: This tool helps you describe a certain knowledge point and content, and finally write it into Twitter or Facebook style content\n",
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"- Translate: This is a tool that helps you translate into any language, using plain language as required\n",
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"\n",
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"To use these tools you must always respond in JSON format containing `\"tool_name\"` and `\"input\"` key-value pairs. For example, to answer the question, \"Build Muliti Agents with MOE models\" you must use the calculator tool like so:\n",
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"\n",
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"```json\n",
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"\n",
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"{\n",
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" \"tool_name\": \"Blog\",\n",
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" \"input\": \"Build Muliti Agents with MOE models\"\n",
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"}\n",
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"\n",
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"```\n",
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"\n",
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"Or to translate the question \"can you introduce yourself in Chinese\" you must respond:\n",
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"\n",
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"```json\n",
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"\n",
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"{\n",
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" \"tool_name\": \"Search\",\n",
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" \"input\": \"can you introduce yourself in Chinese\"\n",
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"}\n",
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"\n",
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"```\n",
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"\n",
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"Remember just output the final result, output in JSON format containing `\"agentid\"`,`\"tool_name\"` , `\"input\"` and `\"output\"` key-value pairs .:\n",
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"\n",
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"```json\n",
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"\n",
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"[\n",
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"\n",
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"\n",
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"{ \"agentid\": \"step1\",\n",
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" \"tool_name\": \"Blog\",\n",
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" \"input\": \"Build Muliti Agents with MOE models\",\n",
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" \"output\": \".........\"\n",
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"},\n",
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"\n",
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"{ \"agentid\": \"step2\",\n",
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" \"tool_name\": \"Search\",\n",
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" \"input\": \"can you introduce yourself in Chinese\",\n",
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" \"output\": \".........\"\n",
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"},\n",
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"{\n",
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" \"agentid\": \"final\"\n",
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" \"tool_name\": \"Result\",\n",
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" \"output\": \".........\"\n",
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"}\n",
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"]\n",
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"\n",
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"```\n",
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"\n",
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"The users answer is as follows.\n",
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"\"\"\""
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],
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"outputs": [],
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"execution_count": 12,
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"metadata": {
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"jupyter": {
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"source_hidden": false,
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"outputs_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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},
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"gather": {
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"logged": 1724129523768
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}
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}
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},
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{
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"cell_type": "code",
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"source": [
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"def instruction_format(sys_message: str, query: str):\n",
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" # note, don't \"</s>\" to the end\n",
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" return f'<|system|> {sys_message} <|end|>\\n<|user|> {query} <|end|>\\n<|assistant|>'"
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],
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"outputs": [],
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"execution_count": 13,
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"metadata": {
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"jupyter": {
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"source_hidden": false,
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"outputs_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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},
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"gather": {
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"logged": 1724129523822
|
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}
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}
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},
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{
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"cell_type": "code",
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"source": [
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"query ='Write something about Generative AI with MOE , translate it to Chinese'"
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],
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"outputs": [],
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"execution_count": 14,
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||
"metadata": {
|
||
"jupyter": {
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||
"source_hidden": false,
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"outputs_hidden": false
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},
|
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"nteract": {
|
||
"transient": {
|
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"deleting": false
|
||
}
|
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},
|
||
"gather": {
|
||
"logged": 1724129523875
|
||
}
|
||
}
|
||
},
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{
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"cell_type": "code",
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"source": [
|
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"input_prompt = instruction_format(sys_msg, query)\n",
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"\n"
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],
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"outputs": [],
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"execution_count": 15,
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||
"metadata": {
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"jupyter": {
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||
"source_hidden": false,
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||
"outputs_hidden": false
|
||
},
|
||
"nteract": {
|
||
"transient": {
|
||
"deleting": false
|
||
}
|
||
},
|
||
"gather": {
|
||
"logged": 1724129523921
|
||
}
|
||
}
|
||
},
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{
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||
"cell_type": "code",
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||
"source": [
|
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"input_prompt"
|
||
],
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"outputs": [
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||
{
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||
"output_type": "execute_result",
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||
"execution_count": 16,
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"data": {
|
||
"text/plain": "'<|system|> You are a helpful AI assistant, you are an agent capable of using a variety of tools to answer a question. Here are a few of the tools available to you:\\n\\n- Blog: This tool helps you describe a certain knowledge point and content, and finally write it into Twitter or Facebook style content\\n- Translate: This is a tool that helps you translate into any language, using plain language as required\\n\\nTo use these tools you must always respond in JSON format containing `\"tool_name\"` and `\"input\"` key-value pairs. For example, to answer the question, \"Build Muliti Agents with MOE models\" you must use the calculator tool like so:\\n\\n```json\\n\\n{\\n \"tool_name\": \"Blog\",\\n \"input\": \"Build Muliti Agents with MOE models\"\\n}\\n\\n```\\n\\nOr to translate the question \"can you introduce yourself in Chinese\" you must respond:\\n\\n```json\\n\\n{\\n \"tool_name\": \"Search\",\\n \"input\": \"can you introduce yourself in Chinese\"\\n}\\n\\n```\\n\\nRemember just output the final result, output in JSON format containing `\"agentid\"`,`\"tool_name\"` , `\"input\"` and `\"output\"` key-value pairs .:\\n\\n```json\\n\\n[\\n\\n\\n{ \"agentid\": \"step1\",\\n \"tool_name\": \"Blog\",\\n \"input\": \"Build Muliti Agents with MOE models\",\\n \"output\": \".........\"\\n},\\n\\n{ \"agentid\": \"step2\",\\n \"tool_name\": \"Search\",\\n \"input\": \"can you introduce yourself in Chinese\",\\n \"output\": \".........\"\\n},\\n{\\n \"agentid\": \"final\"\\n \"tool_name\": \"Result\",\\n \"output\": \".........\"\\n}\\n]\\n\\n```\\n\\nThe users answer is as follows.\\n <|end|>\\n<|user|> Write something about Generative AI with MOE , translate it to Chinese <|end|>\\n<|assistant|>'"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
],
|
||
"execution_count": 16,
|
||
"metadata": {
|
||
"jupyter": {
|
||
"source_hidden": false,
|
||
"outputs_hidden": false
|
||
},
|
||
"nteract": {
|
||
"transient": {
|
||
"deleting": false
|
||
}
|
||
},
|
||
"gather": {
|
||
"logged": 1724129523983
|
||
}
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import torch\n",
|
||
"\n",
|
||
"torch.cuda.empty_cache() \n",
|
||
"\n",
|
||
"import os\n",
|
||
"\n",
|
||
"os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True \""
|
||
],
|
||
"outputs": [],
|
||
"execution_count": 17,
|
||
"metadata": {
|
||
"jupyter": {
|
||
"source_hidden": false,
|
||
"outputs_hidden": false
|
||
},
|
||
"nteract": {
|
||
"transient": {
|
||
"deleting": false
|
||
}
|
||
},
|
||
"gather": {
|
||
"logged": 1724129524034
|
||
}
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# res = generate_text(input_prompt)\n",
|
||
"\n",
|
||
"output = pipe(input_prompt, **generation_args)"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": "/anaconda/envs/azureml_py38/lib/python3.9/site-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.3` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n warnings.warn(\nThe `seen_tokens` attribute is deprecated and will be removed in v4.41. Use the `cache_position` model input instead.\n"
|
||
}
|
||
],
|
||
"execution_count": 18,
|
||
"metadata": {
|
||
"jupyter": {
|
||
"source_hidden": false,
|
||
"outputs_hidden": false
|
||
},
|
||
"nteract": {
|
||
"transient": {
|
||
"deleting": false
|
||
}
|
||
},
|
||
"gather": {
|
||
"logged": 1724130076595
|
||
}
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"output[0]['generated_text']"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"execution_count": 19,
|
||
"data": {
|
||
"text/plain": "' ```json\\n\\n[\\n\\n{ \"agentid\": \"step1\",\\n \"tool_name\": \"Blog\",\\n \"input\": \"Generative AI with MOE\",\\n \"output\": \"Generative AI with MOE (Mixture of Experts) is a powerful approach that combines the strengths of generative models and the flexibility of MOE architecture. This hybrid model can generate high-quality, diverse, and contextually relevant content, making it suitable for various applications such as content creation, data augmentation, and more.\"\\n},\\n\\n{ \"agentid\": \"step2\",\\n \"tool_name\": \"Translate\",\\n \"input\": \"Generative AI with MOE is a powerful approach that combines the strengths of generative models and the flexibility of MOE architecture. This hybrid model can generate high-quality, diverse, and contextually relevant content, making it suitable for various applications such as content creation, data augmentation, and more.\",\\n \"output\": \"基于生成AI的MOE（Mixture of Experts）是一种强大的方法，它结合了生成模型的优势和MOE架构的灵活性。这种混合模型可以生成高质量、多样化且上下文相关的内容，使其适用于各种应用，如内容创建、数据增强等。\"\\n},\\n{\\n \"agentid\": \"final\",\\n \"tool_name\": \"Result\",\\n \"output\": \"基于生成AI的MOE（Mixture of Experts）是一种强大的方法，它结合了生成模型的优势和MOE架构的灵活性。这种混合模型可以生成高质量、多样化且上下文相关的内容，使其适用于各种应用，如内容创建、数据增强等。\"\\n}\\n]\\n```'"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
],
|
||
"execution_count": 19,
|
||
"metadata": {
|
||
"jupyter": {
|
||
"source_hidden": false,
|
||
"outputs_hidden": false
|
||
},
|
||
"nteract": {
|
||
"transient": {
|
||
"deleting": false
|
||
}
|
||
},
|
||
"gather": {
|
||
"logged": 1724130076669
|
||
}
|
||
}
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"name": "python38-azureml",
|
||
"language": "python",
|
||
"display_name": "Python 3.8 - AzureML"
|
||
},
|
||
"language_info": {
|
||
"name": "python",
|
||
"version": "3.9.19",
|
||
"mimetype": "text/x-python",
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"pygments_lexer": "ipython3",
|
||
"nbconvert_exporter": "python",
|
||
"file_extension": ".py"
|
||
},
|
||
"microsoft": {
|
||
"ms_spell_check": {
|
||
"ms_spell_check_language": "en"
|
||
},
|
||
"host": {
|
||
"AzureML": {
|
||
"notebookHasBeenCompleted": true
|
||
}
|
||
}
|
||
},
|
||
"kernel_info": {
|
||
"name": "python38-azureml"
|
||
},
|
||
"nteract": {
|
||
"version": "nteract-front-end@1.0.0"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|