[Operator] scatter & gather

src/flag_gems/__init__.py

@@ -111,6 +111,10 @@ def enable(lib=aten_lib):
     lib.impl("log_softmax.int", log_softmax, "AutogradCUDA")
     lib.impl("outer", outer, "AutogradCUDA")
     lib.impl("cross_entropy_loss", cross_entropy_loss, "AutogradCUDA")
+    # lib.impl("scatter.src", scatter_src, "CUDA")
+    # lib.impl("scatter.reduce", scatter_reduce, "CUDA")
+    # lib.impl("gather", gather, "CUDA")
+    # lib.impl("gather.out", gather_out, "CUDA")
     lib.impl("isclose", isclose, "CUDA")
     lib.impl("allclose", allclose, "CUDA")
     lib.impl("flip", flip, "CUDA")

src/flag_gems/ops/__init__.py

@@ -27,6 +27,7 @@
 from .flip import flip
 from .full import full
 from .full_like import full_like
+from .gather import gather, gather_out
 from .ge import ge, ge_scalar
 from .gelu import gelu
 from .groupnorm import group_norm
@@ -71,6 +72,7 @@
 from .resolve_neg import resolve_neg
 from .rms_norm import rms_norm
 from .rsqrt import rsqrt
+from .scatter import scatter_reduce, scatter_src
 from .sigmoid import sigmoid
 from .silu import silu
 from .sin import sin
@@ -121,6 +123,8 @@
     "eq_scalar",
     "exp",
     "exponential_",
+    "gather",
+    "gather_out",
     "flip",
     "ones_like",
     "full_like",
@@ -168,6 +172,8 @@
     "reciprocal",
     "relu",
     "rsqrt",
+    "scatter_src",
+    "scatter_reduce",
     "sigmoid",
     "silu",
     "sin",

src/flag_gems/ops/gather.py

@@ -0,0 +1,130 @@
+import logging
+
+import torch
+import triton
+import triton.language as tl
+
+from ..utils import libentry, offset_calculator, restride_dim
+
+
+def cfggen():
+    block_m = [1, 2, 4, 8]
+    configs = [
+        triton.Config({"BLOCK_M": m, "BLOCK_N": 1024}, num_warps=4) for m in block_m
+    ]
+    return configs
+
+
+@libentry()
+@triton.autotune(configs=cfggen(), key=["M", "N"])
+@triton.jit
+def gather_kernel(
+    inp,
+    inp_offsets,
+    out,
+    index,
+    idx_offsets,
+    M,
+    N,
+    stride_dim,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+):
+    pid = tl.program_id(0)
+    rows_offsets = pid * BLOCK_M + tl.arange(0, BLOCK_M)[:, None]
+    rows_mask = rows_offsets < M
+
+    for off in range(0, N, BLOCK_N):
+        cols_offsets = off + tl.arange(0, BLOCK_N)[None, :]
+        cols_mask = cols_offsets < N
+
+        offsets = rows_offsets * N + cols_offsets
+        mask = rows_mask and cols_mask
+
+        inp_indices = tl.load(inp_offsets + offsets, mask=mask, other=0)
+        idx_indices = tl.load(idx_offsets + offsets, mask=mask, other=0)
+
+        cur_index = tl.load(index + idx_indices, mask=mask, other=0)
+        inp_indices += cur_index * stride_dim
+        cur_inp = tl.load(inp + inp_indices, mask=mask, other=0)
+
+        tl.store(out + idx_indices, cur_inp, mask=mask)
+
+
+def gather(inp, dim, index, sparse_grad=False):
+    logging.debug("GEMS GATHER")
+    assert (
+        inp.ndim == index.ndim
+    ), "self and index should all have the same number of dimensions"
+    assert (
+        ((0 <= index.size(i) and index.size(i) <= inp.size(i)) or i == dim)
+        for i in range(0, index.ndim)
+    ), "index.size(d) <= self.size(d) for all dimensions d != dim"
+    assert ((0 <= index) * (index < inp.size(dim))).equal(
+        torch.ones(tuple(index.shape), dtype=torch.bool, device=inp.device)
+    ), "0 <= index < self.size(dim)"
+    inp = inp.contiguous()
+    index = index.contiguous()
+    out = torch.empty_like(index, dtype=inp.dtype, device=inp.device)
+
+    inp_strided = restride_dim(inp, dim, index.shape)
+    # FIXME: Are there any other way to get the "flatten offset" of a tensor?
+    idx = torch.arange(0, index.numel(), device=inp.device).reshape(index.shape)
+    # Temporarily call offsetCalculator() outside the block(although it can actually proceed in parallel),
+    # because the triton jit.function cannot accept Tuple as input in version 2.2.0(in 3.0.0, it's available),
+    # and we do need **the whole stride[]** to accomplish this calculation!
+    # FIXME: If stride[] can be wholely passed to triton jit.function, we can do this calculation in the kernel
+    # so that the offset calculation can proceed in parallel
+    inp_offsets = offset_calculator(inp_strided, idx, inp.stride(), dim, isInp=True)
+    idx_offsets = offset_calculator(index, idx, index.stride(), dim, isInp=False)
+    N = list(index.shape)[index.ndim - 1]
+    M = index.numel() // N
+
+    grid = lambda meta: (triton.cdiv(M, meta["BLOCK_M"]),)
+    gather_kernel[grid](
+        inp, inp_offsets, out, index, idx_offsets, M, N, inp.stride(dim)
+    )
+    return out
+
+
+def gather_out(inp, dim, index, sparse_grad=False, out=None):
+    logging.debug("GEMS GATHER OUT")
+    assert (
+        inp.ndim == index.ndim and inp.ndim == out.ndim
+    ), "self, index and out (if it is a Tensor) should all have the same number of dimensions"
+    assert (
+        (0 <= index.size(i) and index.size(i) <= out.size(i))
+        for i in range(0, index.ndim)
+    ), "index.size(d) <= out.size(d) for all dimensions d"
+    assert (
+        ((0 <= index.size(i) and index.size(i) <= inp.size(i)) or i == dim)
+        for i in range(0, index.ndim)
+    ), "index.size(d) <= self.size(d) for all dimensions d != dim"
+    assert index.shape == out.shape, "out will have the same shape as index"
+    assert ((0 <= index) * (index < inp.size(dim))).equal(
+        torch.ones(tuple(index.shape), dtype=torch.bool, device=inp.device)
+    ), "0 <= index < self.size(dim)"
+    if out is None:
+        out = torch.empty_like(index, dtype=inp.dtype, device=inp.device)
+    inp = inp.contiguous()
+    index = index.contiguous()
+    out = out.contiguous()
+
+    inp_strided = restride_dim(inp, dim, index.shape)
+    # FIXME: Are there any other way to get the "flatten offset" of a tensor?
+    idx = torch.arange(0, index.numel(), device=inp.device).reshape(index.shape)
+    # Temporarily call offsetCalculator() outside the block(although it can actually proceed in parallel),
+    # because the triton jit.function cannot accept Tuple as input in version 2.2.0(in 3.0.0, it's available),
+    # and we do need **the whole stride[]** to accomplish this calculation!
+    # FIXME: If stride[] can be wholely passed to triton jit.function, we can do this calculation in the kernel
+    # so that the offset calculation can proceed in parallel
+    inp_offsets = offset_calculator(inp_strided, idx, inp.stride(), dim, isInp=True)
+    idx_offsets = offset_calculator(index, idx, index.stride(), dim, isInp=False)
+    N = list(index.shape)[index.ndim - 1]
+    M = index.numel() // N
+
+    grid = lambda meta: (triton.cdiv(M, meta["BLOCK_M"]),)
+    gather_kernel[grid](
+        inp, inp_offsets, out, index, idx_offsets, M, N, inp.stride(dim)
+    )
+    return out

src/flag_gems/ops/scatter.py

@@ -0,0 +1,216 @@
+import logging
+
+import torch
+import triton
+import triton.language as tl
+
+from ..utils import libentry, offset_calculator, restride_dim
+
+
+def cfggen():
+    block_m = [1, 2, 4, 8]
+    configs = [
+        triton.Config({"BLOCK_M": m, "BLOCK_N": 1024}, num_warps=4) for m in block_m
+    ]
+    return configs
+
+
+@libentry()
+@triton.autotune(configs=cfggen(), key=["M", "N"])
+@triton.jit
+def scatter_kernel(
+    inp_offsets,
+    src,
+    index,
+    idx_offsets,
+    out,
+    M,
+    N,
+    stride_dim,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+):
+    pid = tl.program_id(0)
+    rows_offsets = pid * BLOCK_M + tl.arange(0, BLOCK_M)[:, None]
+    rows_mask = rows_offsets < M
+
+    for off in range(0, N, BLOCK_N):
+        cols_offsets = off + tl.arange(0, BLOCK_N)[None, :]
+        cols_mask = cols_offsets < N
+
+        offsets = rows_offsets * N + cols_offsets
+        mask = rows_mask and cols_mask
+
+        inp_indices = tl.load(inp_offsets + offsets, mask=mask, other=0)
+        idx_indices = tl.load(idx_offsets + offsets, mask=mask, other=0)
+
+        cur_src = tl.load(src + idx_indices, mask=mask, other=0)
+        cur_index = tl.load(index + idx_indices, mask=mask, other=0)
+
+        inp_indices += cur_index * stride_dim
+        tl.store(out + inp_indices, cur_src, mask=mask)
+
+
+@libentry()
+@triton.autotune(configs=cfggen(), key=["M", "N"])
+@triton.jit
+def scatter_add_kernel(
+    inp,
+    inp_offsets,
+    src,
+    index,
+    idx_offsets,
+    out,
+    M,
+    N,
+    stride_dim,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+):
+    pid = tl.program_id(0)
+    rows_offsets = pid * BLOCK_M + tl.arange(0, BLOCK_M)[:, None]
+    rows_mask = rows_offsets < M
+
+    for off in range(0, N, BLOCK_N):
+        cols_offsets = off + tl.arange(0, BLOCK_N)[None, :]
+        cols_mask = cols_offsets < N
+
+        offsets = rows_offsets * N + cols_offsets
+        mask = rows_mask and cols_mask
+
+        inp_indices = tl.load(inp_offsets + offsets, mask=mask, other=0)
+        idx_indices = tl.load(idx_offsets + offsets, mask=mask, other=0)
+
+        cur_src = tl.load(src + idx_indices, mask=mask, other=0).to(tl.float32)
+        cur_index = tl.load(index + idx_indices, mask=mask, other=0)
+
+        inp_indices += cur_index * stride_dim
+        cur_inp = tl.load(inp + inp_indices, mask=mask, other=0).to(tl.float32)
+        res = cur_inp + cur_src
+        tl.store(out + inp_indices, res, mask=mask)
+
+
+@libentry()
+@triton.autotune(configs=cfggen(), key=["M", "N"])
+@triton.jit
+def scatter_mul_kernel(
+    inp,
+    inp_offsets,
+    src,
+    index,
+    idx_offsets,
+    out,
+    M,
+    N,
+    stride_dim,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+):
+    pid = tl.program_id(0)
+    rows_offsets = pid * BLOCK_M + tl.arange(0, BLOCK_M)[:, None]
+    rows_mask = rows_offsets < M
+
+    for off in range(0, N, BLOCK_N):
+        cols_offsets = off + tl.arange(0, BLOCK_N)[None, :]
+        cols_mask = cols_offsets < N
+
+        offsets = rows_offsets * N + cols_offsets
+        mask = rows_mask and cols_mask
+
+        inp_indices = tl.load(inp_offsets + offsets, mask=mask, other=0)
+        idx_indices = tl.load(idx_offsets + offsets, mask=mask, other=0)
+
+        cur_src = tl.load(src + idx_indices, mask=mask, other=0).to(tl.float32)
+        cur_index = tl.load(index + idx_indices, mask=mask, other=0)
+
+        inp_indices += cur_index * stride_dim
+        cur_inp = tl.load(inp + inp_indices, mask=mask, other=0).to(tl.float32)
+        res = cur_inp * cur_src
+        tl.store(out + inp_indices, res, mask=mask)
+
+
+def scatter(inp, dim, index, src, reduction=None):
+    assert (
+        inp.ndim == index.ndim and inp.ndim == src.ndim
+    ), "self, index and src (if it is a Tensor) should all have the same number of dimensions"
+    assert (
+        (0 <= index.size(i) and index.size(i) <= src.size(i))
+        for i in range(0, index.ndim)
+    ), "index.size(d) <= src.size(d) for all dimensions d"
+    assert (
+        ((0 <= index.size(i) and index.size(i) <= inp.size(i)) or i == dim)
+        for i in range(0, index.ndim)
+    ), "index.size(d) <= self.size(d) for all dimensions d != dim"
+    inp = inp.contiguous()
+    index = index.contiguous()
+    src = src.contiguous()
+    out = inp.clone()
+
+    src_strided = src.as_strided(index.shape, src.stride()).contiguous()
+    inp_strided = restride_dim(inp, dim, index.shape)
+    # FIXME: Are there any other way to get the "flatten offset" of a tensor?
+    idx = torch.arange(0, index.numel(), device=inp.device).reshape(index.shape)
+    # Temporarily call offsetCalculator() outside the block(although it can actually proceed in parallel),
+    # because the triton jit.function cannot accept Tuple as input in version 2.2.0(in 3.0.0, it's available),
+    # and we do need **the whole stride[]** to accomplish this calculation!
+    # FIXME: If stride[] can be wholely passed to triton jit.function, we can do this calculation in the kernel
+    # so that the offset calculation can proceed in parallel
+    inp_offsets = offset_calculator(inp_strided, idx, inp.stride(), dim, isInp=True)
+    idx_offsets = offset_calculator(index, idx, index.stride(), dim, isInp=False)
+    N = list(index.shape)[index.ndim - 1]
+    M = index.numel() // N
+
+    grid = lambda meta: (triton.cdiv(M, meta["BLOCK_M"]),)
+    if reduction is None:
+        scatter_kernel[grid](
+            inp_offsets,
+            src_strided,
+            index,
+            idx_offsets,
+            out,
+            M,
+            N,
+            inp.stride(dim),
+        )
+    elif reduction == "add":
+        scatter_add_kernel[grid](
+            inp,
+            inp_offsets,
+            src_strided,
+            index,
+            idx_offsets,
+            out,
+            M,
+            N,
+            inp.stride(dim),
+        )
+    elif reduction == "multiply":
+        scatter_mul_kernel[grid](
+            inp,
+            inp_offsets,
+            src_strided,
+            index,
+            idx_offsets,
+            out,
+            M,
+            N,
+            inp.stride(dim),
+        )
+    return out
+
+
+def scatter_src(inp, dim, index, src):
+    logging.debug("GEMS SCATTER SRC")
+    return scatter(inp, dim, index, src)
+
+
+def scatter_reduce(inp, dim, index, src, reduce):
+    logging.debug("GEMS SCATTER REDUCE")
+    # TODO: As is shown in PyTorch's document(torch.Tensor.scatter_reduce_),
+    # this function is still **in beta** and may change in the near future.
+    # So for now, we're just going to stick with the original "add" and "multiply" parameters.
+    # Maybe we can add reduction options like "sum", "prod", "mean", "amax" and "amin" in the future.
+    if reduce == "add":
+        return scatter(inp, dim, index, src, reduction="add")
+    elif reduce == "multiply":
+        return scatter(inp, dim, index, src, reduction="multiply")