add liger kernels to gemma2

turbo_alignment/common/tf/liger_kernels/cross_entropy.py

@@ -0,0 +1,335 @@
+import torch
+import triton
+import triton.language as tl
+
+from torch.nn import CrossEntropyLoss
+
+
+@triton.jit
+def liger_cross_entropy_kernel(
+    X_ptr,
+    X_stride,
+    Y_ptr,
+    Y_stride,
+    loss_ptr,
+    loss_stride,
+    n_cols,
+    n_non_ignore,
+    ignore_index,
+    label_smoothing: tl.constexpr,
+    reduction: tl.constexpr,  # set it as constexpr since reduction is always known at compile time
+    BLOCK_SIZE: tl.constexpr,
+):
+    """
+    This kernel computes both cross entropy loss and the gradient of the input.
+    We only consider hard label + mean reduction for now. Please refer to https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html for the math.
+
+    Parameters:
+    X_ptr: Pointer to input tensor.
+    X_stride (int): The stride of the input tensor.
+    Y_ptr: Pointer to target tensor.
+    Y_stride (int): The stride of the target tensor.
+    loss_ptr: Pointer to tensor to store the loss.
+    loss_stride (int): The stride of the loss tensor.
+    n_cols (int): The number of columns in the input tensor.
+    n_non_ignore (int): The number of non-ignored elements in the batch.
+    ignore_index (int): The index to ignore in the target.
+    label_smoothing (float): The amount of smoothing when computing the loss, where 0.0 means no smoothing.
+    reduction (str): The string for the reduction to apply
+    BLOCK_SIZE (int): The block size for Triton operations.
+    """
+
+    # https://github.com/triton-lang/triton/issues/1058
+    # If B*T*V is too large, program_id * stride will overflow out of int32, so we convert to int64
+    program_id = tl.program_id(0).to(tl.int64)
+
+    # 1. Load Y_ptr first because if the target is ignore_index, we can return right away
+    Y_ptr += program_id * Y_stride
+    y = tl.load(Y_ptr)
+
+    # 2. locate the start index
+    X_ptr += program_id * X_stride
+
+    if y == ignore_index:
+        # set all X_ptr as 0
+        for i in range(0, n_cols, BLOCK_SIZE):
+            X_offsets = i + tl.arange(0, BLOCK_SIZE)
+            tl.store(X_ptr + X_offsets, 0.0, mask=X_offsets < n_cols)
+        return
+
+    loss_ptr += program_id * loss_stride
+
+    # Online softmax: 2 loads + 1 store (compared with 3 loads + 1 store for the safe softmax)
+    # Refer to Algorithm 3 in the paper: https://arxiv.org/pdf/1805.02867
+
+    # 3. [Online softmax] first pass: find max + sum
+    m = float("-inf")  # m is the max value. use the notation from the paper
+    d = 0.0  # d is the sum. use the notation from the paper
+    ori_X_y = tl.load(
+        X_ptr + y
+    )  # we need to store the original value of X_y for the loss calculation
+
+    # Label smoothing is a general case of normal cross entropy
+    # See the full derivation at https://github.com/linkedin/Liger-Kernel/pull/198#issue-2503665310
+    scaled_x_sum = 0.0
+    eps = label_smoothing / n_cols
+
+    for i in range(0, n_cols, BLOCK_SIZE):
+        X_offsets = i + tl.arange(0, BLOCK_SIZE)
+        X_block = tl.load(
+            X_ptr + X_offsets, mask=X_offsets < n_cols, other=float("-inf")
+        )
+        block_max = tl.max(X_block)
+        if label_smoothing > 0:
+            # scale X beforehand to avoid overflow
+            scaled_x_sum += tl.sum(tl.where(X_offsets < n_cols, -eps * X_block, 0.0))
+        m_new = tl.maximum(m, block_max)
+        d = d * tl.exp(m - m_new) + tl.sum(tl.exp(X_block - m_new))
+        m = m_new
+
+    # 4. [Online Softmax] Second pass: compute gradients
+    # For 'mean' reduction, gradients are normalized by number of non-ignored elements (N)
+    # dx_y = (softmax(x_y) - 1) / N
+    # dx_i = softmax(x_i) / N, i != y
+    # For label smoothing:
+    # dx_i = (softmax(x_y) - label_smoothing / V) / N, V = n_cols, i != y
+    # dx_y = (softmax(x_y) - label_smoothing / V - (1 - label_smoothing)) / N
+    #      = dx_i - (1 - label_smoothing) / N
+    #
+    # For 'sum' reduction, no normalization is applied:
+    # dx_y = softmax(x_y) - 1
+    # dx_i = softmax(x_i), for i ≠ y
+    # For label smoothing:
+    # dx_i = (softmax(x_y) - label_smoothing / V), V = n_cols, i != y
+    # dx_y = (softmax(x_y) - label_smoothing / V - (1 - label_smoothing))
+    #      = dx_i - (1 - label_smoothing)
+
+    for i in range(0, n_cols, BLOCK_SIZE):
+        X_offsets = i + tl.arange(0, BLOCK_SIZE)
+        X_block = tl.load(
+            X_ptr + X_offsets, mask=X_offsets < n_cols, other=float("-inf")
+        )
+        if reduction == "mean":
+            X_block = (tl.exp(X_block - m) / d - eps) / (n_non_ignore)
+        else:
+            X_block = tl.exp(X_block - m) / d - eps
+
+        tl.store(X_ptr + X_offsets, X_block, mask=X_offsets < n_cols)
+
+    # We need tl.debug_barrier() to ensure the new result of X_ptr is written as mentioned in
+    # https://github.com/triton-lang/triton/blob/ba42a5c68fd0505f8c42f4202d53be0f8d9a5fe0/python/triton/ops/cross_entropy.py#L34
+    tl.debug_barrier()
+
+    # 5. Calculate the loss
+
+    # loss = log (softmax(X_y)) = log ((e ^ (X_y - max(X)) / sum(e ^ (X - max(X))))
+    #      = (X_y - max(X)) - log(sum(e ^ (X - max(X))))
+    # sum(e ^ (X - max(X))) must >= 1 because the max term is e ^ 0 = 1
+    # So we can safely calculate log (softmax(X_y)) without overflow
+    loss = -(ori_X_y - m - tl.log(d))
+
+    # Orginal loss = H(q, p),  with label smoothing regularization = H(q', p) and (label_smoothing / V) = eps
+    # H(q', p) = (1 - label_smoothing) * H(q, p) + label_smoothing * H(u, p)
+    #          = (1 - label_smoothing) * H(q, p) + eps * sum(logsoftmax(x_i))
+    # By using m (global max of xi) and d (sum of e^(xi-m)), we can simplify as:
+    #          = (1 - label_smoothing) * H(q, p) + (-sum(x_i * eps) + label_smoothing * (m + logd))
+    # Refer to H(q', p) in section 7 of the paper: https://arxiv.org/pdf/1512.00567
+    # pytorch: https://github.com/pytorch/pytorch/blob/2981534f54d49fa3a9755c9b0855e7929c2527f0/aten/src/ATen/native/LossNLL.cpp#L516
+    # See full derivation at https://github.com/linkedin/Liger-Kernel/pull/198#issuecomment-2333753087
+    if label_smoothing > 0:
+        smooth_loss = scaled_x_sum + label_smoothing * (m + tl.log(d))
+        loss = loss * (1 - label_smoothing) + smooth_loss
+
+    # Normalize the loss by the number of non-ignored elements if reduction is "mean"
+    if reduction == "mean":
+        loss = loss / n_non_ignore
+
+    # 6. Specially handle the i==y case where `dx_y = (softmax(x_y) - (1 - label_smoothing) / N`
+    X_y = tl.load(X_ptr + y)
+    if reduction == "mean":
+        X_y += -(1 - label_smoothing) / (n_non_ignore)
+    else:
+        X_y += -(1 - label_smoothing)
+
+    tl.store(loss_ptr, loss)
+    tl.store(X_ptr + y, X_y)
+
+
+# The hard limit of TRITON_MAX_TENSOR_NUMEL is 1048576 https://github.com/triton-lang/triton/blob/ba42a5c68fd0505f8c42f4202d53be0f8d9a5fe0/python/triton/language/core.py#L19
+# However, setting limit as 65536 as in LayerNorm tutorial is faster because of less register spilling
+# The optimal maximum block size depends on your hardware, your kernel, and your dtype
+MAX_FUSED_SIZE = 65536 // 2  # the best size we found by manually tuning
+
+
+@triton.jit
+def element_mul_kernel(
+    X_ptr,
+    X_stride,
+    grad_output_ptr,
+    n_cols,
+    BLOCK_SIZE: tl.constexpr,
+):
+    """
+    This function multiplies each element of the tensor pointed by X_ptr with the value pointed by grad_output_ptr.
+    The multiplication is performed in-place on the tensor pointed by X_ptr.
+
+    Parameters:
+    X_ptr: Pointer to the input tensor.
+    X_stride (int): The stride of the input tensor.
+    grad_output_ptr: Pointer to the gradient output value.
+    n_cols (int): The number of columns in the input tensor.
+    BLOCK_SIZE (int): The block size for Triton operations.
+    """
+
+    # Get the program ID and convert it to int64 to avoid overflow
+    program_id = tl.program_id(0).to(tl.int64)
+
+    # Locate the start index
+    X_ptr += program_id * X_stride
+
+    # Load the gradient output value
+    grad_output = tl.load(grad_output_ptr)
+
+    # Perform the element-wise multiplication
+    for i in range(0, n_cols, BLOCK_SIZE):
+        X_offsets = i + tl.arange(0, BLOCK_SIZE)
+        X_block = tl.load(X_ptr + X_offsets, mask=X_offsets < n_cols)
+        tl.store(X_ptr + X_offsets, X_block * grad_output, mask=X_offsets < n_cols)
+
+
+def cross_entropy_forward(_input, target, ignore_index, label_smoothing, reduction):
+    BT, V = _input.shape
+    n_rows = BT
+
+    BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(V))
+
+    # unreduced loss
+    loss_1d = torch.zeros(n_rows, dtype=_input.dtype, device=_input.device)
+
+    n_non_ignore = (target != ignore_index).sum().item()
+
+    # ensure _input and target are contiguous in the last dimension
+    if _input.stride(-1) != 1:
+        _input = _input.contiguous()
+    if target.stride(-1) != 1:
+        target = target.contiguous()
+
+    # Here we use a trick to store X_ptr gradient in X_ptr so we can save memory
+    liger_cross_entropy_kernel[(n_rows,)](
+        X_ptr=_input,
+        X_stride=_input.stride(-2),
+        Y_ptr=target,
+        Y_stride=target.stride(-1),  # always 1
+        loss_ptr=loss_1d,
+        loss_stride=loss_1d.stride(-1),  # always 1
+        n_cols=V,
+        n_non_ignore=n_non_ignore,
+        ignore_index=ignore_index,
+        label_smoothing=label_smoothing,
+        reduction=reduction,
+        BLOCK_SIZE=BLOCK_SIZE,
+        # TODO: 32 seems to give the best performance
+        # Performance is quite sensitive to num_warps
+        num_warps=32,
+    )
+
+    loss = torch.sum(loss_1d)
+    return loss, _input
+
+
+def cross_entropy_backward(_input, grad_output):
+    # If cross entropy is the last layer, grad_output is 1.0. Skip the mul to save time
+    if torch.equal(grad_output, torch.tensor(1.0, device=grad_output.device)):
+        pass
+
+    # We use a Triton kernel instead of a PyTorch operation because modifying inputs in-place
+    # for gradient storage and backward multiple times causes anomalies with PyTorch but not with Triton.
+    else:
+        BT, V = _input.shape
+        n_rows = BT
+        BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(V))
+
+        element_mul_kernel[(n_rows,)](
+            _input,
+            _input.stride(-2),
+            grad_output,
+            V,
+            BLOCK_SIZE=BLOCK_SIZE,
+            num_warps=32,
+        )
+
+    return _input
+
+
+class LigerCrossEntropyFunction(torch.autograd.Function):
+    """
+    This class implements a custom autograd function for the Liger Cross Entropy loss.
+    It overrides the forward and backward methods of the torch.autograd.Function class.
+    """
+
+    @staticmethod
+    def forward(
+        ctx, _input, target, ignore_index=-100, label_smoothing=0.0, reduction="mean"
+    ):
+        """
+        The forward pass of the Liger Cross Entropy loss.
+
+        Parameters:
+        ctx : The context object.
+        _input (tensor): The input tensor of shape (BT, V) where B is batch size, T is sequence length, V is vocab size.
+        target (tensor): The target tensor of shape (BT) where each value is in [0, V-1].
+        ignore_index (int): The index to ignore in the target.
+        label_smoothing (float): The amount of smoothing when computing the loss, where 0.0 means no smoothing.
+        reduction (str): The reduction to apply to the output: "none" | "mean | "sum".
+
+        Returns:
+        tensor: The computed loss.
+        """
+        loss, _input = cross_entropy_forward(
+            _input, target, ignore_index, label_smoothing, reduction
+        )
+        # TODO: investigation
+        # If we don't detach the _input tensor, the memory will double
+        # Not sure why but seems that there will be a time both grad and value exist but in different location
+        ctx.save_for_backward(_input.detach())
+        return loss
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        """
+        The backward pass of the Liger Cross Entropy loss.
+
+        Parameters:
+        ctx : The context object with saved tensors.
+        grad_output (tensor): The tensor containing the gradient of the loss with respect to the output.
+
+        Returns:
+        tuple: A tuple with the gradients with respect to the inputs. The elements are tensors or None.
+        """
+        (_input,) = ctx.saved_tensors
+        _input = cross_entropy_backward(_input, grad_output)
+        return (
+            _input,
+            None,
+            None,
+            None,
+            None,
+        )
+
+
+class LigerCrossEntropyLoss(CrossEntropyLoss):
+    def __init__(self, *args, **kwargs):
+        super(LigerCrossEntropyLoss, self).__init__(*args, **kwargs)
+        assert (self.label_smoothing >= 0) and (
+            self.label_smoothing <= 1
+        ), f"label_smoothing must be between 0.0 and 1.0. Got: {self.label_smoothing}"
+        assert self.reduction in {
+            "mean",
+            "sum",
+            "none",
+        }, f"reduction must be one of 'mean', 'sum', or 'none'. Got: {self.reduction}"
+
+    def forward(self, _input, target):
+        return LigerCrossEntropyFunction.apply(
+            _input, target, self.ignore_index, self.label_smoothing, self.reduction
+        )