mirror of
https://github.com/turboderp-org/exllamav3.git
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199 lines
7.4 KiB
Python
199 lines
7.4 KiB
Python
import sys, os
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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import argparse
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from exllamav3.util.file import disk_lru_cache
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from exllamav3.util.progress import ProgressBar
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from exllamav3.util.memory import free_mem
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from exllamav3 import Config, Model, Tokenizer
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from datasets import load_dataset
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import torch
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import torch.nn.functional as F
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import math
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@disk_lru_cache("get_dataset_text")
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def get_dataset_text(spec: dict):
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assert spec["dataset"] == "wiki2", "Only wiki2 implemented atm"
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dataset_text = "\n\n".join(
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load_dataset("wikitext", "wikitext-2-raw-v1", split = "test")
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["text"]
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)
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return dataset_text
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def get_test_tokens(tokenizer, rows, eval_len = 2048, eval_stride = 512):
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with ProgressBar("Tokenizing", rows) as pb:
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dataset_spec = { "dataset": "wiki2" }
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eval_tokens = tokenizer.encode(get_dataset_text(dataset_spec))
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num_tokens = eval_tokens.shape[-1]
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seqs = []
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for a in range(0, num_tokens - eval_len, eval_stride):
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b = a + eval_len
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seqs.append(eval_tokens[:, a:b])
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pb.update(len(seqs))
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if len(seqs) >= rows:
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break
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return torch.cat(seqs, dim = 0)[:, :]
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@torch.inference_mode()
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def main(args):
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config_a = Config.from_directory(args.model_a)
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config_a.override_dynamic_seq_len(2048)
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tokenizer = Tokenizer.from_config(config_a)
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model_a = Model.from_config(config_a)
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config_b = Config.from_directory(args.model_b)
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config_b.override_dynamic_seq_len(2048)
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model_b = Model.from_config(config_b)
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# Dataset
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eval_ids = get_test_tokens(tokenizer, args.rows)
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state_a = eval_ids
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state_b = eval_ids
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for idx, (module_a, module_b) in enumerate(zip(model_a.modules, model_b.modules)):
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module_a.load("cuda:0" if not module_a.caps.get("prefer_cpu") else "cpu")
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params_a = {}
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state_a = module_a.prepare_for_device(state_a, params_a)
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state_a = module_a.forward(state_a, params_a)
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module_a.unload()
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free_mem()
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module_b.load("cuda:0" if not module_b.caps.get("prefer_cpu") else "cpu")
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params_b = {}
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state_b = module_b.prepare_for_device(state_b, params_b)
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state_b = module_b.forward(state_b, params_b)
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module_b.unload()
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free_mem()
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if idx < args.keep_b:
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state_a = state_b.clone()
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max_diff = 0
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rfn_error_sum = 0
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rows = state_a.shape[0]
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for i in range(rows):
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sa = state_a[i].to(float, copy = True)
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sb = state_b[i].to(float)
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sa -= sb
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rfn_error_sum += (torch.linalg.norm(sa, 'fro') / torch.linalg.norm(sb, 'fro').mean()).item()
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sa.abs_()
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md = ((sa.max().item()) / torch.linalg.norm(sb, 'fro').mean()).item()
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max_diff = max(max_diff, md)
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del sa, sb
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rfn_error = rfn_error_sum / rows
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print(f" -- {module_a.key:40} error: {rfn_error:.6f} max_diff/norm: {max_diff:.6f}")
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# Compare logits
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topk_max = args.topk_max
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logprob_sum = [0, 0]
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logprob_count = [0, 0]
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kl_div_sum_ab = 0
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kl_div_sum_ba = 0
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topk_hits_sum = [[0] * topk_max, [0] * topk_max]
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topk_hits_count = [[0] * topk_max, [0] * topk_max]
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topk_agreement_sum = [0] * topk_max
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topk_agreement_count = [0] * topk_max
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def ppl(input_ids_, logits_):
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nonlocal logprob_sum, logprob_count
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logprob_sum_ = 0.0
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logprob_count_ = 0
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chunksize = logits_.shape[1] * 10240 // logits_.shape[1]
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b_ = 0
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while b_ < logits_.shape[1]:
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a_ = b_
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b_ = min(b_ + chunksize, logits_.shape[1])
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logits_f = logits_[a_:b_, :].float() + 1e-10
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target_ids = input_ids_[a_ + 1:b_ + 1].to(logits_.device)
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log_probs = F.log_softmax(logits_f, dim=-1)
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token_log_probs = log_probs.gather(-1, target_ids.unsqueeze(-1)).squeeze(-1)
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logprob_sum_ += token_log_probs.sum().item()
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logprob_count_ += target_ids.numel()
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return logprob_sum_, logprob_count_
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rows = state_a.shape[0]
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for j in range(rows):
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x = (state_a[j], state_b[j])
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input_ids = eval_ids[j]
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top_indices = []
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for i in [0, 1]:
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logits = x[i][:-1, :]
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logprob_sum__, logprob_count__ = ppl(input_ids, logits)
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logprob_sum[i] += logprob_sum__
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logprob_count[i] += logprob_count__
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_, top_index = torch.topk(logits, topk_max, dim = -1)
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top_index = top_index.cpu().view(-1, topk_max)
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top_indices.append(top_index)
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targets = input_ids[1:].view(-1, 1)
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for t in range(topk_max):
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top_slice = top_index[:, :t + 1]
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hits = torch.eq(targets, top_slice)
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row_hits = hits.any(dim = 1)
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topk_hits_sum[i][t] += row_hits.sum().item()
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topk_hits_count[i][t] += top_slice.shape[0]
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for t in range(topk_max):
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top_slice_a = top_indices[0][:, :t + 1]
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top_slice_b = top_indices[1][:, :t + 1]
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hits = torch.eq(top_slice_a, top_slice_b)
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row_hits = hits.all(dim = 1)
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topk_agreement_sum[t] += row_hits.sum().item()
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topk_agreement_count[t] += top_slice_a.shape[0]
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epsilon = 1e-10
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probs_a = torch.softmax(x[0].float(), dim = -1)
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probs_b = torch.softmax(x[1].float(), dim = -1)
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kl_div = F.kl_div(torch.log(probs_a + epsilon), probs_b, reduction = 'none')
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kl_div_sum_ab += kl_div.sum(dim = -1).mean().item()
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kl_div = F.kl_div(torch.log(probs_b + epsilon), probs_a, reduction = 'none')
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kl_div_sum_ba += kl_div.sum(dim = -1).mean().item()
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perplexity = [math.exp(-logprob_sum[i] / logprob_count[i]) for i in (0, 1)]
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kl_div_ab = kl_div_sum_ab / rows
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kl_div_ba = kl_div_sum_ba / rows
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# Perplexity for each model
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print(f" -- A perplexity: {perplexity[0]:11.8f}")
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print(f" -- B perplexity: {perplexity[1]:11.8f}")
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# Probability of the test label being in the top K tokens, for each model
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print(f" -- A label in top-K:")
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for t in range(topk_max):
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a_acc_ = topk_hits_sum[0][t] / topk_hits_count[0][t]
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print(f" K = {t+1}: {a_acc_:6.4f}")
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print(f" -- B label in top-K:")
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for t in range(topk_max):
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a_acc_ = topk_hits_sum[1][t] / topk_hits_count[1][t]
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print(f" K = {t+1}: {a_acc_:6.4f}")
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# Probability of exact top-K token match between models
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print(f" -- Top-K agreement, A vs B:")
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for t in range(topk_max):
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topk_agree_ = topk_agreement_sum[t] / topk_agreement_count[t]
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print(f" K = {t+1}: {topk_agree_:6.4f}")
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# KLD, either way around
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print(f" -- KL divergence (A, B): {kl_div_ab:11.8f}")
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print(f" -- KL divergence (B, A): {kl_div_ba:11.8f}")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("-ma", "--model_a", type = str, help = "Model A", required = True)
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parser.add_argument("-mb", "--model_b", type = str, help = "Model B", required = True)
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parser.add_argument("-r", "--rows", type = int, help = "Number of rows", default = 100)
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parser.add_argument("-kb", "--keep_b", type = int, help = "Maintain B state for number of modules", default = 0)
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parser.add_argument("-tkm", "--topk_max", type = int, default = 5, help = "Max top-K interval to test")
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_args = parser.parse_args()
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main(_args)
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