Remove copy after bmm (#7441)
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@@ -1084,13 +1084,16 @@ class DeepseekV2AttentionMLA(nn.Module):
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masked_m,
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expected_m,
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)
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attn_bmm_output = attn_bmm_output[:, :expected_m, :]
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attn_bmm_output = (
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attn_bmm_output[:, :expected_m, :].transpose(0, 1).flatten(1, 2)
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)
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elif _is_hip:
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# TODO(haishaw): add bmm_fp8 to ROCm
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attn_bmm_output = torch.bmm(
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attn_output.to(torch.bfloat16).transpose(0, 1),
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self.w_vc.to(torch.bfloat16) * self.w_scale,
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)
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attn_bmm_output = attn_bmm_output.transpose(0, 1).flatten(1, 2)
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elif self.w_vc.dtype == torch.float8_e4m3fn:
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attn_output_val, attn_output_scale = per_tensor_quant_mla_fp8(
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attn_output.transpose(0, 1),
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@@ -1103,10 +1106,21 @@ class DeepseekV2AttentionMLA(nn.Module):
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self.w_scale,
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torch.bfloat16,
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)
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attn_bmm_output = attn_bmm_output.transpose(0, 1).flatten(1, 2)
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else:
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attn_bmm_output = torch.bmm(attn_output.transpose(0, 1), self.w_vc)
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attn_output = attn_bmm_output.transpose(0, 1).flatten(1, 2)
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output, _ = self.o_proj(attn_output)
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attn_bmm_output = torch.empty(
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(attn_output.shape[0], self.num_local_heads * self.v_head_dim),
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dtype=attn_output.dtype,
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device=attn_output.device,
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)
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torch.bmm(
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attn_output.transpose(0, 1),
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self.w_vc,
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out=attn_bmm_output.view(
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-1, self.num_local_heads, self.v_head_dim
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).transpose(0, 1),
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)
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output, _ = self.o_proj(attn_bmm_output)
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return output
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