Skip to content

chore: improve ktransformers maintenance path #2033

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
lphuc2250gma wants to merge 1 commit into
base: main
Choose a base branch
from
Open

chore: improve ktransformers maintenance path #2033

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
12 changes: 6 additions & 6 deletions kt-kernel/examples/test_mla.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -58,15 +58,15 @@ def get_torch_tensor_and_type_from_gguf(gguf_weights, name):
return torch.from_numpy(gguf_weights[name].data).contiguous(), gguf_weights[name].tensor_type.name


def type_to_ggml_type(type):
if type == "F32":
def type_to_ggml_type(dtype_str: str) -> ggml_type:
if dtype_str == "F32":
return ggml_type.FP32
elif type == "F16":
elif dtype_str == "F16":
return ggml_type.FP16
elif type == "BF16":
elif dtype_str == "BF16":
return ggml_type.BF16
else:
raise ValueError(f"Unsupported data type: {type}")
raise ValueError(f"Unsupported data type: {dtype_str}")


use_real_weights = True
Expand Down Expand Up @@ -721,4 +721,4 @@ def torch_attn(
print(
f"Diff: ave:{diff.mean()}, max:{diff.max()}, min:{diff.min()}, relative_mean:{diff_relative_mean}, relative_max:{diff_relative.max()}, relative_min:{diff_relative.min()}"
)
assert diff_relative_mean < 2e-1, "CPU and Torch outputs are not close enough!"
assert diff_relative_mean < 2e-1, f"CPU and Torch outputs diverge: relative_mean={diff_relative_mean:.4e} >= 2e-1"

@gemini-code-assist gemini-code-assist Bot Jun 5, 2026

Copy link
Copy Markdown
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

medium

The variable diff_relative_mean is a PyTorch Tensor (0-dimensional). Directly formatting a PyTorch tensor with float format specifiers like :.4e can raise a TypeError or produce unexpected formatting in older PyTorch versions. It is safer and more idiomatic to call .item() to retrieve the underlying Python float before formatting.

Suggested change
assert diff_relative_mean < 2e-1, f"CPU and Torch outputs diverge: relative_mean={diff_relative_mean:.4e} >= 2e-1"
assert diff_relative_mean < 2e-1, f"CPU and Torch outputs diverge: relative_mean={diff_relative_mean.item():.4e} >= 2e-1"

6 changes: 3 additions & 3 deletions kt-kernel/examples/test_mlp.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -30,11 +30,11 @@
validation_iter = 100


def act_fn(x):
def act_fn(x: torch.Tensor) -> torch.Tensor:
return x / (1.0 + torch.exp(-x))


def mlp_torch(input, gate_proj, up_proj, down_proj):
def mlp_torch(input: torch.Tensor, gate_proj: torch.Tensor, up_proj: torch.Tensor, down_proj: torch.Tensor) -> torch.Tensor:
gate_buf = torch.mm(input, gate_proj.t())
up_buf = torch.mm(input, up_proj.t())
intermediate = act_fn(gate_buf) * up_buf
Expand Down Expand Up @@ -95,4 +95,4 @@ def mlp_torch(input, gate_proj, up_proj, down_proj):

diff = torch.mean(torch.abs(output - t_output)) / torch.mean(torch.abs(t_output))
print("diff = ", diff)
assert diff < 0.001
assert diff < 0.001, f"MLP output mismatch: diff={diff:.4e} exceeds threshold 0.001"

@gemini-code-assist gemini-code-assist Bot Jun 5, 2026

Copy link
Copy Markdown
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

medium

The variable diff is a PyTorch Tensor (0-dimensional). Directly formatting a PyTorch tensor with float format specifiers like :.4e can raise a TypeError or produce unexpected formatting in older PyTorch versions. It is safer and more idiomatic to call .item() to retrieve the underlying Python float before formatting.

Suggested change
assert diff < 0.001, f"MLP output mismatch: diff={diff:.4e} exceeds threshold 0.001"
assert diff < 0.001, f"MLP output mismatch: diff={diff.item():.4e} exceeds threshold 0.001"