[moe fp8 training] use transpose method when quantizing to avoid uncoalesced gmem accesses

stack-info: PR: #2864, branch: danielvegamyhre/stack/58

Author: danielvegamyhre

benchmarks/prototype/moe_training/benchmark_rowwise_3d_quant_kernels.py

@@ -31,6 +31,7 @@
 class ExperimentConfig:
     high_precision_dtype: torch.dtype
     input_shape: tuple[int]
+    power_of_2_scales: bool
 
 
 @dataclass(frozen=True)
@@ -48,7 +49,7 @@ class Experiment:
 
 
 def get_configs() -> List[ExperimentConfig]:
-    # Llama4 shapes
+    # Llama4 shapes (E, N, K)
     input_shapes = [
         (1, 8192, 5120),  # w1, w3
         (1, 5120, 8192),  # w2
@@ -58,14 +59,16 @@ def get_configs() -> List[ExperimentConfig]:
         (128, 5120, 8192),  # w2
     ]
     high_precision_dtypes = [torch.bfloat16]
+    power_of_2_scales = [True, False]
     configs = []
-    for input_shape, high_precision_dtype in itertools.product(
-        input_shapes, high_precision_dtypes
+    for input_shape, high_precision_dtype, power_of_2_scale in itertools.product(
+        input_shapes, high_precision_dtypes, power_of_2_scales
     ):
         configs.append(
             ExperimentConfig(
                 input_shape=input_shape,
                 high_precision_dtype=high_precision_dtype,
+                power_of_2_scales=power_of_2_scale,
             )
         )
     return configs
@@ -87,18 +90,16 @@ def run_torch(input_tensor: torch.Tensor):
         out = torch_to_3d_rowwise_float8_transpose_rhs(
             input_tensor,
             target_dtype=torch.float8_e4m3fn,
-            round_scales_to_power_of_2=True,
+            round_scales_to_power_of_2=config.power_of_2_scales,
         )
-        torch.cuda.synchronize()
         return out
 
     def run_triton(input_tensor: torch.Tensor):
         out = triton_fp8_rowwise_3d_transpose_rhs(
             input_tensor,
             output_dtype=torch.float8_e4m3fn,
-            round_scales_to_power_of_2=True,
+            round_scales_to_power_of_2=config.power_of_2_scales,
         )
-        torch.cuda.synchronize()
         return out
 
     # bench torch
@@ -141,6 +142,7 @@ def run_triton(input_tensor: torch.Tensor):
 def print_results(experiments: List[Experiment]):
     headers = [
         "input_shape",
+        "power_of_2_scales",
         "torch_time_us",
         "triton_time_us",
         "torch_mem_bw_gbps",
@@ -153,6 +155,7 @@ def print_results(experiments: List[Experiment]):
         rows.append(
             [
                 input_shape,
+                experiment.config.power_of_2_scales,
                 experiment.result.torch_time_us,
                 experiment.result.triton_time_us,
                 round(experiment.result.torch_mem_bw_gbps, 3),

benchmarks/prototype/moe_training/benchmark_scaled_grouped_mm_dq.py

@@ -49,7 +49,7 @@ def get_configs() -> List[ExperimentConfig]:
     # Llama4 shapes
     A_shapes = [(16640, 5120)]
     B_shapes = [(16, 8192, 5120)]
-    recipes = [MoEScalingType.MXFP8, MoEScalingType.FP8_ROWWISE]
+    recipes = [MoEScalingType.FP8_ROWWISE]  # MoEScalingType.MXFP8,
     high_precision_dtypes = [torch.bfloat16]
     configs = []
     for A_shape, B_shape, recipe, high_precision_dtype in itertools.product(

torchao/prototype/moe_training/kernels/float8_rowwise.py

@@ -26,10 +26,10 @@
     torch.float64: tl.float64,
 }
 
-block_sizes_n = [32, 128, 256]  # large dim (output_features)
-block_sizes_k = [32, 128, 256]  # small dim (input_features)
-num_warps = [2, 4]
-num_stages = [2, 3, 4, 5, 6]
+block_sizes_n = [128]  # large dim (output_features)
+block_sizes_k = [128]  # small dim (input_features)
+num_warps = [4]
+num_stages = [4]
 kernel_configs_2D = [
     triton.Config(
         {"BLOCK_SIZE_N": block_size_n, "BLOCK_SIZE_K": block_size_k},
@@ -172,9 +172,7 @@ def _triton_fp8_rowwise_3d_transpose_scales_rhs_kernel(
         + (n_offs[None, :] * stride_input_dim2)
     )
     input_mask = (k_offs[:, None] < K) & (n_offs[None, :] < N)
-    input_data = tl.load(input_ptr + input_offs, mask=input_mask, other=0.0).to(
-        input_dtype
-    )
+    input_data = tl.load(input_ptr + input_offs, mask=input_mask, other=0.0)
 
     # In a normal torch implementation, we should transpose the tensor then compute the amax
     # along the dim1 (N), to compute colwise scales for a RHS operand of a scaled grouped gemm:
@@ -243,25 +241,20 @@ def _triton_fp8_rowwise_3d_transpose_cast_rhs_kernel(
         + (n_offs[None, :] * stride_input_dim2)
     )
     input_mask = (k_offs[:, None] < K) & (n_offs[None, :] < N)
-    input_data = tl.load(input_ptr + input_offs, mask=input_mask, other=0.0).to(
-        input_dtype
-    )
+    input_data = tl.load(input_ptr + input_offs, mask=input_mask, other=0.0)
     input_data = input_data.trans(1, 0)  # (K, N) -> (N, K)
 
     # load global scales for this block of the given expert - shape (1, K)
     scales_offs = (
         expert_idx[:, None] * stride_scales_dim0 + k_offs[None, :] * stride_scales_dim1
     )
     scales_mask = k_offs[None, :] < K
-    scales = tl.load(scales_ptr + scales_offs, mask=scales_mask, other=0.0).to(
-        tl.float32
-    )
+    scales = tl.load(scales_ptr + scales_offs, mask=scales_mask, other=0.0)
 
     # transpose data and apply scales - shape (N,K) * (1,K) = (N,K)
-    scaled_data = input_data * scales
-    output_data = tl.clamp(scaled_data, min=fp8_dtype_min, max=fp8_dtype_max).to(
-        output_dtype
-    )
+    output_data = tl.clamp(
+        input_data * scales, min=fp8_dtype_min, max=fp8_dtype_max
+    ).to(output_dtype)
 
     # store transpose and store output data - shape (N, K)
     output_offs = (

torchao/prototype/moe_training/kernels/jagged_float8_scales.py

@@ -31,8 +31,8 @@
     torch.float64: tl.float64,
 }
 
-block_sizes = [1, 16, 32, 64]
-block_sizes_iter = [64, 128, 256]
+block_sizes = [32]  # [16, 32, 64]
+block_sizes_iter = [128]  # [64, 128, 256]
 num_warps = [4]
 num_stages = [3]
 kernel_configs_2D = [

torchao/prototype/moe_training/scaled_grouped_mm.py

@@ -14,7 +14,6 @@
 from torchao.prototype.moe_training.conversion_utils import MoEScalingType
 from torchao.prototype.moe_training.kernels import (
     triton_fp8_per_group_colwise_scales,
-    triton_fp8_per_group_rowwise_scales,
     triton_fp8_rowwise_3d_transpose_rhs,
 )
 from torchao.prototype.moe_training.utils import (
@@ -174,8 +173,8 @@ def backward(ctx, grad_output: torch.Tensor):
         # Convert grad_output to float8, row-major for left operand of grouped GEMM
         # needed for grad_A: grad_output @ B
         #
-        # grad_output shape: (M, N)
-        # grad_output_scale shape: (M, 1)
+        # grad_output shape: (Mg, N)
+        # grad_output_scale shape: (Mg, 1)
         grad_output_scales = tensor_to_scale(
             grad_output,
             torch.float8_e4m3fn,
@@ -226,17 +225,22 @@ def backward(ctx, grad_output: torch.Tensor):
 
         # Convert transpose of grad_output to float8, row-major for left operand of grouped GEMM
         # needed for grad_B: grad_output_t @ A
-        grad_output_t_fp8_row_major, grad_output_t_scales = (
-            triton_fp8_per_group_rowwise_scales(
-                grad_output.transpose(-2, -1),
-                offs,
-                torch.float8_e4m3fn,
-                round_scales_to_power_of_2=True,
-            )
+        # Use transpose method to avoid uncoalesced memory accesses.
+        grad_out_fp8_colwise, grad_out_scales = triton_fp8_per_group_colwise_scales(
+            grad_output.t()
+            .contiguous()
+            .t(),  # Quantization is over 2x faster when input is col major, even with this transformation
+            offs,
+            torch.float8_e4m3fn,
+            round_scales_to_power_of_2=True,
         )
+        grad_output_t_fp8_row_major = grad_out_fp8_colwise.t()
+        grad_output_t_scales = grad_out_scales.t()
 
         A_fp8_col_major, A_scales = triton_fp8_per_group_colwise_scales(
-            A,
+            A.t()
+            .contiguous()
+            .t(),  # Quantization is over 2x faster when input is col major, even with this transformation
             offs,
             torch.float8_e4m3fn,
             round_scales_to_power_of_2=True,

torchao/prototype/moe_training/utils.py

@@ -163,21 +163,21 @@ def torch_to_3d_rowwise_float8_transpose_rhs(
         Scales shape: (E, 1, K
     """
     assert _is_column_major(input_hp_t), "input tensor must be column-major"
-    input_hp = input_hp_t.transpose(-2, -1)  # (E, N, K)
     scales = tensor_to_scale(
-        input_hp,
+        input_hp_t,
         target_dtype,
         scaling_granularity=ScalingGranularity.AXISWISE,
-        axiswise_dim=-2,
+        axiswise_dim=-1,
         round_scales_to_power_of_2=round_scales_to_power_of_2,
-    )  # (E, 1, K)
+    )  # (E, K, 1)
 
     # Apply scales to tensor and convert to float8.
-    tensor_scaled = input_hp.to(torch.float32) * scales
+    tensor_scaled = input_hp_t.to(torch.float32) * scales
     float8_tensor = to_fp8_saturated(tensor_scaled, target_dtype)
 
     # To column major
-    float8_tensor = float8_tensor.transpose(-2, -1).contiguous().transpose(-2, -1)
+    float8_tensor = float8_tensor.contiguous().transpose(-2, -1)
+    scales = scales.transpose(-2, -1)
     return float8_tensor, scales