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Update show and q@k dump.

This commit is contained in:
Colin 2024-01-21 20:50:36 +08:00
parent ae6ea67bbe
commit 17a2df2e6f
2 changed files with 20 additions and 12 deletions
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6
qwen/research_attention.py
View File

@ -45,9 +45,11 @@ class ResearchRunner(QwenRunner):
scale_factor = 1 / math.sqrt(query.size(-1))
attn_weight = query @ key.transpose(-2, -1) * scale_factor
attn_weight = torch.softmax(attn_weight, dim=-1)
size = query.shape[2]
qk = attn_weight[0]
attn_mask = torch.ones(causal_mask.shape, dtype=query.dtype, device=query.device)
attn_mask.masked_fill_(causal_mask.logical_not(), float(0))
qk = attn_weight * attn_mask
qk = qk[0]
prePath = "./temp/"
show.DumpTensorToImage(qk, prePath + "q@k_seq_" + str(size) + "_layer_" + str(attention.index) + ".png")

26
tools/show.py
View File

@ -8,27 +8,33 @@ import numpy as np
import os
def DumpTensorToImage(tensor, name, forceSquare=True, scale=1.0):
def DumpTensorToImage(tensor, name, forceSquare=True, scale=1.0, AutoContrast=True):
if len(tensor.shape) != 2 and len(tensor.shape) != 1 and len(tensor.shape) != 3:
raise ("Error input dims")
if len(tensor.shape) == 3:
channel = tensor.shape[0]
x = math.ceil((channel) ** 0.5)
tensor = F.pad(
tensor, (0, 1, 0, 1, 0, x * x - channel), mode="constant", value=0
)
tensor = tensor.reshape((x, x, tensor.shape[1], tensor.shape[2]))
tensor = F.pad(tensor, (0, 1, 0, 1, 0, x * x - channel), mode="constant", value=0)
if AutoContrast:
calc = tensor.reshape((x * x, tensor.shape[1] * tensor.shape[2]))
tensormax = calc.max(1)[0]
tensormin = calc.min(1)[0]
calc = calc.transpose(1, 0)
calc = ((calc - tensormin) / (tensormax - tensormin)) * 255
calc = calc.transpose(1, 0)
tensor = calc.reshape((x, x, tensor.shape[1], tensor.shape[2]))
tensor = tensor.permute((0, 2, 1, 3))
tensor = tensor.reshape((x * tensor.shape[1], x * tensor.shape[3]))
DumpTensorToImage(tensor, name, forceSquare=False, scale=scale)
DumpTensorToImage(tensor, name, forceSquare=False, scale=scale, AutoContrast=False)
return
tensor = tensor.float()
maxv = torch.max(tensor)
minv = torch.min(tensor)
tensor = (((tensor - minv) / (maxv - minv)) * 255).byte().cpu()
img = tensor.numpy()
if AutoContrast:
maxv = torch.max(tensor)
minv = torch.min(tensor)
tensor = ((tensor - minv) / (maxv - minv)) * 255
img = tensor.byte().cpu().numpy()
srp = img.shape
if len(srp) == 1: # 1D的数据自动折叠成2D图像