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import torch
import torch.nn as nn
import math
from ._ops import ops
def matmul_persistent(
a: torch.Tensor, b: torch.Tensor, bias: torch.Tensor = None
) -> torch.Tensor:
"""
Persistent matrix multiplication with optional bias.
Args:
a: Input tensor of shape (M, K)
b: Input tensor of shape (K, N)
bias: Optional bias tensor of shape (N,)
Returns:
Output tensor of shape (M, N)
"""
assert a.shape[1] == b.shape[0], "Incompatible dimensions"
assert a.dtype == b.dtype, "Incompatible dtypes"
assert bias is None or bias.dim() == 1, "Bias must be 1D"
M, K = a.shape
K, N = b.shape
c = torch.empty((M, N), device=a.device, dtype=a.dtype)
ops.matmul_persistent(a, b, c, bias)
return c
def log_softmax(input: torch.Tensor, dim: int = -1) -> torch.Tensor:
"""
Compute log_softmax using custom CUDA kernel.
Args:
input: Input tensor
dim: Dimension along which to compute log_softmax (only -1 supported)
Returns:
Tensor with log_softmax applied
"""
if dim != -1 and dim != input.ndim - 1:
raise ValueError(
"This implementation only supports log_softmax along the last dimension"
)
output = torch.empty_like(input)
ops.log_softmax(input, output)
return output
def mean_dim(
input: torch.Tensor, dim: int, keepdim: bool = False, dtype: torch.dtype = None
) -> torch.Tensor:
"""
Compute mean along a single dimension.
Args:
input: Input tensor
dim: Single dimension along which to compute mean
keepdim: Whether to keep the reduced dimension
dtype: Output dtype
Returns:
Tensor with mean values along specified dimension
"""
assert input.is_cuda, "Input must be a CUDA tensor"
assert -input.ndim <= dim < input.ndim, f"Invalid dimension {dim}"
if dim < 0:
dim = dim + input.ndim
if dtype is None:
if input.dtype in [torch.int8, torch.int16, torch.int32, torch.int64]:
dtype = torch.float32
else:
dtype = input.dtype
if input.dtype != dtype:
input = input.to(dtype)
shape = list(input.shape)
if keepdim:
output_shape = shape.copy()
output_shape[dim] = 1
else:
output_shape = shape[:dim] + shape[dim + 1 :]
output = torch.empty(output_shape, dtype=dtype, device=input.device)
ops.mean_dim(input, output, dim)
return output
# Batch invariant mode functionality (if you still want the mode switching)
def mm_batch_invariant(a, b):
return matmul_persistent(a, b)
def addmm_batch_invariant(bias, a, b):
return matmul_persistent(a, b, bias=bias)
def _log_softmax_batch_invariant(input, dim, _half_to_float):
assert not _half_to_float, "not implemented"
return log_softmax(input, dim=dim)
def mean_batch_invariant(input, dim, keepdim=False, dtype: torch.dtype = None):
if len(dim) == 1:
return mean_dim(input, dim[0], keepdim=keepdim, dtype=dtype)
else:
# Multi-dimensional mean fallback
n_elems = 1
for d in dim:
n_elems *= input.shape[d]
return torch.sum(input, dim=dim, keepdim=keepdim, dtype=torch.float32) / n_elems
class BatchInvariantAttention(nn.Module):
"""
Batch invariant multi-head attention implementation.
Compatible with transformers library integration.
"""
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.max_position_embeddings = getattr(config, "max_position_embeddings", 2048)
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
)
# Linear projections
self.q_proj = nn.Linear(
self.hidden_size, self.num_heads * self.head_dim, bias=False
)
self.k_proj = nn.Linear(
self.hidden_size, self.num_heads * self.head_dim, bias=False
)
self.v_proj = nn.Linear(
self.hidden_size, self.num_heads * self.head_dim, bias=False
)
self.o_proj = nn.Linear(
self.num_heads * self.head_dim, self.hidden_size, bias=False
)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor = None,
position_ids: torch.Tensor = None,
past_key_value=None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: torch.Tensor = None,
**kwargs,
):
batch_size, seq_len, _ = hidden_states.size()
# Project to Q, K, V using batch invariant matrix multiplication
query_states = self._batch_invariant_linear(hidden_states, self.q_proj.weight)
key_states = self._batch_invariant_linear(hidden_states, self.k_proj.weight)
value_states = self._batch_invariant_linear(hidden_states, self.v_proj.weight)
# Reshape for multi-head attention
query_states = query_states.view(
batch_size, seq_len, self.num_heads, self.head_dim
).transpose(1, 2)
key_states = key_states.view(
batch_size, seq_len, self.num_heads, self.head_dim
).transpose(1, 2)
value_states = value_states.view(
batch_size, seq_len, self.num_heads, self.head_dim
).transpose(1, 2)
# Compute attention scores
attn_weights = torch.matmul(
query_states, key_states.transpose(2, 3)
) / math.sqrt(self.head_dim)
# Apply attention mask if provided
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
# Apply softmax using batch invariant log_softmax
attn_weights_log = log_softmax(attn_weights, dim=-1)
attn_weights = torch.exp(attn_weights_log)
# Apply attention to values
attn_output = torch.matmul(attn_weights, value_states)
# Reshape and apply output projection
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(batch_size, seq_len, self.hidden_size)
attn_output = self._batch_invariant_linear(attn_output, self.o_proj.weight)
outputs = (attn_output,)
if output_attentions:
outputs += (attn_weights,)
if use_cache:
outputs += (past_key_value,)
return outputs
def _batch_invariant_linear(
self, input_tensor: torch.Tensor, weight: torch.Tensor
) -> torch.Tensor:
"""Apply linear transformation using batch invariant matrix multiplication"""
original_shape = input_tensor.shape
input_2d = input_tensor.view(-1, original_shape[-1])
output_2d = matmul_persistent(input_2d, weight.t())
return output_2d.view(*original_shape[:-1], -1)
class BatchInvariantMLP(nn.Module):
"""
Batch invariant MLP implementation.
"""
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
self.act_fn = (
nn.SiLU()
) # or whatever activation function is specified in config
def forward(self, x: torch.Tensor) -> torch.Tensor:
# Use batch invariant matrix multiplication for projections
gate = self._batch_invariant_linear(x, self.gate_proj.weight)
up = self._batch_invariant_linear(x, self.up_proj.weight)
# Apply activation
intermediate = self.act_fn(gate) * up
# Down projection
output = self._batch_invariant_linear(intermediate, self.down_proj.weight)
return output
def _batch_invariant_linear(
self, input_tensor: torch.Tensor, weight: torch.Tensor
) -> torch.Tensor:
"""Apply linear transformation using batch invariant matrix multiplication"""
original_shape = input_tensor.shape
input_2d = input_tensor.view(-1, original_shape[-1])
output_2d = matmul_persistent(input_2d, weight.t())
return output_2d.view(*original_shape[:-1], -1)
class BatchInvariantRMSNorm(nn.Module):
"""
Batch invariant RMS normalization implementation.
"""
def __init__(self, hidden_size, eps=1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
# Compute mean square using batch invariant mean
variance = mean_dim(hidden_states.pow(2), dim=-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
# Export the layer classes
__all__ += ["BatchInvariantAttention", "BatchInvariantMLP", "BatchInvariantRMSNorm"] |