add trellis quant mode

mlx/backend/metal/kernels/quantized.h

@@ -684,6 +684,115 @@ METAL_FUNC void qmv_fast_impl(
   }
 }
 
+template <uint32_t a = 89226354, uint32_t b = 64248484, uint32_t m = 996162400>
+float inst3(uint16_t xi) {
+  uint32_t x = xi;
+  x = a * x + b;
+  x = (x & 0b10001111111111111000111111111111) ^ m;
+  auto xf = reinterpret_cast<thread float16_t*>(&x);
+  return xf[0] + xf[1];
+}
+
+template <typename T, int bits>
+METAL_FUNC void qmv_trellis_impl(
+    const device uint32_t* w,
+    const device T* scales,
+    const device T* biases,
+    const device T* x,
+    device T* y,
+    const constant int& in_vec_size,
+    const constant int& out_vec_size,
+    uint3 tid [[threadgroup_position_in_grid]],
+    uint simd_gid [[simdgroup_index_in_threadgroup]],
+    uint simd_lid [[thread_index_in_simdgroup]]) {
+  constexpr int power_of_2_bits = (bits & (bits - 1)) == 0;
+  constexpr int packs_per_thread = 2;
+  constexpr int num_simdgroups = 2;
+  constexpr int results_per_simdgroup = 4;
+  constexpr int pack_factor = bits == 3 ? 8 : bits == 6 ? 4 : 32 / bits;
+  constexpr int bytes_per_pack = power_of_2_bits ? 4 : 3;
+  constexpr int values_per_thread = pack_factor * packs_per_thread;
+  constexpr int block_size = values_per_thread * SIMD_SIZE;
+  constexpr int reads_per = 16 / bits;
+  constexpr int local_w_size =
+      results_per_simdgroup * values_per_thread / reads_per;
+
+  const device uint8_t* ws = (const device uint8_t*)w;
+
+  typedef float U;
+
+  thread U x_thread[values_per_thread];
+  thread uint16_t w_thread[local_w_size];
+  thread U result[results_per_simdgroup] = {0};
+
+  // Adjust positions
+  const int in_vec_size_w = in_vec_size * bytes_per_pack / pack_factor;
+  const int out_row = tid.y * (num_simdgroups * results_per_simdgroup) +
+      simd_gid * results_per_simdgroup;
+
+  ws += out_row * in_vec_size_w + simd_lid * packs_per_thread * bytes_per_pack;
+  x += tid.x * in_vec_size + simd_lid * values_per_thread;
+  y += tid.x * out_vec_size + out_row;
+
+  T scale = scales[0];
+
+  for (int k = 0; k < in_vec_size; k += block_size) {
+#pragma clang loop unroll(full)
+    for (int i = 0; i < values_per_thread; i++) {
+      x_thread[i] = x[i];
+    }
+
+#pragma clang loop unroll(full)
+    for (int row = 0; row < results_per_simdgroup; row++) {
+#pragma clang loop unroll(full)
+      for (int i = 0; i < values_per_thread / reads_per; i++) {
+        auto wl = (const device uint16_t*)(ws + row * in_vec_size_w);
+        w_thread[row * values_per_thread / reads_per + i] = wl[i];
+      }
+    }
+
+#pragma clang loop unroll(full)
+    for (int row = 0; row < results_per_simdgroup; row++) {
+#pragma clang loop unroll(full)
+      for (int i = 0; i < values_per_thread / reads_per; i++) {
+        int index = row * values_per_thread / reads_per + i;
+        uint16_t w0 = w_thread[index];
+        uint16_t w1 = w_thread[(index + 1) % local_w_size];
+
+        uint16_t wx = w0 ^ w1;
+        uint16_t wx1 = wx ^ 1;
+        uint16_t wf = w0 ^ (1 << bits);
+
+        if (bits == 2) {
+          result[row] += x_thread[8 * i] * inst3(w0);
+          result[row] += x_thread[8 * i + 1] * inst3(wf ^ (wx1 & 0x3));
+          result[row] += x_thread[8 * i + 2] * inst3(w0 ^ (wx & 0xf));
+          result[row] += x_thread[8 * i + 3] * inst3(w0 ^ (wx1 & 0x3f));
+          result[row] += x_thread[8 * i + 4] * inst3(w0 ^ (wx & 0xff));
+          result[row] += x_thread[8 * i + 5] * inst3(w0 ^ (wx1 & 0x3ff));
+          result[row] += x_thread[8 * i + 6] * inst3(w0 ^ (wx & 0xfff));
+          result[row] += x_thread[8 * i + 7] * inst3(w0 ^ (wx1 & 0x3fff));
+        } else if (bits == 4) {
+          result[row] += x_thread[4 * i] * inst3(w0);
+          result[row] += x_thread[4 * i + 1] * inst3(wf ^ (wx1 & 0xf));
+          result[row] += x_thread[4 * i + 2] * inst3(w0 ^ (wx & 0xff));
+          result[row] += x_thread[4 * i + 3] * inst3(w0 ^ (wx1 & 0xfff));
+        }
+      }
+    }
+
+    ws += block_size * bytes_per_pack / pack_factor;
+    x += block_size;
+  }
+
+  for (int row = 0; row < results_per_simdgroup; row++) {
+    result[row] = simd_sum(result[row]);
+    if (simd_lid == 0) {
+      y[row] = static_cast<T>(scale * result[row]);
+    }
+  }
+}
+
 template <typename T, int group_size, int bits>
 METAL_FUNC void qmv_impl(
     const device uint32_t* w,
@@ -1302,7 +1411,13 @@ METAL_FUNC void adjust_matrix_offsets(
   y += tid.z * output_stride;
 }
 
-template <typename T, int group_size, int bits, int D, bool batched>
+template <
+    typename T,
+    int group_size,
+    int bits,
+    int D,
+    bool batched,
+    bool trellis = false>
 [[kernel]] void qmv_quad(
     const device uint32_t* w [[buffer(0)]],
     const device T* scales [[buffer(1)]],
@@ -1354,7 +1469,12 @@ template <typename T, int group_size, int bits, int D, bool batched>
       quad_lid);
 }
 
-template <typename T, int group_size, int bits, bool batched>
+template <
+    typename T,
+    int group_size,
+    int bits,
+    bool batched,
+    bool trellis = false>
 [[kernel]] void qmv_fast(
     const device uint32_t* w [[buffer(0)]],
     const device T* scales [[buffer(1)]],
@@ -1393,6 +1513,19 @@ template <typename T, int group_size, int bits, bool batched>
         b_strides,
         tid);
   }
+  if (trellis) {
+    qmv_trellis_impl<T, bits>(
+        w,
+        scales,
+        biases,
+        x,
+        y,
+        in_vec_size,
+        out_vec_size,
+        tid,
+        simd_gid,
+        simd_lid);
+  } else {
     qmv_fast_impl<T, group_size, bits>(
         w,
         scales,
@@ -1404,9 +1537,15 @@ template <typename T, int group_size, int bits, bool batched>
         tid,
         simd_gid,
         simd_lid);
+  }
 }
 
-template <typename T, const int group_size, const int bits, bool batched>
+template <
+    typename T,
+    const int group_size,
+    const int bits,
+    bool batched,
+    bool trellis = false>
 [[kernel]] void qmv(
     const device uint32_t* w [[buffer(0)]],
     const device T* scales [[buffer(1)]],
@@ -1458,7 +1597,12 @@ template <typename T, const int group_size, const int bits, bool batched>
       simd_lid);
 }
 
-template <typename T, const int group_size, const int bits, bool batched>
+template <
+    typename T,
+    const int group_size,
+    const int bits,
+    bool batched,
+    bool trellis = false>
 [[kernel]] void qvm(
     const device uint32_t* w [[buffer(0)]],
     const device T* scales [[buffer(1)]],
@@ -1572,6 +1716,7 @@ template <
     const int bits,
     const bool aligned_N,
     const bool batched,
+    bool trellis = false,
     const int BM = 32,
     const int BK = 32,
     const int BN = 32>
@@ -1630,6 +1775,7 @@ template <
     const int group_size,
     const int bits,
     const bool batched,
+    bool trellis = false,
     const int BM = 32,
     const int BK = 32,
     const int BN = 32>
@@ -1685,7 +1831,7 @@ template <
       w, scales, biases, x, y, Xs, Ws, K, N, M, tid, lid, simd_gid, simd_lid);
 }
 
-template <typename T, int group_size, int bits>
+template <typename T, int group_size, int bits, bool trellis = false>
 [[kernel]] void bs_qmv_fast(
     const device uint32_t* w [[buffer(0)]],
     const device T* scales [[buffer(1)]],
@@ -1734,6 +1880,19 @@ template <typename T, int group_size, int bits>
       s_strides,
       b_strides,
       tid);
+  if (trellis) {
+    qmv_trellis_impl<T, bits>(
+        w,
+        scales,
+        biases,
+        x,
+        y,
+        in_vec_size,
+        out_vec_size,
+        tid,
+        simd_gid,
+        simd_lid);
+  } else {
     qmv_fast_impl<T, group_size, bits>(
         w,
         scales,
@@ -1745,9 +1904,10 @@ template <typename T, int group_size, int bits>
         tid,
         simd_gid,
         simd_lid);
+  }
 }
 
-template <typename T, int group_size, int bits>
+template <typename T, int group_size, int bits, bool trellis = false>
 [[kernel]] void bs_qmv(
     const device uint32_t* w [[buffer(0)]],
     const device T* scales [[buffer(1)]],
@@ -1809,7 +1969,7 @@ template <typename T, int group_size, int bits>
       simd_lid);
 }
 
-template <typename T, int group_size, int bits>
+template <typename T, int group_size, int bits, bool trellis = false>
 [[kernel]] void bs_qvm(
     const device uint32_t* w [[buffer(0)]],
     const device T* scales [[buffer(1)]],
@@ -1876,6 +2036,7 @@ template <
     const int group_size,
     const int bits,
     const bool aligned_N,
+    bool trellis = false,
     const int BM = 32,
     const int BK = 32,
     const int BN = 32>
@@ -1943,6 +2104,7 @@ template <
     typename T,
     const int group_size,
     const int bits,
+    bool trellis = false,
     const int BM = 32,
     const int BK = 32,
     const int BN = 32>
@@ -2157,3 +2319,211 @@ template <typename T, const int group_size, const int bits>
     }
   }
 }
+
+template <
+    typename T,
+    const bool use_overlap,
+    const int bits = 2,
+    const int timesteps = 128>
+[[kernel]] void trellis_viterbi(
+    const device T* w [[buffer(0)]],
+    device float16_t* score [[buffer(1)]],
+    device uint8_t* pointers [[buffer(2)]],
+    const device uint16_t* overlap [[buffer(3)]],
+    uint3 tid [[thread_position_in_grid]]) {
+  constexpr uint16_t L = 16;
+  constexpr uint L2 = 1 << L;
+
+  uint16_t idx = tid.y * 16;
+
+  threadgroup float16_t swap_V[16384];
+
+  thread float16_t min_V[16] = {0};
+
+  for (uint16_t t = 0; t < timesteps; t++) {
+    uint16_t tt = t % 8 == 0 ? L / bits : t % 8;
+    uint16_t shift = ((tt - 1) % (L / bits)) * bits;
+    uint16_t flip = (t == 0 || (t > 1 && t % 8 == 1)) ? (1 << bits) + 1 : t % 2;
+
+    uint16_t s000 = 1 << (shift - 6);
+    uint16_t s0 = 1 << (shift - 2);
+    uint16_t s1 = 1 << (shift);
+    uint16_t s2 = 1 << (shift + 2);
+    uint16_t s4 = 1 << (shift + 4);
+
+    if (t > 1) {
+      uint16_t i = 0;
+      uint16_t loff = 1 << (metal::clamp((shift + 14) % 16, 2, 12));
+      uint16_t hoff = shift > 4 ? 4 : shift == 4 ? 16 : 1;
+      uint16_t ind = idx;
+
+      if (shift == 0) {
+        ind >>= 2;
+      } else if (shift == 14) {
+        ind = (ind & 0xfff) + (ind >> 12);
+      } else if (shift == 2) {
+      } else if (shift == 4) {
+        ind = ((ind >> 4) & 0x3) + (ind & ~0x3f);
+      } else if (shift == 6) {
+        ind = ((ind / s0) % 4) * s1 + ((ind / s1) % 4) + (ind / s2) * s2;
+      } else {
+        ind = ((ind / 16) % s000) * 16 + ((ind / s0) % 4) * s1 +
+            ((ind / s1) % 4) + (ind / s2) * s2;
+      }
+
+      for (uint16_t high = 0; high < 4; high++) {
+        uint16_t sub_ind = ind;
+        for (uint16_t low = 0; low < 4; low++) {
+          swap_V[sub_ind] = min_V[i];
+          i++;
+          sub_ind += loff;
+        }
+        ind += hoff;
+      }
+      threadgroup_barrier(mem_flags::mem_threadgroup);
+      for (uint16_t i = 0; i < 16; i++) {
+        min_V[i] = swap_V[idx + i];
+      }
+      threadgroup_barrier(mem_flags::mem_threadgroup);
+    }
+
+    uint16_t rolled_t = use_overlap ? t : (t + 64) % 128;
+    T w_t = w[tid.x * timesteps + rolled_t];
+
+    for (uint16_t i = 0; i < 4; i++) {
+      thread float16_t min_val[4] = {INFINITY, INFINITY, INFINITY, INFINITY};
+      thread uint16_t min_idx[4] = {0};
+
+      uint16_t ii = idx * 4 + i * 16;
+      uint16_t big_idx = ii;
+      if (shift > 0 && shift < 14) {
+        big_idx = ((ii / s2) % 4) + (ii / s4 * s4);
+        if (shift > 2) {
+          big_idx += ((ii / 16) % s0) * 4;
+        }
+      } else if (shift == 14 && t > 0) {
+        big_idx >>= 2;
+      }
+
+      uint16_t loff = t == 0 ? 4 : s1;
+      uint16_t hoff = (t == 0 || shift == 14) ? 1 : s2;
+
+      for (uint16_t high = 0; high < 4; high++) {
+        uint16_t sub_ind = big_idx;
+        for (uint16_t low = 0; low < 4; low++) {
+          float mse = inst3(sub_ind ^ flip) - w_t;
+          mse *= mse;
+
+          float16_t new_val = min_V[i * 4 + high] + mse;
+          if (new_val < min_val[low]) {
+            min_val[low] = new_val;
+            min_idx[low] = high;
+          }
+          sub_ind += loff;
+        }
+        big_idx += hoff;
+      }
+
+      for (uint16_t j = 0; j < 4; j++) {
+        min_V[i * 4 + j] = min_val[j];
+        pointers[tid.x * L2 / 4 * timesteps + t * L2 / 4 + idx + i * 4 + j] =
+            min_idx[j];
+      }
+    }
+    if (t == 0 && use_overlap) {
+      uint16_t over = overlap[tid.x * 128 + 64];
+      over = over & ((1 << 14) - 1);
+      for (uint16_t i = 0; i < 16; i++) {
+        uint16_t rs = (over >> 2) ^ 1;
+        uint16_t ls = (idx + i) & ((1 << 12) - 1);
+        min_V[i] = rs == ls ? min_V[i] : INFINITY;
+      }
+    }
+  }
+  if (use_overlap) {
+    uint16_t over = overlap[tid.x * 128 + 64];
+    over = over & ((1 << 14) - 1);
+    uint16_t node =
+        (over % 4) * 4096 + ((over / 4) % 1024) * 4 + (over / 4096) % 4;
+    for (uint16_t i = 0; i < 16; i++) {
+      min_V[i] = (idx + i) == node ? min_V[i] : INFINITY;
+    }
+  }
+  for (uint16_t i = 0; i < 16; i++) {
+    score[tid.x * L2 / 4 + idx + i] = min_V[i];
+  }
+}
+
+uint16_t remove_bits(uint16_t i, uint16_t shift) {
+  uint16_t lower = i & ((1 << shift) - 1);
+  uint16_t upper = i & ~((1 << (shift + 2)) - 1);
+  return lower + (upper >> 2);
+}
+
+uint16_t swap_bits(uint16_t i, uint16_t shift) {
+  uint16_t diff = ((i >> shift) ^ i) & 0x3;
+  i = i ^ diff;
+  i ^= diff << shift;
+  return i;
+}
+
+template <const bool use_overlap, const int bits = 2, const int timesteps = 128>
+[[kernel]] void trellis_backtrack(
+    const device uint32_t* start [[buffer(0)]],
+    const device uint8_t* pointers [[buffer(1)]],
+    device uint16_t* out [[buffer(2)]],
+    const device uint16_t* overlap [[buffer(3)]],
+    uint3 tid [[thread_position_in_grid]]) {
+  constexpr uint16_t L = 16;
+
+  uint16_t node = start[tid.x];
+
+  uint16_t dir =
+      pointers[tid.x * timesteps * 16384 + (timesteps - 1) * 16384 + node];
+
+  node = (node % 4) * 4096 + ((node / 4) % 1024) * 4 + (node / 4096) % 4;
+  node ^= 1;
+  node += dir * 16384;
+
+  out[tid.x * timesteps + timesteps - 1] = node;
+
+  for (int t = timesteps - 2; t >= 0; t--) {
+    uint16_t shift = (t % (L / bits)) * bits;
+    uint16_t mask = ((1 << L) - 1) ^ (((1 << bits) - 1) << shift);
+    uint16_t flip = t % (L / bits) == 0 ? 1 << bits : 1;
+    uint16_t i = (node & mask) ^ flip;
+
+    if (shift > 0) {
+      i = remove_bits(i, shift);
+    }
+
+    if (t == 0) {
+      i >>= 2;
+    }
+
+    if (t % 2 == 1 || t == 0) {
+      i ^= 1;
+    }
+
+    shift = shift == 0 ? L : shift;
+
+    if (t > 0) {
+      i = swap_bits(i, shift - 2);
+    }
+
+    shift = shift == L ? 0 : shift;
+
+    uint16_t last_p = pointers[tid.x * timesteps * 16384 + t * 16384 + i];
+    if ((t % 8 == 1 && t > 1) || t == 0) {
+      last_p ^= 1;
+    }
+
+    node = ((node & mask) ^ flip) | (last_p << shift);
+    if (t == 0 && use_overlap) {
+      uint16_t over = overlap[tid.x * 128 + 64];
+      over = over & ((1 << 14) - 1);
+      node = (node & 0xfffc) + (over & 0x3);
+    }
+    out[tid.x * timesteps + t] = node;
+  }
+}

mlx/backend/metal/primitives.cpp

@@ -16,6 +16,8 @@
 #include "mlx/scheduler.h"
 #include "mlx/utils.h"
 
+#include <iostream>
+
 namespace mlx::core {
 
 template <typename T>
@@ -158,33 +160,25 @@ void Arange::eval_gpu(const std::vector<array>& inputs, array& out) {
   compute_encoder.dispatch_threads(grid_dims, group_dims);
 }
 
-void ArgReduce::eval_gpu(const std::vector<array>& inputs, array& out) {
+void arg_reduce_dispatch(
-  assert(inputs.size() == 1);
+    const array& in,
-  auto& in = inputs[0];
+    array& out,
-  out.set_data(allocator::malloc(out.nbytes()));
+    int axis,
-  auto& s = stream();
+    std::string op_name,
+    const Stream& s) {
   auto& d = metal::device(s.device);
-  std::string op_name;
-  switch (reduce_type_) {
-    case ArgReduce::ArgMin:
-      op_name = "argmin_";
-      break;
-    case ArgReduce::ArgMax:
-      op_name = "argmax_";
-      break;
-  }
 
   // Prepare the shapes, strides and axis arguments.
   auto in_strides = in.strides();
   auto shape = in.shape();
   auto out_strides = out.strides();
-  auto axis_stride = in_strides[axis_];
+  auto axis_stride = in_strides[axis];
-  size_t axis_size = shape[axis_];
+  size_t axis_size = shape[axis];
   if (out_strides.size() == in_strides.size()) {
-    out_strides.erase(out_strides.begin() + axis_);
+    out_strides.erase(out_strides.begin() + axis);
   }
-  in_strides.erase(in_strides.begin() + axis_);
+  in_strides.erase(in_strides.begin() + axis);
-  shape.erase(shape.begin() + axis_);
+  shape.erase(shape.begin() + axis);
   size_t ndim = shape.size();
 
   // ArgReduce
@@ -192,7 +186,7 @@ void ArgReduce::eval_gpu(const std::vector<array>& inputs, array& out) {
   int n_reads = 4;
   auto& compute_encoder = d.get_command_encoder(s.index);
   {
-    auto kernel = d.get_kernel(op_name + type_to_name(in));
+    auto kernel = d.get_kernel(op_name + "_" + type_to_name(in));
     NS::UInteger thread_group_size = std::min(
         (axis_size + n_reads - 1) / n_reads,
         kernel->maxTotalThreadsPerThreadgroup());
@@ -226,6 +220,23 @@ void ArgReduce::eval_gpu(const std::vector<array>& inputs, array& out) {
   }
 }
 
+void ArgReduce::eval_gpu(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 1);
+  std::string op_name;
+  switch (reduce_type_) {
+    case ArgReduce::ArgMin:
+      op_name = "argmin";
+      break;
+    case ArgReduce::ArgMax:
+      op_name = "argmax";
+      break;
+  }
+  auto& in = inputs[0];
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  auto& s = stream();
+  arg_reduce_dispatch(in, out, axis_, op_name, s);
+}
+
 void AsType::eval_gpu(const std::vector<array>& inputs, array& out) {
   CopyType ctype =
       inputs[0].flags().contiguous ? CopyType::Vector : CopyType::General;

mlx/backend/metal/quantized.cpp

@@ -7,11 +7,14 @@
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/kernels.h"
 #include "mlx/backend/metal/reduce.h"
+#include "mlx/backend/metal/slicing.h"
 #include "mlx/backend/metal/utils.h"
 #include "mlx/fast_primitives.h"
 #include "mlx/primitives.h"
 #include "mlx/utils.h"
 
+#include <iostream>
+
 namespace mlx::core {
 
 void launch_qmm(
@@ -31,6 +34,7 @@ void launch_qmm(
     bool gather,
     bool aligned,
     bool quad,
+    const std::string& mode,
     const Stream& s) {
   auto& x_pre = inputs[0];
   auto& w_pre = inputs[1];
@@ -54,8 +58,12 @@ void launch_qmm(
   };
   auto x = ensure_row_contiguous_last_dims(x_pre);
   auto w = ensure_row_contiguous_last_dims(w_pre);
-  auto scales = ensure_row_contiguous_last_dims(scales_pre);
+  auto scales = scales_pre;
-  auto biases = ensure_row_contiguous_last_dims(biases_pre);
+  auto biases = biases_pre;
+  if (mode == "affine") {
+    scales = ensure_row_contiguous_last_dims(scales_pre);
+    biases = ensure_row_contiguous_last_dims(biases_pre);
+  }
 
   int x_batch_ndims = x.ndim() - 2;
   auto& x_shape = x.shape();
@@ -68,6 +76,8 @@ void launch_qmm(
 
   std::string aligned_n = (O % 32) == 0 ? "true" : "false";
 
+  bool is_trellis = (mode == "trellis");
+
   std::ostringstream kname;
   auto type_string = get_type_string(x.dtype());
   kname << name << "_" << type_string << "_gs_" << group_size << "_b_" << bits;
@@ -80,24 +90,47 @@ void launch_qmm(
   if (!gather) {
     kname << "_batch_" << batched;
   }
+  if (mode == "trellis") {
+    kname << "_mode_" << is_trellis;
+  }
 
   // Encode and dispatch kernel
   std::string template_def;
   if (quad) {
     template_def = get_template_definition(
-        kname.str(), name, type_string, group_size, bits, D, batched);
+        kname.str(),
+        name,
+        type_string,
+        group_size,
+        bits,
+        D,
+        batched,
+        is_trellis);
   } else if (aligned && !gather) {
     template_def = get_template_definition(
-        kname.str(), name, type_string, group_size, bits, aligned_n, batched);
+        kname.str(),
+        name,
+        type_string,
+        group_size,
+        bits,
+        aligned_n,
+        batched,
+        is_trellis);
   } else if (!gather && !aligned) {
     template_def = get_template_definition(
-        kname.str(), name, type_string, group_size, bits, batched);
+        kname.str(), name, type_string, group_size, bits, batched, is_trellis);
   } else if (aligned && gather) {
     template_def = get_template_definition(
-        kname.str(), name, type_string, group_size, bits, aligned_n);
+        kname.str(),
+        name,
+        type_string,
+        group_size,
+        bits,
+        aligned_n,
+        is_trellis);
   } else {
     template_def = get_template_definition(
-        kname.str(), name, type_string, group_size, bits);
+        kname.str(), name, type_string, group_size, bits, is_trellis);
   }
   auto& d = metal::device(s.device);
   auto kernel = get_quantized_kernel(d, kname.str(), template_def);
@@ -276,6 +309,7 @@ void qmm_op(
     int group_size,
     int bits,
     bool gather,
+    const std::string& mode,
     const Stream& s) {
   out.set_data(allocator::malloc(out.nbytes()));
 
@@ -354,7 +388,7 @@ void qmm_op(
       group_dims = MTL::Size(simdgroup_size, 1, 1);
       grid_dims = MTL::Size(B, (O + bo - 1) / bo, N);
       quad = true;
-    } else if (B < qmv_batch_limit && O % 8 == 0 && D % 512 == 0 && D >= 512) {
+    } else if (B < 10000 && O % 8 == 0 && D % 512 == 0 && D >= 512) {
       name += "qmv_fast";
       int bo = 8;
       int bd = 32;
@@ -420,19 +454,34 @@ void qmm_op(
       gather,
       aligned,
       quad,
+      mode,
       s);
 }
 
 void QuantizedMatmul::eval_gpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 4);
   qmm_op(
-      inputs, out, transpose_, group_size_, bits_, /*gather=*/false, stream());
+      inputs,
+      out,
+      transpose_,
+      group_size_,
+      bits_,
+      /*gather=*/false,
+      mode_,
+      stream());
 }
 
 void GatherQMM::eval_gpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 6);
   qmm_op(
-      inputs, out, transpose_, group_size_, bits_, /*gather=*/true, stream());
+      inputs,
+      out,
+      transpose_,
+      group_size_,
+      bits_,
+      /*gather=*/true,
+      mode_,
+      stream());
 }
 
 void fast::AffineQuantize::eval_gpu(
@@ -516,4 +565,123 @@ void fast::AffineQuantize::eval_gpu(
   d.add_temporaries(std::move(copies), s.index);
 }
 
+void viterbi(
+    array& w,
+    array& scores,
+    array& pointers,
+    array& start,
+    array& overlap,
+    bool use_overlap,
+    const Stream& s) {
+  int B = scores.shape(0);
+  auto& d = metal::device(s.device);
+
+  auto& compute_encoder = d.get_command_encoder(s.index);
+  compute_encoder.set_input_array(w, 0);
+  compute_encoder.set_output_array(scores, 1);
+  compute_encoder.set_output_array(pointers, 2);
+  if (use_overlap) {
+    compute_encoder.set_input_array(overlap, 3);
+  }
+
+  std::ostringstream kname;
+  auto type_string = get_type_string(w.dtype());
+  kname << "trellis_viterbi_" << type_string << "_overlap_" << use_overlap;
+  auto template_def = get_template_definition(
+      kname.str(), "trellis_viterbi", type_string, use_overlap);
+  auto kernel = get_quantized_kernel(d, kname.str(), template_def);
+  compute_encoder.set_compute_pipeline_state(kernel);
+
+  auto group_dims = MTL::Size(1, 1024, 1);
+  auto grid_dims = MTL::Size(B, 1024, 1);
+
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
+  arg_reduce_dispatch(scores, start, 1, "argmin", s);
+}
+
+void viterbi_backtrack(
+    array& start,
+    array& pointers,
+    array& out,
+    array& overlap,
+    bool use_overlap,
+    const Stream& s) {
+  int B = start.shape(0);
+
+  auto& d = metal::device(s.device);
+
+  auto& compute_encoder = d.get_command_encoder(s.index);
+  compute_encoder.set_input_array(start, 0);
+  compute_encoder.set_input_array(pointers, 1);
+  compute_encoder.set_output_array(out, 2);
+  if (use_overlap) {
+    compute_encoder.set_input_array(overlap, 3);
+  }
+
+  std::ostringstream kname;
+  kname << "trellis_backtrack" << "_overlap_" << use_overlap;
+  auto template_def =
+      get_template_definition(kname.str(), "trellis_backtrack", use_overlap);
+  auto kernel = get_quantized_kernel(d, kname.str(), template_def);
+  compute_encoder.set_compute_pipeline_state(kernel);
+
+  auto group_dims = MTL::Size(256, 1, 1);
+  auto grid_dims = MTL::Size(B, 1, 1);
+
+  compute_encoder.dispatch_threads(grid_dims, group_dims);
+}
+
+void fast::TrellisQuantize::eval_gpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  auto& w_pre = inputs[0];
+  auto& out = outputs[0];
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+
+  auto& s = stream();
+  auto& d = metal::device(s.device);
+
+  std::vector<array> copies;
+  auto ensure_row_contiguous = [&copies, &s](const array& arr) {
+    if (arr.flags().row_contiguous) {
+      return arr;
+    } else {
+      array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
+      copy_gpu(arr, arr_copy, CopyType::General, s);
+      copies.push_back(arr_copy);
+      return arr_copy;
+    }
+  };
+  auto w = ensure_row_contiguous(w_pre);
+
+  int B = w.shape(0);
+  int T = w.shape(1);
+
+  constexpr int num_states = 1 << 14;
+
+  array scores({B, num_states}, float16, nullptr, {});
+  scores.set_data(allocator::malloc_or_wait(scores.nbytes()));
+  copies.push_back(scores);
+
+  array pointers({B, T, num_states}, uint8, nullptr, {});
+  pointers.set_data(allocator::malloc_or_wait(pointers.nbytes()));
+  copies.push_back(pointers);
+
+  array start({B}, uint32, nullptr, {});
+  start.set_data(allocator::malloc_or_wait(start.nbytes()));
+  copies.push_back(start);
+
+  array rolled({B, T}, uint16, nullptr, {});
+  rolled.set_data(allocator::malloc_or_wait(rolled.nbytes()));
+  copies.push_back(rolled);
+
+  viterbi(w, scores, pointers, start, out, false, s);
+  viterbi_backtrack(start, pointers, rolled, out, false, s);
+
+  viterbi(w, scores, pointers, start, rolled, true, s);
+  viterbi_backtrack(start, pointers, out, rolled, true, s);
+
+  d.add_temporaries(std::move(copies), s.index);
+}
+
 } // namespace mlx::core

mlx/backend/metal/reduce.h

@@ -38,4 +38,11 @@ void strided_reduce_general_dispatch(
     metal::Device& d,
     const Stream& s);
 
+void arg_reduce_dispatch(
+    const array& in,
+    array& out,
+    int axis,
+    std::string op_name,
+    const Stream& s);
+
 } // namespace mlx::core

mlx/fast.cpp

@@ -11,6 +11,8 @@
 #include "mlx/transforms.h"
 #include "mlx/transforms_impl.h"
 
+#include <iostream>
+
 namespace mlx::core::fast {
 
 std::vector<array> Custom::vjp(
@@ -832,7 +834,7 @@ array pack_and_quantize(
   return packed_w;
 }
 
-std::tuple<array, array, array>
+std::vector<array>
 affine_quantize(const array& w, int group_size, int bits, StreamOrDevice s_) {
   auto s = to_stream(s_);
 
@@ -1028,6 +1030,54 @@ array affine_dequantize(
   return fallback({w, scales, biases})[0];
 }
 
+std::vector<array>
+trellis_quantize(const array& w_, int bits, StreamOrDevice s_) {
+  if (bits != 2) {
+    throw std::invalid_argument(
+        "Only 2 bit Trellis quants are currently supported.");
+  }
+
+  int Tx = 4;
+  int Ty = 32;
+  int batch_size = 256;
+
+  auto s = to_stream(s_);
+
+  int L = 16;
+  int M = w_.shape(-2);
+  int T = Tx * Ty;
+  auto scale = std(astype(w_, float32, s), s);
+  auto w = divide(w_, scale, s);
+  w = astype(w, float16, s);
+
+  w = reshape(w, {M / Tx, Tx, -1, Ty}, s);
+  w = transpose(w, {0, 2, 1, 3}, s);
+  w = reshape(w, {-1, T}, s);
+
+  auto fallback = [bits, s](const std::vector<array>& inputs) mutable
+      -> std::vector<array> { return {inputs[0]}; };
+
+  auto q = zeros({w.shape(0), w.shape(1) * bits / L}, uint16, s);
+  for (int i = 0; i < w.shape(0); i += batch_size) {
+    auto w_batch = slice(w, {i, 0}, {i + batch_size, w.shape(-1)}, s);
+    auto q_batch = array(
+        w_batch.shape(),
+        uint16,
+        std::make_shared<TrellisQuantize>(s, fallback, bits, true),
+        {w_batch});
+    q_batch = slice(q_batch, {0, 0}, q_batch.shape(), {1, L / bits}, s);
+    q = slice_update(q, q_batch, {i, 0}, {i + batch_size, q.shape(-1)}, s);
+    eval(q);
+  }
+
+  q = reshape(q, {M / Tx, -1, Tx, Ty * bits / L}, s);
+  q = transpose(q, {0, 2, 1, 3}, s);
+  q = reshape(q, {M, -1}, s);
+  q = view(q, uint32, s);
+
+  return {q, scale, scale};
+}
+
 bool AffineQuantize::is_equivalent(const Primitive& other) const {
   const AffineQuantize& p_other = static_cast<const AffineQuantize&>(other);
   return (

mlx/fast.h

@@ -52,7 +52,7 @@ array scaled_dot_product_attention(
     const std::vector<array>& mask_arrs = {},
     StreamOrDevice s = {});
 
-std::tuple<array, array, array> affine_quantize(
+std::vector<array> affine_quantize(
     const array& w,
     int group_size = 64,
     int bits = 4,
@@ -66,6 +66,9 @@ array affine_dequantize(
     int bits = 4,
     StreamOrDevice s = {});
 
+std::vector<array>
+trellis_quantize(const array& w, int bits = 4, StreamOrDevice s = {});
+
 typedef std::variant<int, bool, Dtype> TemplateArg;
 
 typedef std::function<std::vector<array>(

mlx/fast_primitives.h

@@ -269,6 +269,38 @@ class AffineQuantize : public Custom {
   bool dequantize_;
 };
 
+class TrellisQuantize : public Custom {
+ public:
+  explicit TrellisQuantize(
+      Stream stream,
+      std::function<std::vector<array>(std::vector<array>)> fallback,
+      int bits,
+      bool dequantize)
+      : Custom(stream, fallback), bits_(bits), dequantize_(dequantize) {}
+
+  void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
+      override {
+    throw std::runtime_error("NYI");
+  };
+
+  void eval_gpu(const std::vector<array>& inputs, std::vector<array>& outputs)
+      override;
+
+  DEFINE_PRINT(TrellisQuantize);
+
+  // bool is_equivalent(const Primitive& other) const override;
+  // std::vector<Shape> output_shapes(const std::vector<array>& inputs)
+  // override;
+  auto state() const {
+    return std::make_tuple(nullptr, bits_, dequantize_);
+  }
+
+ private:
+  std::function<std::vector<array>(std::vector<array>)> fallback_;
+  int bits_;
+  bool dequantize_;
+};
+
 struct CustomKernelShapeInfo {
   bool shape = false;
   bool strides = false;

mlx/ops.cpp

@@ -17,6 +17,8 @@
 #include "mlx/transforms_impl.h"
 #include "mlx/utils.h"
 
+#include <iostream>
+
 namespace mlx::core {
 
 namespace {
@@ -79,7 +81,8 @@ std::pair<int, int> extract_quantized_matmul_dims(
     const array& biases,
     bool transpose,
     int group_size,
-    int bits) {
+    int bits,
+    const std::string& mode) {
   if (w.dtype() != uint32) {
     std::ostringstream msg;
     msg << "[" << tag << "] The weight matrix should be uint32 "
@@ -87,6 +90,7 @@ std::pair<int, int> extract_quantized_matmul_dims(
     throw std::invalid_argument(msg.str());
   }
 
+  if (mode == "affine") {
     if (scales.shape() != biases.shape()) {
       std::ostringstream msg;
       msg << "[" << tag << "] Scales and biases should have the same shape. "
@@ -95,16 +99,6 @@ std::pair<int, int> extract_quantized_matmul_dims(
       throw std::invalid_argument(msg.str());
     }
 
-  if (!std::equal(
-          w.shape().begin(), w.shape().end() - 2, scales.shape().begin())) {
-    std::ostringstream msg;
-    msg << "[" << tag
-        << "] Weight, scales and biases should have the same batch shape. "
-        << "Received weight with shape " << w.shape() << ", scales with "
-        << scales.shape() << " and biases with " << biases.shape();
-    throw std::invalid_argument(msg.str());
-  }
-
     if (w.shape(-1) * 32 / bits != scales.shape(-1) * group_size) {
       std::ostringstream msg;
       msg << "[" << tag << "] The shapes of the weight and scales are "
@@ -113,6 +107,17 @@ std::pair<int, int> extract_quantized_matmul_dims(
           << " with group_size=" << group_size << " and bits=" << bits;
       throw std::invalid_argument(msg.str());
     }
+  }
+
+  if (!std::equal(
+          w.shape().begin(), w.shape().end() - 2, scales.shape().begin())) {
+    std::ostringstream msg;
+    msg << "[" << tag
+        << "] Weight, scales and biases should have the same batch shape. "
+        << "Received weight with shape " << w.shape() << ", scales with "
+        << scales.shape() << " and biases with " << biases.shape();
+    throw std::invalid_argument(msg.str());
+  }
 
   int x_inner_dims = x.shape(-1);
 
@@ -717,6 +722,9 @@ array slice(
         << "array with dimension " << a.ndim() << ".";
     throw std::invalid_argument(msg.str());
   }
+  // std::cout << "start " << start << std::endl;
+  // std::cout << "stop " << stop << std::endl;
+  // std::cout << "strides " << strides << std::endl;
 
   auto [has_neg_strides, out_shape] =
       normalize_slice(a.shape(), start, stop, strides);
@@ -3969,10 +3977,19 @@ array quantized_matmul(
     bool transpose /* = true */,
     int group_size /* = 64 */,
     int bits /* = 4 */,
+    const std::string& mode /* = "affine" */,
     StreamOrDevice s /* = {} */) {
   // Check and extract the quantized matrix shape against x
   auto [w_inner_dims, w_outer_dims] = extract_quantized_matmul_dims(
-      "quantized_matmul", x, w, scales, biases, transpose, group_size, bits);
+      "quantized_matmul",
+      x,
+      w,
+      scales,
+      biases,
+      transpose,
+      group_size,
+      bits,
+      mode);
 
   auto dtype = result_type(x, scales, biases);
   if (!issubdtype(dtype, floating)) {
@@ -3996,16 +4013,26 @@ array quantized_matmul(
       std::move(out_shape),
       dtype,
       std::make_shared<QuantizedMatmul>(
-          to_stream(s), group_size, bits, transpose),
+          to_stream(s), group_size, bits, transpose, mode),
       std::move(inputs));
 }
 
-std::tuple<array, array, array> quantize(
+std::vector<array> quantize(
     const array& w,
     int group_size /* = 64 */,
     int bits /* = 4 */,
+    const std::string& mode, /* = affine */
     StreamOrDevice s /* = {} */) {
+  if (mode == "affine") {
     return fast::affine_quantize(w, group_size, bits, s);
+  } else if (mode == "trellis") {
+    return fast::trellis_quantize(w, bits, s);
+  } else {
+    std::ostringstream msg;
+    msg << "[quantize] Unsupported quantization mode " << mode << "."
+        << std::endl;
+    throw std::invalid_argument(msg.str());
+  }
 }
 
 array dequantize(
@@ -4028,14 +4055,15 @@ array gather_qmm(
     bool transpose /* = true */,
     int group_size /* = 64 */,
     int bits /* = 4 */,
+    const std::string& mode /* = "affine" */,
     StreamOrDevice s /* = {} */) {
   if (!lhs_indices_ && !rhs_indices_) {
     return quantized_matmul(
-        x, w, scales, biases, transpose, group_size, bits, s);
+        x, w, scales, biases, transpose, group_size, bits, mode, s);
   }
 
   auto [w_inner_dims, w_outer_dims] = extract_quantized_matmul_dims(
-      "gather_qmm", x, w, scales, biases, transpose, group_size, bits);
+      "gather_qmm", x, w, scales, biases, transpose, group_size, bits, mode);
 
   // Extract indices and broadcast them
   array lhs_indices = indices_or_default(lhs_indices_, x, s);
@@ -4067,7 +4095,8 @@ array gather_qmm(
   return array(
       std::move(out_shape),
       out_type,
-      std::make_shared<GatherQMM>(to_stream(s), group_size, bits, transpose),
+      std::make_shared<GatherQMM>(
+          to_stream(s), group_size, bits, transpose, mode),
       {astype(x, out_type, s),
        w,
        astype(scales, out_type, s),

mlx/ops.h

@@ -1323,13 +1323,15 @@ array quantized_matmul(
     bool transpose = true,
     int group_size = 64,
     int bits = 4,
+    const std::string& mode = "affine",
     StreamOrDevice s = {});
 
 /** Quantize a matrix along its last axis */
-std::tuple<array, array, array> quantize(
+std::vector<array> quantize(
     const array& w,
     int group_size = 64,
     int bits = 4,
+    const std::string& mode = "affine",
     StreamOrDevice s = {});
 
 /** Dequantize a matrix produced by quantize() */
@@ -1352,6 +1354,7 @@ array gather_qmm(
     bool transpose = true,
     int group_size = 64,
     int bits = 4,
+    const std::string& mode = "affine",
     StreamOrDevice s = {});
 
 /** Returns a contraction of a and b over multiple dimensions. */

mlx/primitives.cpp

@@ -3012,6 +3012,7 @@ std::vector<array> QuantizedMatmul::vjp(
           !transpose_,
           group_size_,
           bits_,
+          mode_,
           stream()));
     }
 
@@ -3040,6 +3041,7 @@ std::vector<array> QuantizedMatmul::jvp(
       transpose_,
       group_size_,
       bits_,
+      mode_,
       stream())};
 }
 
@@ -3098,6 +3100,7 @@ std::vector<array> GatherQMM::vjp(
                       !transpose_,
                       group_size_,
                       bits_,
+                      mode_,
                       stream()),
                   -3,
                   stream()),

mlx/primitives.h

@@ -1552,11 +1552,13 @@ class QuantizedMatmul : public UnaryPrimitive {
       Stream stream,
       int group_size,
       int bits,
-      bool transpose)
+      bool transpose,
+      const std::string mode)
       : UnaryPrimitive(stream),
         group_size_(group_size),
         bits_(bits),
-        transpose_(transpose) {}
+        transpose_(transpose),
+        mode_(mode) {}
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1567,22 +1569,29 @@ class QuantizedMatmul : public UnaryPrimitive {
   bool is_equivalent(const Primitive& other) const override;
   std::vector<Shape> output_shapes(const std::vector<array>& inputs) override;
   auto state() const {
-    return std::make_tuple(group_size_, bits_, transpose_);
+    return std::make_tuple(group_size_, bits_, transpose_, mode_);
   }
 
  private:
   int group_size_;
   int bits_;
   bool transpose_;
+  const std::string mode_;
 };
 
 class GatherQMM : public UnaryPrimitive {
  public:
-  explicit GatherQMM(Stream stream, int group_size, int bits, bool transpose)
+  explicit GatherQMM(
+      Stream stream,
+      int group_size,
+      int bits,
+      bool transpose,
+      const std::string& mode)
       : UnaryPrimitive(stream),
         group_size_(group_size),
         bits_(bits),
-        transpose_(transpose) {}
+        transpose_(transpose),
+        mode_(mode) {}
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1592,13 +1601,14 @@ class GatherQMM : public UnaryPrimitive {
   DEFINE_PRINT(GatherQMM)
   bool is_equivalent(const Primitive& other) const override;
   auto state() const {
-    return std::make_tuple(group_size_, bits_, transpose_);
+    return std::make_tuple(group_size_, bits_, transpose_, mode_);
   }
 
  private:
   int group_size_;
   int bits_;
   bool transpose_;
+  const std::string mode_;
 };
 
 class RandomBits : public UnaryPrimitive {

python/mlx/nn/layers/embedding.py

@@ -1,6 +1,7 @@
 # Copyright © 2023-2024 Apple Inc.
 
 import math
+from typing import Literal
 
 import mlx.core as mx
 from mlx.nn.layers.base import Module
@@ -39,6 +40,12 @@ class Embedding(Module):
         """
         return x @ self.weight.T
 
-    def to_quantized(self, group_size: int = 64, bits: int = 4):
+    def to_quantized(
+        self,
+        group_size: int = 64,
+        bits: int = 4,
+        mode: Literal["affine", "trellis"] = "affine",
+        fake: bool = False,
+    ):
         """Return a :obj:`QuantizedEmbedding` layer that approximates this embedding layer."""
         return QuantizedEmbedding.from_embedding(self, group_size, bits)

python/mlx/nn/layers/linear.py

@@ -1,11 +1,12 @@
 # Copyright © 2023 Apple Inc.
 
 import math
-from typing import Any
+from typing import Any, Literal
 
 import mlx.core as mx
 from mlx.nn.layers.base import Module
 from mlx.nn.layers.quantized import QuantizedLinear
+from mlx.nn.layers.viterbi import quantize as trellis_quantize
 
 
 class Identity(Module):
@@ -70,9 +71,15 @@ class Linear(Module):
             x = x @ self["weight"].T
         return x
 
-    def to_quantized(self, group_size: int = 64, bits: int = 4):
+    def to_quantized(
+        self,
+        group_size: int = 64,
+        bits: int = 4,
+        mode: Literal["affine", "trellis"] = "affine",
+        fake: bool = False,
+    ):
         """Return a :obj:`QuantizedLinear` layer that approximates this layer."""
-        return QuantizedLinear.from_linear(self, group_size, bits)
+        return QuantizedLinear.from_linear(self, group_size, bits, mode=mode, fake=fake)
 
 
 class Bilinear(Module):

python/mlx/nn/layers/quantized.py

@@ -1,10 +1,11 @@
 # Copyright © 2023-2024 Apple Inc.
 
 import math
-from typing import Callable, Optional, Union
+from typing import Callable, Literal, Optional, Union
 
 import mlx.core as mx
 from mlx.nn.layers.base import Module
+from mlx.nn.layers.viterbi import quantize as trellis_quantize
 from mlx.utils import tree_map_with_path
 
 
@@ -12,7 +13,9 @@ def quantize(
     model: Module,
     group_size: int = 64,
     bits: int = 4,
+    mode: Literal["affine", "trellis"] = "affine",
     class_predicate: Optional[Callable[[str, Module], Union[bool, dict]]] = None,
+    fake: bool = False,
 ):
     """Quantize the sub-modules of a module according to a predicate.
 
@@ -21,7 +24,7 @@ def quantize(
     will be quantized. Note also, the module is updated in-place.
 
     Args:
-        model (mlx.nn.Module): The model whose leaf modules may be quantized.
+        model (mlx.nn.Module):, mode: Literal["affine", "trellis"] = "affine" The model whose leaf modules may be quantized.
         group_size (int): The quantization group size (see
            :func:`mlx.core.quantize`). Default: ``64``.
         bits (int): The number of bits per parameter (see
@@ -36,12 +39,15 @@ def quantize(
     class_predicate = class_predicate or (lambda _, m: hasattr(m, "to_quantized"))
 
     def _maybe_quantize(path, m):
+        print(path)
         if bool_or_params := class_predicate(path, m):
             if hasattr(m, "to_quantized"):
                 if isinstance(bool_or_params, bool):
-                    return m.to_quantized(group_size=group_size, bits=bits)
+                    return m.to_quantized(
+                        group_size=group_size, bits=bits, mode=mode, fake=fake
+                    )
                 elif isinstance(bool_or_params, dict):
-                    return m.to_quantized(**bool_or_params)
+                    return m.to_quantized(**bool_or_params, fake=fake)
                 else:
                     raise ValueError(
                         "``class_predicate`` must return a bool"
@@ -131,7 +137,11 @@ class QuantizedEmbedding(Module):
 
     @classmethod
     def from_embedding(
-        cls, embedding_layer: Module, group_size: int = 64, bits: int = 4
+        cls,
+        embedding_layer: Module,
+        group_size: int = 64,
+        bits: int = 4,
+        mode: Literal["affine", "trellis"] = "affine",
     ):
         """Create a :obj:`QuantizedEmbedding` layer from an :obj:`Embedding` layer."""
         embedding_dims, dims = embedding_layer.weight.shape
@@ -170,12 +180,14 @@ class QuantizedLinear(Module):
         bias: bool = True,
         group_size: int = 64,
         bits: int = 4,
+        mode: Literal["affine", "trellis"] = "affine",
     ):
         super().__init__()
 
         # Quantization config
         self.group_size = group_size
         self.bits = bits
+        self.mode = mode
 
         # Initialize the quantized weight
         scale = math.sqrt(1 / input_dims)
@@ -216,19 +228,40 @@ class QuantizedLinear(Module):
             transpose=True,
             group_size=self.group_size,
             bits=self.bits,
+            mode=self.mode,
         )
         if "bias" in self:
             x = x + self["bias"]
         return x
 
     @classmethod
-    def from_linear(cls, linear_layer: Module, group_size: int = 64, bits: int = 4):
+    def from_linear(
+        cls,
+        linear_layer: Module,
+        group_size: int = 64,
+        bits: int = 4,
+        mode: Literal["affine", "trellis"] = "affine",
+        fake: bool = False,
+    ):
         """Create a :obj:`QuantizedLinear` layer from a :obj:`Linear` layer."""
         output_dims, input_dims = linear_layer.weight.shape
-        ql = cls(input_dims, output_dims, False, group_size, bits)
+        ql = cls(input_dims, output_dims, False, group_size, bits, mode)
-        ql.weight, ql.scales, ql.biases = mx.quantize(
+        if mode == "trellis":
-            linear_layer.weight, group_size, bits
+            if fake:
+                ql.weight = mx.zeros(
+                    (output_dims, input_dims // 32 * bits), dtype=mx.uint32
                 )
+                ql.scales = mx.array(0.0)
+                ql.biases = mx.array(0.0)
+            else:
+                ql.weight, ql.scales, ql.biases = mx.quantize(
+                    linear_layer.weight, bits=bits, mode="trellis"
+                )
+        else:
+            ql.weight, ql.scales, ql.biases = mx.quantize(
+                linear_layer.weight, group_size, bits, mode="affine"
+            )
+
         if "bias" in linear_layer:
             ql.bias = linear_layer.bias
 

python/src/ops.cpp

@@ -4116,10 +4116,11 @@ void init_ops(nb::module_& m) {
       "transpose"_a = true,
       "group_size"_a = 64,
       "bits"_a = 4,
+      "mode"_a = "affine",
       nb::kw_only(),
       "stream"_a = nb::none(),
       nb::sig(
-          "def quantized_matmul(x: array, w: array, /, scales: array, biases: array, transpose: bool = True, group_size: int = 64, bits: int = 4, *, stream: Union[None, Stream, Device] = None) -> array"),
+          "def quantized_matmul(x: array, w: array, /, scales: array, biases: array, transpose: bool = True, group_size: int = 64, bits: int = 4, mode: Literal['affine', 'trellis'], *, stream: Union[None, Stream, Device] = None) -> array"),
       R"pbdoc(
         Perform the matrix multiplication with the quantized matrix ``w``. The
         quantization uses one floating point scale and bias per ``group_size`` of
@@ -4138,6 +4139,8 @@ void init_ops(nb::module_& m) {
             shares a scale and bias. Default: ``64``.
           bits (int, optional): The number of bits occupied by each element in
             ``w``. Default: ``4``.
+          mode (str, optional): The mode to use for quantization.
+            Default: ``affine``.
 
         Returns:
           array: The result of the multiplication of ``x`` with ``w``.
@@ -4149,9 +4152,10 @@ void init_ops(nb::module_& m) {
       "group_size"_a = 64,
       "bits"_a = 4,
       nb::kw_only(),
+      "mode"_a = "affine",
       "stream"_a = nb::none(),
       nb::sig(
-          "def quantize(w: array, /, group_size: int = 64, bits : int = 4, *, stream: Union[None, Stream, Device] = None) -> tuple[array, array, array]"),
+          "def quantize(w: array, /, group_size: int = 64, bits : int = 4, *, mode: Literal['affine', 'trellis'], stream: Union[None, Stream, Device] = None) -> tuple[array, array, array]"),
       R"pbdoc(
         Quantize the matrix ``w`` using ``bits`` bits per element.
 
@@ -4193,6 +4197,7 @@ void init_ops(nb::module_& m) {
             scale and bias. Default: ``64``.
           bits (int, optional): The number of bits occupied by each element of
             ``w`` in the returned quantized matrix. Default: ``4``.
+          mode (str): The quantization mode to use. Default: ``affine``.
 
         Returns:
           tuple: A tuple containing
@@ -4249,10 +4254,11 @@ void init_ops(nb::module_& m) {
       "transpose"_a = true,
       "group_size"_a = 64,
       "bits"_a = 4,
+      "mode"_a = "affine",
       nb::kw_only(),
       "stream"_a = nb::none(),
       nb::sig(
-          "def gather_qmm(x: array, w: array, /, scales: array, biases: array, lhs_indices: Optional[array] = None, rhs_indices: Optional[array] = None, transpose: bool = True, group_size: int = 64, bits: int = 4, *, stream: Union[None, Stream, Device] = None) -> array"),
+          "def gather_qmm(x: array, w: array, /, scales: array, biases: array, lhs_indices: Optional[array] = None, rhs_indices: Optional[array] = None, transpose: bool = True, group_size: int = 64, bits: int = 4, mode: Literal['affine', 'trellis'], *, stream: Union[None, Stream, Device] = None) -> array"),
       R"pbdoc(
         Perform quantized matrix multiplication with matrix-level gather.
 
@@ -4278,6 +4284,8 @@ void init_ops(nb::module_& m) {
             shares a scale and bias. Default: ``64``.
           bits (int, optional): The number of bits occupied by each element in
             ``w``. Default: ``4``.
+          mode (str, optional): The mode to use for quantization.
+            Default: ``affine``.
 
         Returns:
           array: The result of the multiplication of ``x`` with ``w``

python/tests/test_quantized.py

@@ -10,6 +10,9 @@ import mlx_tests
 class TestQuantized(mlx_tests.MLXTestCase):
     def test_quantize_dequantize(self):
         w = mx.random.normal(shape=(128, 512))
+        w_q, scales, biases = mx.quantize(w, bits=2, mode="trellis")
+        print(w_q, scales, biases)
+
         for gs in [32, 64, 128]:
             for b in [2, 3, 6, 4, 8]:
                 with self.subTest(gs=gs, b=b):