ggml-impl : do not flush bf16 subnormals to zero

* ggml : add reference fp32 to bf16 conversion

The fast version is no longer equivalent for all platforms
because of the handling of subnormal values.

* gguf-py : remove flush to zero for bf16 subnormals

* gguf-py : remove float32 truncation to bf16

Rounding achieves the same thing in the cases where this was used.

convert-hf-to-gguf.py

@@ -295,7 +295,7 @@ class Model:
 
                 if self.ftype != gguf.LlamaFileType.ALL_F32 and extra_f16 and not extra_f32:
                     if self.ftype == gguf.LlamaFileType.MOSTLY_BF16:
-                        data = gguf.truncate_bf16(data) if old_dtype == torch.bfloat16 else gguf.quantize_bf16(data)
+                        data = gguf.quantize_bf16(data)
                         assert data.dtype == np.uint16
                         data_qtype = gguf.GGMLQuantizationType.BF16
 

ggml-impl.h

@@ -80,8 +80,9 @@ static inline float ggml_compute_bf16_to_fp32(ggml_bf16_t h) {
 /**
  * Converts float32 to brain16.
  *
- * This function is binary identical to AMD Zen4 VCVTNEPS2BF16.
- * Subnormals shall be flushed to zero, and NANs will be quiet.
+ * This is binary identical with Google Brain float conversion.
+ * Floats shall round to nearest even, and NANs shall be quiet.
+ * Subnormals aren't flushed to zero, except perhaps when used.
  * This code should vectorize nicely if using modern compilers.
  */
 static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) {
@@ -95,10 +96,6 @@ static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) {
         h.bits = (u.i >> 16) | 64; /* force to quiet */
         return h;
     }
-    if (!(u.i & 0x7f800000)) { /* subnormal */
-        h.bits = (u.i & 0x80000000) >> 16; /* flush to zero */
-        return h;
-    }
     h.bits = (u.i + (0x7fff + ((u.i >> 16) & 1))) >> 16;
     return h;
 }

ggml.c

@@ -411,9 +411,16 @@ void ggml_bf16_to_fp32_row(const ggml_bf16_t * x, float * y, int64_t n) {
     }
 }
 
+void ggml_fp32_to_bf16_row_reference(const float * x, ggml_bf16_t * y, int64_t n) {
+    for (int i = 0; i < n; i++) {
+        y[i] = ggml_compute_fp32_to_bf16(x[i]);
+    }
+}
+
 void ggml_fp32_to_bf16_row(const float * x, ggml_bf16_t * y, int64_t n) {
   int i = 0;
 #if defined(__AVX512BF16__)
+  // subnormals are flushed to zero on this platform
   for (; i + 32 <= n; i += 32) {
         _mm512_storeu_si512(
             (__m512i *)(y + i),
@@ -904,7 +911,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = false,
         .to_float                 = (ggml_to_float_t) ggml_bf16_to_fp32_row,
         .from_float               = (ggml_from_float_t) ggml_fp32_to_bf16_row,
-        .from_float_reference     = (ggml_from_float_t) ggml_fp32_to_bf16_row,
+        .from_float_reference     = (ggml_from_float_t) ggml_fp32_to_bf16_row_reference,
         .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_bf16,
         .vec_dot_type             = GGML_TYPE_BF16,
         .nrows                    = 1,
@@ -21334,7 +21341,7 @@ size_t ggml_quantize_chunk(
         case GGML_TYPE_BF16:
             {
                 size_t elemsize = sizeof(ggml_bf16_t);
-                ggml_fp32_to_bf16_row(src + start, (ggml_bf16_t *)dst + start, n);
+                ggml_fp32_to_bf16_row_reference(src + start, (ggml_bf16_t *)dst + start, n);
                 result = n * elemsize;
             } break;
         case GGML_TYPE_F32:

ggml.h

@@ -339,6 +339,7 @@ extern "C" {
     GGML_API ggml_bf16_t ggml_fp32_to_bf16(float);
     GGML_API float       ggml_bf16_to_fp32(ggml_bf16_t);  // consider just doing << 16
     GGML_API void        ggml_bf16_to_fp32_row(const ggml_bf16_t *, float *, int64_t);
+    GGML_API void        ggml_fp32_to_bf16_row_reference(const float *, ggml_bf16_t *, int64_t);
     GGML_API void        ggml_fp32_to_bf16_row(const float *, ggml_bf16_t *, int64_t);
 
     struct ggml_object;

gguf-py/gguf/quants.py

@@ -28,19 +28,11 @@ def __compute_fp32_to_bf16(n: np.ndarray) -> np.ndarray:
     n = n.astype(np.float32, copy=False).view(np.uint32)
     # force nan to quiet
     n = np.where((n & 0x7fffffff) > 0x7f800000, (n & np.uint32(0xffff0000)) | np.uint32(64 << 16), n)
-    # flush subnormals to zero
-    n = np.where((n & 0x7f800000) == 0, n & np.uint32(0x80000000), n)
     # round to nearest even
     n = (np.uint64(n) + (0x7fff + ((n >> 16) & 1))) >> 16
     return n.astype(np.uint16)
 
 
-# for fp32 values that are just extended bf16
-def __truncate_fp32_to_bf16(n: np.ndarray) -> np.ndarray:
-    n = n.astype(np.float32, copy=False).view(np.uint32) >> 16
-    return n.astype(np.uint16)
-
-
 # This is faster than np.vectorize and np.apply_along_axis because it works on more than one row at a time
 def __apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.ndarray, otype: DTypeLike, oshape: tuple[int, ...]) -> np.ndarray:
     rows = arr.reshape((-1, arr.shape[-1]))
@@ -68,20 +60,6 @@ def quantize_bf16(n: np.ndarray):
         return __quantize_bf16_array(n)
 
 
-def __truncate_bf16_array(n: np.ndarray) -> np.ndarray:
-    return __apply_over_grouped_rows(__truncate_fp32_to_bf16, arr=n, otype=np.uint16, oshape=n.shape)
-
-
-__truncate_bf16_lazy = LazyNumpyTensor._wrap_fn(__truncate_bf16_array, meta_noop=np.uint16)
-
-
-def truncate_bf16(n: np.ndarray):
-    if type(n) is LazyNumpyTensor:
-        return __truncate_bf16_lazy(n)
-    else:
-        return __truncate_bf16_array(n)
-
-
 __q8_block_size, __q8_type_size = GGML_QUANT_SIZES[GGMLQuantizationType.Q8_0]