Affine quant always in fp32 (#1925)

* do affine quant in fp32

* static cast

Author: barronalex

mlx/backend/cpu/quantized.cpp

@@ -543,8 +543,8 @@ void quantize(
   T* scales = scales_.data<T>();
   T* biases = biases_.data<T>();
 
-  T n_bins = (1 << bits) - 1;
-  T eps = 1e-7;
+  float n_bins = (1 << bits) - 1;
+  float eps = 1e-7;
   bool power_of_2_bits = is_power_of_2(bits);
   int el_per_int = bits == 3 ? 8 : bits == 6 ? 4 : 32 / bits;
   // For 3/6 bits we read 3 uint8s at a time instead of 1 uint32
@@ -554,32 +554,30 @@ void quantize(
 
   for (size_t i = 0; i < n_groups; ++i) {
     size_t w_idx = i * group_size;
-    T w_min = std::numeric_limits<float>::infinity();
-    T w_max = -w_min;
+    float w_min = std::numeric_limits<float>::infinity();
+    float w_max = -w_min;
     for (int j = 0; j < group_size; ++j) {
-      w_max = std::max(w_max, w[w_idx + j]);
-      w_min = std::min(w_min, w[w_idx + j]);
+      w_max = std::max(w_max, (float)w[w_idx + j]);
+      w_min = std::min(w_min, (float)w[w_idx + j]);
     }
     bool mask = std::abs(w_min) > std::abs(w_max);
-    T scale = std::max(T((w_max - w_min) / n_bins), eps);
+    float scale = std::max((w_max - w_min) / n_bins, eps);
     scale = mask ? scale : -scale;
 
-    auto edge = mask ? w_min : w_max;
-    auto q0 = std::rint(edge / scale);
-    if (q0 == 0) {
-      scales[i] = scale;
-      biases[i] = 0;
-    } else {
-      scales[i] = edge / q0;
-      biases[i] = edge;
+    float edge = mask ? w_min : w_max;
+    float q0 = std::rint(edge / scale);
+    float bias = 0;
+    if (q0 != 0) {
+      scale = edge / q0;
+      bias = edge;
     }
     size_t out_idx = i * int_per_group;
     for (int j = 0; j < int_per_group / bytes_per_pack; ++j) {
       uint32_t out_el = 0;
       for (int k = 0; k < el_per_int; ++k) {
-        T w_el = w[w_idx + j * el_per_int + k];
-        w_el = std::rint((w_el - biases[i]) / scales[i]);
-        w_el = std::min(std::max(w_el, T(0)), n_bins);
+        float w_el = w[w_idx + j * el_per_int + k];
+        w_el = std::rint((w_el - bias) / scale);
+        w_el = std::min(std::max(w_el, 0.0f), n_bins);
         out_el |= static_cast<uint32_t>(w_el) << (k * bits);
       }
       if (power_of_2_bits) {
@@ -590,6 +588,8 @@ void quantize(
         out[out_idx + bytes_per_pack * j + 2] = (out_el & 0xff0000) >> 16;
       }
     }
+    scales[i] = static_cast<T>(scale);
+    biases[i] = static_cast<T>(bias);
   }
 }
 

mlx/backend/metal/kernels/quantized.h

@@ -2015,9 +2015,9 @@ template <typename T, const int group_size, const int bits>
     device T* biases [[buffer(3)]],
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  constexpr T eps = T(1e-7);
+  constexpr float eps = 1e-7;
   constexpr int simd_size = 32;
-  constexpr T n_bins = (1 << bits) - 1;
+  constexpr float n_bins = (1 << bits) - 1;
   constexpr int packs_per_int = bits == 3 ? 8 : bits == 6 ? 4 : 8 / bits;
   constexpr int values_per_reduce = group_size / simd_size;
   constexpr int writes_per_reduce = packs_per_int / values_per_reduce;
@@ -2036,13 +2036,13 @@ template <typename T, const int group_size, const int bits>
       ? offset * writes_per_pack
       : offset * bytes_per_pack / writes_per_reduce;
 
-  T w_thread[values_per_reduce];
-  T w_min = Limits<T>::max;
-  T w_max = 0;
+  float w_thread[values_per_reduce];
+  float w_min = Limits<T>::max;
+  float w_max = 0;
 
 #pragma clang loop unroll(full)
   for (int i = 0; i < values_per_reduce; i++) {
-    T val = w[in_index + i];
+    float val = w[in_index + i];
     w_thread[i] = val;
     w_min = min(w_min, val);
     w_max = max(w_max, val);
@@ -2051,20 +2051,20 @@ template <typename T, const int group_size, const int bits>
   w_min = simd_min(w_min);
   w_max = simd_max(w_max);
 
-  T scale = max((w_max - w_min) / n_bins, eps);
+  float scale = max((w_max - w_min) / n_bins, eps);
   bool side = abs(w_min) > abs(w_max);
   scale = side ? scale : -scale;
-  T edge = side ? w_min : w_max;
-  T q0 = round(edge / scale);
+  float edge = side ? w_min : w_max;
+  float q0 = round(edge / scale);
   bool at_zero = q0 == 0.0f;
   scale = at_zero ? scale : edge / q0;
-  T bias = at_zero ? T(0) : edge;
+  float bias = at_zero ? 0 : edge;
 
   // Write out the scales and biases
   size_t gindex = in_index / group_size;
   if (in_index % group_size == 0) {
-    scales[gindex] = scale;
-    biases[gindex] = bias;
+    scales[gindex] = static_cast<T>(scale);
+    biases[gindex] = static_cast<T>(bias);
   }
 
   // We accumulate 3 bytes worth for 3/6 bit so we need a uint32_t

mlx/fast.cpp

@@ -827,14 +827,17 @@ affine_quantize(const array& w, int group_size, int bits, StreamOrDevice s_) {
     auto wshape = w.shape();
     wshape.back() = -1;
 
-    array zero(0, w.dtype());
-    array n_bins((1 << bits) - 1, w.dtype()); // 2**bits - 1
-    array eps(1e-7, w.dtype());
+    array zero(0, float32);
+    array n_bins((1 << bits) - 1, float32); // 2**bits - 1
+    array eps(1e-7, float32);
 
     array packed_w = reshape(w, {-1, 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);
+    w_max = astype(w_max, float32, s);
+    w_min = astype(w_min, float32, s);
+
     array mask = greater(abs(w_min, s), abs(w_max, s), s);
     array scales =
         maximum(divide(subtract(w_max, w_min, s), n_bins, s), eps, s);
@@ -845,6 +848,9 @@ affine_quantize(const array& w, int group_size, int bits, StreamOrDevice s_) {
     array biases = where(equal(q0, zero, s), zero, edge, s);
 
     packed_w = pack_and_quantize(packed_w, scales, biases, bits, s);
+
+    scales = astype(scales, w.dtype(), s);
+    biases = astype(biases, w.dtype(), s);
     return {
         reshape(packed_w, wshape, s),
         reshape(scales, wshape, s),