[PyTorch] Integration test for Megatron-LM

qa/L1_pytorch_mcore_integration/test.sh

@@ -0,0 +1,58 @@
+# Copyright (c) 2022-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# See LICENSE for license information.
+
+set -e
+
+# Paths
+: ${TE_PATH:=/opt/transformerengine}
+: ${MCORE_PATH:=${TE_PATH}/qa/L1_pytorch_mcore_integration/Megatron-LM}
+
+# Download Megatron-LM if needed
+if [ ! -d "${MCORE_PATH}" ]; then
+    pushd $(dirname ${MCORE_PATH})
+    git clone -b core_r0.9.0 https://github.com/NVIDIA/Megatron-LM.git Megatron-LM
+    popd
+fi
+
+# Megatron-LM invocation
+COMMAND="
+NVTE_TORCH_COMPILE=0
+NVTE_ALLOW_NONDETERMINISTIC_ALGO=0
+NVTE_FLASH_ATTN=1
+NVTE_FWD_LAYERNORM_SM_MARGIN=0
+NVTE_BWD_LAYERNORM_SM_MARGIN=0
+CUDA_DEVICE_MAX_CONNECTIONS=1
+NVTE_BIAS_GELU_NVFUSION=0
+NVTE_BIAS_DROPOUT_FUSION=0
+
+python
+-m torch.distributed.launch
+--use_env
+--nnodes=1
+--nproc_per_node=1
+
+${MCORE_PATH}/pretrain_gpt.py
+--tensor-model-parallel-size 1
+--pipeline-model-parallel-size 1
+--use-cpu-initialization
+--num-layers 2
+--hidden-size 128
+--num-attention-heads 8
+--seq-length 128
+--max-position-embeddings 2048
+--micro-batch-size 1
+--global-batch-size 8
+--train-iters 10
+--eval-iters 10
+--lr 1e-4
+--mock-data
+--vocab-file /data/gpt3/pile-cc1-cc2-shuf/bpe/gpt2-vocab.json
+--merge-file /data/gpt3/pile-cc1-cc2-shuf/bpe/gpt2-merges.txt
+--transformer-impl transformer_engine
+--fp8-format hybrid
+"
+COMMAND=$(echo "${COMMAND}" | tr '\n' ' ')
+
+# Launch Megatron-LM
+bash -c "${COMMAND}"

transformer_engine/pytorch/module/layernorm.py

@@ -61,15 +61,32 @@ class LayerNorm(_LayerNormOp):
 
     def __init__(
         self,
-        normalized_shape: Union[Iterable[int], int],
+        normalized_shape: Union[Iterable[int], int, None] = None,
         eps: float = 1e-5,
         sequence_parallel: Optional[bool] = None,  # legacy
         params_dtype: Optional[torch.dtype] = None,  # deprecated
         zero_centered_gamma: bool = False,
+        hidden_size: Optional[int] = None,  # deprecated
         **kwargs,
     ) -> None:
 
         # Handle deprecated options
+        if normalized_shape is None:
+            if hidden_size is None:
+                raise RuntimeError(
+                    "Neither `normalized_shape` nor `hidden_size` (deprecated) args are provided"
+                )
+            warnings.warn(
+                "`hidden_size` arg has been renamed to `normalized_shape` "
+                "for compatibility with `torch.nn.LayerNorm`.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            normalized_shape = hidden_size
+        elif hidden_size is not None:
+            raise RuntimeError(
+                "Both `normalized_shape` and `hidden_size` (deprecated) args are provided"
+            )
         if params_dtype is not None:
             if "dtype" in kwargs:
                 raise RuntimeError(

transformer_engine/pytorch/module/rmsnorm.py

@@ -65,15 +65,32 @@ class RMSNorm(_RMSNormOp):
 
     def __init__(
         self,
-        normalized_shape: Union[Iterable[int], int],
+        normalized_shape: Union[Iterable[int], int, None] = None,
         eps: float = 1e-5,
         sequence_parallel: Optional[bool] = None,  # legacy
         params_dtype: Optional[torch.dtype] = None,  # deprecated
         zero_centered_gamma: bool = False,
+        hidden_size: Optional[int] = None,  # deprecated
         **kwargs,
     ) -> None:
 
         # Handle deprecated options
+        if normalized_shape is None:
+            if hidden_size is None:
+                raise RuntimeError(
+                    "Neither `normalized_shape` nor `hidden_size` (deprecated) args are provided"
+                )
+            warnings.warn(
+                "`hidden_size` arg has been renamed to `normalized_shape` "
+                "for compatibility with `torch.nn.LayerNorm`.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            normalized_shape = hidden_size
+        elif hidden_size is not None:
+            raise RuntimeError(
+                "Both `normalized_shape` and `hidden_size` (deprecated) args are provided"
+            )
         if params_dtype is not None:
             if "dtype" in kwargs:
                 raise RuntimeError(

transformer_engine/pytorch/ops/basic/layer_norm.py

@@ -20,7 +20,12 @@
 )
 from ...fp8 import FP8GlobalStateManager, get_fp8_te_dtype
 from ...tensor import Float8Tensor, QuantizedTensor
-from ...utils import canonicalize_device, canonicalize_dtype, clear_tensor_data
+from ...utils import (
+    canonicalize_device,
+    canonicalize_dtype,
+    clear_tensor_data,
+    devices_match,
+)
 from ..op import BasicOperation, OperationContext
 from .._common import maybe_autocast_dtype, reshape
 
@@ -84,28 +89,23 @@ def __init__(
             normalized_shape = (normalized_shape,)
         else:
             normalized_shape = tuple(normalized_shape)
-        self._shape: tuple[int, ...] = normalized_shape
 
         # Parameter device
         defer_param_init = False
         device = canonicalize_device(device)
         if device.type == "meta":
             defer_param_init = True
-            device = canonicalize_device(None)
-        if device.type != "cuda":
-            raise ValueError(f"Only CUDA devices are supported (got {device})")
-        self.device: torch.device = device
 
         # Initialize parameters if needed
         dtype = canonicalize_dtype(dtype)
         weight = torch.empty(
-            self._shape,
-            device="meta",
+            normalized_shape,
+            device=device,
             dtype=dtype,
         )
         bias = torch.empty(
-            self._shape,
-            device="meta",
+            normalized_shape,
+            device=device,
             dtype=dtype,
         )
         weight = torch.nn.Parameter(weight)
@@ -143,17 +143,18 @@ def getenv(name: str) -> int:
     def reset_parameters(self) -> None:
         """Initialize parameter buffers and values"""
 
-        # Make sure parameter is initialized
+        # Parameter device
         weight = self.weight
         bias = self.bias
-        if weight.device.type != "cuda":
-            weight = torch.empty_like(weight, device=self.device)
-        else:
-            weight = weight.to(device=self.device)
-        if bias.device.type != "cuda":
-            bias = torch.empty_like(bias, device=self.device)
-        else:
-            bias = bias.to(device=self.device)
+        device = weight.device
+        if device.type == "meta":
+            device = canonicalize_device(None)
+
+        # Initialize param buffers
+        if not devices_match(weight.device, device):
+            weight = torch.empty_like(weight, device=device)
+        if not devices_match(bias.device, device):
+            bias = torch.empty_like(bias, device=device)
 
         # Initialize values
         if self.zero_centered_gamma:
@@ -184,17 +185,21 @@ def op_forward(
     ) -> torch.Tensor:
 
         # Check tensor dims
+        weight = self.weight
+        weight_dims = tuple(weight.size())
         input_dims = tuple(input_.size())
-        if len(input_dims) < len(self._shape) or input_dims[-len(self._shape) :] != self._shape:
+        if len(input_dims) < len(weight_dims) or input_dims[-len(weight_dims) :] != weight_dims:
             raise ValueError(
                 f"Input tensor (shape={input_dims}) "
-                f"and weight tensor (shape={self._shape}) are not compatible"
+                f"and weight tensor (shape={weight_dims}) are not compatible"
             )
 
         # Check input tensors
-        inner_dim = math.prod(self._shape)
-        device = self.device
-        dtype = maybe_autocast_dtype(default_dtype=self.weight.dtype)
+        inner_dim = math.prod(weight_dims)
+        device = weight.device
+        if device.type != "cuda":
+            device = canonicalize_device(None)
+        dtype = maybe_autocast_dtype(default_dtype=weight.dtype)
         x = reshape(input_, (-1, inner_dim), device=device, dtype=dtype)
         w = reshape(self.weight, (inner_dim,), device=device, dtype=dtype)
         b = reshape(self.bias, (inner_dim,), device=device, dtype=dtype)
@@ -266,6 +271,7 @@ def op_forward(
         # Save state for backward pass
         if requires_grad:
             ctx.save_for_backward(x, means, rstdevs)
+            ctx.device = device
             ctx.dtype = dtype
             ctx.has_prev_op = prev_op is not None
 
@@ -282,9 +288,12 @@ def op_backward(
         # Saved tensors from forward pass
         x, means, rstdevs = ctx.saved_tensors
 
+        # Tensor dims
+        weight_dims = self.weight.size()
+        inner_dim = math.prod(weight_dims)
+
         # Check input tensors
-        inner_dim = x.size(-1)
-        device = self.device
+        device = ctx.device
         dtype = ctx.dtype
         dy = reshape(grad_output, x.size(), device=device, dtype=dtype)
         w = reshape(self.weight, (inner_dim,), device=device, dtype=dtype)
@@ -312,6 +321,6 @@ def op_backward(
 
         # Reshape results
         grad_input = reshape(dx, grad_output.size())
-        grad_weight = reshape(dw, self._shape)
-        grad_bias = reshape(db, self._shape)
+        grad_weight = reshape(dw, weight_dims)
+        grad_bias = reshape(db, weight_dims)
         return grad_input, (grad_weight, grad_bias)

transformer_engine/pytorch/ops/basic/rmsnorm.py

@@ -20,7 +20,12 @@
 )
 from ...fp8 import FP8GlobalStateManager, get_fp8_te_dtype
 from ...tensor import Float8Tensor, QuantizedTensor
-from ...utils import canonicalize_device, canonicalize_dtype, clear_tensor_data
+from ...utils import (
+    canonicalize_device,
+    canonicalize_dtype,
+    clear_tensor_data,
+    devices_match,
+)
 from ..op import BasicOperation, OperationContext
 from .._common import maybe_autocast_dtype, reshape
 
@@ -83,22 +88,17 @@ def __init__(
             normalized_shape = (normalized_shape,)
         else:
             normalized_shape = tuple(normalized_shape)
-        self._shape: tuple[int, ...] = normalized_shape
 
         # Parameter device
         defer_param_init = False
         device = canonicalize_device(device)
         if device.type == "meta":
             defer_param_init = True
-            device = canonicalize_device(None)
-        if device.type != "cuda":
-            raise ValueError(f"Only CUDA devices are supported (got {device})")
-        self.device: torch.device = device
 
         # Initialize parameters if needed
         weight = torch.empty(
-            self._shape,
-            device="meta",
+            normalized_shape,
+            device=device,
             dtype=canonicalize_dtype(dtype),
         )
         weight = torch.nn.Parameter(weight)
@@ -133,12 +133,15 @@ def getenv(name: str) -> int:
     def reset_parameters(self) -> None:
         """Initialize parameter buffers and values"""
 
-        # Make sure parameter is initialized
+        # Parameter device
         weight = self.weight
-        if weight.device.type != "cuda":
-            weight = torch.empty_like(weight, device=self.device)
-        else:
-            weight = weight.to(device=self.device)
+        device = weight.device
+        if device.type == "meta":
+            device = canonicalize_device(None)
+
+        # Initialize param buffers
+        if not devices_match(weight.device, device):
+            weight = torch.empty_like(weight, device=device)
 
         # Initialize values
         if self.zero_centered_gamma:
@@ -165,17 +168,21 @@ def op_forward(
     ) -> torch.Tensor:
 
         # Check tensor dims
+        weight = self.weight
+        weight_dims = tuple(weight.size())
         input_dims = tuple(input_.size())
-        if len(input_dims) < len(self._shape) or input_dims[-len(self._shape) :] != self._shape:
+        if len(input_dims) < len(weight_dims) or input_dims[-len(weight_dims) :] != weight_dims:
             raise ValueError(
                 f"Input tensor (shape={input_dims}) "
-                f"and weight tensor (shape={self._shape}) are not compatible"
+                f"and weight tensor (shape={weight_dims}) are not compatible"
             )
 
         # Check input tensors
-        inner_dim = math.prod(self._shape)
-        device = self.device
-        dtype = maybe_autocast_dtype(default_dtype=self.weight.dtype)
+        inner_dim = math.prod(weight_dims)
+        device = weight.device
+        if device.type != "cuda":
+            device = canonicalize_device(None)
+        dtype = maybe_autocast_dtype(default_dtype=weight.dtype)
         x = reshape(input_, (-1, inner_dim), device=device, dtype=dtype)
         w = reshape(self.weight, (inner_dim,), device=device, dtype=dtype)
         if isinstance(x, QuantizedTensor):
@@ -241,6 +248,7 @@ def op_forward(
         # Save state for backward pass
         if requires_grad:
             ctx.save_for_backward(x, rstdevs)
+            ctx.device = device
             ctx.dtype = dtype
             ctx.has_prev_op = prev_op is not None
 
@@ -257,9 +265,12 @@ def op_backward(
         # Saved tensors from forward pass
         x, rstdevs = ctx.saved_tensors
 
+        # Tensor dims
+        weight_dims = self.weight.size()
+        inner_dim = math.prod(weight_dims)
+
         # Check input tensors
-        inner_dim = x.size(-1)
-        device = self.device
+        device = ctx.device
         dtype = ctx.dtype
         dy = reshape(grad_output, x.size(), device=device, dtype=dtype)
         w = reshape(self.weight, (inner_dim,), device=device, dtype=dtype)
@@ -285,5 +296,5 @@ def op_backward(
 
         # Reshape results
         grad_input = reshape(dx, grad_output.size())
-        grad_weight = reshape(dw, self._shape)
+        grad_weight = reshape(dw, weight_dims)
         return grad_input, (grad_weight,)

transformer_engine/pytorch/ops/fuser.py

@@ -135,7 +135,11 @@ def forward(
                     requires_grad = any(any(x.requires_grad for x in xs) for xs in extra_inputs)
             for idx in basic_op_idxs:
                 basic_op_ctxs[idx].requires_grad = requires_grad
-            x.requires_grad_(requires_grad=requires_grad)
+            if requires_grad != x.requires_grad:
+                if requires_grad:
+                    x.requires_grad_()
+                else:
+                    x = x.detach()
 
             # Forward op
             extra_inputs = [basic_op_extra_inputs[idx] for idx in basic_op_idxs]