Forward matmul_fp4 tests pass.

bitsandbytes/__init__.py

@@ -10,6 +10,7 @@ from .autograd._functions import (
     matmul,
     matmul_cublas,
     mm_cublas,
+    matmul_fp4
 )
 from .cextension import COMPILED_WITH_CUDA
 from .nn import modules

bitsandbytes/autograd/_functions.py

@@ -2,7 +2,7 @@ import operator
 import warnings
 from dataclasses import dataclass
 from functools import reduce  # Required in Python 3
-from typing import Tuple, Optional
+from typing import Tuple, Optional, List
 
 import torch
 
@@ -474,6 +474,67 @@ class MatMul8bitLt(torch.autograd.Function):
         return grad_A, grad_B, None, grad_bias, None
 
 
+class MatMulFP4(torch.autograd.Function):
+    # forward is the same, but we added the fallback for pre-turing GPUs
+    # backward is mostly the same, but adds one extra clause (see "elif state.CxB is not None")
+
+    @staticmethod
+    def forward(ctx, A, B, out=None, bias=None, state=None):
+        # default of pytorch behavior if inputs are empty
+        ctx.is_empty = False
+        if prod(A.shape) == 0:
+            ctx.is_empty = True
+            ctx.A = A
+            ctx.B = B
+            ctx.bias = bias
+            B_shape = state[1]
+            if A.shape[-1] == B_shape[0]:
+                return torch.empty(A.shape[:-1] + B_shape[1:], dtype=A.dtype, device=A.device)
+            else:
+                return torch.empty(A.shape[:-1] + B_shape[:1], dtype=A.dtype, device=A.device)
+
+
+        # 1. Dequantize
+        # 2. Matmul
+        output = torch.nn.functional.linear(A, F.dequantize_fp4(B, state).to(A.dtype), bias)
+
+        # 3. Save state
+        ctx.state = state
+        ctx.dtype_A, ctx.dtype_B, ctx.dtype_bias = A.dtype, B.dtype, None if bias is None else bias.dtype
+
+        if any(ctx.needs_input_grad[:2]):
+            ctx.tensors = A
+        else:
+            ctx.tensors = [None, None]
+            ctx.tensor_states = (None, None)
+            ctx.save_for_backward(None, None)
+
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        if ctx.is_empty:
+            bias_grad = None if ctx.bias is None else torch.zeros_like(ctx.bias)
+            return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None
+
+        req_gradA, req_gradB, _, req_gradBias, _ = ctx.needs_input_grad
+        A = ctx.tensors
+        state = ctx.state
+
+        if req_gradBias:
+            # compute grad_bias first before changing grad_output dtype
+            grad_bias = grad_output.sum(0, dtype=ctx.dtype_bias)
+
+        # Cast grad_output to fp16
+        if len(grad_output.shape) == 3:
+            grad_output = grad_output.reshape(-1, grad_output.shape[-1]).contiguous()
+
+        if req_gradB: grad_B = torch.matmul(grad_output.t(), A)
+        if req_gradA: grad_A = torch.matmul(grad_output, F.dequantize_fp4(B, ctx.state).to(ctx.dtype_A))
+
+        return grad_A, grad_B, None, grad_bias, None
+
+
 def matmul(
     A: tensor,
     B: tensor,
@@ -486,3 +547,7 @@ def matmul(
     if threshold > 0.0:
         state.threshold = threshold
     return MatMul8bitLt.apply(A, B, out, bias, state)
+
+
+def matmul_fp4(A: tensor, B: tensor, out: tensor = None, quant_state: List = None, bias=None):
+    return MatMulFP4.apply(A, B, out, bias, quant_state)

bitsandbytes/functional.py

@@ -626,7 +626,7 @@ def quantize_fp4(A: Tensor, absmax: Tensor = None, out: Tensor = None, blocksize
     -------
     torch.Tensor:
         The 8-bit tensor with packed 4-bit values.
-    tuple(torch.Tensor, torch.Size, torch.dtype):
+    tuple(torch.Tensor, torch.Size, torch.dtype, int):
         The quantization state to undo the quantization.
     """
     if A.device.type != 'cuda':
@@ -640,10 +640,10 @@ def quantize_fp4(A: Tensor, absmax: Tensor = None, out: Tensor = None, blocksize
         blocks += 1 if n % blocksize > 0 else 0
         absmax = torch.zeros((blocks,), device=A.device)
 
-    state = (absmax, input_shape, A.dtype)
+    state = (absmax, input_shape, A.dtype, blocksize)
 
     if out is None:
-        out = torch.zeros(((n+1)//2,), dtype=torch.uint8, device=A.device)
+        out = torch.zeros(((n+1)//2, 1), dtype=torch.uint8, device=A.device)
 
     assert blocksize in [4096, 2048, 1024, 512, 256, 128, 64]
 
@@ -692,7 +692,7 @@ def dequantize_fp4(A: Tensor,quant_state: Tuple[Tensor, Tensor] = None, absmax:
         shape = out.shape
         dtype = out.dtype
     else:
-        absmax, shape, dtype = quant_state
+        absmax, shape, dtype, blocksize = quant_state
 
 
     if out is None:
@@ -700,6 +700,7 @@ def dequantize_fp4(A: Tensor,quant_state: Tuple[Tensor, Tensor] = None, absmax:
 
     n = out.numel()
 
+
     device = pre_call(A.device)
     is_on_gpu([A, absmax, out])
     if out.dtype == torch.float32:
@@ -710,9 +711,9 @@ def dequantize_fp4(A: Tensor,quant_state: Tuple[Tensor, Tensor] = None, absmax:
         raise ValueError(f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}")
     post_call(A.device)
 
-    return out
-
-
+    is_transposed = (True if A.shape[0] == 1 else False)
+    if is_transposed: return out.t()
+    else: return out
 
 
 def quantize(A: Tensor, code: Tensor = None, out: Tensor = None) -> Tensor:

bitsandbytes/nn/modules.py

@@ -133,6 +133,67 @@ class Embedding(torch.nn.Embedding):
 
         return emb
 
+class FP4Params(torch.nn.Parameter):
+    def __new__(cls, data=None, requires_grad=True, quant_state=None):
+        cls.quant_state = None
+        if data is None:
+            data = torch.empty(0)
+        return torch.Tensor._make_subclass(cls, data, requires_grad)
+
+    def cuda(self, device):
+        w = self.data.contiguous().half().cuda(device)
+        w_fp4, quant_state = bnb.functional.quantize_fp4(w)
+        self.data = w_fp4
+        self.quant_state = quant_state
+
+        return self
+
+    @overload
+    def to(self: T, device: Optional[Union[int, device]] = ..., dtype: Optional[Union[dtype, str]] = ..., non_blocking: bool = ...,) -> T:
+        ...
+
+    @overload
+    def to(self: T, dtype: Union[dtype, str], non_blocking: bool = ...) -> T:
+        ...
+
+    @overload
+    def to(self: T, tensor: Tensor, non_blocking: bool = ...) -> T:
+        ...
+
+    def to(self, *args, **kwargs):
+        device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(*args, **kwargs)
+
+        if (device is not None and device.type == "cuda" and self.data.device.type == "cpu"):
+            return self.cuda(device)
+        else:
+            new_param = FP4Params(super().to(device=device, dtype=dtype, non_blocking=non_blocking),
+                                  requires_grad=self.requires_grad, quant_state=self.quant_state)
+
+            return new_param
+
+
+class LinearFP4(nn.Linear):
+    def __init__(self, input_features, output_features, bias=True):
+        super().__init__(input_features, output_features, bias)
+        self.state = bnb.MatmulLtState()
+        self.weight = FP4Params(self.weight.data, requires_grad=False)
+
+    def init_8bit_state(self):
+        pass
+
+    def forward(self, x: torch.Tensor):
+        self.state.is_training = self.training
+
+        # weights are cast automatically as Int8Params, but the bias has to be cast manually
+        if self.bias is not None and self.bias.dtype != x.dtype:
+            self.bias.data = self.bias.data.to(x.dtype)
+
+        if getattr(self.weight, 'state', None) is None:
+            print('FP4 state not initialized. Please call .cuda() or .to(device) on the LinearFP4 layer first.')
+        out = bnb.matmul_fp(x, self.weight, bias=self.bias, state=self.weight.state)
+
+        return out
+
 
 class Int8Params(torch.nn.Parameter):
     def __new__(
@@ -208,6 +269,7 @@ class Int8Params(torch.nn.Parameter):
             return new_param
 
 
+
 class Linear8bitLt(nn.Linear):
     def __init__(self, input_features, output_features, bias=True, has_fp16_weights=True,
                        memory_efficient_backward=False, threshold=0.0, index=None):

tests/test_autograd.py

@@ -429,3 +429,118 @@ def test_matmullt(
 
                 if req_grad[2]:
                     torch.testing.assert_allclose(gradBias1, gradBias2)
+
+
+n = 1
+k = 3
+dim1 = torch.randint(16, 64, size=(n,)).tolist()
+dim2 = torch.randint(32, 96, size=(n,)).tolist()
+dim3 = torch.randint(32, 96, size=(n,)).tolist()
+dim4 = torch.randint(32, 96, size=(n,)).tolist()
+
+dim2.append(0)
+
+funcs = [(torch.matmul, bnb.matmul_fp4)]
+str_funcs = ["matmul"]
+req_grad = list(product([True, False], repeat=3))
+req_grad_str = []
+for c in req_grad:
+    strval = ''
+    for v in c:
+        if v == True: strval += 'T'
+        else: strval += 'F'
+    req_grad_str.append(strval)
+
+transpose = [(False, True), (False, False)]
+str_transpose = ["NT", "NN"]
+dtype = [torch.float16, torch.float32]
+has_fp16_weights = [True, False]
+has_bias = [True, False]
+values = list(product(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, has_bias))
+str_values = list(product(dim1, dim2, dim3, dim4, str_funcs, dtype, req_grad_str, str_transpose, has_bias))
+names = ["dim1_{}_dim2_{}_dim3_{}_dim4_{}_func_{}_dtype_{}_requires_grad_{}_transpose_{}_has_bias_{}".format(*vals) for vals in str_values]
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="this test requires a GPU")
+@pytest.mark.parametrize( "dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, has_bias", values, ids=names)
+def test_matmul_fp4( dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, has_bias):
+    dimA = (dim2, dim3) if not transpose[0] else (dim3, dim2)
+    dimB = (dim3, dim4) if not transpose[1] else (dim4, dim3)
+    if has_bias == False:
+        req_grad = list(req_grad)
+        req_grad[2] = False
+
+    for i in range(k):
+        # normal multiply
+        if funcs[0] in [torch.mm, torch.matmul]:
+            A = torch.randn(size=dimA, device="cuda", requires_grad=req_grad[0], dtype=dtype)
+            B = torch.randn(size=dimB, device="cuda", requires_grad=req_grad[1], dtype=dtype)
+            target = torch.randn(size=(dim2, dim4), device="cuda", requires_grad=req_grad[1], dtype=dtype)
+            bias = None
+            bias2 = None
+            if has_bias:
+                bias = torch.randn(dim4, device='cuda', dtype=dtype, requires_grad=req_grad[2])
+                bias2 = bias.clone()
+            torch.nn.init.xavier_uniform_(B)
+            B2 = B.clone()
+
+            B2, quant_state = bnb.functional.quantize_fp4(B)
+
+            if not transpose[0] and transpose[1]:
+                out_torch = funcs[0](A, B.t())
+                out_bnb = funcs[1](A, B2, quant_state=quant_state, bias=bias2)
+            elif not transpose[0] and not transpose[1]:
+                out_torch = funcs[0](A, B)
+                out_bnb = funcs[1](A, B2.t(), quant_state=quant_state, bias=bias2)
+
+            if has_bias:
+                out_torch += bias
+
+            assert out_bnb.dtype == A.dtype, f"bnb matmullt received {A.dtype} but returned {out_bnb.dtype}"
+
+            n = out_bnb.numel()
+            err = torch.abs(out_bnb - out_torch).float().mean().item()
+            if n > 0:
+                assert err < 0.11
+
+            if any(req_grad):
+                out_bnb.data.copy_(out_torch)
+                torch.cuda.synchronize()
+                loss_bnb = torch.nn.functional.mse_loss(out_bnb, target).mean()
+                loss_bnb.backward()
+                gradA1 = A.grad
+                gradB1 = B.grad
+                A.grad = None
+                B.grad = None
+                if has_bias:
+                    gradBias1 = bias.grad
+                    bias.grad = None
+
+                loss_torch = torch.nn.functional.mse_loss( out_torch, target ).mean()
+                loss_torch.backward()
+                gradA2 = A.grad
+                gradB2 = B.grad
+                A.grad = None
+                B.grad = None
+                if has_bias:
+                    gradBias2 = bias.grad
+                    bias.grad = None
+
+                if req_grad[0]:
+                    torch.testing.assert_allclose( gradA1, gradA2, atol=0.015, rtol=0.1)
+                if req_grad[1]:
+                    n = gradB1.numel()
+                    if dim2 > 0:
+                        assert torch.abs(gradB1).sum() > 0.0
+                        assert torch.abs(gradB2).sum() > 0.0
+                    else:
+                        assert torch.abs(gradB1).sum() == 0.0
+                        assert torch.abs(gradB2).sum() == 0.0
+                    idx = torch.isclose(gradB1, gradB2, atol=0.06, rtol=0.3)
+
+                    assert (idx == 0).sum().item() <= n * 0.1
+                    idx = torch.isclose(gradB1, gradB2, atol=0.10, rtol=0.3)
+                    assert (idx == 0).sum().item() <= n * 0.02
+                    torch.testing.assert_allclose(gradB1, gradB2, atol=0.18, rtol=0.3
+                    )
+
+                if req_grad[2]:
+                    torch.testing.assert_allclose(gradBias1, gradBias2)

tests/test_functional.py

@@ -2221,26 +2221,13 @@ def test_fp4_quant():
     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
+    assert err.item() < 0.1
+    assert relerr.item() < 0.28
 
 
 def test_bench_fp4_dequant():