Add some code for evaluating FPx (not enabled)

exllamav2/experimental/fpx.py

@@ -0,0 +1,249 @@
+import torch
+from torch import Tensor
+import gc
+
+# From https://github.com/PygmalionAI/aphrodite-engine/blob/main/aphrodite/quantization/utils/fp6_utils.py
+
+def _n_ones(n: int) -> int:
+    return (1 << n) - 1
+
+EBITS_F32, MBITS_F32 = 8, 23
+F32_EXP_BIAS = _n_ones(EBITS_F32 - 1)
+
+_ONES_TABLE = [_n_ones(i) for i in range(8)]
+
+def _f32_to_fpx_unpacked(x: Tensor, ebits: int, mbits: int) -> Tensor:
+    """Convert FP32 numbers to sub-byte floating point numbers with the given
+    number of exponent and mantissa bits.
+    Input: torch.Tensor of dtype torch.float
+    Output: torch.Tensor of dtype torch.uint8, where the bit encoding is stored
+    in the least significant bits. e.g.
+      fp4: bits 0-3 empty and bits 4-7 in fp4_e2m1 encoding
+      fp6: bits 0-1 empty and bits 2-7 in fp6_e2m3 or fp6_e3m2 encoding
+    Note: there are no special values (NaN, inf) support in this code. Values
+    outside the representable range of FPx after rounding are clamped to the
+    maximum FPx magnitude (sign is preserved).
+    Code below is an adaptation of https://fburl.com/code/ciwofcg4
+    Background 1: last answer in https://stackoverflow.com/questions/8981913/how-to-perform-round-to-even-with-floating-point-numbers  # noqa: E501
+    Background 2: Computer Organization and Design, RISC-V edition, Chapter 3.5
+    """
+    assert x.dtype == torch.float
+    assert 1 + ebits + mbits <= 8
+
+    # calculate constants
+    exp_bias = _n_ones(ebits - 1)
+    max_int = _n_ones(ebits + mbits)
+    sign_mask = 1 << (ebits + mbits)
+
+    # TODO document this better
+    magic_adder = _n_ones(MBITS_F32 - mbits - 1)
+
+    # all E bits and M bits are 1s
+    max_normal = 2 ** (_n_ones(ebits) - exp_bias) * (_n_ones(mbits + 1) / (2 ** mbits))
+
+    # E bits = 1, M bits = 0
+    min_normal = 2 ** (1 - exp_bias)
+
+    denorm_exp = (
+        # exp bias conversion between formats
+        (F32_EXP_BIAS - exp_bias)
+        # mantissa length difference between formats
+        + (MBITS_F32 - mbits)
+        # add one to encoded exponent for denormalized numbers
+        + 1
+    )
+    denorm_mask_int = denorm_exp << MBITS_F32
+
+    # reinterpret int32 as float32
+    denorm_mask_float = torch.tensor(denorm_mask_int, dtype=torch.int32).view(torch.float32)
+
+    # save the sign
+    # Note that we have torch.uint32, but some ops like cpu bit shifts
+    # do not work on it. So, we stay in int32.
+    x = x.view(torch.int32)
+    sign = x & 0x80000000
+
+    # set everything to positive, will add sign back at the end
+    x = x ^ sign
+
+    # TODO: can the branch floating point comparisons below be done without
+    # converting to float? probably but need to verify
+    x = x.view(torch.float)
+
+    # rewrite saturate/denorm/norm branches without explicit data dependent
+    # control flow, to be more compiler friendly
+    saturate_mask = x >= max_normal
+    denormal_mask = torch.logical_and(torch.logical_not(saturate_mask), x < min_normal)
+    normal_mask = torch.logical_not(torch.logical_or(saturate_mask, denormal_mask))
+
+    #
+    # branch 1: saturate to max val - handled later in the code which combines
+    #   the branches
+    #
+
+    #
+    # branch 2: to conversion to denormal as well as rounding up to normal
+    #
+    denormal_x = x + denorm_mask_float
+    denormal_x = denormal_x.view(torch.int32)
+    denormal_x -= denorm_mask_int
+    denormal_x = denormal_x.to(torch.uint8)
+
+    #
+    # branch 3: stay in normal range, adjust the exponent and round
+    #
+    normal_x = x.view(torch.int32)
+    # resulting mantissa is odd
+    mant_odd = (normal_x >> (MBITS_F32 - mbits)) & 1
+    # update exponent, rounding bias part 1
+    val_to_add = ((exp_bias - F32_EXP_BIAS) << MBITS_F32) + magic_adder
+    normal_x += val_to_add
+    # rounding bias part 2
+    normal_x += mant_odd
+    # take the bits!
+    normal_x = normal_x >> (MBITS_F32 - mbits)
+    normal_x = normal_x.to(torch.uint8)
+
+    #
+    # combine the branches
+    #
+    x = torch.full_like(x, max_int, dtype=torch.uint8)
+    x = torch.where(denormal_mask, denormal_x, x)
+    x = torch.where(normal_mask, normal_x, x)
+
+    # add sign back
+    sign_lp = sign >> (MBITS_F32 + EBITS_F32 - mbits - ebits)
+    sign_lp = sign_lp.to(torch.uint8)
+    # Right shift of a negative signed integer can fill the least significant
+    # bits with either 1s or 0s, depending on the implementation. Since PyTorch
+    # doesn't have an uint32 dtype, we mask out these bits to get just the
+    # f4 sign bit
+    sign_lp = sign_lp & sign_mask
+    x = x | sign_lp
+
+    return x.to(torch.uint8)
+
+
+def _fpx_unpacked_to_f32(x: Tensor, ebits: int, mbits: int) -> Tensor:
+    """Convert sub-byte floating point numbers with the given number of exponent
+    and mantissa bits to FP32.
+    Input: torch.Tensor of dtype uint8, where the bit encoding is stored
+    in the least significant bits. e.g.
+      fp4: bits 0-3 empty and bits 4-7 in fp4_e2m1 encoding
+      fp6: bits 0-1 empty and bits 2-7 in fp6_e2m3 or fp6_e3m2 encoding
+    Output: torch.Tensor of dtype fp32 with the dequantized value
+    """
+    assert x.dtype == torch.uint8
+    assert 1 + ebits + mbits <= 8
+
+    sign_mask = 1 << (ebits + mbits)
+    exp_bias = _n_ones(ebits - 1)
+    mantissa_mask = _n_ones(mbits)
+
+    # save the sign
+    sign_lp = x & sign_mask
+
+    # set everything to positive, will add sign back at the end
+    x_pos = x ^ sign_lp
+
+    #
+    # 1. Calculate zero mask
+    #
+    zero_mask = x_pos == 0
+
+    #
+    # 2. Calculate the denormal path mask
+    #
+    denormal_mask = torch.logical_and((x_pos > 0), ((x_pos >> mbits) == 0))
+
+    #
+    # 3. Calculate the normal path
+    #
+
+    # calculate the new exponent and shift it to bits 2:9 of the result
+    exp_biased_lp = x_pos >> mbits
+    exp_biased_f32 = exp_biased_lp - exp_bias + F32_EXP_BIAS
+    exp_biased_f32 = exp_biased_f32.to(torch.int32) << MBITS_F32
+
+    # shift the mantissa to bits 10:32 of the result
+    mantissa_lp_int32 = (x_pos & mantissa_mask).to(torch.int32)
+    mantissa_f32 = mantissa_lp_int32 << (MBITS_F32 - mbits)
+    result = exp_biased_f32 | mantissa_f32
+
+    #
+    # 4. Add the zero and denormal casts to the already casted normal path
+    #
+    result[zero_mask] = 0
+
+    denormal_exp_biased = 1 - exp_bias + F32_EXP_BIAS
+
+    # fast path.
+    # without this, performance for FP4_E2M1 is slower by 2x
+    if mbits == 1:
+        result[denormal_mask] = (denormal_exp_biased - mbits) << MBITS_F32
+
+    else:
+        # iterate over all possible values of mantissa
+        # i=0, j=1
+        # i=1, j=10,11
+        # i=2, j=100,101,110,111
+        # and so on
+        for i in range(mbits):
+            for mantissa_cmp in range(1 << i, 1 << (i+1)):
+                # left shift mantissa until it overflows (create an implicit 1)
+                # subtract exponent by the same amount
+                left_shift = mbits - i
+                mantissa_f32 = (mantissa_cmp - (1 << i)) << (left_shift + MBITS_F32 - mbits)
+                exp_biased_f32 = (denormal_exp_biased - left_shift) << MBITS_F32
+
+                # we can update this in-place since the values won't overlap
+                # torch.compile() may complain unsupported operand type(s) for |: 'SymInt' and 'int'
+                # thus we use + instead of | here
+                mantissa_lp_int32[mantissa_lp_int32 == mantissa_cmp] = exp_biased_f32 + mantissa_f32
+
+        result = torch.where(denormal_mask, mantissa_lp_int32, result)
+
+    # add sign back
+    sign_f32 = sign_lp.to(torch.int32) << (MBITS_F32 - mbits + EBITS_F32 - ebits)
+    result = result | sign_f32
+
+    return result.view(torch.float)
+
+
+def to_scaled_tc_fpx(tensor: Tensor, ebits: int, mbits: int) -> tuple[Tensor, Tensor]:
+    # _n_ones() is not compatible with torch.compile() due to << operator
+    # https://github.com/pytorch/pytorch/issues/119152
+    # exp_bias = _n_ones(ebits - 1)
+    # max_normal = 2 ** (_n_ones(ebits) - exp_bias) * (_n_ones(mbits + 1) / (2 ** mbits))
+
+    # workaround: global lookup table
+    exp_bias = _ONES_TABLE[ebits - 1]
+    max_normal = 2 ** (_ONES_TABLE[ebits] - exp_bias) * (_ONES_TABLE[mbits + 1] / (2 ** mbits))
+
+    tensor = tensor.float()
+    scale = tensor.abs().amax(1).clamp(min=1e-12) / max_normal
+    tensor_fpx = _f32_to_fpx_unpacked(tensor / scale.view(-1, 1), ebits, mbits)
+    return tensor_fpx, scale.half()
+    # tensor_tc_fpx = pack_tc_fpx(tensor_fpx, 1 + ebits + mbits)
+    # return tensor_tc_fpx, scale.half()
+
+
+def from_scaled_tc_fpx(fpx_unpacked: Tensor, ebits: int, mbits: int, scale = None) -> Tensor:
+    # fpx_unpacked = unpack_tc_fpx(tensor, 1 + ebits + mbits)
+    tensor = _fpx_unpacked_to_f32(fpx_unpacked, ebits, mbits)
+    if scale is not None:
+        tensor = tensor * scale.float().view(-1, 1)
+    return tensor
+
+
+
+def fpxify(tensor: torch.Tensor, exponent: int, mantissa: int) -> torch.Tensor:
+    """
+    Convert to eXmY and back again
+    """
+
+    a = tensor.to("cuda:0").float()
+    b, scale = to_scaled_tc_fpx(a, exponent, mantissa)
+    c = from_scaled_tc_fpx(b, exponent, mantissa, scale)
+    d = c.half().to(tensor.device)
+    return d

exllamav2/generator/dynamic.py

@@ -1989,6 +1989,7 @@ class ExLlamaV2DynamicJob:
             return emit(results, emit_eos = True, eos_reason = "stop_token", stop_token = next_token.item())
 
         # Stop if we reach max_new_tokens
+        # TODO: Auto-extend option
 
         if self.new_tokens >= self.max_new_tokens - self.generator.num_draft_tokens:
             return emit(results, emit_eos = True, emit_held = True, eos_reason = "max_new_tokens")

exllamav2/linear.py

@@ -9,6 +9,7 @@ from exllamav2.compat import safe_move_tensor
 from exllamav2.tensor_p import BROADCAST_VC
 from exllamav2.util import unpack_4bit, pack_4bit
 import gc
+from exllamav2.experimental.fpx import fpxify
 
 from typing import TYPE_CHECKING
 
@@ -170,6 +171,7 @@ class ExLlamaV2Linear(ExLlamaV2Module):
 
         elif isinstance(w, nn.Parameter):
             assert not self.has_bias, self.key + " has no bias tensor but bias is expected"
+            # w = nn.Parameter(fpxify(w.data, 2, 3), requires_grad = False)
             if self.normalize_unq:
                 w = self.normalize(w)
             if self.padding > 0: w = nn.Parameter(F.pad(w.data, (0, 0, 0, self.padding)).contiguous())
@@ -188,6 +190,7 @@ class ExLlamaV2Linear(ExLlamaV2Module):
             if self.normalize_unq:
                 w = self.normalize(w[0]), w[1]
             ww = w[0]
+            # ww = nn.Parameter(fpxify(ww.data, 2, 3), requires_grad = False)
             wb = w[1]
             if self.padding > 0:
                 ww = nn.Parameter(F.pad(ww.data, (0, 0, 0, self.padding)).contiguous())