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[lint] Update lint (#16)

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* update lint

* update readme

* update ruff lint
This commit is contained in:
Lufang Chen
2025-09-11 13:18:33 +08:00
committed by GitHub
parent a89dce95aa
commit e9332a283d
28 changed files with 2406 additions and 1074 deletions
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wall_x/fusions/backend.py
+119 -90
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@@ -13,28 +13,29 @@ from typing import Tuple, Optional
import wallx_csrc as backend
def _allocate_asymmetric_dual_outputs(input_expert0: torch.Tensor,
input_expert1: torch.Tensor,
weight_expert0: torch.Tensor,
weight_expert1: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
def _allocate_asymmetric_dual_outputs(
input_expert0: torch.Tensor,
input_expert1: torch.Tensor,
weight_expert0: torch.Tensor,
weight_expert1: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Allocate output tensors for asymmetric dual expert GEMM operations.
This function handles the case where two experts may have different output
dimensions, which is common in heterogeneous MoE architectures.
Args:
input_expert0 (torch.Tensor): Expert 0 input tensor of shape [m0, k]
input_expert1 (torch.Tensor): Expert 1 input tensor of shape [m1, k]
weight_expert0 (torch.Tensor): Expert 0 weight tensor of shape [k, n0]
weight_expert1 (torch.Tensor): Expert 1 weight tensor of shape [k, n1]
Returns:
Tuple[torch.Tensor, torch.Tensor]: Pre-allocated output tensors
- output_expert0: Shape [m0, n0]
- output_expert1: Shape [m1, n1]
Raises:
AssertionError: If tensor dimensions are incompatible
"""
@@ -43,42 +44,50 @@ def _allocate_asymmetric_dual_outputs(input_expert0: torch.Tensor,
assert input_expert1.ndim == 2, "Expected 2D tensor for input_expert1"
assert weight_expert0.ndim == 2, "Expected 2D tensor for weight_expert0"
assert weight_expert1.ndim == 2, "Expected 2D tensor for weight_expert1"
# Verify dimension compatibility for matrix multiplication
assert input_expert0.size(1) == weight_expert0.size(0), \
f"Input expert0 K dimension {input_expert0.size(1)} != weight expert0 K dimension {weight_expert0.size(0)}"
assert input_expert1.size(1) == weight_expert1.size(0), \
f"Input expert1 K dimension {input_expert1.size(1)} != weight expert1 K dimension {weight_expert1.size(0)}"
assert input_expert0.size(1) == weight_expert0.size(
0
), f"Input expert0 K dimension {input_expert0.size(1)} != weight expert0 K dimension {weight_expert0.size(0)}"
assert input_expert1.size(1) == weight_expert1.size(
0
), f"Input expert1 K dimension {input_expert1.size(1)} != weight expert1 K dimension {weight_expert1.size(0)}"
# Calculate output shapes: [m, k] × [k, n] = [m, n]
m0, n0 = input_expert0.size(0), weight_expert0.size(1)
m1, n1 = input_expert1.size(0), weight_expert1.size(1)
# Allocate output tensors with matching device and dtype
output_expert0 = torch.empty(m0, n0, device=input_expert0.device, dtype=input_expert0.dtype)
output_expert1 = torch.empty(m1, n1, device=input_expert1.device, dtype=input_expert1.dtype)
output_expert0 = torch.empty(
m0, n0, device=input_expert0.device, dtype=input_expert0.dtype
)
output_expert1 = torch.empty(
m1, n1, device=input_expert1.device, dtype=input_expert1.dtype
)
return output_expert0, output_expert1
def asym_dual_gmm_separated(input_expert0: torch.Tensor,
input_expert1: torch.Tensor,
weight_expert0: torch.Tensor,
weight_expert1: torch.Tensor,
output_expert0: Optional[torch.Tensor] = None,
output_expert1: Optional[torch.Tensor] = None,
trans_a: bool = False,
trans_b: bool = False) -> Tuple[torch.Tensor, torch.Tensor]:
def asym_dual_gmm_separated(
input_expert0: torch.Tensor,
input_expert1: torch.Tensor,
weight_expert0: torch.Tensor,
weight_expert1: torch.Tensor,
output_expert0: Optional[torch.Tensor] = None,
output_expert1: Optional[torch.Tensor] = None,
trans_a: bool = False,
trans_b: bool = False,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Asymmetric dual expert grouped GEMM with separated inputs and outputs.
This is the recommended interface for maximum flexibility and performance when
dealing with two experts that may have different intermediate dimensions.
The operation is equivalent to:
output_expert0 = input_expert0 @ weight_expert0
output_expert1 = input_expert1 @ weight_expert1
But optimized as a single fused kernel call.
Args:
input_expert0 (torch.Tensor): Expert 0 input tensor of shape [m0, k]
input_expert1 (torch.Tensor): Expert 1 input tensor of shape [m1, k]
@@ -89,10 +98,10 @@ def asym_dual_gmm_separated(input_expert0: torch.Tensor,
output_expert1 (torch.Tensor, optional): Pre-allocated output for expert 1 [m1, n1]
trans_a (bool, optional): Whether to transpose input tensors. Defaults to False.
trans_b (bool, optional): Whether to transpose weight tensors. Defaults to False.
Returns:
Tuple[torch.Tensor, torch.Tensor]: Output tensors (output_expert0, output_expert1)
Example:
>>> # Two experts with different output dimensions
>>> input0 = torch.randn(512, 1024, device='cuda') # 512 tokens for expert 0
@@ -110,67 +119,77 @@ def asym_dual_gmm_separated(input_expert0: torch.Tensor,
output_expert0 = alloc_out0
if output_expert1 is None:
output_expert1 = alloc_out1
# Call optimized C++ backend kernel
backend.asym_dual_gmm(
input_expert0, input_expert1,
weight_expert0, weight_expert1,
output_expert0, output_expert1,
trans_a, trans_b
input_expert0,
input_expert1,
weight_expert0,
weight_expert1,
output_expert0,
output_expert1,
trans_a,
trans_b,
)
return output_expert0, output_expert1
def permute(input: torch.Tensor,
indices: torch.Tensor,
num_out_tokens: int,
workspace: torch.Tensor,
max_expanded_token_num: int) -> torch.Tensor:
def permute(
input: torch.Tensor,
indices: torch.Tensor,
num_out_tokens: int,
workspace: torch.Tensor,
max_expanded_token_num: int,
) -> torch.Tensor:
"""
Permute input tokens according to expert assignment indices for MoE routing.
This function reorders tokens based on their assigned experts to enable
efficient grouped processing. Used in the forward pass of MoE layers.
Args:
input (torch.Tensor): Input tokens to permute
indices (torch.Tensor): Expert assignment indices for each token
num_out_tokens (int): Number of output tokens after expansion
workspace (torch.Tensor): Temporary workspace tensor for intermediate computations
max_expanded_token_num (int): Maximum number of tokens after top-k expansion
Returns:
torch.Tensor: Permuted tokens grouped by expert assignment
Note:
This is typically used with top-k expert selection where each token
can be routed to multiple experts.
"""
return backend.permute(input, indices, num_out_tokens, workspace, max_expanded_token_num)
return backend.permute(
input, indices, num_out_tokens, workspace, max_expanded_token_num
)
def unpermute(input: torch.Tensor,
row_id_map: torch.Tensor,
prob: torch.Tensor,
max_tokens: int,
num_topK: int) -> torch.Tensor:
def unpermute(
input: torch.Tensor,
row_id_map: torch.Tensor,
prob: torch.Tensor,
max_tokens: int,
num_topK: int,
) -> torch.Tensor:
"""
Unpermute expert outputs back to original token order with probability weighting.
This function reverses the permutation applied in the forward pass and combines
outputs from multiple experts using their routing probabilities.
Args:
input (torch.Tensor): Permuted expert outputs to unpermute
row_id_map (torch.Tensor): Mapping from permuted positions to original positions
prob (torch.Tensor): Expert routing probabilities for weighted combination
max_tokens (int): Maximum number of tokens in the sequence
num_topK (int): Number of top experts selected per token
Returns:
torch.Tensor: Unpermuted tokens in original order with expert outputs combined
Note:
The output combines multiple expert predictions for each token using
the routing probabilities as weights.
@@ -178,57 +197,63 @@ def unpermute(input: torch.Tensor,
return backend.unpermute(input, row_id_map, prob, max_tokens, num_topK)
def unpermute_bwd(input_bwd: torch.Tensor,
input_fwd: torch.Tensor,
row_id_map: torch.Tensor,
prob: Optional[torch.Tensor]) -> torch.Tensor:
def unpermute_bwd(
input_bwd: torch.Tensor,
input_fwd: torch.Tensor,
row_id_map: torch.Tensor,
prob: Optional[torch.Tensor],
) -> torch.Tensor:
"""
Backward pass for unpermute operation with gradient flow.
This function handles the backward pass through the unpermute operation,
ensuring proper gradient flow for training MoE models.
Args:
input_bwd (torch.Tensor): Backward gradients from the next layer
input_fwd (torch.Tensor): Forward pass inputs (for gradient computation)
row_id_map (torch.Tensor): Row mapping used in forward unpermute
prob (torch.Tensor, optional): Expert probabilities. If None, uniform weights are used.
Returns:
torch.Tensor: Gradients with respect to the input of unpermute forward pass
Note:
If prob is None, uniform probabilities are assumed for gradient computation.
"""
# Handle case where probabilities are not provided
if prob is None:
prob = torch.ones([input_bwd.size(0), 1], dtype=torch.float32, device=input_bwd.device)
prob = torch.ones(
[input_bwd.size(0), 1], dtype=torch.float32, device=input_bwd.device
)
return backend.unpermute_bwd(input_bwd, input_fwd, row_id_map, prob)
def rope(q: torch.Tensor,
k: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
q_out: torch.Tensor,
k_out: torch.Tensor,
mrope_section_doubled: bool) -> None:
def rope(
q: torch.Tensor,
k: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
q_out: torch.Tensor,
k_out: torch.Tensor,
mrope_section_doubled: bool,
) -> None:
"""
Apply RoPE (Rotary Position Embedding) to query and key tensors.
Applies rotary position embeddings to query and key tensors using precomputed
cosine and sine values. Supports both standard RoPE and multi-dimensional RoPE (mRoPE).
Args:
q (torch.Tensor): Query tensor to apply RoPE to
k (torch.Tensor): Key tensor to apply RoPE to
k (torch.Tensor): Key tensor to apply RoPE to
cos (torch.Tensor): Precomputed cosine values for rotation
sin (torch.Tensor): Precomputed sine values for rotation
q_out (torch.Tensor): Output tensor for rotated queries (in-place operation supported)
k_out (torch.Tensor): Output tensor for rotated keys (in-place operation supported)
mrope_section_doubled (bool): Whether using multi-dimensional RoPE with doubled sections
Note:
This function performs in-place operations if q_out and k_out point to the same
memory as q and k respectively. The rotation is applied using the standard
@@ -237,21 +262,23 @@ def rope(q: torch.Tensor,
return backend.rope(q, k, cos, sin, q_out, k_out, mrope_section_doubled)
def rope_bwd(grad_q_out: torch.Tensor,
grad_k_out: torch.Tensor,
q: torch.Tensor,
k: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
grad_q: torch.Tensor,
grad_k: torch.Tensor,
mrope_section_doubled: bool) -> None:
def rope_bwd(
grad_q_out: torch.Tensor,
grad_k_out: torch.Tensor,
q: torch.Tensor,
k: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
grad_q: torch.Tensor,
grad_k: torch.Tensor,
mrope_section_doubled: bool,
) -> None:
"""
Backward pass for RoPE operation with gradient computation.
Computes gradients with respect to the input query and key tensors
for the RoPE operation used in transformer attention mechanisms.
Args:
grad_q_out (torch.Tensor): Gradient with respect to output queries
grad_k_out (torch.Tensor): Gradient with respect to output keys
@@ -262,9 +289,11 @@ def rope_bwd(grad_q_out: torch.Tensor,
grad_q (torch.Tensor): Output tensor for query gradients
grad_k (torch.Tensor): Output tensor for key gradients
mrope_section_doubled (bool): Whether using multi-dimensional RoPE configuration
Note:
This function computes the analytical gradient of the RoPE operation,
which involves the inverse rotation compared to the forward pass.
"""
return backend.rope_bwd(grad_q_out, grad_k_out, q, k, cos, sin, grad_q, grad_k, mrope_section_doubled)
return backend.rope_bwd(
grad_q_out, grad_k_out, q, k, cos, sin, grad_q, grad_k, mrope_section_doubled
)