Add dump tool.

This commit is contained in:
Colin 2023-12-21 19:52:19 +08:00
parent a451def299
commit 68417fdc12
5 changed files with 64 additions and 10 deletions

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@ -502,13 +502,9 @@ class ChatGLMModel(nn.Module):
# Rotary positional embeddings # Rotary positional embeddings
rotary_pos_emb = self.rotary_pos_emb(self.seq_length) rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
from tools import show
import plotly_express as px show.DumpTensorToImage(rotary_pos_emb[:, :, 0], "plot.png", scale=0.1)
img = px.imshow((rotary_pos_emb[:,:,0]*256).byte().cpu())
img.write_image("plot.png")
if position_ids is not None: if position_ids is not None:
rotary_pos_emb = rotary_pos_emb[position_ids] rotary_pos_emb = rotary_pos_emb[position_ids]
@ -709,8 +705,8 @@ class ChatGLMForConditionalGeneration(nn.Module):
input_ids, input_ids,
pad_token_id=generation_config.pad_token_id, pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id, eos_token_id=generation_config.eos_token_id,
output_hidden_states = generation_config.output_hidden_states, output_hidden_states=generation_config.output_hidden_states,
use_cache = generation_config.use_cache use_cache=generation_config.use_cache,
) )
outputs = outputs.tolist()[0][len(inputs["input_ids"][0]) : -1] outputs = outputs.tolist()[0][len(inputs["input_ids"][0]) : -1]
@ -724,7 +720,7 @@ class ChatGLMForConditionalGeneration(nn.Module):
pad_token_id: Optional[int] = None, pad_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None, eos_token_id: Optional[Union[int, List[int]]] = None,
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
use_cache: Optional[bool] = None use_cache: Optional[bool] = None,
): ):
if isinstance(eos_token_id, int): if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id] eos_token_id = [eos_token_id]

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0
tools/__init__.py Normal file
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52
tools/show.py Normal file
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@ -0,0 +1,52 @@
import plotly_express as px
import torch
import torch.nn.functional as F
import torchvision.transforms.functional as Vision
import cv2
def DumpTensorToImage(tensor, name, autoPad=True, scale=1.0):
if len(tensor.shape) != 2:
raise ("Error input dims")
tensor = tensor.float()
maxv = torch.max(tensor)
minv = torch.min(tensor)
tensor = (((tensor - minv) / (maxv - minv)) * 256).byte().cpu()
img = tensor.numpy()
srp = img.shape
if autoPad and (max(srp) / min(srp) > 16):
img = cv2.resize(img,[max(srp),max(srp)])
srp = img.shape
if scale != 1.0:
img = cv2.resize(img, [int(srp[0] * scale), int(srp[1] * scale)])
srp = img.shape
cv2.imwrite(name, img)
# def DumpTensorToImage(tensor, name, autoPad=True, scale=1.0):
# if len(tensor.shape) != 2:
# raise ("Error input dims")
# tensor = tensor.float()
# maxv = torch.max(tensor)
# minv = torch.min(tensor)
# tensor = (((tensor - minv) / (maxv - minv)) * 256).byte().cpu()
# srp = tensor.shape
# if autoPad and (max(srp) / min(srp) > 16):
# if srp[0] == min(srp):
# tensor = F.pad(tensor, [max(srp) - min(srp), 0], "replicate")
# else:
# tensor = F.pad(tensor, [0, max(srp) - min(srp)], "replicate")
# srp = tensor.shape
# tensor = tensor.unsqueeze(0)
# if scale != 1.0:
# tensor = Vision.resize(tensor, [int(srp[0] * scale), int(srp[1] * scale)])
# tensor = tensor.view([int(srp[0] * scale), int(srp[1] * scale)])
# srp = tensor.shape
# w = 1024 if max(srp) > 1024 else max(srp)
# scale = max(srp) / w
# # img = px.imshow(tensor)
# # img.write_image(name)
# cv2.imwrite(name, tensor.numpy())
# cv2.CreateMat(name, tensor.numpy())

6
tools/test.py Normal file
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@ -0,0 +1,6 @@
import show
import torch
radata = torch.randn(8192, 128)
show.DumpTensorToImage(radata, "test.png", autoPad=True,scale=0.2)