Improve names of quantization arguments (#235)

* Change the default quantization group_size to 64 * Rename groups to group_size and width to bits
2025-09-19 19:38:16 +08:00 · 2023-12-20 16:53:53 -08:00
parent 57fe918cf8
commit b3916cbf2b
11 changed files with 184 additions and 180 deletions
--- a/mlx/backend/accelerate/quantized.cpp
+++ b/mlx/backend/accelerate/quantized.cpp
@@ -19,12 +19,12 @@ void _qmm_t_4_64(
    int M,
    int N,
    int K) {
-  constexpr int width = 4;
-  constexpr int groups = 64;
-  constexpr int bitmask = (1 << width) - 1;
-  constexpr int pack_factor = 32 / width;
-  constexpr int packs_in_group = groups / pack_factor;
-  const int Kg = K / groups;
+  constexpr int bits = 4;
+  constexpr int group_size = 64;
+  constexpr int bitmask = (1 << bits) - 1;
+  constexpr int pack_factor = 32 / bits;
+  constexpr int packs_in_group = group_size / pack_factor;
+  const int Kg = K / group_size;
  const int Kw = K / pack_factor;

  for (int m = 0; m < M; m++) {
@@ -35,7 +35,7 @@ void _qmm_t_4_64(
    for (int n = 0; n < N; n++) {
      const simd_float16* x_local = (simd_float16*)x;
      simd_float16 sum = 0;
-      for (int k = 0; k < K; k += groups) {
+      for (int k = 0; k < K; k += group_size) {
        float scale = *scales_local++;
        float bias = *biases_local++;

@@ -46,7 +46,7 @@ void _qmm_t_4_64(
            uint32_t wii = *w_local++;
            for (int p = 0; p < 8; p++) {
              wi[e * 8 + p] = wii & bitmask;
-              wii >>= width;
+              wii >>= bits;
            }
          }
          simd_float16 wf = simd_float(wi);
@@ -85,7 +85,7 @@ void QuantizedMatmul::eval_cpu(const std::vector<array>& inputs, array& out) {
    throw std::runtime_error("x, scales and biases should be row contiguous.");
  }

-  if (x.dtype() == float32 && width_ == 4 && groups_ == 64) {
+  if (x.dtype() == float32 && bits_ == 4 && group_size_ == 64) {
    out.set_data(allocator::malloc_or_wait(out.nbytes()));
    int K = x.shape(-1);
    int M = x.size() / K;
--- a/mlx/backend/common/quantized.cpp
+++ b/mlx/backend/common/quantized.cpp
@@ -8,7 +8,7 @@ namespace mlx::core {

 namespace {

-template <typename T, int width, int groups>
+template <typename T, int bits, int group_size>
 void _qmm_t(
    T* result,
    const T* x,
@@ -18,10 +18,10 @@ void _qmm_t(
    int M,
    int N,
    int K) {
-  constexpr int bitmask = (1 << width) - 1;
-  constexpr int pack_factor = 32 / width;
-  constexpr int packs_in_group = groups / pack_factor;
-  const int Kg = K / groups;
+  constexpr int bitmask = (1 << bits) - 1;
+  constexpr int pack_factor = 32 / bits;
+  constexpr int packs_in_group = group_size / pack_factor;
+  const int Kg = K / group_size;
  const int Kw = K / pack_factor;

  for (int m = 0; m < M; m++) {
@@ -32,7 +32,7 @@ void _qmm_t(
    for (int n = 0; n < N; n++) {
      const T* x_local = x;
      T sum = 0;
-      for (int k = 0; k < K; k += groups) {
+      for (int k = 0; k < K; k += group_size) {
        T scale = *scales_local++;
        T bias = *biases_local++;

@@ -42,7 +42,7 @@ void _qmm_t(
 #pragma clang loop unroll(full)
          for (int p = 0; p < pack_factor; p++) {
            sum += (*x_local++) * (scale * static_cast<T>(wi & bitmask) + bias);
-            wi >>= width;
+            wi >>= bits;
          }
        }
      }
@@ -64,11 +64,11 @@ void _qmm_t_dispatch_typed(
    int M,
    int N,
    int K,
-    int width,
-    int groups) {
-  switch (width) {
+    int group_size,
+    int bits) {
+  switch (bits) {
    case 2: {
-      switch (groups) {
+      switch (group_size) {
        case 64:
          return _qmm_t<T, 2, 64>(result, x, w, scales, biases, M, N, K);
        case 128:
@@ -76,7 +76,7 @@ void _qmm_t_dispatch_typed(
      }
    }
    case 4: {
-      switch (groups) {
+      switch (group_size) {
        case 64:
          return _qmm_t<T, 4, 64>(result, x, w, scales, biases, M, N, K);
        case 128:
@@ -84,7 +84,7 @@ void _qmm_t_dispatch_typed(
      }
    }
    case 8: {
-      switch (groups) {
+      switch (group_size) {
        case 64:
          return _qmm_t<T, 8, 64>(result, x, w, scales, biases, M, N, K);
        case 128:
@@ -93,9 +93,10 @@ void _qmm_t_dispatch_typed(
    }
  }
  std::ostringstream msg;
-  msg << "Quantization type not supported. Provided bit width=" << width
-      << " and groups=" << groups << ". The supported options are width in "
-      << "{2, 4, 8} and groups in {64, 128}.";
+  msg << "Quantization type not supported. Provided bits=" << bits
+      << " and group_size=" << group_size
+      << ". The supported options are bits in "
+      << "{2, 4, 8} and group_size in {64, 128}.";
  throw std::invalid_argument(msg.str());
 }

@@ -105,8 +106,8 @@ void _qmm_t_dispatch(
    const array& w,
    const array& scales,
    const array& biases,
-    int width,
-    int groups) {
+    int bits,
+    int group_size) {
  int K = x.shape(-1);
  int M = x.size() / K;
  int N = w.shape(1);
@@ -122,8 +123,8 @@ void _qmm_t_dispatch(
          M,
          N,
          K,
-          width,
-          groups);
+          bits,
+          group_size);
      break;
    case float16:
      _qmm_t_dispatch_typed<float16_t>(
@@ -135,8 +136,8 @@ void _qmm_t_dispatch(
          M,
          N,
          K,
-          width,
-          groups);
+          bits,
+          group_size);
      break;
    case bfloat16:
      _qmm_t_dispatch_typed<bfloat16_t>(
@@ -148,8 +149,8 @@ void _qmm_t_dispatch(
          M,
          N,
          K,
-          width,
-          groups);
+          bits,
+          group_size);
      break;
    default:
      throw std::invalid_argument(
@@ -177,7 +178,7 @@ void QuantizedMatmul::eval(const std::vector<array>& inputs, array& out) {
  }

  out.set_data(allocator::malloc_or_wait(out.nbytes()));
-  _qmm_t_dispatch(out, x, w, scales, biases, width_, groups_);
+  _qmm_t_dispatch(out, x, w, scales, biases, group_size_, bits_);
 }

 } // namespace mlx::core
--- a/mlx/backend/metal/kernels/quantized.metal
+++ b/mlx/backend/metal/kernels/quantized.metal
@@ -14,7 +14,7 @@ using namespace metal;

 MLX_MTL_CONST int SIMD_SIZE = 32;

-template <typename T, const int BM, const int BN, const int groups, const int width>
+template <typename T, const int BM, const int BN, const int group_size, const int bits>
 [[kernel]] void qmv(
    const device uint32_t* w [[buffer(0)]],
    const device T* scales [[buffer(1)]],
@@ -30,10 +30,10 @@ template <typename T, const int BM, const int BN, const int groups, const int wi

  static_assert(BN == SIMD_SIZE, "qmv expects BN to be equal to SIMD_SIZE");

-  constexpr int bitmask = (1 << width) - 1;
-  constexpr int el_per_thread = 32 / width;
+  constexpr int bitmask = (1 << bits) - 1;
+  constexpr int el_per_thread = 32 / bits;
  constexpr int colgroup = BN * el_per_thread;
-  constexpr int groups_per_block = colgroup / groups;
+  constexpr int groups_per_block = colgroup / group_size;
  constexpr int simdgroups_fetching_vec = colgroup / SIMD_SIZE;

  threadgroup T scales_block[BM * groups_per_block];
@@ -48,7 +48,7 @@ template <typename T, const int BM, const int BN, const int groups, const int wi

  // Adjust positions
  const int in_vec_size_w = in_vec_size / el_per_thread;
-  const int in_vec_size_g = in_vec_size / groups;
+  const int in_vec_size_g = in_vec_size / group_size;
  int out_row = tid.y * BM + simd_gid;
  w += out_row * in_vec_size_w;
  scales += out_row * in_vec_size_g;
@@ -66,11 +66,11 @@ template <typename T, const int BM, const int BN, const int groups, const int wi
    if (simd_lid == 0) {
      #pragma clang loop unroll(full)
      for (int j=0; j<groups_per_block; j++) {
-        scales_block[simd_gid * groups_per_block + j] = scales[i / groups + j];
+        scales_block[simd_gid * groups_per_block + j] = scales[i / group_size + j];
      }
      #pragma clang loop unroll(full)
      for (int j=0; j<groups_per_block; j++) {
-        biases_block[simd_gid * groups_per_block + j] = biases[i / groups + j];
+        biases_block[simd_gid * groups_per_block + j] = biases[i / group_size + j];
      }
    }
    threadgroup_barrier(mem_flags::mem_threadgroup);
@@ -80,8 +80,8 @@ template <typename T, const int BM, const int BN, const int groups, const int wi
    for (int j=0; j<el_per_thread; j++) {
      x_thread[j] = x_block[simd_lid*el_per_thread + j];
    }
-    scale = scales_block[simd_gid * groups_per_block + simd_lid * el_per_thread / groups];
-    bias = biases_block[simd_gid * groups_per_block + simd_lid * el_per_thread / groups];
+    scale = scales_block[simd_gid * groups_per_block + simd_lid * el_per_thread / group_size];
+    bias = biases_block[simd_gid * groups_per_block + simd_lid * el_per_thread / group_size];

    // Load the matrix elements
    w_local = w[i / el_per_thread + simd_lid];
@@ -90,7 +90,7 @@ template <typename T, const int BM, const int BN, const int groups, const int wi
    #pragma clang loop unroll(full)
    for (int k=0; k<el_per_thread; k++) {
      result += (scale * static_cast<T>(w_local & bitmask) + bias) * x_thread[k];
-      w_local >>= width;
+      w_local >>= bits;
    }
  }

@@ -104,7 +104,7 @@ template <typename T, const int BM, const int BN, const int groups, const int wi
 }


-template <typename T, const int BM, const int BK, const int BN, const int groups, const int width>
+template <typename T, const int BM, const int BK, const int BN, const int group_size, const int bits>
 [[kernel]] void qmm_t(
    const device T* x [[buffer(0)]],
    const device uint32_t* w [[buffer(1)]],
@@ -126,10 +126,10 @@ template <typename T, const int BM, const int BK, const int BN, const int groups

  constexpr int WM = 2;
  constexpr int WN = 2;
-  constexpr int bitmask = (1 << width) - 1;
-  constexpr int el_per_int = 32 / width;
+  constexpr int bitmask = (1 << bits) - 1;
+  constexpr int el_per_int = 32 / bits;
  constexpr int ints_per_block = BK / el_per_int;
-  constexpr int groups_per_block = (BK / groups > 0) ? (BK / groups) : 1;
+  constexpr int groups_per_block = (BK / group_size > 0) ? (BK / group_size) : 1;
  constexpr int groups_per_simd = BN / (WM * WN);
  constexpr int w_els_per_thread = (BN * BK / el_per_int) / (SIMD_SIZE * WM * WN);

@@ -145,7 +145,7 @@ template <typename T, const int BM, const int BK, const int BN, const int groups

  // Set the block
  const int K_w = K / el_per_int;
-  const int K_g = K / groups;
+  const int K_g = K / group_size;
  const int y_row = tid.y * BM;
  const int y_col = tid.x * BN;
  x += y_row * K;
@@ -172,8 +172,8 @@ template <typename T, const int BM, const int BK, const int BN, const int groups
    if (simd_lid == 0) {
      threadgroup T *scales_block_local = scales_block + lidy * groups_per_block * groups_per_simd;
      threadgroup T *biases_block_local = biases_block + lidy * groups_per_block * groups_per_simd;
-      const device T *scales_local = scales + lidy * groups_per_simd * K_g + k / groups;
-      const device T *biases_local = biases + lidy * groups_per_simd * K_g + k / groups;
+      const device T *scales_local = scales + lidy * groups_per_simd * K_g + k / group_size;
+      const device T *biases_local = biases + lidy * groups_per_simd * K_g + k / group_size;
      #pragma clang loop unroll(full)
      for (int gs=0; gs<groups_per_simd; gs++) {
        #pragma clang loop unroll(full)
@@ -199,13 +199,13 @@ template <typename T, const int BM, const int BK, const int BN, const int groups
        threadgroup T * Ws_local = Ws + offset_row * BK + offset_col * el_per_int;

        uint32_t wi = *w_local;
-        T scale = scales_block[offset_row * groups_per_block + offset_col / (groups / el_per_int)];
-        T bias = biases_block[offset_row * groups_per_block + offset_col / (groups / el_per_int)];
+        T scale = scales_block[offset_row * groups_per_block + offset_col / (group_size / el_per_int)];
+        T bias = biases_block[offset_row * groups_per_block + offset_col / (group_size / el_per_int)];

        #pragma clang loop unroll(full)
        for (int t=0; t<el_per_int; t++) {
          Ws_local[t] = scale * static_cast<T>(wi & bitmask) + bias;
-          wi >>= width;
+          wi >>= bits;
        }
      }
    }
@@ -231,9 +231,9 @@ template <typename T, const int BM, const int BK, const int BN, const int groups
 }


-#define instantiate_qmv(name, itype, groups, width) \
-  template [[host_name("qmv_n_" #name "_groups_" #groups "_width_" #width)]] \
-  [[kernel]] void qmv<itype, 32, 32, groups, width>( \
+#define instantiate_qmv(name, itype, group_size, bits) \
+  template [[host_name("qmv_n_" #name "_gs_" #group_size "_b_" #bits)]] \
+  [[kernel]] void qmv<itype, 32, 32, group_size, bits>( \
    const device uint32_t* w [[buffer(0)]], \
    const device itype* scales [[buffer(1)]], \
    const device itype* biases [[buffer(2)]], \
@@ -246,10 +246,10 @@ template <typename T, const int BM, const int BK, const int BN, const int groups
    uint simd_gid [[simdgroup_index_in_threadgroup]], \
    uint simd_lid [[thread_index_in_simdgroup]]);

-#define instantiate_qmv_types(groups, width) \
-  instantiate_qmv(float32, float, groups, width) \
-  instantiate_qmv(float16, half, groups, width) \
-  instantiate_qmv(bfloat16, bfloat16_t, groups, width)
+#define instantiate_qmv_types(group_size, bits) \
+  instantiate_qmv(float32, float, group_size, bits) \
+  instantiate_qmv(float16, half, group_size, bits) \
+  instantiate_qmv(bfloat16, bfloat16_t, group_size, bits)

 instantiate_qmv_types(128, 2)
 instantiate_qmv_types(128, 4)
@@ -258,9 +258,9 @@ instantiate_qmv_types( 64, 2)
 instantiate_qmv_types( 64, 4)
 instantiate_qmv_types( 64, 8)

-#define instantiate_qmm_t(name, itype, groups, width) \
-  template [[host_name("qmm_t_" #name "_groups_" #groups "_width_" #width)]] \
-  [[kernel]] void qmm_t<itype, 32, 64, 32, groups, width>( \
+#define instantiate_qmm_t(name, itype, group_size, bits) \
+  template [[host_name("qmm_t_" #name "_gs_" #group_size "_b_" #bits)]] \
+  [[kernel]] void qmm_t<itype, 32, 64, 32, group_size, bits>( \
      const device itype* x [[buffer(0)]], \
      const device uint32_t* w [[buffer(1)]], \
      const device itype* scales [[buffer(2)]], \
@@ -274,10 +274,10 @@ instantiate_qmv_types( 64, 8)
      uint simd_gid [[simdgroup_index_in_threadgroup]], \
      uint simd_lid [[thread_index_in_simdgroup]]);

-#define instantiate_qmm_t_types(groups, width) \
-  instantiate_qmm_t(float32, float, groups, width) \
-  instantiate_qmm_t(float16, half, groups, width) \
-  instantiate_qmm_t(bfloat16, bfloat16_t, groups, width)
+#define instantiate_qmm_t_types(group_size, bits) \
+  instantiate_qmm_t(float32, float, group_size, bits) \
+  instantiate_qmm_t(float16, half, group_size, bits) \
+  instantiate_qmm_t(bfloat16, bfloat16_t, group_size, bits)

 instantiate_qmm_t_types(128, 2)
 instantiate_qmm_t_types(128, 4)
--- a/mlx/backend/metal/quantized.cpp
+++ b/mlx/backend/metal/quantized.cpp
@@ -58,7 +58,7 @@ void QuantizedMatmul::eval_gpu(const std::vector<array>& inputs, array& out) {
  if (B == 1) {
    std::ostringstream kname;
    kname << "qmv_" << (w_transposed ? "n_" : "t_") << type_to_name(out)
-          << "_groups_" << groups_ << "_width_" << width_;
+          << "_gs_" << group_size_ << "_b_" << bits_;

    // Encode and dispatch kernel
    auto compute_encoder = d.get_command_encoder(s.index);
@@ -87,7 +87,7 @@ void QuantizedMatmul::eval_gpu(const std::vector<array>& inputs, array& out) {
  else {
    std::ostringstream kname;
    kname << "qmm_" << (w_transposed ? "t_" : "n_") << type_to_name(out)
-          << "_groups_" << groups_ << "_width_" << width_;
+          << "_gs_" << group_size_ << "_b_" << bits_;

    // Encode and dispatch kernel
    auto compute_encoder = d.get_command_encoder(s.index);
--- a/mlx/ops.cpp
+++ b/mlx/ops.cpp
@@ -2583,8 +2583,8 @@ array quantized_matmul(
    const array& w,
    const array& scales,
    const array& biases,
-    int groups /* = 128 */,
-    int width /* = 4 */,
+    int group_size /* = 64 */,
+    int bits /* = 4 */,
    StreamOrDevice s /* = {} */) {
  auto x = in_x;

@@ -2611,24 +2611,25 @@ array quantized_matmul(
    x = reshape(x, {-1, x_inner_dims}, s);
  }

-  int w_inner_dims = w.shape(0) * (32 / width);
+  int w_inner_dims = w.shape(0) * (32 / bits);
  if (w_inner_dims != x_inner_dims) {
    std::ostringstream msg;
    msg << "[quantized_matmul] Last dimension of first input with "
        << "shape (..., " << x_inner_dims
        << ") does not match the expanded first "
        << "dimension of the quantized matrix " << w_inner_dims
-        << ", computed from shape " << w.shape() << " with groups=" << groups
-        << " and width=" << width;
+        << ", computed from shape " << w.shape()
+        << " with group_size=" << group_size << " and bits=" << bits;
    throw std::invalid_argument(msg.str());
  }

-  int n_groups = x_inner_dims / groups;
+  int n_groups = x_inner_dims / group_size;
  if (scales.shape(-1) != n_groups || biases.shape(-1) != n_groups) {
    std::ostringstream msg;
    msg << "[quantized_matmul] Scales and biases provided do not match the "
-        << "quantization arguments (groups=" << groups << ", width=" << width
-        << "). Expected shapes (" << w.shape(1) << ", " << x_inner_dims / groups
+        << "quantization arguments (group_size=" << group_size
+        << ", bits=" << bits << "). Expected shapes (" << w.shape(1) << ", "
+        << x_inner_dims / group_size
        << "), but got scales.shape=" << scales.shape()
        << " and biases.shape=" << biases.shape();
    throw std::invalid_argument(msg.str());
@@ -2637,7 +2638,7 @@ array quantized_matmul(
  auto out = array(
      {x.shape(0), w.shape(1)},
      x.dtype(),
-      std::make_unique<QuantizedMatmul>(to_stream(s), groups, width),
+      std::make_unique<QuantizedMatmul>(to_stream(s), group_size, bits),
      {x, w, scales, biases});

  // If needed reshape x to the original batch shape
@@ -2651,8 +2652,8 @@ array quantized_matmul(

 std::tuple<array, array, array> quantize(
    const array& w,
-    int groups /* = 128 */,
-    int width /* = 4 */,
+    int group_size /* = 64 */,
+    int bits /* = 4 */,
    StreamOrDevice s /* = {} */) {
  if (w.ndim() != 2) {
    throw std::invalid_argument("[quantize] Only matrices supported for now");
@@ -2663,23 +2664,24 @@ std::tuple<array, array, array> quantize(
        "[quantize] All dimensions should be divisible by 32 for now");
  }

-  if ((w.shape(-1) % groups) != 0) {
+  if ((w.shape(-1) % group_size) != 0) {
    std::ostringstream msg;
    msg << "[quantize] The last dimension of the matrix needs to be divisible by "
-        << "the quantization group size " << groups
-        << ". However the provided matrix"
-        << " has shape " << w.shape();
+        << "the quantization group size " << group_size
+        << ". However the provided "
+        << " matrix has shape " << w.shape();
    throw std::invalid_argument(msg.str());
  }

  // Compute some constants used for the quantization
-  int n_bins = (1 << width) - 1; // 2**width - 1
-  int el_per_int = 32 / width;
-  array shifts = power(array(2, uint32), arange(0, 32, width, uint32, s), s);
+  int n_bins = (1 << bits) - 1; // 2**bits - 1
+  int el_per_int = 32 / bits;
+  array shifts = power(array(2, uint32), arange(0, 32, bits, uint32, s), s);
  shifts = reshape(shifts, {1, 1, -1}, s);

  // Compute scales and biases
-  array packed_w = reshape(w, {w.shape(0), w.shape(1) / groups, groups}, s);
+  array packed_w =
+      reshape(w, {w.shape(0), w.shape(1) / group_size, group_size}, s);
  array w_max = max(packed_w, /* axis= */ -1, /* keepdims= */ true, s);
  array w_min = min(packed_w, /* axis= */ -1, /* keepdims= */ true, s);
  array delta = divide(subtract(w_max, w_min, s), array(n_bins, w.dtype()), s);
@@ -2700,8 +2702,8 @@ array dequantize(
    const array& w,
    const array& scales,
    const array& biases,
-    int groups /* = 128 */,
-    int width /* = 4 */,
+    int group_size /* = 64 */,
+    int bits /* = 4 */,
    StreamOrDevice s /* = {} */) {
  if (w.ndim() != 2 || scales.ndim() != 2 || biases.ndim() != 2) {
    throw std::invalid_argument("[dequantize] Only matrices supported for now");
@@ -2723,22 +2725,22 @@ array dequantize(
  }

  // Compute some constants for the dequantization
-  int el_per_int = 32 / width;
+  int el_per_int = 32 / bits;

-  if (w.shape(1) * el_per_int != scales.shape(1) * groups) {
+  if (w.shape(1) * el_per_int != scales.shape(1) * group_size) {
    std::ostringstream msg;
    msg << "[dequantize] Shape of scales and biases does not match the matrix "
        << "given the quantization parameters. Provided matrix of shape "
        << w.shape() << " and scales/biases of shape " << scales.shape()
-        << " with groups=" << groups << " and width=" << width << ".";
+        << " with group_size=" << group_size << " and bits=" << bits << ".";
    throw std::invalid_argument(msg.str());
  }

  // Extract the pieces from the passed quantized matrix
  std::vector<array> parts;
-  for (int start = 0; start < 32; start += width) {
+  for (int start = 0; start < 32; start += bits) {
    // TODO: Implement bitwise operators for integral types
-    int shift_left = 32 - (start + width);
+    int shift_left = 32 - (start + bits);
    int shift_right = shift_left + start;
    array p = multiply(w, array(1 << shift_left, uint32), s);
    p = floor_divide(p, array(1 << shift_right, uint32), s);
@@ -2748,7 +2750,7 @@ array dequantize(
  array w_full = concatenate(parts, -1, s);

  // Dequantize
-  w_full = reshape(w_full, {w.shape(0), -1, groups}, s);
+  w_full = reshape(w_full, {w.shape(0), -1, group_size}, s);
  w_full = multiply(w_full, expand_dims(scales, -1, s), s);
  w_full = add(w_full, expand_dims(biases, -1, s), s);
  w_full = reshape(w_full, {w.shape(0), -1}, s);
--- a/mlx/ops.h
+++ b/mlx/ops.h
@@ -1037,15 +1037,15 @@ array quantized_matmul(
    const array& w,
    const array& scales,
    const array& biases,
-    int groups = 128,
-    int width = 4,
+    int group_size = 64,
+    int bits = 4,
    StreamOrDevice s = {});

 /** Quantize a matrix along its last axis */
 std::tuple<array, array, array> quantize(
    const array& w,
-    int groups = 128,
-    int width = 4,
+    int group_size = 64,
+    int bits = 4,
    StreamOrDevice s = {});

 /** Dequantize a matrix produced by quantize() */
@@ -1053,8 +1053,8 @@ array dequantize(
    const array& w,
    const array& scales,
    const array& biases,
-    int groups = 128,
-    int width = 4,
+    int group_size = 64,
+    int bits = 4,
    StreamOrDevice s = {});

 } // namespace mlx::core
--- a/mlx/primitives.cpp
+++ b/mlx/primitives.cpp
@@ -1718,7 +1718,7 @@ array QuantizedMatmul::jvp(

 bool QuantizedMatmul::is_equivalent(const Primitive& other) const {
  const QuantizedMatmul& qm_other = static_cast<const QuantizedMatmul&>(other);
-  return groups_ == qm_other.groups_ && width_ == qm_other.width_;
+  return group_size_ == qm_other.group_size_ && bits_ == qm_other.bits_;
 }

 std::pair<array, int> RandomBits::vmap(
--- a/mlx/primitives.h
+++ b/mlx/primitives.h
@@ -1112,8 +1112,8 @@ class Power : public Primitive {

 class QuantizedMatmul : public Primitive {
 public:
-  explicit QuantizedMatmul(Stream stream, int groups, int width)
-      : Primitive(stream), groups_(groups), width_(width){};
+  explicit QuantizedMatmul(Stream stream, int group_size, int bits)
+      : Primitive(stream), group_size_(group_size), bits_(bits){};

  void eval_cpu(const std::vector<array>& inputs, array& out) override;
  void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1127,8 +1127,8 @@ class QuantizedMatmul : public Primitive {
  bool is_equivalent(const Primitive& other) const override;

 private:
-  int groups_;
-  int width_;
+  int group_size_;
+  int bits_;

  void eval(const std::vector<array>& inputs, array& out);
 };