patrickvonplaten
commited on
Commit
•
5211e0c
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Parent(s):
015300c
upload
Browse files- model/restored.pt +2 -2
- model/special_tokens_map.json +23 -1
- model/tokenizer_config.json +31 -1
- model/vocab.json +0 -0
- run_model.py +29 -19
model/restored.pt
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:e8185e89d0ebf9a63a207216b0e5ef89e67d74601b2cfec424a9eef2eba0dfbe
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size 59949410247
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model/special_tokens_map.json
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@@ -1 +1,23 @@
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{
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{
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"bos_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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}
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}
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model/tokenizer_config.json
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{
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{
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"add_bos_token": false,
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"add_prefix_space": false,
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"bos_token": {
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"__type": "AddedToken",
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"__type": "AddedToken",
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"errors": "replace",
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"pad_token": null,
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"tokenizer_class": "GPT2Tokenizer",
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"unk_token": {
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"__type": "AddedToken",
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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}
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}
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model/vocab.json
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The diff for this file is too large to render.
See raw diff
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run_model.py
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#!/usr/bin/env python3
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#!/usr/bin/env python3
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import os
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from transformers import AutoTokenizer, GPT2Tokenizer
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from megatron.initialize import initialize_megatron
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from metaseq import checkpoint_utils
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import torch
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path = "./model"
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# just need to initialize args with something,
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# => doesn't need to correspond to the "correct" architecture for this checkpoint
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initialize_megatron(args_defaults={
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"micro_batch_size": 1,
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"num_layers": 12,
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"hidden_size": 768,
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"num_attention_heads": 12,
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"max_position_embeddings": 2048,
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"encoder_seq_length": 2048
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})
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vocab_file = os.path.join(path, "gpt2-vocab.json")
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merges_file = os.path.join(path, "gpt2-merges.txt")
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)
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model = checkpoint[0][0].eval()
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model = model
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# forward passes
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def single_batch_forward_logits(prompts):
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input_ids = tokenizer(prompts, return_tensors="pt").input_ids
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input_ids = torch.cat([torch.tensor([[0]]), input_ids], dim=-1)
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input_ids = input_ids
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with torch.no_grad():
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logits = model(input_ids)[0]
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return logits
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prompts = [
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-
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-
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]
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print("Next word generation")
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for prompt in prompts:
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print("-------------")
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print(f"Prompt: {prompt}...\n")
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pred_next_token = torch.argmax(logits[0, -1], -1)
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next_token = tokenizer.convert_ids_to_tokens([pred_next_token])
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next_token = next_token[0].replace("Ġ", "")
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print(f"Next word: {next_token}")
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print("-------------")
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#!/usr/bin/env python3
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import os
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from transformers import AutoTokenizer, GPT2Tokenizer
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#from megatron.initialize import initialize_megatron
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from metaseq import checkpoint_utils
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from transformers import OPTForCausalLM
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import torch
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path = "./model"
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hf_path = "/home/patrick/facebook/opt-30b"
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vocab_file = os.path.join(path, "gpt2-vocab.json")
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merges_file = os.path.join(path, "gpt2-merges.txt")
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)
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model = checkpoint[0][0].eval()
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model = model
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hf_model = OPTForCausalLM.from_pretrained(hf_path)
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# forward passes
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def single_batch_forward_logits(prompts):
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input_ids = tokenizer(prompts, return_tensors="pt").input_ids
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input_ids = torch.cat([torch.tensor([[0]]), input_ids], dim=-1)
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input_ids = input_ids
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with torch.no_grad():
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logits = model(input_ids)[0]
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return logits
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# forward hf
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def forward_hf(prompts):
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input_ids = tokenizer(prompts, return_tensors="pt").input_ids
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input_ids = torch.cat([torch.tensor([[0]]), input_ids], dim=-1)
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input_ids = input_ids
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with torch.no_grad():
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logits = hf_model(input_ids)[0]
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return logits
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prompts = [
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"Today is a beautiful day and I want to",
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"In the city of",
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"Paris is the capital of France and",
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"Computers and mobile phones have taken",
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]
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print("Next word generation")
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for prompt in prompts:
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print("-------------")
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print(f"Prompt: {prompt}...\n")
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logits_fsq = single_batch_forward_logits(prompt)
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pred_next_token = torch.argmax(logits_fsq[0, -1], -1)
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next_token = tokenizer.convert_ids_to_tokens([pred_next_token])
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next_token = next_token[0].replace("Ġ", "")
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print(f"Next word: {next_token}")
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print("-------------")
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logits = forward_hf(prompt)
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pred_next_token = torch.argmax(logits[0, -1], -1)
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next_token = tokenizer.convert_ids_to_tokens([pred_next_token])
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next_token = next_token[0].replace("Ġ", "")
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print(f"Next word: {next_token}")
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print("-------------")
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print("Is equal:", torch.allclose(logits_fsq.cpu(), logits.cpu(), atol=1e-3))
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