Add Bf16 Support (#136)

.gitignore

@@ -150,4 +150,5 @@ log
 .vscode
 
 !bmtrain/dist
-tests/test_log.txt
+tests/test_log.txt
+tests/*.opt

bmtrain/loss/_function.py

@@ -2,26 +2,28 @@
 from .. import C 
 import torch
 CHECK_INPUT = lambda x: x.is_contiguous() and x.is_cuda
-def has_inf_nan(g_fp16: torch.Tensor, out: torch.Tensor) -> None:
-    assert g_fp16.dtype == torch.float16, "g_fp16 must be a half tensor"
+def has_inf_nan(g_half: torch.Tensor, out: torch.Tensor) -> None:
     assert out.dtype == torch.uint8, "out must be a uint8 tensor"
-    assert CHECK_INPUT(g_fp16), "g_fp16 must be contiguous and on cuda"
+    assert CHECK_INPUT(g_half), "g_fp16 must be contiguous and on cuda"
     assert CHECK_INPUT(out), "out must be contiguous and on cuda"
     mid = torch.zeros(1024, device=out.device, dtype=out.dtype)
     stream = torch.cuda.current_stream().cuda_stream
-    C.has_nan_inf_launcher(g_fp16.numel(), g_fp16.data_ptr(), mid.data_ptr(), out.data_ptr(), stream)
-
-
+    if g_half.dtype == torch.float16:
+        C.has_nan_inf_fp16_launcher(g_half.numel(), g_half.data_ptr(), mid.data_ptr(), out.data_ptr(), stream)
+    elif g_half.dtype == torch.bfloat16:
+        if not C.is_bf16_supported():
+            raise NotImplementedError(f"bfloat16 is not supported on current GPU")
+        C.has_nan_inf_bf16_launcher(g_half.numel(), g_half.data_ptr(), mid.data_ptr(), out.data_ptr(), stream)
+    else:
+        raise ValueError(f"has_inf_nan not supported for dtype {g_half.dtype}")
 
 def cross_entropy_forward(m: int, n: int, input: torch.Tensor, target: torch.Tensor,
                             softmax: torch.Tensor, output: torch.Tensor, ignore_index: int) -> None:
     CHECK_INPUT(input)
     CHECK_INPUT(target)
     CHECK_INPUT(softmax)
     CHECK_INPUT(output)
-    assert input.dtype == torch.float16, "input must be a half tensor"
     assert target.dtype == torch.int32, "target must be an int tensor"
-    assert softmax.dtype == torch.float16, "softmax must be a half tensor"
     assert output.dtype == torch.float32, "output must be a float tensor"
     assert input.numel() == softmax.numel(), "input and softmax must have the same number of elements"
     assert target.numel() == output.numel(), "target and output must have the same number of elements"
@@ -30,43 +32,14 @@ def cross_entropy_forward(m: int, n: int, input: torch.Tensor, target: torch.Ten
     softmax_ptr = softmax.data_ptr()
     output_ptr = output.data_ptr()
     cuda_stream = torch.cuda.current_stream().cuda_stream
-    C.cross_entropy_forward_launcher(m, n, input_ptr, target_ptr, softmax_ptr, output_ptr, ignore_index, cuda_stream)
-
-def cross_entropy_backward(m: int, n: int, grad_output: torch.Tensor, target: torch.Tensor,
-                             softmax: torch.Tensor, grad_input: torch.Tensor, ignore_index: int) -> None:
-    CHECK_INPUT(grad_output)
-    CHECK_INPUT(target)
-    CHECK_INPUT(softmax)
-    CHECK_INPUT(grad_input)
-    assert grad_output.dtype == torch.float32, "grad_output must be a float tensor"
-    assert target.dtype == torch.int32, "target must be an int tensor"
-    assert softmax.dtype == torch.float16, "softmax must be a half tensor"
-    assert grad_input.dtype == torch.float16, "grad_input must be a half tensor"
-    assert grad_input.numel() == softmax.numel(), "grad_input and softmax must have the same number of elements"
-    assert target.numel() == grad_output.numel(), "target and grad_output must have the same number of elements"
-    grad_output_ptr = grad_output.data_ptr()
-    target_ptr = target.data_ptr()
-    softmax_ptr = softmax.data_ptr()
-    grad_input_ptr = grad_input.data_ptr()
-    cuda_stream = torch.cuda.current_stream().cuda_stream
-    C.cross_entropy_backward_launcher(m, n, grad_output_ptr, target_ptr, softmax_ptr, grad_input_ptr, ignore_index, cuda_stream)
-
-def cross_entropy_forward_inplace(m: int, n: int, x: torch.Tensor, target: torch.Tensor,
-                                    output: torch.Tensor, ignore_index: int) -> None:
-    CHECK_INPUT(x)
-    CHECK_INPUT(target)
-    CHECK_INPUT(output)
-    assert x.dtype == torch.float16, "x must be a half tensor"
-    assert target.dtype == torch.int32, "target must be an int tensor"
-    assert output.dtype == torch.float32, "output must be a float tensor"
-    assert target.numel() == output.numel(), "target and output must have the same number of elements"
-    cuda_stream = torch.cuda.current_stream().cuda_stream
-    x_ptr = x.data_ptr()
-    output_ptr = output.data_ptr()
-    target_ptr = target.data_ptr()
-    output_ptr = output.data_ptr()
-
-    C.cross_entropy_forward_inplace_launcher(m, n, x_ptr, target_ptr, output_ptr, ignore_index, cuda_stream)
+    if input.dtype == torch.float16:
+        C.cross_entropy_forward_fp16_launcher(m, n, input_ptr, target_ptr, softmax_ptr, output_ptr, ignore_index, cuda_stream)
+    elif input.dtype == torch.bfloat16:
+        if not C.is_bf16_supported():
+            raise NotImplementedError(f"bfloat16 is not supported on current GPU")
+        C.cross_entropy_forward_bf16_launcher(m, n, input_ptr, target_ptr, softmax_ptr, output_ptr, ignore_index, cuda_stream)
+    else:
+        raise ValueError(f"cross_entropy_forward not supported for dtype {input.dtype}")
 
 def cross_entropy_backward_inplace(m: int, n: int, grad_output: torch.Tensor, target: torch.Tensor,
                                      x: torch.Tensor, ignore_index: int) -> None:
@@ -75,12 +48,17 @@ def cross_entropy_backward_inplace(m: int, n: int, grad_output: torch.Tensor, ta
     CHECK_INPUT(x)
     assert grad_output.dtype == torch.float32, "grad_output must be a float tensor"
     assert target.dtype == torch.int32, "target must be an int tensor"
-    assert x.dtype == torch.float16, "x must be a half tensor"
     assert target.numel() == grad_output.numel(), "target and grad_output must have the same number of elements"
     cuda_stream = torch.cuda.current_stream().cuda_stream
     grad_output_ptr = grad_output.data_ptr()
     target_ptr = target.data_ptr()
     x_ptr = x.data_ptr()
 
-    C.cross_entropy_backward_inplace_launcher(m, n, grad_output_ptr, target_ptr, x_ptr, ignore_index, cuda_stream)
-
+    if x.dtype == torch.float16:
+        C.cross_entropy_backward_inplace_fp16_launcher(m, n, grad_output_ptr, target_ptr, x_ptr, ignore_index, cuda_stream)
+    elif x.dtype == torch.bfloat16:
+        if not C.is_bf16_supported():
+            raise NotImplementedError(f"bfloat16 is not supported on current GPU")
+        C.cross_entropy_backward_inplace_bf16_launcher(m, n, grad_output_ptr, target_ptr, x_ptr, ignore_index, cuda_stream)
+    else:
+        raise ValueError(f"cross_entropy_backward not supported for dtype {input.dtype}")

bmtrain/loss/cross_entropy.py

@@ -36,36 +36,6 @@ def backward(ctx, grad_output : torch.Tensor):
         )
         return (softmax, None, None)
 
-class OpFusedCrossEntropyInplace(torch.autograd.Function):
-    """
-    CrossEntropy dim = 1
-    """
-    @staticmethod
-    def forward(ctx, x : torch.Tensor, target : torch.Tensor, ignore_index: int):
-        assert x.ndim == 2
-        out = torch.empty(x.size(0), device=x.device, dtype=torch.float)
-        F.cross_entropy_forward_inplace(
-            x.size(0), x.size(1),
-            x, target,
-            out,
-            ignore_index,
-        ) # x is inplace modify to softmax result
-        ctx.ignore_index = ignore_index
-        ctx.save_for_backward(x, target)
-        return out # float tensor
-
-    @staticmethod
-    def backward(ctx, grad_output : torch.Tensor):
-        grad_output = grad_output.contiguous()
-        softmax, target = ctx.saved_tensors
-        F.cross_entropy_backward_inplace(
-            softmax.size(0), softmax.size(1),
-            grad_output, target,
-            softmax,
-            ctx.ignore_index,
-        ) # softmax is inplace modify to grad_input
-        return (softmax, None, None)
-
 class FusedCrossEntropy(torch.nn.Module):
     r"""This criterion computes the cross entropy loss between input and target.
 

bmtrain/optim/_function.py

@@ -1,4 +1,3 @@
-
 from .. import C
 import torch
 CHECK_INPUT = lambda x: x.is_contiguous() and x.is_cuda
@@ -11,8 +10,8 @@ def adam_cpu(param_fp32: torch.Tensor, param_fp16: torch.Tensor, g_fp16: torch.T
     assert m_fp32.is_contiguous(), "m_fp32 must be contiguous"
     assert v_fp32.is_contiguous(), "v_fp32 must be contiguous"
     assert param_fp32.dtype == torch.float32, "param_fp32 must be float32 tensor"
-    assert param_fp16.dtype == torch.float16, "param_fp16 must be float16 tensor"
-    assert g_fp16.dtype == torch.float16, "g_fp16 must be float16 tensor"
+    assert param_fp16.dtype == torch.float16 or param_fp16.dtype == torch.bfloat16, "param_fp16 must be float16/bfloat16 tensor"
+    assert g_fp16.dtype == torch.float16 or g_fp16.dtype == torch.bfloat16, "g_fp16 must be float16/bfloat16 tensor"
     assert m_fp32.dtype == torch.float32, "m_fp32 must be float32 tensor"
     assert v_fp32.dtype == torch.float32, "v_fp32 must be float32 tensor"
     assert param_fp32.device == torch.device("cpu"), "param_fp32 must be a cpu tensor"
@@ -26,22 +25,28 @@ def adam_cpu(param_fp32: torch.Tensor, param_fp16: torch.Tensor, g_fp16: torch.T
     assert param_fp32.numel() == v_fp32.numel(), "param_fp32 and v_fp32 must have the same number of elements"
     bias_correction1 = 1 - beta1 ** step
     bias_correction2 = 1 - beta2 ** step
-    C.adam_cpu_launcher(
-                    param_fp32.numel(),
-                    param_fp32.data_ptr(),
-                    param_fp16.data_ptr(),
-                    g_fp16.data_ptr(),
-                    m_fp32.data_ptr(),
-                    v_fp32.data_ptr(),
-                    beta1, beta2,
-                    eps, lr,
-                    scale,
-                    weight_decay,
-                    bias_correction1,
-                    bias_correction2,
-                )
+    if g_fp16.dtype == torch.float16:
+        launcher = C.adam_cpu_fp16_launcher
+    elif g_fp16.dtype == torch.bfloat16:
+        if not C.is_bf16_supported():
+            raise NotImplementedError(f"bfloat16 is not supported on current GPU")
+        launcher = C.adam_cpu_bf16_launcher
+    launcher(
+        param_fp32.numel(),
+        param_fp32.data_ptr(),
+        param_fp16.data_ptr(),
+        g_fp16.data_ptr(),
+        m_fp32.data_ptr(),
+        v_fp32.data_ptr(),
+        beta1, beta2,
+        eps, lr,
+        scale,
+        weight_decay,
+        bias_correction1,
+        bias_correction2,
+    )
 
-def adam(param_fp32: torch.Tensor, param_fp16: torch.Tensor, g_fp16: torch.Tensor, m_fp16: torch.Tensor,
+def adam_fp16(param_fp32: torch.Tensor, param_fp16: torch.Tensor, g_fp16: torch.Tensor, m_fp16: torch.Tensor,
              v_fp32: torch.Tensor, beta1: float, beta2: float, eps: float, lr: float, scale: float,
              weight_decay: float, step: int) -> None:
     assert CHECK_INPUT(param_fp32), "param_fp32 must be contiguous and on cuda"
@@ -61,7 +66,7 @@ def adam(param_fp32: torch.Tensor, param_fp16: torch.Tensor, g_fp16: torch.Tenso
     bias_correction1 = 1 - beta1 ** step
     bias_correction2 = 1 - beta2 ** step
     stream = torch.cuda.current_stream().cuda_stream
-    C.adam_launcher(
+    C.adam_fp16_launcher(
         param_fp32.numel(),
         param_fp32.data_ptr(),
         param_fp16.data_ptr(),
@@ -76,3 +81,41 @@ def adam(param_fp32: torch.Tensor, param_fp16: torch.Tensor, g_fp16: torch.Tenso
         bias_correction2,
         stream
     )
+
+def adam_bf16(param_fp32: torch.Tensor, param_bf16: torch.Tensor, g_bf16: torch.Tensor, m_fp32: torch.Tensor,
+             v_fp32: torch.Tensor, beta1: float, beta2: float, eps: float, lr: float, scale: float,
+             weight_decay: float, step: int) -> None:
+    assert CHECK_INPUT(param_fp32), "param_fp32 must be contiguous and on cuda"
+    assert CHECK_INPUT(param_bf16), "param_bf16 must be contiguous and on cuda"
+    assert CHECK_INPUT(g_bf16), "g_bf16 must be contiguous and on cuda"
+    assert CHECK_INPUT(m_fp32), "m_fp32 must be contiguous and on cuda"
+    assert CHECK_INPUT(v_fp32), "v_fp32 must be contiguous and on cuda"
+    assert param_fp32.dtype == torch.float32, "param_fp32 must be float32 tensor"
+    assert param_bf16.dtype == torch.bfloat16, "param_fp16 must be float16 tensor"
+    assert g_bf16.dtype == torch.bfloat16, "g_bf16 must be bfloat16 tensor"
+    assert m_fp32.dtype == torch.float32, "m_fp32 must be bfloat16 tensor"
+    assert v_fp32.dtype == torch.float32, "v_fp32 must be float32 tensor"
+    assert param_fp32.numel() == param_bf16.numel(), "param_fp32 and param_bf16 must have the same number of elements"
+    assert param_fp32.numel() == g_bf16.numel(), "param_fp32 and g_fp16 must have the same number of elements"
+    assert param_fp32.numel() == m_fp32.numel(), "param_fp32 and m_m_fp32 must have the same number of elements"
+    assert param_fp32.numel() == v_fp32.numel(), "param_fp32 and v_fp32 must have the same number of elements"
+    bias_correction1 = 1 - beta1 ** step
+    bias_correction2 = 1 - beta2 ** step
+    stream = torch.cuda.current_stream().cuda_stream
+    if not C.is_bf16_supported():
+        raise NotImplementedError(f"bfloat16 is not supported on current GPU")
+    C.adam_bf16_launcher(
+        param_fp32.numel(),
+        param_fp32.data_ptr(),
+        param_bf16.data_ptr(),
+        g_bf16.data_ptr(),
+        m_fp32.data_ptr(),
+        v_fp32.data_ptr(),
+        beta1, beta2,
+        eps, lr,
+        scale,
+        weight_decay,
+        bias_correction1,
+        bias_correction2,
+        stream
+    )

bmtrain/optim/adam.py

@@ -40,8 +40,9 @@ def _on_justify_scale(self, old_scale, new_scale):
                 if p in self.state:
                     state = self.state[p]
                     if len(state) > 0:
-                        state['exp_avg'] *= delta
-                        state['exp_avg_sq'] *= delta
+                        if p.dtype == torch.float16:
+                            state['exp_avg'] *= delta
+                            state['exp_avg_sq'] *= delta
 
     @torch.no_grad()
     def step(self, closure=None, scale=1):
@@ -63,45 +64,32 @@ def step(self, closure=None, scale=1):
                 if p.grad is not None and p.requires_grad:
                     if p.grad.is_sparse:
                         raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
-                    if p.dtype not in [torch.float16, torch.float32]:
-                        raise RuntimeError('Adam only supports fp32 or fp16 gradients')
+                    if p.dtype not in [torch.float32, torch.half, torch.bfloat16]:
+                        raise RuntimeError('Adam only supports fp32, fp16 and bf16 gradients')
 
                     state = self.state[p]
                     # Lazy state initialization
                     if len(state) == 0:
                         state['step'] = 0
                         # Exponential moving average of gradient values
-                        state['exp_avg'] = torch.zeros(p.size(), dtype=p.dtype, device=p.device) # on device
+                        if p.dtype == torch.float16:
+                            state['exp_avg'] = torch.zeros(p.size(), dtype=torch.float16, device=p.device) # on device
+                        else:
+                            state['exp_avg'] = torch.zeros(p.size(), dtype=torch.float32, device=p.device) # on device
                         # Exponential moving average of squared gradient values
-                        state['exp_avg_sq'] = torch.zeros(p.size(), dtype=torch.float32, device=p.device)   # on device
-
-                        if p.dtype == torch.half:
+                        state['exp_avg_sq'] = torch.zeros(p.size(), dtype=torch.float32, device=p.device)# on device
+                  
+                        if p.dtype != torch.float32:
                             state['_param_fp32'] = torch.empty(p.size(), dtype=torch.float32, device=p.device)   # on device
                             state['_param_fp32'].copy_(p)
 
                     # update the steps for each param group update
-                    state['step'] += 1
-
                     if ('maximize' in group) and (group['maximize'] is True):
                         grad = -p.grad
                     else:
                         grad = p.grad
 
-                    if p.dtype == torch.half:
-                        F.adam(
-                            state["_param_fp32"],    # fp32
-                            p,                      # fp16
-                            grad,                 # fp16
-                            state['exp_avg'],       # fp16: m
-                            state["exp_avg_sq"],    # fp32: v
-                            group['betas'][0], group['betas'][1],
-                            group['eps'],
-                            0.0 if state["step"] <= self._hold_steps else group['lr'],
-                            scale,
-                            group['weight_decay'],
-                            state['step']
-                        )
-                    else:
+                    if p.dtype == torch.float32:
                         other_kwargs = {}
                         if 'maximize' in inspect.signature(torch.optim._functional.adam).parameters:
                             other_kwargs['maximize'] = False
@@ -116,11 +104,30 @@ def step(self, closure=None, scale=1):
                             amsgrad=False,
                             beta1=group['betas'][0],
                             beta2=group['betas'][1],
-                            lr=0.0 if state["step"] <= self._hold_steps else group['lr'],
+                            lr=0.0 if state["step"] < self._hold_steps else group['lr'],
                             weight_decay=group['weight_decay'],
                             eps=group['eps'],
                             **other_kwargs
                         )
+                        state['step'] += 1
+                    else:
+                        f = F.adam_fp16 if p.dtype == torch.float16 else F.adam_bf16
+                        state['step'] += 1
+                        f(
+                            state["_param_fp32"],    # fp32
+                            p,                      # fp16
+                            grad,                 # fp16
+                            state['exp_avg'],       # fp16: m
+                            state["exp_avg_sq"],    # fp32: v
+                            group['betas'][0], group['betas'][1],
+                            group['eps'],
+                            0.0 if state["step"] < self._hold_steps else group['lr'],
+                            scale,
+                            group['weight_decay'],
+                            state['step']
+                        )
+
+
 
         return loss
 
@@ -159,11 +166,11 @@ def load_state_dict(self, state_dict: dict) -> None:
             if k in id_map:
                 param = id_map[k]
 
-                if param.dtype == torch.half and "_param_fp32" not in v:
+                if param.dtype != torch.float32 and "_param_fp32" not in v:
                     v["_param_fp32"] = torch.empty(param.size(), dtype=torch.float32, device=param.device)
                     v["_param_fp32"].copy_(param)
 
-                for name, dtype in [("exp_avg", param.dtype), ("exp_avg_sq", torch.float32), ("_param_fp32", torch.float32)]:
+                for name, dtype in [("exp_avg", torch.float16 if param.dtype == torch.float16 else torch.float32), ("exp_avg_sq", torch.float32), ("_param_fp32", torch.float32)]:
                     if name in v:
                         v[name] = v[name].to(param.device).to(dtype)