Custom primitive + RoPE fat op

benchmarks/python/rope_bench.py

@@ -0,0 +1,35 @@
+# Copyright © 2023-2024 Apple Inc.
+
+import mlx.core as mx
+import mlx.nn as nn
+from time_utils import time_fn
+
+
+def time_rope():
+    rope = nn.RoPE(4096)
+
+    # vec
+    x = mx.random.uniform(shape=(1, 4096)).astype(mx.float16)
+    mx.eval(x)
+
+    def rope_vec(x):
+        for _ in range(32):
+            x = rope(x)
+        return x
+
+    time_fn(rope_vec, x)
+
+    # matrix
+    x = mx.random.uniform(shape=(1024, 4096)).astype(mx.float16)
+    mx.eval(x)
+
+    def rope_mat(x):
+        for _ in range(32):
+            x = rope(x)
+        return x
+
+    time_fn(rope_mat, x)
+
+
+if __name__ == "__main__":
+    time_rope()

mlx/CMakeLists.txt

@@ -3,9 +3,10 @@ target_sources(
   PRIVATE
   ${CMAKE_CURRENT_SOURCE_DIR}/allocator.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/array.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/compile.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/dtype.cpp
-  ${CMAKE_CURRENT_SOURCE_DIR}/compile.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/fast.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/fft.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/ops.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/graph_utils.cpp

mlx/backend/common/CMakeLists.txt

@@ -11,6 +11,7 @@ target_sources(
   ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/quantized.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/reduce.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/rope.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/scan.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/softmax.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/sort.cpp

mlx/backend/common/rope.cpp

@@ -0,0 +1,14 @@
+// Copyright © 2023-2024 Apple Inc.
+
+#include "mlx/fast.h"
+#include "mlx/primitives.h"
+
+namespace mlx::core::fast {
+
+void RoPE::eval_cpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  throw std::runtime_error("NYI");
+}
+
+} // namespace mlx::core::fast

mlx/backend/metal/CMakeLists.txt

@@ -32,6 +32,7 @@ target_sources(
   ${CMAKE_CURRENT_SOURCE_DIR}/metal.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/quantized.cpp
+  ${CMAKE_CURRENT_SOURCE_DIR}/rope.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/scan.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/softmax.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/sort.cpp

mlx/backend/metal/kernels/CMakeLists.txt

@@ -23,6 +23,7 @@ set(
   "quantized"
   "random"
   "reduce"
+  "rope"
   "scan"
   "softmax"
   "sort"

mlx/backend/metal/kernels/rope.metal

@@ -0,0 +1,68 @@
+// Copyright © 2023-2024 Apple Inc.
+
+#include <metal_math>
+
+#include "mlx/backend/metal/kernels/bf16.h"
+#include "mlx/backend/metal/kernels/utils.h"
+
+template <typename T, bool traditional>
+[[kernel]] void rope(
+    const device T *in [[buffer(0)]],
+    device T * out [[buffer(1)]],
+    constant const size_t strides[3],
+    constant const int& offset,
+    constant const float& base,
+    constant const float& scale,
+    uint3 pos [[thread_position_in_grid]],
+    uint3 grid [[threads_per_grid]]) {
+  // Compute the input and output indices
+  uint in_index_1, in_index_2;
+  uint out_index_1, out_index_2;
+  if (traditional) {
+    out_index_1 = 2 * (pos.x + grid.x * (pos.y + grid.y * pos.z));
+    out_index_2 = out_index_1 + 1;
+    in_index_1 = 2 * pos.x * strides[2] + pos.y * strides[1] + pos.z * strides[0];
+    in_index_2 = in_index_1 + strides[2];
+  } else {
+    out_index_1 = pos.x + 2*(grid.x * (pos.y + grid.y * pos.z));
+    out_index_2 = out_index_1 + grid.x;
+    in_index_1 = pos.x * strides[2] + pos.y * strides[1] + pos.z * strides[0];
+    in_index_2 = in_index_1 + grid.x * strides[2];
+  }
+
+  // Figure out L and d.
+  float L = scale * static_cast<float>(pos.y + offset);
+  float d = static_cast<float>(pos.x) / static_cast<float>(grid.x);
+
+  // Compute costheta, sintheta
+  float theta = L * metal::exp2(-d * base);
+  float costheta = metal::fast::cos(theta);
+  float sintheta = metal::fast::sin(theta);
+
+  // Read and write the output
+  float x1 = static_cast<float>(in[in_index_1]);
+  float x2 = static_cast<float>(in[in_index_2]);
+  float rx1 = x1 * costheta - x2 * sintheta;
+  float rx2 = x1 * sintheta + x2 * costheta;
+  out[out_index_1] = static_cast<T>(rx1);
+  out[out_index_2] = static_cast<T>(rx2);
+}
+
+#define instantiate_rope(name, type, traditional) \
+  template [[host_name("rope_" #name)]] \
+  [[kernel]] void rope<type, traditional>( \
+      const device type* in [[buffer(0)]], \
+      device type* out [[buffer(1)]], \
+    constant const size_t strides[3], \
+    constant const int& offset, \
+    constant const float& base, \
+    constant const float& scale, \
+    uint3 pos [[thread_position_in_grid]], \
+    uint3 grid [[threads_per_grid]]);
+
+instantiate_rope(traditional_float16, half, true)
+instantiate_rope(traditional_bfloat16, bfloat16_t, true)
+instantiate_rope(traditional_float32, float, true)
+instantiate_rope(float16, half, false)
+instantiate_rope(bfloat16, bfloat16_t, false)
+instantiate_rope(float32, float, false)

mlx/backend/metal/rope.cpp

@@ -0,0 +1,55 @@
+// Copyright © 2023-2024 Apple Inc.
+
+#include "mlx/backend/metal/utils.h"
+#include "mlx/fast.h"
+#include "mlx/primitives.h"
+
+namespace mlx::core::fast {
+
+void RoPE::eval_gpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  assert(inputs.size() == 1);
+  assert(outputs.size() == 1);
+  auto& in = inputs[0];
+  auto& out = outputs[0];
+
+  if (in.ndim() != 3) {
+    throw std::runtime_error(
+        "[RoPE] Only 3 dimensions are supported (batch x sequence x dims)");
+  }
+  if (dims_ != in.shape(-1)) {
+    throw std::runtime_error("[RoPE] Partial RoPE application not supported");
+  }
+  if (in.flags().row_contiguous && in.is_donatable()) {
+    out.move_shared_buffer(in);
+  } else {
+    out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  }
+
+  auto& s = out.primitive().stream();
+  auto& d = metal::device(s.device);
+  std::ostringstream kname;
+  kname << "rope_" << (traditional_ ? "traditional_" : "") << type_to_name(in);
+  auto kernel = d.get_kernel(kname.str());
+  auto compute_encoder = d.get_command_encoder(s.index);
+
+  bool donated = in.data_shared_ptr() == nullptr;
+  float base = std::log2(base_);
+  compute_encoder->setComputePipelineState(kernel);
+  set_array_buffer(compute_encoder, donated ? out : in, 0);
+  set_array_buffer(compute_encoder, out, 1);
+  compute_encoder->setBytes(in.strides().data(), 3 * sizeof(size_t), 2);
+  compute_encoder->setBytes(&offset_, sizeof(int), 3);
+  compute_encoder->setBytes(&base, sizeof(float), 4);
+  compute_encoder->setBytes(&scale_, sizeof(float), 5);
+
+  int dim0 = in.shape(2) / 2;
+  int dim1 = in.shape(1);
+  int dim2 = in.shape(0);
+  auto group_dims = get_block_dims(dim0, dim1, dim2);
+  auto grid_dims = MTL::Size(dim0, dim1, dim2);
+  compute_encoder->dispatchThreads(grid_dims, group_dims);
+}
+
+} // namespace mlx::core::fast

mlx/backend/no_metal/primitives.cpp

@@ -1,6 +1,7 @@
 // Copyright © 2023-2024 Apple Inc.
 
 #include "mlx/primitives.h"
+#include "mlx/fast.h"
 
 #define NO_GPU_MULTI(func)                                             \
   void func::eval_gpu(                                                 \
@@ -95,4 +96,8 @@ NO_GPU(Tan)
 NO_GPU(Tanh)
 NO_GPU(Transpose)
 
+namespace fast {
+NO_GPU_MULTI(RoPE)
+} // namespace fast
+
 } // namespace mlx::core

mlx/fast.cpp

@@ -0,0 +1,128 @@
+// Copyright © 2023-2024 Apple Inc.
+
+#include "mlx/fast.h"
+#include "mlx/transforms.h"
+
+namespace mlx::core::fast {
+
+std::vector<array> Custom::vjp(
+    const std::vector<array>& primals,
+    const std::vector<array>& cotangents,
+    const std::vector<int>& argnums,
+    const std::vector<array>& outputs) {
+  auto [_, vjps] = mlx::core::vjp(fallback_, primals, cotangents);
+  std::vector<array> vjp_outs;
+  for (int i = 0, j = 0; i < vjps.size(); ++i) {
+    if (i < argnums.size() && i == argnums[j]) {
+      vjp_outs.push_back(vjps[i]);
+      j++;
+    }
+  }
+  return vjp_outs;
+}
+
+std::vector<array> Custom::jvp(
+    const std::vector<array>& primals,
+    const std::vector<array>& tangents,
+    const std::vector<int>& argnums) {
+  auto [_, jvps] = mlx::core::jvp(fallback_, primals, tangents);
+  std::vector<array> jvp_outs;
+  for (int i = 0, j = 0; i < jvps.size(); ++i) {
+    if (i < argnums.size() && i == argnums[j]) {
+      jvp_outs.push_back(jvps[i]);
+      j++;
+    }
+  }
+  return jvp_outs;
+}
+
+std::pair<std::vector<array>, std::vector<int>> Custom::vmap(
+    const std::vector<array>& inputs,
+    const std::vector<int>& axes) {
+  auto outputs = mlx::core::vmap(fallback_, axes)(inputs);
+  auto out_axes = std::vector<int>(outputs.size(), 0);
+  return {outputs, out_axes};
+}
+
+array rope(
+    const array& x,
+    int dims,
+    bool traditional,
+    float base,
+    float scale,
+    int offset,
+    StreamOrDevice s /* = {} */) {
+  if (x.ndim() != 3) {
+    std::ostringstream msg;
+    msg << "[rope] Input must have 3 dimensions but got input with " << x.ndim()
+        << " dimensions.";
+    throw std::invalid_argument(msg.str());
+  }
+  if (traditional && x.shape(-1) != dims) {
+    throw std::invalid_argument(
+        "[rope] Does not support partial traditional application.");
+  }
+
+  auto fallback = [dims, traditional, base, scale, offset, s](
+                      const std::vector<array>& inputs) {
+    auto& x = inputs[0];
+    auto t = x.dtype();
+    auto N = x.shape(1) + offset;
+    // Compute sines and cosines
+    auto half_dims = dims / 2;
+    auto positions = multiply(arange(offset, N, t, s), array(scale, t), s);
+    auto freqs = negative(arange(0, half_dims, t, s), s);
+    freqs = exp(multiply(freqs, array(std::log(base) / half_dims, t), s), s);
+    auto theta =
+        multiply(expand_dims(positions, 1, s), expand_dims(freqs, 0, s), s);
+    auto coss = cos(theta, s);
+    auto sins = sin(theta, s);
+
+    if (traditional) {
+      auto x1 = slice(x, {0, 0, 0}, x.shape(), {1, 1, 2}, s);
+      auto x2 = slice(x, {0, 0, 1}, x.shape(), {1, 1, 2}, s);
+      std::vector<array> outs;
+      outs.push_back(subtract(multiply(x1, coss, s), multiply(x2, sins, s), s));
+      outs.push_back(add(multiply(x1, sins, s), multiply(x2, coss, s), s));
+      for (auto& o : outs) {
+        o = expand_dims(o, 3, s);
+      }
+      return std::vector<array>{reshape(concatenate(outs, 3, s), x.shape(), s)};
+    } else {
+      auto out_s = x.shape();
+      out_s.back() = half_dims;
+      auto x1 = slice(x, {0, 0, 0}, out_s, s);
+      out_s.back() = dims;
+      auto x2 = slice(x, {0, 0, half_dims}, out_s, s);
+
+      std::vector<array> outs;
+      outs.push_back(subtract(multiply(x1, coss, s), multiply(x2, sins, s), s));
+      outs.push_back(add(multiply(x1, sins, s), multiply(x2, coss, s), s));
+      if (dims < x.shape(-1)) {
+        outs.push_back(slice(x, {0, 0, dims}, x.shape(), s));
+      }
+      return std::vector<array>{concatenate(outs, 2, s)};
+    }
+  };
+  // TODO change to condition for using custom prim
+  auto stream = to_stream(s);
+  if (stream.device == Device::gpu && x.shape(-1) == dims) {
+    return array(
+        x.shape(),
+        x.dtype(),
+        std::make_unique<RoPE>(
+            stream, fallback, dims, traditional, base, scale, offset),
+        {x});
+  }
+  return fallback({x})[0];
+}
+
+bool RoPE::is_equivalent(const Primitive& other) const {
+  const RoPE& a_other = static_cast<const RoPE&>(other);
+  return (
+      dims_ == a_other.dims_ && base_ == a_other.base_ &&
+      scale_ == a_other.scale_ && traditional_ == a_other.traditional_ &&
+      offset_ == a_other.offset_);
+}
+
+} // namespace mlx::core::fast