[PyTorch] Fix small errors (#2396)

* fix

Signed-off-by: Pawel Gadzinski <[email protected]>

* fix

Signed-off-by: Pawel Gadzinski <[email protected]>

---------

Signed-off-by: Pawel Gadzinski <[email protected]>

Author: pggPL

tests/pytorch/distributed/run_gemm_with_overlap.py

@@ -24,10 +24,8 @@
     MXFP8Quantizer,
 )
 import transformer_engine.pytorch.cpp_extensions as tex
-from transformer_engine.pytorch.module.base import (
-    fill_userbuffers_buffer_for_all_gather,
-    get_cublas_workspace_size_bytes,
-)
+from transformer_engine.pytorch.cpp_extensions.gemm import get_cublas_workspace_size_bytes
+from transformer_engine.pytorch.module.base import fill_userbuffers_buffer_for_all_gather
 
 warnings.filterwarnings("ignore", category=DeprecationWarning)
 warnings.filterwarnings("ignore", category=FutureWarning)
@@ -417,10 +415,6 @@ def dist_print(msg, src=None, info=False, error=False, section=False, group=None
             std=opts.std,
         )
 
-    # Allocate cuBLAS workspace
-    workspace_size = 3 * get_cublas_workspace_size_bytes()
-    workspace = torch.empty(workspace_size, dtype=torch.uint8, device="cuda")
-
     # Gather global tensors and calculate reference result (need these first for Fp8 scales)
     if opts.bulk_overlap:
         ker_g = torch.transpose(kernel_t, 0, 1)
@@ -617,7 +611,6 @@ def _fp8_gemm():
         return tex.general_gemm(
             kernel_t_fp8,
             gemm_inp,
-            workspace,
             out_dtype=torch.float8_e4m3fn if opts.fp8_output else torch.bfloat16,
             quantization_params=out_quantizer,
             use_split_accumulator=te.module.base._2X_ACC_FPROP,
@@ -635,7 +628,6 @@ def _fp8_gemm2(gemm1_out):
         return tex.general_gemm(
             kernel2_t_fp8,
             gemm2_inp,
-            workspace,
             out_dtype=torch.float8_e4m3fn if opts.fp8_output else torch.bfloat16,
             quantization_params=out2_quantizer,
             use_split_accumulator=te.module.base._2X_ACC_FPROP,
@@ -648,7 +640,6 @@ def _gemm():
         return tex.general_gemm(
             kernel_t,
             gemm_inp,
-            workspace,
             out_dtype=torch.bfloat16,
             use_split_accumulator=te.module.base._2X_ACC_FPROP,
             ub=ub_obj,

transformer_engine/pytorch/cpu_offload.py

@@ -471,6 +471,8 @@ def fwd_step(self) -> int:
         """
         if self.num_of_fwds in [None, self.num_layers - 1]:
             # reset the offload synchronizer
+            for layer_id in self.layer_states:
+                self.layer_states[layer_id].release_all_memory()
             self.num_of_fwds = 0
         else:
             self.num_of_fwds += 1

transformer_engine/pytorch/distributed.py

@@ -948,7 +948,13 @@ def _all_gather_fp8(
         if isinstance(inp, Float8Tensor):
             dtype = inp.dtype
             device = inp.device
+        # Temporarily ensure rowwise usage for output tensor creation
+        # since we're gathering rowwise data, not the transpose
+        init_rowwise_usage = quantizer.rowwise_usage
+        init_columnwise_usage = quantizer.columnwise_usage
+        quantizer.set_usage(rowwise=True, columnwise=init_columnwise_usage)
         out = quantizer.make_empty(out_shape, dtype=dtype, device=device)
+        quantizer.set_usage(rowwise=init_rowwise_usage, columnwise=init_columnwise_usage)
     elif isinstance(inp, Float8Tensor):
         out = inp.make_like(inp, shape=out_shape)
         out._data = torch.empty(

transformer_engine/pytorch/tensor/mxfp8_tensor.py

@@ -134,7 +134,9 @@ def make_empty(
         columnwise_data = None
         columnwise_scale_inv = None
         if self.columnwise_usage:
-            columnwise_data = torch.empty_like(data, pin_memory=pin_memory)
+            columnwise_data = torch.empty(
+                shape, dtype=torch.uint8, device=device, pin_memory=pin_memory
+            )
             columnwise_scale_inv = torch.empty(
                 round_up_to_nearest_multiple(math.prod(shape[:-1]) // MXFP8_BLOCK_SCALING_SIZE, 4),
                 round_up_to_nearest_multiple(shape[-1], 128),