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Commit 3ac5840c authored by Tim Dettmers's avatar Tim Dettmers
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Added fp4 quant/dequant and dequant optimizations.

parent 0f5c3948
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Showing with 471 additions and 116 deletions
bitsandbytes/cextension.py
View file @ 3ac5840c
......@@ -9,7 +9,7 @@ from bitsandbytes.cuda_setup.main import CUDASetup
setup = CUDASetup.get_instance()
if setup.initialized != True:
if not setup.initialized:
setup.run_cuda_setup()
if 'BITSANDBYTES_NOWELCOME' not in os.environ or str(os.environ['BITSANDBYTES_NOWELCOME']) == '0':
setup.print_log_stack()
......
bitsandbytes/cuda_setup/main.py
View file @ 3ac5840c
......@@ -35,6 +35,9 @@ class CUDASetup:
raise RuntimeError("Call get_instance() instead")
def generate_instructions(self):
if getattr(self, 'error', False): return
print(self.error)
self.error = True
if self.cuda is None:
self.add_log_entry('CUDA SETUP: Problem: The main issue seems to be that the main CUDA library was not detected.')
self.add_log_entry('CUDA SETUP: Solution 1): Your paths are probably not up-to-date. You can update them via: sudo ldconfig.')
......@@ -84,6 +87,7 @@ class CUDASetup:
self.has_printed = False
self.lib = None
self.initialized = False
self.error = False
def run_cuda_setup(self):
self.initialized = True
......
bitsandbytes/functional.py
View file @ 3ac5840c
......@@ -168,7 +168,8 @@ def create_fp8_map(signed=True, exponent_bits=5, precision_bits=2, total_bits=8)
values = []
lst = list(itertools.product([0, 1], repeat=precision_bits))
#for ev in evalues:
bias = 2**(exponent_bits-1)-1
bias = 2**(exponent_bits-1)+1
print(bias)
for evalue in range(2**(exponent_bits)):
for bit_pattern in lst:
value = (1 if evalue != 0 else 0)
......@@ -176,10 +177,12 @@ def create_fp8_map(signed=True, exponent_bits=5, precision_bits=2, total_bits=8)
value += pval*(2**-(i+1))
if evalue == 0:
# subnormals
value = value*2**-(bias-1)
value = value*2**-(bias)
else:
# normals
value = value*2**-(evalue-bias-2)
print(value, 1)
value = value*2**-(evalue-bias-1)
print(value, 2)
values.append(value)
if signed:
values.append(-value)
......@@ -193,7 +196,7 @@ def create_fp8_map(signed=True, exponent_bits=5, precision_bits=2, total_bits=8)
values.append(0)
values.sort()
code = torch.Tensor(values)
code /= code.max()
#code /= code.max()
return code
......@@ -587,7 +590,7 @@ def dequantize_blockwise(
code = code.to(A.device)
if blocksize not in [2048, 4096, 1024, 512, 256, 128, 64]:
raise ValueError(f"The blockwise of {blocksize} is not supported. Supported values: [2048, 4096, 1024, 512, 256, 128, 64]")
is_on_gpu([A, out])
is_on_gpu([A, absmax, out])
if out.dtype == torch.float32:
lib.cdequantize_blockwise_fp32(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(blocksize), ct.c_int(A.numel()))
elif out.dtype == torch.float16:
......@@ -602,6 +605,116 @@ def dequantize_blockwise(
return out
def quantize_fp4(A: Tensor, absmax: Tensor = None, out: Tensor = None, blocksize=64) -> Tensor:
"""
Quantize tensor A in blocks of FP4 values.
Quantizes tensor A by dividing it into blocks which are independently quantized to FP4.
Parameters
----------
A : torch.Tensor
The input tensor.
absmax : torch.Tensor
The absmax values.
out : torch.Tensor
The output tensor (8-bit).
blocksize : int
The blocksize used in quantization.
Returns
-------
torch.Tensor:
The 8-bit tensor with packed 4-bit values.
tuple(torch.Tensor, torch.Size, torch.dtype):
The quantization state to undo the quantization.
"""
if A.device.type != 'cuda':
raise NotImplementedError(f'Device type not supported for FP4 quantization: {A.device.type}')
n = A.numel()
input_shape = A.shape
if absmax is None:
blocks = n // blocksize
blocks += 1 if n % blocksize > 0 else 0
absmax = torch.zeros((blocks,), device=A.device)
state = (absmax, input_shape, A.dtype)
if out is None:
out = torch.zeros(((n+1)//2,), dtype=torch.uint8, device=A.device)
assert blocksize in [4096, 2048, 1024, 512, 256, 128, 64]
prev_device = pre_call(A.device)
is_on_gpu([A, out, absmax])
if A.dtype == torch.float32:
lib.cquantize_blockwise_fp32_fp4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int32(blocksize), ct.c_int(n))
elif A.dtype == torch.float16:
lib.cquantize_blockwise_fp16_fp4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int32(blocksize), ct.c_int(n))
else:
raise ValueError(f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}")
post_call(A.device)
return out, state
def dequantize_fp4(A: Tensor,quant_state: Tuple[Tensor, Tensor] = None, absmax: Tensor = None, out: Tensor = None, blocksize: int = 64) -> Tensor:
"""
Dequantizes FP4 blockwise quantized values.
Dequantizes the tensor A with maximum absolute values absmax in blocks of size blocksize.
Parameters
----------
A : torch.Tensor
The input 8-bit tensor (packed 4-bit values).
quant_state : tuple(torch.Tensor, torch.Size, torch.dtype)
Tuple of absmax values, original tensor shape and original dtype.
absmax : torch.Tensor
The absmax values.
out : torch.Tensor
Dequantized output tensor.
Returns
-------
torch.Tensor:
Dequantized tensor.
"""
if blocksize not in [2048, 4096, 1024, 512, 256, 128, 64]:
raise ValueError(f"The blockwise of {blocksize} is not supported. Supported values: [2048, 4096, 1024, 512, 256, 128, 64]")
if quant_state is None:
assert absmax is not None and out is not None
shape = out.shape
dtype = out.dtype
else:
absmax, shape, dtype = quant_state
if out is None:
out = torch.empty(shape, dtype=dtype, device=A.device)
n = out.numel()
device = pre_call(A.device)
is_on_gpu([A, absmax, out])
if out.dtype == torch.float32:
lib.cdequantize_blockwise_fp32_fp4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(blocksize), ct.c_int(n))
elif out.dtype == torch.float16:
lib.cdequantize_blockwise_fp16_fp4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(blocksize), ct.c_int(n))
else:
raise ValueError(f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}")
post_call(A.device)
return out
def quantize(A: Tensor, code: Tensor = None, out: Tensor = None) -> Tensor:
if code is None:
if "dynamic" not in name2qmap:
......
csrc/kernels.cu
View file @ 3ac5840c
This diff is collapsed.
csrc/kernels.cuh
View file @ 3ac5840c
......@@ -14,8 +14,8 @@ template<typename T>__global__ void kEstimateQuantiles(T *__restrict__ const A,
__global__ void kQuantize(float * code, float * __restrict__ const A, unsigned char *out, const int n);
__global__ void kDequantize(float *code, unsigned char *A, float *out, const int n);
template<typename T, int BLOCK_SIZE, int NUM_PER_TH, int STOCHASTIC> __global__ void kQuantizeBlockwise(float * code, T * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template<typename T, int BLOCK_SIZE, int THREADS, int NUM_PER_TH> __global__ void kDequantizeBlockwise(float *code, unsigned char * A, float * absmax, T *out, const int n);
template<typename T, int BLOCK_SIZE, int NUM_PER_TH, int STOCHASTIC, int FP4> __global__ void kQuantizeBlockwise(float * code, T * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
template<typename T, int BLOCK_SIZE, int THREADS, int NUM_PER_TH, int FP4> __global__ void kDequantizeBlockwise(float *code, unsigned char * A, float * absmax, T *out, const int blocksize, const int n);
template<typename T, int OPTIMIZER, int BLOCK_SIZE, int NUM_VALS>
__global__ void kPreconditionOptimizer32bit2State(T* g, T* p,
......
csrc/ops.cu
View file @ 3ac5840c
......@@ -50,7 +50,7 @@ void dequantize(float *code, unsigned char *A, float *out, int n)
CUDA_CHECK_RETURN(cudaPeekAtLastError());
}
template <typename T, int STOCHASTIC> void quantizeBlockwise(float * code, T *A, float *absmax, unsigned char *out, float *rand, int rand_offset, int blocksize, const int n)
template <typename T, int STOCHASTIC, int FP4> void quantizeBlockwise(float * code, T *A, float *absmax, unsigned char *out, float *rand, int rand_offset, int blocksize, const int n)
{
int num_blocks = n/blocksize;
num_blocks = n % blocksize == 0 ? num_blocks : num_blocks + 1;
......@@ -58,42 +58,34 @@ template <typename T, int STOCHASTIC> void quantizeBlockwise(float * code, T *A,
assert(blocksize == 4096);
if(blocksize == 4096)
kQuantizeBlockwise<T, 4096, 4, STOCHASTIC><<<num_blocks, 1024>>>(code, A, absmax, out, rand, rand_offset, n);
kQuantizeBlockwise<T, 4096, 4, STOCHASTIC, 0><<<num_blocks, 1024>>>(code, A, absmax, out, rand, rand_offset, n);
else if(blocksize == 2048)
kQuantizeBlockwise<T, 2048, 4, 0><<<num_blocks, 512>>>(code, A, absmax, out, rand, rand_offset, n);
kQuantizeBlockwise<T, 2048, 4, 0, FP4><<<num_blocks, 512>>>(code, A, absmax, out, rand, rand_offset, n);
else if(blocksize == 1024)
kQuantizeBlockwise<T, 1024, 4, 0><<<num_blocks, 256>>>(code, A, absmax, out, rand, rand_offset, n);
kQuantizeBlockwise<T, 1024, 4, 0, FP4><<<num_blocks, 256>>>(code, A, absmax, out, rand, rand_offset, n);
else if(blocksize == 512)
kQuantizeBlockwise<T, 512, 2, 0><<<num_blocks, 256>>>(code, A, absmax, out, rand, rand_offset, n);
kQuantizeBlockwise<T, 512, 2, 0, FP4><<<num_blocks, 256>>>(code, A, absmax, out, rand, rand_offset, n);
else if(blocksize == 256)
kQuantizeBlockwise<T, 256, 2, 0><<<num_blocks, 128>>>(code, A, absmax, out, rand, rand_offset, n);
kQuantizeBlockwise<T, 256, 2, 0, FP4><<<num_blocks, 128>>>(code, A, absmax, out, rand, rand_offset, n);
else if(blocksize == 128)
kQuantizeBlockwise<T, 128, 2, 0><<<num_blocks, 64>>>(code, A, absmax, out, rand, rand_offset, n);
kQuantizeBlockwise<T, 128, 2, 0, FP4><<<num_blocks, 64>>>(code, A, absmax, out, rand, rand_offset, n);
else if(blocksize == 64)
kQuantizeBlockwise<T, 64, 1, 0><<<num_blocks, 64>>>(code, A, absmax, out, rand, rand_offset, n);
kQuantizeBlockwise<T, 64, 2, 0, FP4><<<num_blocks, 32>>>(code, A, absmax, out, rand, rand_offset, n);
CUDA_CHECK_RETURN(cudaPeekAtLastError());
}
template<typename T> void dequantizeBlockwise(float *code, unsigned char *A, float *absmax, T *out, int blocksize, const int n)
template<typename T, int FP4> void dequantizeBlockwise(float *code, unsigned char *A, float *absmax, T *out, int blocksize, const int n)
{
int num_blocks = n/blocksize;
num_blocks = n % blocksize == 0 ? num_blocks : num_blocks + 1;
if(blocksize == 4096)
kDequantizeBlockwise<T, 4096, 1024, 4><<<num_blocks, 4096/4>>>(code, A, absmax, out, n);
else if(blocksize == 2048)
kDequantizeBlockwise<T, 2048, 512, 4><<<num_blocks, 2048/4>>>(code, A, absmax, out, n);
else if(blocksize == 1024)
kDequantizeBlockwise<T, 1024, 256, 4><<<num_blocks, 1024/4>>>(code, A, absmax, out, n);
else if(blocksize == 512)
kDequantizeBlockwise<T, 512, 256, 2><<<num_blocks, 512/2>>>(code, A, absmax, out, n);
else if(blocksize == 256)
kDequantizeBlockwise<T, 256, 128, 2><<<num_blocks, 256/2>>>(code, A, absmax, out, n);
else if(blocksize == 128)
kDequantizeBlockwise<T, 128, 64, 2><<<num_blocks, 128/2>>>(code, A, absmax, out, n);
else if(blocksize == 64)
kDequantizeBlockwise<T, 64, 64, 1><<<num_blocks, 64/1>>>(code, A, absmax, out, n);
int tile_size = FP4 ? 1024 : 512;
if(FP4)
kDequantizeBlockwise<T, 512, 64, 8, FP4><<<(n+tile_size-1)/tile_size, 64>>>(code, A, absmax, out, blocksize/2, n);
else
kDequantizeBlockwise<T, 512, 64, 8, FP4><<<(n+tile_size-1)/tile_size, 64>>>(code, A, absmax, out, blocksize, n);
CUDA_CHECK_RETURN(cudaPeekAtLastError());
}
......@@ -688,12 +680,16 @@ template void transformRowToFormat<COL_AMPERE, 1>(char * A, char *out, int rows,
template void estimateQuantiles(half *A, float *code, float offset, int n);
template void estimateQuantiles(float *A, float *code, float offset, int n);
template void quantizeBlockwise<half, 0>(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void quantizeBlockwise<float, 0>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void quantizeBlockwise<half, 1>(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void quantizeBlockwise<float, 1>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void dequantizeBlockwise<half>(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n);
template void dequantizeBlockwise<float>(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n);
template void quantizeBlockwise<half, 0, 0>(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void quantizeBlockwise<float, 0, 0>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void quantizeBlockwise<half, 0, 1>(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void quantizeBlockwise<float, 0, 1>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void quantizeBlockwise<half, 1, 0>(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void quantizeBlockwise<float, 1, 0>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template void dequantizeBlockwise<half, 0>(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n);
template void dequantizeBlockwise<float, 0>(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n);
template void dequantizeBlockwise<half, 1>(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n);
template void dequantizeBlockwise<float, 1>(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n);
#define MAKE_optimizer32bit(name, gtype) \
template void optimizer32bit<gtype, name>(gtype* g, gtype* p, \
......
csrc/ops.cuh
View file @ 3ac5840c
......@@ -128,8 +128,8 @@ template <typename T> void estimateQuantiles(T *A, float *code, float offset, in
void quantize(float *code, float *A, unsigned char *out, int n);
void dequantize(float *code, unsigned char *A, float *out, int n);
template <typename T, int STOCHASTIC> void quantizeBlockwise(float * code, T *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template<typename T> void dequantizeBlockwise(float *code, unsigned char *A, float *absmax, T *out, int block_size, const int n);
template <typename T, int STOCHASTIC, int FP4> void quantizeBlockwise(float * code, T *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template<typename T, int FP4> void dequantizeBlockwise(float *code, unsigned char *A, float *absmax, T *out, int block_size, const int n);
template<typename T, int OPTIMIZER> void optimizer32bit(T* g, T* p,
float* state1, float* state2, float *unorm, float max_unorm, float param_norm,
......
csrc/pythonInterface.c
View file @ 3ac5840c
......@@ -75,13 +75,17 @@ MAKE_BLOCKWISE8(adagrad, ADAGRAD, float, 32)
void percentileClipping_g32(float * g, float *gnorm_vec, int step, const int n){ percentileClipping<float>(g, gnorm_vec, step, n); }
void percentileClipping_g16(half * g, float *gnorm_vec, int step, const int n){ percentileClipping<half>(g, gnorm_vec, step, n); }
void quantizeBlockwise_fp16(float * code, half *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<half, 0>(code, A, absmax, out, NULL, 0, blocksize, n); }
void quantizeBlockwise_fp32(float * code, float *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<float, 0>(code, A, absmax, out, NULL, 0, blocksize, n); }
void quantizeBlockwise_stochastic_fp16(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, const int n){ quantizeBlockwise<half, 1>(code, A, absmax, out, rand, rand_offset, 4096, n); }
void quantizeBlockwise_stochastic_fp32(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, const int n){ quantizeBlockwise<float, 1>(code, A, absmax, out, rand, rand_offset, 4096, n); }
void dequantizeBlockwise_fp16(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n){ dequantizeBlockwise<half>(code, A, absmax, out, blocksize, n); } \
void dequantizeBlockwise_fp32(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n){ dequantizeBlockwise<float>(code, A, absmax, out, blocksize, n); }
void quantizeBlockwise_fp16(float * code, half *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<half, 0, 0>(code, A, absmax, out, NULL, 0, blocksize, n); }
void quantizeBlockwise_fp32(float * code, float *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<float, 0, 0>(code, A, absmax, out, NULL, 0, blocksize, n); }
void quantizeBlockwise_stochastic_fp16(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, const int n){ quantizeBlockwise<half, 1, 0>(code, A, absmax, out, rand, rand_offset, 4096, n); }
void quantizeBlockwise_stochastic_fp32(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, const int n){ quantizeBlockwise<float, 1, 0>(code, A, absmax, out, rand, rand_offset, 4096, n); }
void quantizeBlockwise_fp16_fp4(float * code, half *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<half, 0, 1>(NULL, A, absmax, out, NULL, 0, blocksize, n); }
void quantizeBlockwise_fp32_fp4(float * code, float *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<float, 0, 1>(NULL, A, absmax, out, NULL, 0, blocksize, n); }
void dequantizeBlockwise_fp16(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n){ dequantizeBlockwise<half, 0>(code, A, absmax, out, blocksize, n); } \
void dequantizeBlockwise_fp32(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n){ dequantizeBlockwise<float, 0>(code, A, absmax, out, blocksize, n); }
void dequantizeBlockwise_fp16_fp4(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n){ dequantizeBlockwise<half, 1>(NULL, A, absmax, out, blocksize, n); } \
void dequantizeBlockwise_fp32_fp4(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n){ dequantizeBlockwise<float, 1>(NULL, A, absmax, out, blocksize, n); }
#define MAKE_FUNC_TRANSFORM(fbits, fsrc, ftrgt, ftranspose, dtype, src, target, transpose, bits) \
void transform_##fbits##_##fsrc##_to_##ftrgt##_##ftranspose(cublasLtHandle_t ltHandle, dtype *A, dtype *out, int dim1, int dim2) \
......@@ -148,6 +152,11 @@ extern "C"
void cdequantize_blockwise_fp16(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n){ dequantizeBlockwise_fp16(code, A, absmax, out, blocksize, n); }
void cdequantize_blockwise_fp32(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n){ dequantizeBlockwise_fp32(code, A, absmax, out, blocksize, n); }
void cquantize_blockwise_fp16_fp4(float * code, half *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise_fp16_fp4(code, A, absmax, out, blocksize, n); }
void cquantize_blockwise_fp32_fp4(float * code, float *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise_fp32_fp4(code, A, absmax, out, blocksize, n); }
void cdequantize_blockwise_fp16_fp4(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n){ dequantizeBlockwise_fp16_fp4(code, A, absmax, out, blocksize, n); }
void cdequantize_blockwise_fp32_fp4(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n){ dequantizeBlockwise_fp32_fp4(code, A, absmax, out, blocksize, n); }
#define MAKE_CFUNC32(name, gtype, gbits) \
void c##name##32bit_g##gbits(gtype *g, gtype *p, \
float* state1, float* state2, float *unorm, float max_unorm, float param_norm, \
......
tests/test_functional.py
View file @ 3ac5840c
......@@ -152,7 +152,7 @@ def test_dynamic_quantization():
def test_dynamic_blockwise_quantization():
#print('')
for blocksize in [4096, 2048, 1024, 512]:
for blocksize in [4096, 2048, 1024, 512, 256, 128, 64]:
diffs = []
reldiffs = []
for i in range(100):
......@@ -2189,7 +2189,88 @@ def test_bench_dequantization():
torch.cuda.synchronize()
t0 = time.time()
for i in range(100):
F.dequantize_blockwise(qa, SA, blocksize=2048)
#F.dequantize_blockwise(qa, SA, blocksize=2048)
qa, SA = F.quantize_blockwise(a)
torch.cuda.synchronize()
#print((time.time()-t0)/1e6)
def test_fp4_quant():
vals = list(product([0, 1], repeat=4))
code = {}
for bits in vals:
result = 0
bias = 3
sign, e1, e2, p1 = bits
idx = sign*8 + e1*4 + e2*2 + p1*1
sign = -1.0 if sign else 1.0
exp = e1*2 + e2*1
if exp == 0:
# sub-normal
if p1 == 0: result = 0
else: result = sign*0.0625
else:
# normal
exp = 2**(-exp + bias + 1)
frac = 1.5 if p1 else 1.0
result = sign*exp*frac
code[idx] = result
A1 = torch.randn(1024, 1024, device='cuda').half()
qa, SA = F.quantize_fp4(A1, blocksize=64)
A2 = F.dequantize_fp4(qa, SA)
#qa, SA = F.quantize_fp4(A1, blocksize=128)
#A2 = F.dequantize_fp4(qa, SA, blocksize=128)
#A1 = A1.flatten().sort()[0]
#A2 = A2.flatten().sort()[0]
#print(A1)
#print(A2)
err = (A1 - A2).abs().float()
relerr = (err/A1.abs().float()).mean()
err = err.mean()
print(err, relerr)
#assert err.item() < 0.1
#assert relerr.item() < 0.28
def test_bench_fp4_dequant():
blocksize = 256
a = torch.rand(1024*12*4, 1024*12, device='cuda').half()
qa, SA = F.quantize_fp4(a, blocksize=blocksize)
input_size = a.numel()/2
output_size = a.numel()*2
num_bytes = input_size+output_size
GB = num_bytes/1e9
max_theoretical_s = GB/768
print(max_theoretical_s*1e6)
b = torch.randn(128, 1024*12, device='cuda').half()
iters = 5
torch.cuda.synchronize()
t0 = time.time()
for i in range(iters):
F.dequantize_fp4(qa, SA, blocksize=blocksize)
#b.copy_(a)
torch.cuda.synchronize()
print((time.time()-t0)/iters*1e6)
torch.cuda.synchronize()
t0 = time.time()
for i in range(iters):
torch.matmul(b, a.t())
torch.cuda.synchronize()
print((time.time()-t0)/iters*1e6)
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