Triton kernel (#78)

* add triton kernel

* add softmax kernel

* fix bugs in triton kernel

* remove eviction_policy

* refactoring triton kernel for softmax

fastfold/model/fastnn/evoformer.py

@@ -15,8 +15,8 @@ class Evoformer(nn.Module):
 
     def forward(self, node, pair, node_mask, pair_mask):
         node = self.msa_stack(node, pair, node_mask)
-        pair = pair + self.communication(node, node_mask)
+        pair = self.communication(node, node_mask, pair)
         node, work = All_to_All_Async.apply(node, 1, 2)
         pair = self.pair_stack(pair, pair_mask)
         node = All_to_All_Async_Opp.apply(node, work, 1, 2)
-        return node, pair
+        return node, pair
\ No newline at end of file

fastfold/model/fastnn/kernel/__init__.py

 from .jit.fused_ops import bias_dropout_add, bias_sigmod_ele, bias_ele_dropout_residual
-from .cuda_native.layer_norm import MixedFusedLayerNorm as LayerNorm
-from .cuda_native.softmax import softmax, mask_softmax, mask_bias_softmax
+from .layer_norm import FusedLayerNorm as LayerNorm
+from .softmax import fused_softmax
 
 __all__ = [
-    "bias_dropout_add", "bias_sigmod_ele", "bias_ele_dropout_residual", "LayerNorm", "softmax",
-    "mask_softmax", "mask_bias_softmax"
+    "bias_dropout_add",
+    "bias_sigmod_ele",
+    "bias_ele_dropout_residual",
+    "LayerNorm",
+    "fused_softmax",
 ]
\ No newline at end of file

fastfold/model/fastnn/kernel/cuda_native/layer_norm.py

 import importlib
-import numbers
 
 import torch
-from torch.nn import init
-from torch.nn.parameter import Parameter
 
-global fastfold_layer_norm_cuda
-fastfold_layer_norm_cuda = None
+fastfold_layer_norm_cuda = importlib.import_module("fastfold_layer_norm_cuda")
 
 
 class FusedLayerNormAffineFunction(torch.autograd.Function):
@@ -37,34 +33,3 @@ class FusedLayerNormAffineFunction(torch.autograd.Function):
                 weight_, bias_, ctx.eps)
 
         return grad_input, grad_weight, grad_bias, None, None
-
-
-class MixedFusedLayerNorm(torch.nn.Module):
-
-    def __init__(self, normalized_shape, eps=1e-5):
-        super(MixedFusedLayerNorm, self).__init__()
-
-        global fastfold_layer_norm_cuda
-        if fastfold_layer_norm_cuda is None:
-            try:
-                fastfold_layer_norm_cuda = importlib.import_module("fastfold_layer_norm_cuda")
-            except ImportError:
-                raise RuntimeError('MixedFusedLayerNorm requires cuda extensions')
-
-        if isinstance(normalized_shape, numbers.Integral):
-            normalized_shape = (normalized_shape,)
-        self.normalized_shape = torch.Size(normalized_shape)
-        self.eps = eps
-        self.weight = Parameter(torch.Tensor(*normalized_shape))
-        self.bias = Parameter(torch.Tensor(*normalized_shape))
-        self.reset_parameters()
-
-    def reset_parameters(self):
-
-        init.ones_(self.weight)
-        init.zeros_(self.bias)
-
-    def forward(self, input):
-
-        return FusedLayerNormAffineFunction.apply(input, self.weight, self.bias,
-                                                  self.normalized_shape, self.eps)

fastfold/model/fastnn/kernel/cuda_native/softmax.py

 import importlib
-from functools import reduce
-from operator import mul
-
-import torch
 
 fastfold_softmax_cuda = importlib.import_module("fastfold_softmax_cuda")
 
 
-class SoftmaxAffineFunction(torch.autograd.Function):
-
-    @staticmethod
-    def forward(ctx, input):
-        input_ = input.contiguous()
-        ctx.cols = input_.shape[-1]
-        ctx.rows = reduce(mul, input.shape[:-1])
-        output = fastfold_softmax_cuda.forward(input_, ctx.rows, ctx.cols)
-        ctx.save_for_backward(output)
-
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-
-        output = ctx.saved_tensors[0]
-
-        grad_input = None
-        grad_input = fastfold_softmax_cuda.backward(grad_output.contiguous(), output,
-                                                           ctx.rows, ctx.cols)
-
-        return grad_input
-
-
-class FusedMaskSoftmaxFunction(torch.autograd.Function):
-
-    @staticmethod
-    def forward(ctx, input, mask):
-        input_ = input.contiguous()
-        mask_ = mask.contiguous()
-        ctx.cols = input_.shape[-1]
-        ctx.rows = reduce(mul, input.shape[:-1])
-        output = fastfold_softmax_cuda.fused_mask_softmax_forward(
-            input_, mask_, ctx.rows, ctx.cols)
-        ctx.save_for_backward(output, mask_)
-
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-
-        output, mask_ = ctx.saved_tensors
-
-        grad_input = None
-        grad_input = fastfold_softmax_cuda.fused_mask_softmax_backward(
-            grad_output.contiguous(), output, mask_, ctx.rows, ctx.cols)
-
-        return grad_input.contiguous(), None
-
-
-class FusedMaskBiasSoftmaxFunction(torch.autograd.Function):
-
-    @staticmethod
-    def forward(ctx, input, mask, bias):
-        input_ = input.contiguous()
-        mask_ = mask.contiguous()
-        bias_ = bias.contiguous()
-        ctx.cols = input_.shape[-1]
-        ctx.rows = reduce(mul, input.shape[:-1])
-        output = fastfold_softmax_cuda.fused_mask_bias_softmax_forward(
-            input_, mask_, bias_, ctx.rows, ctx.cols)
-        ctx.save_for_backward(output, mask_, bias_)
-
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-
-        output, mask_, bias_ = ctx.saved_tensors
-
-        grad_input = None
-        grad_input = fastfold_softmax_cuda.fused_mask_bias_softmax_backward(
-            grad_output.contiguous(), output, mask_, bias_, ctx.rows, ctx.cols)
-
-        grad_input = grad_input.contiguous()
-
-        grad_bias = torch.sum(grad_input, dim=1, keepdim=True)
+def softmax_cuda_kernel_wrapper(input_, mask_, bias_, rows, cols):
+    if bias_ is not None:
+        output = fastfold_softmax_cuda.fused_mask_bias_softmax_forward(input_, mask_, bias_, rows, cols)
+    elif mask_ is not None:
+        output = fastfold_softmax_cuda.fused_mask_softmax_forward(input_, mask_, rows, cols)
+    else:
+        output = fastfold_softmax_cuda.forward(input_, rows, cols)
 
-        return grad_input.contiguous(), None, grad_bias
+    return output
 
 
-softmax = SoftmaxAffineFunction.apply
-mask_softmax = FusedMaskSoftmaxFunction.apply
-mask_bias_softmax = FusedMaskBiasSoftmaxFunction.apply
+def softmax_grad_cuda_kernel_wrapper(grad_output, output, mask_, rows, cols):
+    if mask_ is not None:
+        grad_input = fastfold_softmax_cuda.fused_mask_softmax_backward(grad_output, output, mask_, rows, cols)
+    else:
+        grad_input = fastfold_softmax_cuda.backward(grad_output, output, rows, cols)
+    return grad_input

fastfold/model/fastnn/kernel/layer_norm.py

+import numbers
+import logging
+
+import torch
+from torch.nn.parameter import Parameter
+
+_triton_available = True
+if _triton_available:
+    try:
+        from .triton.layer_norm import LayerNormTritonFunc
+
+    except ImportError:
+        logging.warning("Triton is not available, fallback to old kernel.")
+        _triton_available = False
+
+from .cuda_native.layer_norm import FusedLayerNormAffineFunction
+
+
+class FusedLayerNorm(torch.nn.Module):
+
+    def __init__(self, normalized_shape, eps=1e-5):
+        super(FusedLayerNorm, self).__init__()
+
+        if isinstance(normalized_shape, numbers.Integral):
+            normalized_shape = (normalized_shape,)
+        self.normalized_shape = torch.Size(normalized_shape)
+        self.eps = eps
+        self.weight = Parameter(torch.Tensor(*normalized_shape))
+        self.bias = Parameter(torch.Tensor(*normalized_shape))
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        torch.nn.init.ones_(self.weight)
+        torch.nn.init.zeros_(self.bias)
+
+    def forward(self, input):
+        if _triton_available:
+            return LayerNormTritonFunc.apply(input, self.normalized_shape, self.weight, self.bias,
+                                             self.eps)
+        else:
+            return FusedLayerNormAffineFunction.apply(input, self.weight, self.bias,
+                                                      self.normalized_shape, self.eps)

fastfold/model/fastnn/kernel/softmax.py

+from functools import reduce
+from operator import mul
+import logging
+
+import torch
+
+_triton_available = True
+if _triton_available:
+    try:
+        from .triton.softmax import softmax_triton_kernel_wrapper
+        from .triton.softmax import softmax_grad_triton_kernel_wrapper
+
+    except ImportError:
+        logging.warning("Triton is not available, fallback to old kernel.")
+        _triton_available = False
+
+from .cuda_native.softmax import softmax_cuda_kernel_wrapper
+from .cuda_native.softmax import softmax_grad_cuda_kernel_wrapper
+
+
+class FusedSoftmaxFunc(torch.autograd.Function):
+
+    @staticmethod
+    def forward(ctx, input, mask=None, bias=None):
+        input_ = input.contiguous()
+        mask_, bias_ = None, None
+        ctx.use_bias = False
+        if mask is not None:
+            mask_ = mask.contiguous()
+        if bias is not None:
+            bias_ = bias.contiguous()
+            ctx.use_bias = True
+        ctx.cols = input_.shape[-1]
+        ctx.rows = reduce(mul, input.shape[:-1])
+        if _triton_available:
+            output = softmax_triton_kernel_wrapper(input_, mask_, bias_, ctx.rows, ctx.cols)
+        else:
+            output = softmax_cuda_kernel_wrapper(input_, mask_, bias_, ctx.rows, ctx.cols)
+        ctx.save_for_backward(output, mask_)
+
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        grad_output = grad_output.contiguous()
+        output, mask_ = ctx.saved_tensors
+        if _triton_available:
+            grad_input = softmax_grad_triton_kernel_wrapper(grad_output, output, mask_, ctx.rows,
+                                                            ctx.cols)
+        else:
+            grad_input = softmax_grad_cuda_kernel_wrapper(grad_output, output, mask_, ctx.rows,
+                                                          ctx.cols)
+        grad_bias = None
+        if ctx.use_bias:
+            grad_bias = torch.sum(grad_input, dim=1, keepdim=True)
+
+        return grad_input, None, grad_bias
+
+
+fused_softmax = FusedSoftmaxFunc.apply
\ No newline at end of file

fastfold/model/fastnn/kernel/triton/layer_norm.py

+import torch
+
+import triton
+import triton.language as tl
+
+
+@triton.jit
+def _layer_norm_fwd_fused(
+    Out,
+    A,
+    Weight,
+    Bias,
+    Mean,
+    Rstd,
+    stride,
+    N,
+    eps,
+    BLOCK_SIZE: tl.constexpr,
+):
+    # position of elements processed by this program
+    row = tl.program_id(0)
+    Out += row * stride
+    A += row * stride
+    # compute mean
+    mean = 0
+    _mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
+    for off in range(0, N, BLOCK_SIZE):
+        cols = off + tl.arange(0, BLOCK_SIZE)
+        a = tl.load(A + cols, mask=cols < N, other=0.,).to(tl.float32)
+        _mean += a
+    mean = tl.sum(_mean, axis=0) / N
+    # compute variance
+    _var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
+    for off in range(0, N, BLOCK_SIZE):
+        cols = off + tl.arange(0, BLOCK_SIZE)
+        a = tl.load(A + cols, mask=cols < N, other=0.).to(tl.float32)
+        a = tl.where(cols < N, a - mean, 0.)
+        _var += a * a
+    var = tl.sum(_var, axis=0) / N
+    rstd = 1 / tl.sqrt(var + eps)
+    # write-back mean/rstd
+    tl.store(Mean + row, mean)
+    tl.store(Rstd + row, rstd)
+    # multiply by weight and add bias
+    for off in range(0, N, BLOCK_SIZE):
+        cols = off + tl.arange(0, BLOCK_SIZE)
+        mask = cols < N
+        weight = tl.load(Weight + cols, mask=mask)
+        bias = tl.load(Bias + cols, mask=mask)
+        a = tl.load(A + cols, mask=mask, other=0.).to(tl.float32)
+        a_hat = (a - mean) * rstd
+        out = a_hat * weight + bias
+        # # write-back
+        tl.store(Out + cols, out, mask=mask)
+
+
+# Backward pass (DA + partial DW + partial DB)
+@triton.jit
+def _layer_norm_bwd_dx_fused(
+    _DA,
+    _DOut,
+    _A,
+    Weight,
+    Mean,
+    Rstd,
+    stride,
+    NumRows,
+    NumCols,
+    eps,
+    BLOCK_SIZE_N: tl.constexpr,
+):
+    # position of elements processed by this program
+    pid = tl.program_id(0)
+    row = pid
+    A = _A + row * stride
+    DOut = _DOut + row * stride
+    DA = _DA + row * stride
+    mean = tl.load(Mean + row)
+    rstd = tl.load(Rstd + row)
+    # load data to SRAM
+    _mean1 = tl.zeros([BLOCK_SIZE_N], dtype=tl.float32)
+    _mean2 = tl.zeros([BLOCK_SIZE_N], dtype=tl.float32)
+    for off in range(0, NumCols, BLOCK_SIZE_N):
+        cols = off + tl.arange(0, BLOCK_SIZE_N)
+        mask = cols < NumCols
+        a = tl.load(A + cols, mask=mask, other=0).to(tl.float32)
+        dout = tl.load(DOut + cols, mask=mask, other=0).to(tl.float32)
+        weight = tl.load(Weight + cols, mask=mask, other=0).to(tl.float32)
+        a_hat = (a - mean) * rstd
+        wdout = weight * dout
+        _mean1 += a_hat * wdout
+        _mean2 += wdout
+    mean1 = tl.sum(_mean1, axis=0) / NumCols
+    mean2 = 0.
+    mean2 = tl.sum(_mean2, axis=0) / NumCols
+    for off in range(0, NumCols, BLOCK_SIZE_N):
+        cols = off + tl.arange(0, BLOCK_SIZE_N)
+        mask = cols < NumCols
+        a = tl.load(A + cols, mask=mask, other=0).to(tl.float32)
+        dout = tl.load(DOut + cols, mask=mask, other=0).to(tl.float32)
+        weight = tl.load(Weight + cols, mask=mask, other=0).to(tl.float32)
+        a_hat = (a - mean) * rstd
+        wdout = weight * dout
+        da = (wdout - (a_hat * mean1 + mean2)) * rstd
+        # write-back dx
+        tl.store(DA + cols, da, mask=mask)
+
+
+# Backward pass (total DW + total DB)
+@triton.jit
+def _layer_norm_bwd_dwdb(
+    A,
+    DOut,
+    Mean,
+    Var,
+    DW,
+    DB,
+    M,
+    N,
+    BLOCK_SIZE_M: tl.constexpr,
+    BLOCK_SIZE_N: tl.constexpr,
+):
+    pid = tl.program_id(0)
+    cols = pid * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    dw = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+    db = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+    UNROLL: tl.constexpr = 4
+    for i in range(0, M, BLOCK_SIZE_M * UNROLL):
+        for j in range(UNROLL):
+            rows = i + j * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+            mask = (rows[:, None] < M) & (cols[None, :] < N)
+            offs = rows[:, None] * N + cols[None, :]
+            a = tl.load(A + offs, mask=mask, other=0.).to(tl.float32)
+            dout = tl.load(DOut + offs, mask=mask, other=0.).to(tl.float32)
+            mean = tl.load(Mean + rows, mask=rows < M, other=0.)
+            rstd = tl.load(Var + rows, mask=rows < M, other=0.)
+            a_hat = (a - mean[:, None]) * rstd[:, None]
+            dw += dout * a_hat
+            db += dout
+    sum_dw = tl.sum(dw, axis=0)
+    sum_db = tl.sum(db, axis=0)
+    tl.store(DW + cols, sum_dw, mask=cols < N)
+    tl.store(DB + cols, sum_db, mask=cols < N)
+
+
+class LayerNormTritonFunc(torch.autograd.Function):
+
+    def forward(ctx, a_raw, normalized_shape, weight, bias, eps):
+        # allocate output
+        a = a_raw.contiguous()
+        out = torch.empty_like(a)
+        # reshape input data into 2D tensor
+        a_arg = a.reshape(-1, a.shape[-1])
+        M, N = a_arg.shape
+        mean = torch.empty((M,), dtype=torch.float32, device="cuda")
+        rstd = torch.empty((M,), dtype=torch.float32, device="cuda")
+        # Less than 64KB per feature: enqueue fused kernel
+        MAX_FUSED_SIZE = 65536 // a.element_size()
+        BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))
+        BLOCK_SIZE = max(BLOCK_SIZE, 128)
+        BLOCK_SIZE = min(BLOCK_SIZE, 4096)
+        # heuristics for number of warps
+        num_warps = min(max(BLOCK_SIZE // 256, 1), 8)
+        _layer_norm_fwd_fused[(M,)](
+            out,
+            a_arg,
+            weight,
+            bias,
+            mean,
+            rstd,
+            a_arg.stride(0),
+            N,
+            eps,
+            BLOCK_SIZE=BLOCK_SIZE,
+            num_warps=num_warps,
+        )
+        ctx.save_for_backward(
+            a,
+            weight,
+            bias,
+            mean,
+            rstd,
+        )
+        ctx.BLOCK_SIZE = BLOCK_SIZE
+        ctx.num_warps = num_warps
+        ctx.eps = eps
+        return out
+
+    @staticmethod
+    def backward(ctx, dout):
+        assert dout.is_contiguous()
+        a, weight, bias, mean, var = ctx.saved_tensors
+        # heuristics for amount of parallel reduction stream for DG/DB
+        N = weight.shape[0]
+        # allocate output
+        da = torch.empty_like(dout)
+        # enqueue kernel using forward pass heuristics
+        # also compute partial sums for DW and DB
+        x_arg = a.reshape(-1, a.shape[-1])
+        M, N = x_arg.shape
+        dweight = torch.empty((weight.shape[0],), dtype=weight.dtype, device=weight.device)
+        dbias = torch.empty((weight.shape[0],), dtype=weight.dtype, device=weight.device)
+        _layer_norm_bwd_dx_fused[(M,)](
+            da,
+            dout,
+            a,
+            weight,
+            mean,
+            var,
+            x_arg.stride(0),
+            M,
+            N,
+            ctx.eps,
+            BLOCK_SIZE_N=ctx.BLOCK_SIZE,
+            num_warps=ctx.num_warps,
+        )
+        if N > 10240:
+            BLOCK_SIZE_N = 128
+            BLOCK_SIZE_M = 32
+            num_warps = 4
+        if N > 384:
+            BLOCK_SIZE_N = 16
+            BLOCK_SIZE_M = 16
+            num_warps = 8
+        else:
+            # maximize occupancy for small N
+            BLOCK_SIZE_N = 4
+            BLOCK_SIZE_M = 256
+            num_warps = 8
+        grid = lambda meta: [triton.cdiv(N, meta["BLOCK_SIZE_N"])]
+        _layer_norm_bwd_dwdb[grid](a,
+                                   dout,
+                                   mean,
+                                   var,
+                                   dweight,
+                                   dbias,
+                                   M,
+                                   N,
+                                   BLOCK_SIZE_M=BLOCK_SIZE_M,
+                                   BLOCK_SIZE_N=BLOCK_SIZE_N,
+                                   num_warps=num_warps)
+        return (da, None, dweight, dbias, None)

fastfold/model/fastnn/kernel/triton/softmax.py

+import torch
+
+import triton
+import triton.language as tl
+
+
+@triton.jit
+def _softmax_core(input_ptrs, output_ptrs, mask_ptrs, bias_ptrs, col_offsets, n_cols,
+                  use_mask: tl.constexpr, use_bias: tl.constexpr):
+
+    row = tl.load(input_ptrs, mask=col_offsets < n_cols, other=-float('inf')).to(tl.float32)
+
+    if use_bias:
+        bias = tl.load(bias_ptrs, mask=col_offsets < n_cols, other=float("-inf")).to(tl.float32)
+        row += bias
+
+    if use_mask:
+        mask = tl.load(mask_ptrs, mask=col_offsets < n_cols, other=float("-inf")).to(tl.float32)
+        row = tl.where(mask == 0, float("-1e20"), row)
+
+    row_minus_max = row - tl.max(row, axis=0)
+    numerator = tl.exp(row_minus_max)
+    denominator = tl.sum(numerator, axis=0)
+    softmax_output = numerator / denominator
+
+    tl.store(output_ptrs, softmax_output, mask=col_offsets < n_cols)
+
+
+@triton.jit
+def _softmax_grad_core(output_ptrs, d_output_ptrs, d_input_ptrs, mask_ptrs, col_offsets, n_cols,
+                       is_bf16: tl.constexpr, use_mask: tl.constexpr):
+    output_row = tl.load(output_ptrs, mask=col_offsets < n_cols, other=float("-inf"))
+    d_output_row = tl.load(d_output_ptrs, mask=col_offsets < n_cols, other=float("-inf"))
+
+    if is_bf16:
+        output_row = output_row.to(tl.float32)
+        d_output_row = d_output_row.to(tl.float32)
+
+    row_sum = tl.sum(output_row * d_output_row, axis=0)
+    d_softmax_output = (d_output_row - row_sum) * output_row
+
+    if use_mask:
+        mask = tl.load(mask_ptrs, mask=col_offsets < n_cols, other=float("-inf")).to(tl.float32)
+        d_softmax_output = tl.where(mask == 0, float(0), d_softmax_output)
+
+    tl.store(d_input_ptrs, d_softmax_output, mask=col_offsets < n_cols)
+
+
+@triton.jit
+def softmax_mask_bias_kernel(output_ptr, input_ptr, mask_ptr, bias_ptr, input_row_stride,
+                             output_row_stride, n_cols, n_heads, BLOCK_SIZE: tl.constexpr,
+                             use_mask: tl.constexpr, use_bias: tl.constexpr):
+    row_idx = tl.program_id(0)
+    col_offsets = tl.arange(0, BLOCK_SIZE)
+
+    input_row_ptr = input_ptr + row_idx * input_row_stride
+    output_row_ptr = output_ptr + row_idx * output_row_stride
+
+    input_ptrs = input_row_ptr + col_offsets
+    output_ptrs = output_row_ptr + col_offsets
+
+    mask_ptrs = input_ptrs  # place holder, not use if use_mask == False
+    if use_mask:
+        mask_row_ptr = mask_ptr + (row_idx // (n_heads * n_cols)) * n_cols
+        mask_ptrs = mask_row_ptr + col_offsets
+
+    bias_ptrs = input_ptrs  # place holder, not use if use_bias == False
+    if use_bias:
+        bias_row_ptr = bias_ptr + (row_idx % (n_heads * n_cols)) * n_cols
+        bias_ptrs = bias_row_ptr + col_offsets
+
+    _softmax_core(input_ptrs, output_ptrs, mask_ptrs, bias_ptrs, col_offsets, n_cols, use_mask,
+                  use_bias)
+
+
+@triton.jit
+def softmax_mask_bias_kernel_two_rows(output_ptr, input_ptr, mask_ptr, bias_ptr, input_row_stride,
+                                      output_row_stride, n_cols, n_heads, BLOCK_SIZE: tl.constexpr,
+                                      use_mask: tl.constexpr, use_bias: tl.constexpr):
+    row_idx = tl.program_id(0)
+    col_offsets = tl.arange(0, BLOCK_SIZE)
+
+    input_row_ptr = input_ptr + 2 * row_idx * input_row_stride
+    output_row_ptr = output_ptr + 2 * row_idx * output_row_stride
+
+    input_ptrs = input_row_ptr + col_offsets
+    output_ptrs = output_row_ptr + col_offsets
+
+    mask_ptrs = input_ptrs  # place holder, not use if use_mask == False
+    if use_mask:
+        mask_row_ptr = mask_ptr + ((2 * row_idx) // (n_heads * n_cols)) * n_cols
+        mask_ptrs = mask_row_ptr + col_offsets
+
+    bias_ptrs = input_ptrs  # place holder, not use if use_bias == False
+    if use_bias:
+        bias_row_ptr = bias_ptr + ((2 * row_idx) % (n_heads * n_cols)) * n_cols
+        bias_ptrs = bias_row_ptr + col_offsets
+
+    _softmax_core(input_ptrs, output_ptrs, mask_ptrs, bias_ptrs, col_offsets, n_cols, use_mask,
+                  use_bias)
+
+    mask_ptrs = input_ptrs  # place holder, not use if use_mask == False
+    if use_mask:
+        mask_row_ptr = mask_ptr + ((2 * row_idx + 1) // (n_heads * n_cols)) * n_cols
+        mask_ptrs = mask_row_ptr + col_offsets
+
+    bias_ptrs = input_ptrs  # place holder, not use if use_bias == False
+    if use_bias:
+        bias_row_ptr = bias_ptr + ((2 * row_idx + 1) % (n_heads * n_cols)) * n_cols
+        bias_ptrs = bias_row_ptr + col_offsets
+
+    _softmax_core(input_ptrs + n_cols, output_ptrs + n_cols, mask_ptrs, bias_ptrs, col_offsets,
+                  n_cols, use_mask, use_bias)
+
+
+@triton.jit
+def softmax_mask_grad_kernel(d_output_ptr, output_ptr, d_input_ptr, mask_ptr, d_output_row_stride,
+                             output_row_stride, d_input_row_stride, n_cols, n_heads,
+                             BLOCK_SIZE: tl.constexpr, is_bf16: tl.constexpr,
+                             use_mask: tl.constexpr):
+
+    row_idx = tl.program_id(0)
+    col_offsets = tl.arange(0, BLOCK_SIZE)
+
+    output_row_ptr = output_ptr + row_idx * output_row_stride
+    d_output_row_ptr = d_output_ptr + row_idx * d_output_row_stride
+    d_input_row_ptr = d_input_ptr + row_idx * d_input_row_stride
+
+    output_ptrs = output_row_ptr + col_offsets
+    d_output_ptrs = d_output_row_ptr + col_offsets
+    d_input_ptrs = d_input_row_ptr + col_offsets
+
+    mask_ptrs = output_ptrs  # place holder, not use if use_mask == False
+    if use_mask:
+        mask_row_ptr = mask_ptr + (row_idx // (n_heads * n_cols)) * n_cols
+        mask_ptrs = mask_row_ptr + col_offsets
+
+    _softmax_grad_core(output_ptrs, d_output_ptrs, d_input_ptrs, mask_ptrs, col_offsets, n_cols,
+                       is_bf16, use_mask)
+
+
+@triton.jit
+def softmax_mask_grad_kernel_two_rows(d_output_ptr, output_ptr, d_input_ptr, mask_ptr,
+                                      d_output_row_stride, output_row_stride, d_input_row_stride,
+                                      n_cols, n_heads, BLOCK_SIZE: tl.constexpr,
+                                      is_bf16: tl.constexpr, use_mask: tl.constexpr):
+
+    row_idx = tl.program_id(0)
+    col_offsets = tl.arange(0, BLOCK_SIZE)
+
+    output_row_ptr = output_ptr + 2 * row_idx * output_row_stride
+    d_output_row_ptr = d_output_ptr + 2 * row_idx * d_output_row_stride
+    d_input_row_ptr = d_input_ptr + 2 * row_idx * d_input_row_stride
+
+    output_ptrs = output_row_ptr + col_offsets
+    d_output_ptrs = d_output_row_ptr + col_offsets
+    d_input_ptrs = d_input_row_ptr + col_offsets
+
+    mask_ptrs = output_ptrs  # place holder, not use if use_mask == False
+    if use_mask:
+        mask_row_ptr = mask_ptr + ((2 * row_idx) // (n_heads * n_cols)) * n_cols
+        mask_ptrs = mask_row_ptr + col_offsets
+
+    _softmax_grad_core(output_ptrs, d_output_ptrs, d_input_ptrs, mask_ptrs, col_offsets, n_cols,
+                       is_bf16, use_mask)
+
+    mask_ptrs = output_ptrs  # place holder, not use if use_mask == False
+    if use_mask:
+        mask_row_ptr = mask_ptr + ((2 * row_idx + 1) // (n_heads * n_cols)) * n_cols
+        mask_ptrs = mask_row_ptr + col_offsets
+
+    _softmax_grad_core(output_ptrs + n_cols, d_output_ptrs + n_cols, d_input_ptrs + n_cols,
+                       mask_ptrs, col_offsets, n_cols, is_bf16, use_mask)
+
+
+def softmax_triton_kernel_wrapper(x, mask, bias, n_rows, n_cols):
+    y = torch.empty_like(x)
+    n_heads = x.shape[2]
+
+    num_warps = 1
+    BLOCK_SIZE = triton.next_power_of_2(n_cols)
+    if BLOCK_SIZE >= 1024:
+        num_warps = 4
+    if BLOCK_SIZE >= 2048:
+        num_warps = 8
+    if BLOCK_SIZE >= 4096:
+        num_warps = 16
+
+    _dispatch_kernel = softmax_mask_bias_kernel
+    _grid = (n_rows,)
+    if n_cols <= 128 and n_rows % 2 == 0:
+        _dispatch_kernel = softmax_mask_bias_kernel_two_rows
+        _grid = (n_rows // 2,)
+
+    _dispatch_kernel[_grid](
+        y,
+        x,
+        mask,
+        bias,
+        x.stride(-2),
+        y.stride(-2),
+        n_cols,
+        n_heads,
+        num_warps=num_warps,
+        BLOCK_SIZE=BLOCK_SIZE,
+        use_mask=(mask != None),
+        use_bias=(bias != None),
+    )
+    return y
+
+
+def softmax_grad_triton_kernel_wrapper(grad_output, output, mask, n_rows, n_cols):
+    grad_input = torch.empty_like(grad_output)
+    n_heads = output.shape[2]
+
+    num_warps = 1
+    BLOCK_SIZE = triton.next_power_of_2(n_cols)
+    if BLOCK_SIZE >= 1024:
+        num_warps = 4
+    if BLOCK_SIZE >= 2048:
+        num_warps = 8
+    if BLOCK_SIZE >= 4096:
+        num_warps = 16
+    is_bf16 = (output.dtype == torch.bfloat16)
+
+    _dispatch_kernel = softmax_mask_grad_kernel
+    _grid = (n_rows,)
+    if n_cols <= 128 and n_rows % 2 == 0:
+        _dispatch_kernel = softmax_mask_grad_kernel_two_rows
+        _grid = (n_rows // 2,)
+
+    _dispatch_kernel[_grid](
+        grad_output,
+        output,
+        grad_input,
+        mask,
+        grad_output.stride(-2),
+        output.stride(-2),
+        grad_output.stride(-2),
+        n_cols,
+        n_heads,
+        num_warps=num_warps,
+        BLOCK_SIZE=BLOCK_SIZE,
+        is_bf16=is_bf16,
+        use_mask=(mask != None),
+    )
+    return grad_input

fastfold/model/fastnn/msa.py

@@ -17,8 +17,7 @@ import math
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-# from fastfold.model.fastnn.kernel import LayerNorm
-from torch.nn import LayerNorm
+from fastfold.model.fastnn.kernel import LayerNorm
 
 from fastfold.model.fastnn.ops import ChunkMSARowAttentionWithPairBias, ChunkTransition, SelfAttention, GlobalAttention, Transition, ChunkMSAColumnGlobalAttention
 from fastfold.model.fastnn.kernel import bias_dropout_add

fastfold/model/fastnn/ops.py

@@ -18,8 +18,8 @@ import torch.nn.functional as F
 import math
 from einops import rearrange
 from typing import Tuple
-from fastfold.model.fastnn.kernel import mask_softmax, mask_bias_softmax
 from fastfold.model.fastnn.kernel import LayerNorm
+from fastfold.model.fastnn.kernel import fused_softmax
 
 from .initializer import glorot_uniform_af
 
@@ -140,6 +140,7 @@ class OutProductMean(nn.Module):
         self.n_feat_proj = n_feat_proj
 
     def forward(self, M, M_mask, Z_raw):
+        Z = torch.empty_like(Z_raw)
         M = self.layernormM(M)
         right_act = self.linear_b(M)
         right_act_all, work = gather_async(right_act, dim=2)
@@ -165,9 +166,9 @@ class OutProductMean(nn.Module):
             O = rearrange(O, 'b i j d e -> b i j (d e)')
             O = self.o_linear(O)
             norm0 = norm[:, ax:ax + chunk_size, :, :]
-            Z_raw[:, ax:ax + chunk_size, :, :] += O / norm0
+            Z[:, ax:ax + chunk_size, :, :] += O / norm0
 
-        return Z_raw
+        return Z
 
     def inplace(self, M, M_mask, Z_raw):
         
@@ -317,9 +318,9 @@ class SelfAttention(nn.Module):
             logits = torch.matmul(q, k.transpose(-1, -2))
 
             if nonbatched_bias is not None:
-                weights = mask_bias_softmax(logits, mask_part, bias.unsqueeze(1))
+                weights = fused_softmax(logits, mask_part, bias.unsqueeze(1))
             else:
-                weights = mask_softmax(logits, mask)
+                weights = fused_softmax(logits, mask_part)
 
             weighted_avg = torch.matmul(weights, v)
             weighted_avg = rearrange(weighted_avg, 'b1 b2 h n d -> b1 b2 n (h d)')
@@ -1168,7 +1169,7 @@ class GlobalAttention(nn.Module):
 
             logits = torch.matmul(q, k.transpose(-1, -2))
 
-            weights = mask_softmax(logits, mask_part)
+            weights = fused_softmax(logits, mask_part)
 
             weighted_avg = torch.matmul(weights, v)
             weighted_avg = rearrange(weighted_avg, "b1 b2 h d -> b1 b2 (h d)")