Modernize bf16 cutlass grouped gemm (pytorch#3889)

Summary:
Pull Request resolved: pytorch#3889

X-link: facebookresearch/FBGEMM#982

This diff unifies the API between FP8 and BF16 grouped gemm. Specifically we add the same dynamic, concatenated, and stacked APIs that are used for FP8 across both cutlass and CK. After this change, our tests can also be unified into a single grouped gemm test that covers all the various modes.

Reviewed By: jiawenliu64

Differential Revision: D71920813

Author: mxz297

fbgemm_gpu/experimental/gen_ai/bench/quantize_ops.py

@@ -22,6 +22,7 @@
     triton_quantize_fp8_row,
 )
 from fbgemm_gpu.experimental.gemm.triton_gemm.grouped_gemm import (
+    grouped_gemm,
     grouped_gemm_fp8_rowwise,
 )
 from fbgemm_gpu.experimental.gen_ai.quantize import quantize_int4_preshuffle
@@ -729,6 +730,45 @@ def cuda(self) -> bool:
         return True
 
 
+@register_quantize_op
+class BF16TritonStackedGroupedGemm(QuantizeOpBase):
+    """
+    BF16 grouped matmul with stacked inputs implemented with triton.
+    """
+
+    def preprocess(self, x, w):
+        m_values = [i.shape[0] for i in x]
+        # Convert m_values into offsets into grouped tensor.
+        m_sizes = torch.tensor(m_values).to(dtype=torch.int32, device=x[0].device)
+        w = torch.concat(w, dim=0).contiguous()
+        # Also view input as flattened.
+        x = torch.concat(x, dim=0).contiguous()
+        # Return processed tensors.
+        return x, w, m_sizes
+
+    def quantize(self, x, w, m_sizes):
+        return x, w, m_sizes
+
+    def compute(self, x, w, m_sizes):
+        return grouped_gemm(x, w, m_sizes)
+
+    def quantize_and_compute(self, x, w, m_sizes):
+        x, w, m_sizes = self.quantize(x, w, m_sizes)
+        return self.compute(x, w, m_sizes)
+
+    @property
+    def name(self) -> str:
+        return "triton_bf16_grouped_stacked"
+
+    @property
+    def hip(self) -> bool:
+        return True
+
+    @property
+    def cuda(self) -> bool:
+        return True
+
+
 @register_quantize_op
 class FP8TritonStackedGroupedGemm(QuantizeOpBase):
     """
@@ -1488,6 +1528,46 @@ def cuda(self) -> bool:
         return True
 
 
+@register_quantize_op
+class BF16GroupedStacked(QuantizeOpBase):
+    """
+    BF16 grouped matmul with stacked inputs backed by cutlass or ck.
+    """
+
+    def preprocess(self, x, w):
+        m_values = [i.shape[0] for i in x]
+        # Convert m_values into offsets into grouped tensor.
+        m_sizes = torch.tensor(m_values).to(dtype=torch.int64, device=x[0].device)
+        # Group weights as single tensor.
+        w = torch.stack(w, dim=0).contiguous()
+        # Also view input as flattened.
+        x = torch.concat(x, dim=0).contiguous()
+        # Return processed tensors.
+        return x, w, m_sizes
+
+    def quantize(self, x, w, m_sizes):
+        return x, w, m_sizes
+
+    def compute(self, x, w, m_sizes):
+        return torch.ops.fbgemm.bf16bf16bf16_grouped_stacked(x, w, m_sizes)
+
+    def quantize_and_compute(self, x, w, m_sizes):
+        x, w, m_sizes = self.quantize(x, w, m_sizes)
+        return self.compute(x, w, m_sizes)
+
+    @property
+    def name(self) -> str:
+        return "bf16_grouped_stacked"
+
+    @property
+    def hip(self) -> bool:
+        return True
+
+    @property
+    def cuda(self) -> bool:
+        return True
+
+
 @register_quantize_op
 class BF16I4RowwiseGemm(F8I4RowwiseGemm):
     """